Watershed Income Accounting for SEEA-EA Implementation: Mapping Economic Activity in Guatemala Using Satellite Imagery

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The central motivation is to generate spatially explicit economic data aligned with hydrological units, a foundational requirement for implementing the System of Environmental-Economic Accounting, Ecosystem Accounting (SEEA-EA), adopted by the United Nations Statistical Commission in 2021. By integrating satellite-derived luminosity with National Accounts statistics from the Guatemalan Central Bank, the country's 2020 Gross Domestic Product (GDP) was disaggregated to the department, municipality, watershed, and sub-watershed levels. Non-agricultural value added was allocated using nighttime light intensity as a proxy for economic activity. In contrast, agricultural value added was distributed using a land cover map from the Ministry of Agriculture (MAGA). Results reveal that the Motagua watershed (35.1%) and the María Linda watershed (32.2%) generated the largest shares of national GDP, followed by Samalá (4.5%) and Cahabón-Polochic-Lago Izabal (2.0%). The findings demonstrate that combining VIIRS nighttime lights with land-use classification constitutes a valid and reproducible methodology for subnational GDP estimation in data-scarce environments. Beyond its direct policy relevance for regional planning and inequality analysis, this study contributes to Guatemala's SEEA-EA implementation pathway by providing the monetary economic counterpart to spatially explicit ecosystem extent and condition accounts. Watershed-level GDP estimates could enable the computation of ecosystem service flow accounts. watershed accounting GDP regionalization SEEA ecosystem accounting satellite remote sensing natural capital Figures Figure 1 Figure 2 Figure 3 1. Introduction The spatial disaggregation of Gross Domestic Product (GDP) at subnational levels has become an increasingly important tool for understanding territorial economic inequalities and for informing evidence-based public policy. The geographic distribution of economic activity enables researchers and planners to analyze urbanization patterns, industry concentration, and regional development dynamics within a single country (Liang et al., 2020 ). The capacity to disaggregate GDP at subnational scales enables the identification of the structure of goods and services at the territorial level. It quantifies the influence of economic activities at the spatial unit level, an aspect of considerable relevance for public planning and national development strategies (CEPAL, 2023 ). In developing countries where official subnational accounts are absent, satellite-based methods have emerged as the primary alternative, leveraging the global coverage and temporal consistency of remote sensing data to approximate economic activity across administrative and ecological units alike. This study is specifically motivated by the requirements of watershed-level economic accounting as a cornerstone of ecosystem accounting implementation in Guatemala. Watersheds, defined as the geographic areas that drain to a common point in a river or water body, are among the most widely used spatial units for environmental management and natural resource governance. They are also the primary spatial organizational units adopted in Guatemala's institutional framework for environmental management, as reflected in the activities of the Instituto Nacional de Bosques (INAB), the Universidad Rafael Landívar's Institute for Environment and Natural Resources (IARNA-URL), and the regulatory framework governing water resources and land use. The System of Environmental-Economic Accounting, Ecosystem Accounting (SEEA-EA), adopted as an international statistical standard by the United Nations Statistical Commission in 2021, organizes accounts by ecosystem type and spatial extent rather than by administrative or political units, with the explicit goal of linking economic flows to the natural assets and ecosystem services that generate or sustain them (United Nations et al., 2021 ). Watersheds serve as natural ecosystem accounting units. They have well-defined boundaries, they integrate multiple ecosystem types within a coherent hydrological system, and they are the scale at which many critical regulating ecosystem services, water regulation, flood attenuation, sediment retention, and groundwater recharge, are most meaningfully quantified and managed. National-level GDP figures are insufficient for SEEA-EA purposes. They can quantify an economy's aggregate output. However, they cannot identify which economic activities occur within or depend upon ecosystems, or what share of output is generated at the cost of natural capital depletion (Edens & Hein, 2013 ). Subnational GDP disaggregated to watershed and sub-watershed scales, as produced here, fills this gap by providing the economic counterpart to the spatially explicit ecosystem extent and condition accounts compiled by SEEA-EA. This alignment between economic and ecological spatial units is essential for computing ecosystem service flow accounts, for attributing environmental pressure to its territorial source, and for constructing spatially explicit adjusted GDP indicators that reflect natural capital consumption (Hein et al., 2020 ). The paper contributes directly to this broader agenda by generating watershed-level GDP estimates for Guatemala that are immediately compatible with the country’s ongoing SEEA-EA implementation efforts. Guatemala's System of National Accounts follows the 2008 System of National Accounts (SNA 2008), an international methodological framework that regulates the measurement of economic activities through various aggregates, including GDP, in accordance with economic principles. Within the SNA framework, the Banco de Guatemala produces macroeconomic account tables including the Supply and Use Tables (SUT), which register the monetary values of transactions involving goods and services, covering production, exports, imports, intermediate consumption, and final consumption, annually and by economic activity (Banco de Guatemala, 2019 ). Guatemala currently has official GDP data produced at the national level, derived from the sum of total monetary values generated by each product and economic activity annually. However, official subnational GDP estimates at the municipality or watershed level do not exist. This gap limits policymakers' and researchers' ability to make spatially informed decisions on regional development, resource allocation, and inequality reduction. It also prevents Guatemala from advancing toward full SEEA-EA implementation, as the latter requires monetary economic data georeferenced to ecosystem accounting units, such as watersheds and land cover classes (United Nations et al., 2021 ). A sizeable literature has responded to this methodological gap by using satellite-derived nighttime light (NTL) data to estimate economic activity at subnational scales. Nighttime light emissions captured through satellite imagery have been used across a wide range of geophysical and socioeconomic investigations (Cao et al., 2022 ). The main data sources are the Day Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS), onboard the Joint Polar-orbiting Satellite System operated by NASA and NOAA from 2012 onward, and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) of NOAA, which covers 1992 to 2014. Nighttime lights are a well-established proxy for economic activity (Henderson et al., 2012 ) and for the spatial intensity of production in a given area. NTL data are generated by the artificial electricity present in most buildings and urban infrastructure, providing a means to analyze the socioeconomic characteristics of urban dynamics in a given area (Chen et al., 2021 ). Recent applications have demonstrated the viability of NTL-based approaches for subnational GDP estimation across diverse geographic and institutional contexts (Hu & Yao, 2022 ; Yu et al., 2023 ; Gibson et al., 2020 ). This paper disaggregates Guatemala's 2020 GDP across departments, municipalities, watersheds, and sub-watersheds, drawing on VIIRS nighttime light imagery and on land cover and land use data from the Ministry of Agriculture, Livestock, and Food (MAGA). The combined approach is well-suited to countries like Guatemala, where agricultural GDP is poorly captured by satellite luminosity yet accounts for a sizeable share of economic output. The methodology builds on the international literature (Ghosh et al., 2010 ; Keola et al., 2015 ; McCord & Rodriguez-Heredia, 2022 ) and adapts it to the spatial and economic context of Central America. The outputs are designed to be directly compatible with the ongoing and future SEEA-EA efforts in Guatemala. Watershed-level results are emphasized because hydrologically bound economic accounts are not only a methodological contribution but also a concrete step toward SEEA-EA implementation in a country with rich ecological endowments and wide gaps in spatially explicit environmental-economic statistics. Following Rincon-Patino et al. (2021), who developed a watershed-scale ecosystem service accounting framework for Colombia, the exercise shows that watershed GDP accounting is both technically feasible and institutionally relevant in comparable Latin American settings. The paper is organized into five further sections. Section 2 reviews the theoretical and policy framework that links watershed accounting to the implementation of SEEA-EA. Section 3 sets out the methodology and data inputs. Section 4 reports results at four spatial scales with emphasis on watershed and sub-watershed outcomes. Section 5 discusses the findings relative to existing estimates and the broader academic literature and draws out implications for environmental-economic accounting. Section 6 closes with conclusions and future research directions. 2. Theoretical and Policy Framework: Watershed Accounting and the SEEA-EA 2.1 Ecosystem Accounting Units The System of Environmental-Economic Accounting, Ecosystem Accounting (SEEA-EA), represents a fundamental evolution in the conceptual architecture of national accounting. Adopted by the United Nations Statistical Commission in March 2021 as an international statistical standard, the SEEA-EA provides a coherent framework for measuring the extent and condition of ecosystems, the services they provide to the economy and human well-being, and the monetary value of those services as a component of natural capital (United Nations et al., 2021 ). A defining characteristic of the SEEA-EA framework is its spatial organization. All accounts are structured around ecosystem spatial units, geographically defined areas of land and water that share common ecological characteristics, rather than around administrative or political boundaries (Vallecillo et al., 2022 ). This spatial structure reflects the insight that economic flows and ecological processes are both geographically embedded, and that understanding their interactions requires aligning the spatial units at which each is measured. Within the SEEA-EA architecture, ecosystem accounts are compiled at multiple spatial aggregation levels. These include individual ecosystem types (e.g., tropical moist forest, cultivated land), ecosystem accounting areas, and larger spatial units such as watersheds or administrative regions. The monetary supply and use accounts for ecosystem services, which quantify the value of services such as water purification, carbon sequestration, pollination, and flood regulation flowing from ecosystems to economic units, require spatially disaggregated economic data as the receiving end of the supply chain. Without knowing how much economic output is generated within or adjacent to a given watershed, it is impossible to compute meaningful ratios of ecosystem service contribution to GDP, or to assess the degree to which territorial economic activity depends on the ecosystem services supplied by that watershed. This data gap is what the present analysis is designed to close. 2.2 Watersheds as Ecosystem Accounting Units Watersheds occupy a central place in environmental governance and ecosystem accounting for four interrelated reasons. They are ecologically coherent because water, sediments, nutrients, and pollutants move through them under the physical laws of hydrology, which makes the watershed boundary a meaningful delineation of functional ecosystem interactions. They also integrate multiple ecosystem types within a single accounting unit, including forests, wetlands, agricultural lands, rivers, and urban areas, allowing analyses of how different land uses interact to produce combined ecological outcomes, such as water yield or erosion rates. Watersheds are, moreover, the scale at which many regulating and provisioning ecosystem services are most meaningfully quantified, including water regulation, flood attenuation, groundwater recharge, sediment retention, and freshwater supply (Rincon-Patino et al., 2021). Finally, watersheds are institutionally salient in many countries, including Guatemala, where river basin management plans and integrated water resource management frameworks explicitly use hydrological boundaries as the unit of planning and governance. For SEEA-EA implementation, computing economic output at the watershed scale is therefore both theoretically appropriate and practically necessary. The watershed-level GDP estimates produced here open up four concrete applications. They provide the denominator for computing ecosystem service dependency ratios, which capture the fraction of territorial economic output that relies on specific services such as freshwater supply or flood regulation. They also allow the calibration of monetary supply accounts for ecosystem services by situating those services in their economic context. They support the computation of spatially explicit natural capital depletion indicators, linking economic output to physical measures of ecosystem degradation such as deforestation, aquifer drawdown, or soil loss within the same watershed. Moreover, they permit an assessment of environmental pressure by relating the scale of local economic activity to the basin's ecological carrying capacity. 2.3 Guatemala's Institutional Context for SEEA-EA Implementation Guatemala is among the Latin American countries with the most advanced institutional infrastructure for environmental-economic accounting. The Guatemala Central Bank and IARNA-URL have collaborated since 2011, following the SEEA Central Framework (United Nations et al., 2014 ), and have produced a series of physical and monetary accounts covering land, water, forests, and biodiversity (Banco de Guatemala & IARNA-URL, 2011). The adoption of the SEEA-EA as an international standard in 2021 opens a new chapter in this institutional trajectory, requiring the development of ecosystem extent and condition accounts and monetary ecosystem service flow accounts that go beyond the asset-stock orientation of the SEEA Central Framework. A critical constraint on Guatemala's SEEA-EA implementation pathway is the absence of subnational GDP data at scales compatible with ecosystem spatial units. While the country has developed extensive spatial datasets on ecosystem extent, land cover change, and watershed condition, linking these to economic flows has not been possible without corresponding sub-national economic data. The watershed GDP estimates reported here directly address this constraint. By generating monetized economic output data at the watershed scale, and using the same hydrological boundaries as the ecosystem extent accounts, the exercise creates the conditions for integrating physical ecosystem accounts with monetary economic accounts in the manner envisioned by the SEEA-EA framework. This represents a concrete step toward the production of full SEEA-EA accounts for Guatemala, including ecosystem service flow accounts for water regulation, agricultural water use, and urban ecosystem services. 3. Methods and Data The methodology for disaggregating Guatemala's GDP using nighttime light emissions followed a four-phase approach informed by the existing literature (Ghosh et al., 2010 ; McCord & Rodriguez-Heredia, 2022 ; Suarez, 2016 ). The procedure integrates two principal spatial data layers, nighttime light satellite imagery and land cover classification, with national economic statistics to produce spatially explicit estimates of economic output at multiple subnational scales (Fig. 1 ). Methodology for GDP Disaggregation Using Nighttime Lights and Land Use Data Notes. Elaborated by the authors based on the University of Colorado framework (Ghosh et al., 2010 ). 3.1 Phase 1: Satellite Image Preparation A global satellite raster image captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) of NASA/NOAA was used. VIIRS imagery contains radiance data in nanoWatts per square centimeter per steradian (nW/cm²/sr) (Suarez, 2016 ), with values ranging from 0 to 1,223 nW/cm²/sr for Guatemala in 2020. The nighttime light emission data were obtained in TIFF format, from which the national territory was extracted by clipping the satellite imagery with a shapefile of the Republic of Guatemala (Fig. 2 ). The VIIRS Day/Night Band (DNB) product used in this study is an annual composite derived from monthly averages, minimizing the influence of ephemeral light sources such as fires and reducing cloud contamination through temporal averaging. Annual composites have been shown to produce more stable and spatially consistent proxies for underlying economic activity than shorter-period composites (Zheng et al., 2023 ; Yu et al., 2023 ). The raster was then vectorized into shapefile format to enable visualization and extraction of radiance values. A new shapefile was generated by joining the nighttime light layer with the departmental boundaries from the National Geographic Institute (IGN) and the 2020 land use layer from MAGA (MAGA, 2021 ) to calculate departmental GDP using both nighttime lights and land use. The same procedure was applied with watershed, sub-watershed, and municipal layers from IARNA. Although the original methodological framework from the University of Colorado (Ghosh et al., 2010 ) incorporated a population layer for GDP regionalization, prior research has demonstrated a direct correlation between nighttime light emissions and population density, meaning that luminosity data already implicitly incorporate the population factor (Zheng et al., 2023 ). Therefore, a separate population layer was not added in this study. This approach is consistent with the methodology applied by McCord and Rodriguez-Heredia ( 2022 ) for Paraguay and by Pérez-Sindín et al. ( 2021 ) for Colombia, both of which achieved strong correspondence between NTL-based estimates and available reference data without an additional population covariate. Distribution of Nighttime Light Intensity across Guatemala, 2020 Notes. Own elaboration based on VIIRS Day/Night Band annual composite imagery (NASA/NOAA, 2020). 3.2 Phase 2: Calculation of Agricultural Value Added Using MAGA Land Use Data All spatial processing was performed in a Geographic Information System (GIS) environment using vector overlay operations to intersect the nighttime light raster with the respective administrative and hydrological boundary layers. The VIIRS DNB product has a native spatial resolution of approximately 500 meters, which is sufficient for disaggregation to the departmental and watershed scales used in this study but introduces some imprecision at the boundaries of small municipalities or narrow sub-watersheds. At polygon boundaries that straddle raster pixels, radiance values were allocated in proportion to the area of each pixel falling within the respective polygon, using an area-weighted zonal statistics approach. This procedure ensures that light emissions near unit boundaries are not arbitrarily assigned to a single spatial unit, thereby minimizing systematic edge effects in the luminosity shares used for GDP allocation. The watershed and sub-watershed boundary layers provided by IARNA were derived from the national hydrological network at a 1:50,000 scale, consistent with the spatial resolution of the MAGA land use map, ensuring geometric compatibility between the two principal input layers. Prior to the overlay operations, all spatial layers were projected to a common coordinate reference system (WGS 84 / UTM Zone 15N) to preserve areal accuracy across the national territory. The resulting polygon dataset, containing both luminosity values and land-use attributes for each spatial unit, served as the integrated input database for the value-added calculations conducted in Phases 2 and 3. The procedure for calculating GDP at the department, watershed, and sub-watershed levels was consistent across spatial units. Three separate databases were built, one by department, one by municipality, and one by watershed and sub-watershed, because of differences in the input layers used. To calculate agricultural GDP, the land-use polygons generated in Phase 1 were classified according to MAGA's Level 2 categories into two groups of economic activity, agricultural and non-agricultural. After categorizing land uses as agricultural and non-agricultural, the agricultural land uses were associated with the agricultural economic activities listed in the 2020 Supply and Use Table (SUT) produced by the Banco de Guatemala under the SNA 2008 framework. The total area in hectares was calculated for each activity, and the proportional share of each polygon was used to allocate the monetary value added according to the following formula: VApa = Ap × VAt Where VApa represents the value added of the polygon for agricultural activity (millions of Quetzales). Ap is the percentage share of the polygon area within the total area of the economic activity. VAt is the total national value added of the economic activity (millions of Quetzales). This area-proportional approach assumes that the productivity of a given agricultural activity is spatially homogeneous within land-use polygons of the same type, a simplifying assumption discussed in the limitations section. The crosswalk between MAGA Level 2 land use categories and the agricultural activities in the Supply and Use Table required a series of expert judgments about the correspondence between land cover classes and National Accounts categories. For example, the MAGA category “annual crops” was mapped to activities such as maize, bean, and other basic grains production in the SUT. In contrast, “perennial export crops” were linked to coffee, sugarcane, cardamom, banana, and palm oil activities. Pasture and grazing land were associated with livestock and dairy activities. Where a land use class corresponds to multiple SUT activities, as in the case of mixed agroforestry systems, the national value added of those activities was combined and allocated proportionally to the total area of the aggregated land use class. This aggregation introduces some imprecision, but it is necessary given that the MAGA classification does not distinguish between specific crop types at the sub-category level with sufficient spatial resolution for one-to-one SUT matching. The agricultural value-added estimates are therefore best interpreted as broad agricultural category estimates rather than precise sectoral measurements. 3.3 Phase 3: Calculation of Non-Agricultural Value Added Using Nighttime Lights To territorialize non-agricultural GDP, the total nighttime light intensity of all polygons within each spatial unit, department, municipality, watershed, and sub-watershed, was summed. Each polygon's luminosity share was calculated as a percentage of the national total. These luminosity shares were then combined with the total and activity-level value-added data from the Banco de Guatemala for 2020. A coefficient was derived for each economic activity: C = VAe / VAt Where C is the economic activity coefficient, VAe is the value added of the economic activity (millions of Quetzales), and VAt is the total national value added (millions of Quetzales). The non-agricultural value added for each polygon was then calculated as: VApna = NTLp × C × VAt Where VApna is the non-agricultural value added of the polygon (millions of Quetzales). NTLp is the percentage share of nighttime light intensity of the polygon. C is the economic activity coefficient. VA_t is the total national value added (millions of Quetzales). This formulation follows the luminosity-proportional allocation logic established by Ghosh et al. ( 2010 ) and subsequently applied by Suarez ( 2016 ), McCord and Rodriguez-Heredia ( 2022 ), and others. A key assumption is that the spatial distribution of non-agricultural economic activity within each sector is proportional to the spatial distribution of nighttime light intensity, a relationship supported by the extensive empirical literature on the NTL-GDP relationship (Henderson et al., 2012 ; Hu & Yao, 2022 ). The economic activity coefficients (C) were computed separately for each of the non-agricultural activity groups identified in the 2020 Guatemalan Supply and Use Table, which covers activities including manufacturing, construction, commerce, transportation, financial services, real estate, professional services, public administration, education, health, and other services. Each coefficient represents that activity’s share of total national non-agricultural value added. Multiplying the luminosity share of a polygon by the activity-specific coefficient and the corresponding national value added distributes each sector’s output across spatial units in proportion to their share of total luminosity. The underlying assumption is that all non-agricultural sectors follow the same proportional spatial pattern as nighttime light intensity. The assumption is a reasonable approximation for most service and commercial activities, which tend to co-locate with urban populations and infrastructure. It is less precise for activities such as manufacturing, utilities, or construction, which may cluster in industrial zones or peri-urban areas whose luminosity profiles differ from the broader urban core. The limitation is discussed further in Section 5.4 . The non-agricultural value added computed in this phase was aggregated by economic activity sector before being summed to the polygon total, which preserves internal consistency with the SUT structure at the national level. 3.4 Phase 4: Database Consolidation and GDP Calculation To obtain the regionalized GDP values, the value-added data from all polygons, both agricultural and non-agricultural, were consolidated into a single database. Taxes were then allocated to each polygon proportionally to its share of total value added: Ip = It × VApp Where Ip is the taxes allocated to the polygon (millions of Quetzales). It is the total of national taxes (in millions of Quetzales). VApp is the polygon's percentage share of value added. Finally, the GDP for each polygon was calculated by summing the value added and allocated taxes: GDPp = VAp + Ip 4. Results The key result of a database of spatial distribution of agricultural and non-agricultural economic activity across Guatemala for 2020. The map in Fig. 3 confirms the extraordinary concentration of non-agricultural output in the metropolitan core (in gray). Guatemala City, Mixco, Villa Nueva, and San Miguel Petapa emerge as the brightest nodes on the national surface, reflecting the dense luminosity recorded by VIIRS over the central departments. Secondary concentrations appear along the Pacific region, where Escuintla, Quetzaltenango, and Retalhuleu combine urban commercial activity with agro-industrial processing of sugarcane, palm oil, and coffee. Agricultural value added is distributed more broadly across the national territory, with dominant clusters on the southern coastal plain, the Verapaz uplands, and the banana and palm oil belt of Izabal. The northern lowlands of Petén and the Western Highlands register low values on both layers despite covering extensive territory. The map reveals a sharp discontinuity between a small, highly productive metropolitan core and a vast, sparsely lit national periphery. These patterns anchor the quantitative results reported next. Distribution of agricultural and non-agricultural economic activities in the territory (2020). Notes. Based on own calculations Escuintla's high contribution reflects its economic structure. The department hosts extensive sugarcane plantations, palm oil production, and several agro-industrial processing facilities, all of which generate significant agricultural and light industrial GDP. Quetzaltenango's contribution is consistent with its role as Guatemala's second city and the primary economic hub of the western highlands. Huehuetenango and Izabal's relatively higher contributions reflect, respectively, significant remittance-driven economic activity and the presence of export-oriented banana and palm oil production alongside important mining activity in Izabal. Table 1 Estimated GDP by Department, Guatemala 2020 Departments GDP (Q) Departments GDP (Q) Alta Verapaz 13,860 Petén 23,336 Baja Verapaz 3,122 Quetzaltenango 25,557 Chimaltenango 10,125 Quiché 9,415 Chiquimula 8,006 Retalhuleu 6,448 El Progreso 4,400 Sacatepéquez 13,670 Escuintla 34,445 San Marcos 15,572 Guatemala 351,839 Santa Rosa 6,188 Huehuetenango 21,263 Sololá 5,377 Izabal 18,428 Suchitepéquez 7,995 Jalapa 4,694 Totonicapán 3,996 Jutiapa 6,237 Zacapa 6,754 Total 600,727 Notes. Own calculations The watershed-level results constitute the central analytical contribution of this study. At the watershed level, the Motagua watershed registered the highest GDP share (35.1% of the national total), followed by the María Linda watershed (32.2%). These two watersheds together account for more than two-thirds of Guatemala's economic output. The Samalá watershed ranked third (4.5%), followed by the Cahabón-Polochic-Lago Izabal (2.0%) and Ocosito-Naranjo (2.9%) watersheds. The lowest values were recorded for the Candelaria, Coatán, and Mohó watersheds, each contributing less than 0.1% of the national GDP. Table 2 Estimated GDP by Watershed, Guatemala 2020 Rank Watershed GDP Share (%) GDP (Q millions) Drainage Basin 1 Motagua 35.1% 211,059 Atlantic 2 María Linda 32.2% 193,590 Pacific 3 Samalá 4.5% 27,211 Pacific 4 Achiguate 2.9% 17,532 Pacific 5 Ocosito-Naranjo 2.9% 17,399 Pacific 6 Selegua 2.9% 17,261 Gulf of Mexico 7 Cahabón-Polochic-Lago Izabal 2.0% 12,195 Atlantic 8 Chixoy 1.9% 11,263 Gulf of Mexico 9 Bahía de Amatique 1.9% 11,353 Atlantic 10 San Pedro 1.8% 11,108 Gulf of Mexico 11 Los Esclavos 1.7% 10,337 Atlantic / Pacific 12 La Pasión 1.5% 9,088 Gulf of Mexico 13 Coyolate 1.5% 8,737 Pacific 14 Ostúa-Güija 1.3% 7,645 Atlantic 15 Atitlán-Madre Vieja 0.9% 5,336 Pacific 16 Sis-Icán 0.8% 4,947 Pacific 17 Nahualate 0.9% 5,321 Pacific 18 Suchiate 0.7% 4,278 Pacific 19 Mopán 0.4% 2,152 Atlantic 20 Paz 0.4% 2,199 Pacific 21 Cuilco 0.4% 2,558 Gulf of Mexico 23 Paso Hondo 0.2% 1,154 Pacific 24 Ixcán 0.3% 1,642 Gulf of Mexico 25 Usumacinta 0.3% 1,636 Gulf of Mexico 26 Nentón 0.2% 1,018 Gulf of Mexico 27 Sarstún 0.2% 1,062 Atlantic 28 Xaclbal 0.1% 737 Gulf of Mexico 29 Pojóm 0.1% 348 Gulf of Mexico 30 Coatán 0.0% 295 Gulf of Mexico 31 Mohó 0.0% 215 Atlantic 32 Candelaria 0.0% 48 Gulf of Mexico 33 Hondo 0.0% 1 Atlantic TOTAL 100.0% 600,725 Note. The top three watersheds (Motagua, María Linda, and Samalá) account for approximately 72% of the national GDP. Motagua and María Linda combined represent 67.3%. Source: Authors’ own calculations based on VIIRS nighttime lights (NASA/NOAA) and MAGA land use data (2021). The dominance of the Motagua watershed is closely tied to the geographic extent of the department of Guatemala and the associated metropolitan municipalities within its drainage basin. The Motagua river basin is the largest in Guatemala and drains a wide swath of territory from the central highlands to the Caribbean coast through Izabal department. It encompasses the entire Guatemala metropolitan area, as well as significant portions of El Progreso, Zacapa, and Izabal, all of which contribute meaningfully to national economic output. The high NTL intensity in the metropolitan portion of the Motagua basin thus drives the watershed's dominant economic ranking. The high GDP share of the María Linda watershed (32.2%) is similarly driven by the geography of economic concentration. The basin takes in the southern and southwestern portions of the Guatemala metropolitan area, including the heavily urbanized and industrially active municipalities of Villa Nueva and San Miguel Petapa, parts of Guatemala City itself, and the agro-industrial corridor of Escuintla. The María Linda basin is thus at once one of the country’s most economically productive and most ecologically stressed hydrological units. This contrast has direct consequences for SEEA-EA ecosystem service accounting, as discussed in Section 5 . The relatively lower GDP shares of watersheds such as Cahabón-Polochic-Lago Izabal (2.0%), Chixoy (1.9%), and Usumacinta (0.3%) reflect their predominantly rural and forested character, with sparse urban centers and limited industrial activity. These watersheds, paradoxically, are likely to be among the most ecologically important in the country. The Chixoy and Usumacinta basins encompass large areas of humid tropical forest in the Petén, Alta, and Baja Verapaz departments, yet they contribute a small fraction of national economic output. This contrast between ecological significance and economic weight is precisely the type of relationship that watershed-level SEEA-EA accounting is designed to make visible and quantifiable. Below the top-ranked watersheds sits a second tier of economically significant hydrological units. The Samalá watershed (4.5%, Q.27,211 million), Achiguate (2.9%, Q.17,532 million), Selegua (2.9%, Q.17,261 million), and Ocosito-Naranjo (2.9%, Q.17,399 million) each contribute between 2.9% and 4.5% of national GDP and together account for roughly 11.2% of total output. These watersheds share a common structural feature. All four drain the Pacific slope of the Western Highlands and the agro-industrial piedmont, and they combine moderate urban economic activity with significant agricultural production in sugarcane, palm oil, and coffee. The Cahabón-Polochic-Lago Izabal watershed (2.0%, Q.12,195 million) and the Chixoy watershed (1.9%, Q.11,263 million) form a second group of watersheds with meaningful but more modest GDP contributions, a pattern reflecting their mixed rural and agricultural character, combined with urban nodes in Cobán and Salamá. The five lowest-ranked watersheds, Candelaria (0.0%, Q.48 million), Mohó (0.0%, Q.215 million), Hondo (0.0%, Q.1 million), Coatán (0.0%, Q.295 million), and Pojóm (0.1%, Q.348 million), generate a combined total of less than Q.908 million, equivalent to just 0.15% of national GDP. The spatial concentration of Guatemala’s economic output is therefore extreme. The two leading watersheds alone (Motagua and María Linda) account for 67.3% of national GDP, while the ten lowest-ranked watersheds together account for less than 2%. The Pacific-slope watersheds deserve particular attention, given their hybrid economic character. Watersheds such as Achiguate (2.9%), Ocosito-Naranjo (2.9%), and Coyolate (1.5%, Q.8,737 million) generate GDP shares that are substantially larger than their populations might suggest, reflecting the high land productivity of the Pacific piedmont, one of the most fertile agricultural zones in Central America, and the presence of large agro-industrial complexes processing sugarcane and palm oil for export. By contrast, the northern watersheds draining into the Gulf of Mexico, including the Usumacinta (0.3%, Q.1,636 million), La Pasión (1.5%, Q.9,088 million), and Ixcán (0.3%, Q.1,642 million), generate relatively modest economic output despite encompassing large portions of the Petén department, Guatemala’s most biodiverse and ecologically critical region. This spatial pattern has direct implications for environmental-economic accounting. The watersheds generating the least economic output are often those providing the most substantial ecosystem services, including carbon sequestration, biodiversity habitat, and freshwater regulation, pointing to a systematic undervaluation of ecological contributions to national welfare in conventional GDP accounting. The María Linda sub-watershed recorded the highest GDP share at 32.2% of the national total. Since this is the only sub-watershed in the María Linda watershed, the results at both levels are equivalent, making it the second-highest watershed overall. The Las Vacas sub-watershed ranked second (28.6%) and formed part of the Motagua watershed, which ranked highest at the watershed level. The sub-watershed concentration of the Motagua basin's economic output in the Las Vacas unit reflects the geographic concentration of metropolitan Guatemala within the Las Vacas tributary system, which drains the urban core of Guatemala City northward to its confluence with the Motagua River. The Samalá sub-watershed ranked third (4.5%), being the only sub-watershed of the Samalá watershed. Table 3 Estimated GDP by Sub-Watershed: Top 15, Guatemala 2020 Sub-Watershed GDP Share (%) Sub-Watershed GDP Share (%) Nahualate 0.9% Los Esclavos 1.7% Motagua bajo 1.0% Puerto Barrios 1.9% Ostúa-Güija 1.3% Achiguate 2.9% Cahabón 1.4% Selegua 2.9% Coyolate-Acomé 1.5% Samalá 4.5% Grande 1.5% Las Vacas 28.6% Naranjo 1.6% María Linda 32.2% Pixcayá 1.7% Note. Source: Authors’ own calculations based on VIIRS nighttime lights (NASA/NOAA) and MAGA land use data (2021). Examining the full sub-watershed distribution reveals a more granular picture of economic geography within Guatemala’s major basins. Within the Motagua watershed, which contributes 35.1% of the national GDP, economic output is highly unequally distributed across its constituent sub-watersheds. The Las Vacas sub-watershed (28.6%, Q.171,584 million) accounts for the overwhelming majority of the watershed’s total (Q.211,059 million). In contrast, the remaining Motagua sub-watersheds, Pixcayá (1.7%, Q.10,238 million), Motagua bajo (1.0%, Q.5,971 million), El Tambor (0.4%, Q.2,322 million), Motagua alto (0.8%, Q.4,958 million), Chuacús-Uyús (0.7%, Q.3,989 million), and Teculután Huité (0.4%, Q.2,461 million), collectively contribute only 5.0% of national GDP despite spanning a vast lowland territory. This intra-watershed inequality reflects the extraordinary concentration of economic activity in the Las Vacas headwaters region, which drains Guatemala City and its immediate metropolitan fringe, contrasted with the largely rural and sparsely populated middle and lower Motagua valley. The Pixcayá sub-watershed’s relatively higher share (1.7%) reflects the economic activity of the Chimaltenango department and parts of the Guatemala City commuter belt that fall within its drainage area. A further set of sub-watersheds warrants discussion for their intermediate economic weight. The Los Esclavos sub-watershed (1.7%, Q.10,337 million), which drains parts of Jalapa and Santa Rosa departments, and the Naranjo sub-watershed (1.6%, Q.9,376 million) and Grande sub-watershed (1.5%, Q.9,094 million), both part of the Pacific coastal system, rank among the top ten nationally, reflecting the agricultural productivity of their drainage areas. The Coyolate-Acomé sub-watershed (1.5%, Q.8,737 million) captures a comparable share of Pacific piedmont agro-industrial output. At the lower end of the distribution, several sub-watersheds of the northern lowland systems produce extremely low GDP values. The Livingston sub-watershed (0.0%, Q.29 million), Hondo (0.0%, Q.1 million), and Las Pozas (0.0%, Q.237 million) represent hydrological units with minimal human economic activity but potentially high ecological value as part of the Caribbean coastal and lowland forest ecosystem. The Cahabón sub-watershed (1.4%, Q.8,490 million) deserves special mention as the most economically significant unit within the larger Cahabón-Polochic-Lago Izabal watershed, driven by coffee and cardamom production in Alta Verapaz alongside the growing economic activity of Cobán city. In total, the dataset covers 62 named sub-watersheds whose GDP values span five orders of magnitude, from Q.1 million for Hondo to Q.193,590 million for María Linda, illustrating the spatial heterogeneity of economic activity across Guatemala’s hydrological landscape. The results at the sub-watershed scale reveal a marked concentration of economic activity in a small number of spatial units. María Linda, Las Vacas, and Samalá record substantially higher GDP shares than the remaining 59 sub-watersheds. This disparity reflects the dominance of non-agricultural economic activity in Guatemala's GDP and its geographic concentration in densely populated metropolitan and peri-urban areas. The three highest-ranking sub-watersheds are located in departments that rank among the most populous in the 2018 national census (INE, 2018 ). The María Linda sub-watershed spans Guatemala, Sacatepéquez, Santa Rosa, and Escuintla. The Las Vacas sub-watershed lies within the departments of Guatemala and El Progreso. The Samalá sub-watershed covers Totonicapán and Quetzaltenango, which rank eighth and ninth in national population. These findings are consistent with the well-documented relationship between nighttime luminosity, population density, and economic output. The departmental results presented above can now be compared against the recently published official subnational accounts for Guatemala. In 2025, the Banco de Guatemala published its Regional and Departmental Gross Domestic Product estimates covering the period 2013–2023 (Banco de Guatemala, 2025 ), the first official disaggregation of GDP below the national level in the country’s statistical history. This publication constitutes an important benchmark for evaluating the estimates generated in the present study. A comparison of the departmental shares reported in the official accounts with those produced by this NTL-based methodology for 2020 reveals a high degree of correspondence in both relative rankings and orders of magnitude. The department of Guatemala consistently emerges as the dominant economic unit in both datasets, accounting for approximately 55–60% of national GDP, while the ranking of subsequent departments, Escuintla, Quetzaltenango, Huehuetenango, is broadly preserved across the two methodologies. Departments at the lower end of the distribution, such as Baja Verapaz, El Progreso, and Totonicapán, also occupy comparable positions in both sets of estimates. These convergences lend credibility to the NTL-based approach adopted here, demonstrating that satellite-derived luminosity combined with land-use data can approximate the spatial structure of departmental economic activity with reasonable accuracy, even in the absence of official subnational accounts. The exercise reported here extends the official Banco de Guatemala estimates in two ways that are directly relevant to planning and to ecosystem accounting. The official regional accounts are published only at the departmental level, leaving the municipal scale, at which many local development decisions and natural resource management actions are taken, without any GDP reference. The municipal estimates produced here fill that gap for 2020, attributing economic output to Guatemala’s 340 municipalities and providing a spatially granular baseline for subnational investment planning and inequality analysis. A second, more consequential extension concerns the spatial logic of the accounts. The Banco de Guatemala regional accounts are organized around administrative units and cannot be linked directly to ecosystem spatial boundaries. Extending the disaggregation to the watershed and sub-watershed scales generates hydrologically organized economic data that SEEA-EA implementation requires,a dimension absent from the official departmental accounts. The two datasets are therefore best understood as complementary. The official accounts provide a validated reference for departmental magnitudes, while the NTL-watershed methodology developed here provides the spatial granularity needed for environmental-economic accounting and municipal-level policy analysis that the official statistics do not yet cover. At the municipal scale, the four highest-GDP municipalities all sit within the department of Guatemala. These are Guatemala City (33.94%), Mixco (8.87%), Villa Nueva (5.54%), and San Miguel Petapa (2.66%). The two lowest-GDP municipalities are both in Sololá, namely Santa Catarina Palopó (0.001%) and Santa Cruz La Laguna (0.002%) (see Appendix 3). The results across the four spatial scales point to a consistent picture. Economic activity in Guatemala is extraordinarily concentrated in a small metropolitan core, and the gradient between the capital region and the peripheral rural departments is among the steepest in Latin America. 5. Discussion 5.1 Validation Against Alternative Estimates The absence of official subnational GDP statistics in Guatemala makes it difficult to directly validate the results of this exercise. Nevertheless, both FUNDESA (Fundación para el Desarrollo de Guatemala) and ICESH (Instituto de Investigación en Ciencias Socio Humanistas) have produced departmental GDP estimates for Guatemala using distinct methodologies, providing useful benchmarks for comparison. FUNDESA estimated departmental GDP using disaggregated economic activity data from the Banco de Guatemala, combined with Principal Component Analysis to identify behavioral patterns among economic activities and their geographic clustering. Urban and rural concentration estimates from the National Statistics Institute (INE) and the geographic distribution of economic activities were then used to allocate GDP across departments (Córdova, 2019 ). Unlike the present study, the FUNDESA methodology does not explicitly account for population density. In contrast, nighttime lights are directly correlated with population density as an indicator of human activity in a territory. ICESH used four indicators for its departmental GDP estimates: electricity consumption, exports, the number of firms, and the employed population (Díaz, 2023 ). These indicators were averaged to allocate the national GDP across departments. The ICESH approach distributes total GDP without disaggregating by economic activity, whereas the method applied here disaggregates by economic activity using the MAGA land-use classification. Comparing the ICESH estimates with the results reported here reveals a high degree of convergence. The underlying variables drive the overlap. Employed population is embedded in nighttime light intensities, electricity consumption is directly tied to luminosity, and exports are partially captured through the MAGA land use map, which reflects the agricultural production that accounts for the bulk of Guatemalan exports (Díaz, 2023 ). The departmental GDP results obtained here are broadly consistent with the existing estimates from ICESH and FUNDESA in relative rankings and shares. The results support the view that combining nighttime lights with land-use data is a methodologically sound approach for subnational GDP disaggregation in countries where agriculture still accounts for a meaningful share of output. Guatemala is a clear case, with 9.8% of GDP generated in agricultural activities spread across 47.6% of the national territory. The remaining 90.2% of GDP is concentrated in urban zones that cover less than 2% of the country. 5.2 Implications for SEEA Ecosystem Accounting at the Watershed Scale A key application of the spatial GDP estimates reported here lies in their compatibility with the SEEA-EA, particularly for constructing watershed-scale ecosystem service accounts. The SEEA-EA is explicitly designed to link monetary economic flows to their spatial origin in ecosystems and to the natural capital stocks that underpin them (United Nations et al., 2021 ). This linkage is structurally impossible without subnational GDP data organized at scales that correspond to ecosystem boundaries, which is what the present exercise produces for Guatemala. A central challenge in SEEA-EA implementation is matching the spatial scale of economic data to that of ecosystem accounting units. National accounts, by design, aggregate all economic activity without geographic reference, making it impossible to determine how much output is generated within or dependent upon a given watershed, forest, or coastal ecosystem. The watershed and sub-watershed GDP estimates produced here resolve this problem for Guatemala. By attributing economic value to the same hydrological units used in ecosystem extent and condition accounts, the results enable analysts to construct ecosystem service flow accounts that quantify the economic contribution of water regulation, flood control, sediment retention, and other provisioning and regulating services at the watershed scale. This application is particularly pertinent in Guatemala, where watershed-level management is already institutionalized through agencies such as INAB and IARNA, and where freshwater ecosystem services represent a critical but undervalued input to agricultural and urban economic activity (Banco de Guatemala & IARNA-URL, 2011). Spatially disaggregated GDP also enables the computation of environmental pressure indicators tied to specific territories. The SEEA central framework (United Nations et al., 2014 ) distinguishes between the monetary value of economic output and the physical costs of environmental degradation, deforestation, aquifer depletion, and soil erosion that may accompany it. Without subnational GDP, it is possible to document that a given watershed lost X hectares of forest in a given year, but not to attribute that loss to identifiable economic activities generating quantifiable output. By overlaying the GDP estimates from this study with physical flow accounts for land cover change, water extraction, or biomass removal, it is possible to calculate spatially explicit natural capital depletion rates relative to local economic output, which are far more informative for governance than national averages (Edens & Hein, 2013 ). Vallecillo et al. ( 2022 ) have similarly argued that aligning economic and ecological spatial units is a necessary condition for meaningful natural capital accounting. That satellite-derived economic proxies represent a viable approach for bridging this gap in countries with limited statistical infrastructure. Furthermore, the land-use classification approach used in this study, which separately estimates agricultural and non-agricultural value added, based on MAGA’s land-cover classifications, is structurally analogous to the ecosystem-type mapping that anchors SEEA-EA accounts. The land-use categories used here can be crosswalked to the ecosystem typologies used in national ecosystem extent accounts, enabling the GDP estimates to be directly integrated into a broader SEEA-EA framework. This is consistent with the recommendations of Hein et al. ( 2020 ), who argue that integrating standard land use data with economic accounts is an essential and practical pathway for implementing SEEA-EA in countries with limited statistical infrastructure. Obst ( 2015 ) similarly emphasizes that SEEA implementation in developing countries depends on the availability of spatially explicit economic data at scales compatible with ecosystem boundaries, a requirement that the present study helps to address for Guatemala. More recently, Baró et al. ( 2021 ) demonstrated, for urban watersheds in Europe, that linking spatial economic data with ecosystem service accounts can reveal critical dependencies between urban economies and the regulating services provided by surrounding ecosystems, a framework directly applicable to Guatemala's highly urbanized watersheds, such as Las Vacas and María Linda. A further set of implications concerns the valuation of ecosystem services. The SEEA-EA monetary supply accounts require estimates of the economic value of ecosystem services flowing to economic units within a given territory. Knowing that the Motagua watershed generates 35.1% of national GDP, or that the María Linda sub-watershed generates 32.2%, offers an order-of-magnitude estimate of the economic value at stake from ecosystem degradation in those units. It also grounds the computation of ecosystem service dependency ratios, the share of territorial GDP traceable to ecosystem service inputs such as water supply, pollination, or climate regulation, which are increasingly used as indicators for natural capital risk assessment and green finance (United Nations et al., 2021 ). In a country like Guatemala, where ecosystem degradation through deforestation and watershed deterioration is spatially concentrated in the same sub-watersheds that generate the most economic output, such indicators carry immediate policy weight. 5.3 Comparison with Academic Literature The methodology used here falls within a substantial body of peer-reviewed research that uses nighttime satellite imagery to estimate subnational economic activity. That literature supports NTL data as a proxy for GDP and identifies methodological refinements relevant to the Guatemalan case. Henderson et al. ( 2012 ) established the econometric foundation for treating nighttime lights as a measure of economic growth from space, showing that changes in light intensity correlate significantly with GDP growth rates, even in countries with poor national accounts data. Their framework has been widely adopted and extended for subnational applications worldwide. Nordhaus and Chen ( 2015 ) then offered a rigorous assessment of the precision of nighttime light data as an economic proxy, finding that luminosity-based estimates achieve reasonable accuracy at subnational scales while suffering from systematic biases in densely populated areas. That bias is directly relevant to the Guatemala City metropolitan region, which dominates both light intensity and economic output in the present results. Keola et al. ( 2015 ) conducted a particularly relevant study using nighttime light data combined with land cover information to estimate subnational GDP growth across a set of developing countries. Their integration of land cover data to account for agricultural economic activity not captured by luminosity closely parallels the approach adopted here, and their results confirmed that the combination of these two data sources substantially improves estimation accuracy compared to NTL data alone. Similarly, Zhao et al. ( 2017 ) used VIIRS nighttime light time series combined with population data to produce pixel-level GDP estimates for China, demonstrating the utility of this approach for spatially granular economic mapping at a national scale. Their methodology showed strong alignment with official provincial GDP data, lending further credibility to satellite-based approaches in contexts like Guatemala, where official subnational data are absent. Doll et al. ( 2000 ) demonstrated early on that nighttime satellite imagery could be used to map socioeconomic parameters globally, establishing important methodological groundwork for subsequent regional applications. Their findings underscored both the promise and the limitations of luminosity-based proxies, particularly in rural areas with low electrification rates, a concern equally applicable to Guatemala's more remote departments. More recently, Sutton and Costanza ( 2002 ) estimated market and non-market values at the global level by combining nighttime imagery with land cover data and ecosystem service valuation, illustrating how NTL data can be integrated with additional spatial layers to produce more comprehensive economic assessments. This finding anticipates and reinforces the SEEA-EA application dimension of the present study. Subsequent contributions have refined the NTL-GDP methodology and extended its applications. Hu and Yao ( 2022 ) developed an econometric framework to illuminate economic growth using panel data across countries and subnational regions, and they found that VIIRS data provide substantially more accurate economic proxies than the older DMSP-OLS sensor, particularly in rapidly growing economies where light saturation in dense urban areas distorts DMSP-based estimates. Yu et al. ( 2023 ) used VIIRS nighttime light data to estimate subnational GDP growth, confirming the method’s temporal stability and demonstrating its utility for monitoring spatial economic dynamics over time, which is a potential extension for the Guatemalan application reported here. Gibson et al. ( 2020 ) systematically evaluated which NTL data sources and preprocessing approaches are most appropriate for different economic applications, and their finding that annual composites from VIIRS outperform monthly or shorter-period products for GDP estimation is consistent with the data selection made here. At the regional scale, McCord and Rodriguez-Heredia ( 2022 ) estimated departmental GDP in Paraguay using nighttime lights, reporting results that closely matched alternative subnational estimates and confirming the methodology's transferability across Latin American countries. Pérez-Sindín et al. ( 2021 ) examined the use of nighttime lights as a proxy for economic activity in rural areas of Colombia, finding moderate but meaningful correlations at the municipal level, particularly where agricultural activity is relatively minor, suggesting that the NTL-land use combination approach adopted here is especially well suited to countries like Guatemala. Addison and Stewart ( 2015 ) provided an extensive review of the conditions under which NTL data serve as reliable proxies for economic variables, noting that the proxy performs best when complemented by additional spatial covariates that capture economic dimensions not reflected in luminosity, which is exactly the role played by the MAGA land use data in the present exercise. Liang et al. ( 2020 ) extended the NTL-GDP methodology to incorporate machine learning via random forest regression to produce higher-resolution GDP estimates in Ningbo, China, suggesting a possible avenue for future methodological refinement in the Guatemalan context as higher-resolution spatial datasets become available. These studies form a comparative frame within which the Guatemalan watershed GDP accounting exercise can be situated, drawing on an established international literature on satellite-based subnational economic estimation. The watershed focus and the explicit SEEA-EA orientation of the present work add a dimension that has not been previously explored in the Central American context. 5.4 Limitations The GDP estimates generated in this study are an academic exercise and should not be taken as official statistics. A key limitation is the reliance on available land-use information. Guatemala lacks a continuous multi-year time series of land cover and land-use maps, and the most recent available map is from MAGA ( 2021 ) for 2020. The area-proportional allocation of agricultural value added assumes spatial homogeneity in productivity across land-use classes. This assumption does not hold in practice, given substantial variation in soil quality, water availability, and farming technology across Guatemala's diverse agroecological zones. Future work should explore weighting agricultural value added by agro-ecological productivity gradients or by yield data where available. Another limitation concerns the spatial allocation of non-agricultural economic activities, which had to be distributed proportionally based on light intensity, as precise geolocated data on the distribution of non-agricultural businesses and industries were unavailable. The results should therefore be interpreted as approximations that reflect broad spatial economic patterns rather than precise sectoral measurements at fine spatial scales. Light saturation in the Guatemala City metropolitan area, a known limitation of NTL data in high-density urban environments documented by Nordhaus and Chen ( 2015 ), may slightly underestimate the economic output of the metropolitan core compared to that of peri-urban areas. For SEEA-EA applications specifically, these estimates would need to be complemented by physical flow accounts and ecosystem condition data before full ecosystem service valuations or natural capital depletion indicators could be computed. Finally, the study covers a single year (2020), which coincides with the COVID-19 pandemic and associated economic disruptions. The unusual economic conditions of 2020, including contractions in service-sector output, reduced nighttime light intensity in commercial zones, and disruptions to agricultural supply chains, may affect the representativeness of the results relative to a typical pre-pandemic year. Extending the analysis to include 2018 or 2019 data would help assess the robustness of the watershed GDP distribution to inter-annual variability. 6. Conclusions The paper has disaggregated Guatemala’s 2020 GDP across departments, municipalities, watersheds, and sub-watersheds using nighttime light maps and land use data, with particular emphasis on the watershed as the unit most relevant to SEEA-EA ecosystem accounting. The three departments with the highest GDP shares were Guatemala (58.6%), Escuintla (5.7%), and Quetzaltenango (4.3%). At the watershed level, Motagua (35.1%) and María Linda (32.2%) accounted for more than two-thirds of national output. At the sub-watershed scale, María Linda (32.2%) and Las Vacas (28.6%) accounted for the largest share. The four highest-GDP municipalities, Guatemala City (33.94%), Mixco (8.87%), Villa Nueva (5.54%), and San Miguel Petapa (2.66%), are all concentrated in the metropolitan department. The results also show that 9.8% of GDP is generated in agricultural activities spread across 47.6% of the national territory, while urban areas covering less than 2% of the country produce the remaining 90.2%. The watershed-level GDP estimates produced here are a building block for SEEA-EA implementation in Guatemala. Aligning economic output data with the hydrological spatial units used in ecosystem accounting supplies the monetary counterpart needed to construct ecosystem service flow accounts, compute natural capital depletion rates by watershed, and assess ecosystem service dependencies for the country’s most economically productive territories. The contrast between watersheds with high economic output and limited ecological cover, such as María Linda and Las Vacas, and those with low economic output but high ecological significance, such as Chixoy, Polochic, and Usumacinta, is exactly the kind of spatial tension between economic activity and natural capital that SEEA-EA is designed to make visible and governable. The combination of VIIRS nighttime lights with land-use classification offers a viable and reproducible approach for countries like Guatemala, where official subnational GDP estimates do not exist, and agricultural production must be accounted for separately because it is poorly detected by satellite luminosity. The method travels well to other Central American and Latin American countries with similar data constraints that are advancing SEEA-EA implementation, as comparable applications in Paraguay (McCord & Rodriguez-Heredia, 2022 ) and Colombia (Pérez-Sindín et al., 2021 ) illustrate. The approach is also well-suited to temporal extension. VIIRS nighttime light composites are available every year from 2012 onward, so the same procedure can generate a multi-year time series of watershed-level GDP estimates for the analysis of spatial economic dynamics and regional growth patterns. Future extensions of this work could draw on the updated MAGA land cover map, scheduled for publication in 2025 (MAGA, 2024), which will sharpen the allocation of agricultural value added and open up analysis of how land-use change affects watershed economic output. The spatial precision of the non-agricultural GDP estimates could also improve if geolocated data on non-agricultural economic activities became available from business registries or tax records. The most consequential next step, however, is to integrate these GDP estimates with Guatemala’s ongoing work on physical ecosystem accounting, particularly land cover change monitoring and water resource accounts. Such integration would move the country toward full SEEA-EA implementation and toward green GDP indicators at the subnational scale. It would also offer a methodological and policy contribution of value not only to Guatemala but also to the wider set of developing countries working to operationalize the SEEA-EA framework in conditions of statistical scarcity and ecological richness. Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. All three authors were affiliated with Universidad Rafael Landívar at the time the research was conducted. At the time of manuscript preparation and submission, the authors’ affiliations had changed to those indicated in the paper. Author Contribution Paulina Reyes: Methodology, Formal analysis, Investigation, Data curation, Visualization, Writing – original draft. Juan Pablo Castañeda Sánchez: Conceptualization, Supervision, Writing – review and editing. Juan Miguel Goyzueta: Data curation, Resources, Writing – review and editing. References Addison D, Stewart B (2015) Nighttime Lights Revisited: The Use of Nighttime Lights Data as a Proxy for Economic Variables. In the World Bank policy research working paper . https://doi.org/10.1596/1813-9450-7496 Banco de Guatemala y IARNA-URL (2011) Sistema de Contabilidad Ambiental y Económica Integrada de Guatemala (SCAEI): Síntesis explicativa. Autor Banco de Guatemala (2019) Cuentas nacionales de Guatemala. Banco de Guatemala Banco de Guatemala (2025) Producto Interno Bruto Regional y Departamental 2013–2023 . Banco de Guatemala. https://banguat.gob.gt/page/producto-interno-bruto-regional-y-departamental-0 Baró F, Palomo I, Zulian G, Vizcaíno P, Haase D, Gómez-Baggethun E (2021) Mapping ecosystem service capacity, flow, and demand for landscape and urban planning: A conceptual framework and case study for urban watershed accounting. Landsc Urban Plann 208:104031. https://doi.org/10.1016/j.landurbplan.2020.104031 Cao C, Zhang B, Xia F, Bai Y (2022) Exploring VIIRS Night Light Long-Term Time Series with CNN/SI for Urban Change Detection and Aerosol Monitoring. Remote Sens 14(13):3126. https://doi.org/10.3390/rs14133126 CEPAL (2023) Producto Interno Bruto subnacional por enfoque de producción. Naciones Unidas. https://www.cepal.org/es/notas/producto-interno-bruto-subnacional-enfoque-la-produccion Chen Z, Yu B, Yang C, Zhou Y, Yao S, Qian X, Wang C, Wu B, Wu J (2021) An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth Syst Sci Data 13(3):889. https://doi.org/10.5194/essd-13-889-2021 Córdova E (2019) Diagnóstico del Corredor Económico Quetzaltenango-Huehuetenango/La Mesilla para el Proyecto Creando Oportunidades Económicas. USAID Díaz G (2023) Estimación del PIB por departamento. ICESH Doll CNH, Müller J, Elvidge CD (2000) Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions. Ambio 29(3):157. https://doi.org/10.1579/0044-7447-29.3.157 Edens B, Hein L (2013) Towards a consistent approach for ecosystem accounting. Ecol Econ 90:41. https://doi.org/10.1016/j.ecolecon.2013.03.003 Ghosh T, Powell R, Elvidge CD, Baugh K, Sutton PC, Anderson S (2010) Shedding Light on the Global Distribution of Economic Activity. Open Geogr J, 3 (1). https://benthamopen.com/ABSTRACT/TOGEOGJ-3-147 Gibson J, Olivia S, Boe-Gibson G, Li C (2020) Which night lights data should we use in economics, and where? J Dev Econ 149:102602. https://doi.org/10.1016/j.jdeveco.2020.102602 Hein L, Bagstad KJ, Obst C, Edens B, Schenau S, Castillo GB, Soulard F, Brown C, Driver A, Bordt M, Steurer A, Harris R, Caparrós A (2020) Progress in natural capital accounting for ecosystems. Science 367(6477):514. https://doi.org/10.1126/science.aaz8901 Henderson JV, Storeygard A, Weil DN (2012) Measuring economic growth from outer space. Am Econ Rev 102(2):994–1028. https://doi.org/10.1257/aer.102.2.994 Hu Y, Yao J (2022) Illuminating economic growth. J Econ 228(2):359–378. https://doi.org/10.1016/j.jeconom.2021.05.007 INE (2018) Características generales de la población. Instituto Nacional de Estadística, Guatemala. https://www.censopoblacion.gt/censo2018/poblacion.php Keola S, Andersson M, Hall O (2015) World Dev 66:322–334. https://doi.org/10.1016/j.worlddev.2014.08.017 . Monitoring economic development from space: Using nighttime light and land cover data to measure economic growth Liang H, Guo Z, Wu J, Chen Z (2020) Adv Space Res 65(1):481–493. https://doi.org/10.1016/j.asr.2019.09.035 . GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression MAGA (2021) Determinación de la cobertura vegetal y del uso de la tierra a escala 1:50.000 en la República de Guatemala, año 2020. Serviprenta, S.A. MAGA. (2024), April 1 Actualizarán el Mapa de Cobertura Vegetal y de Uso de la Tierra. https://www.maga.gob.gt/actualizaran-mapa-de-cobertura-vegetal-y-uso-de-la-tierra/ McCord GC, Rodriguez-Heredia M (2022) Nightlights and Subnational Economic Activity: Estimating Departmental GDP in Paraguay. Remote Sens 14(5):1150. https://doi.org/10.3390/rs14051150 Nordhaus WD, Chen X (2015) A sharper image? Estimates of the precision of nighttime lights as a proxy for economic statistics. J Econ Geogr 15(1):217. https://doi.org/10.1093/jeg/lbu035 Obst C (2015) Reflections on natural capital accounting at the national level: The state of play for ecosystem accounting. Sustain Acc Manage Policy J 6(3):406–429. https://doi.org/10.1108/SAMPJ-04-2015-0034 Pérez-Sindín X, Chen TK, Prishchepov AV (2021) Are night-time lights a good proxy for economic activity in rural areas of middle- and low-income countries? Examining the empirical evidence from Colombia. Remote Sens Appl Soc Environ 24:100647. https://doi.org/10.1016/j.rsase.2021.100647 Rincón-Patino J, Ramos-Poli D, Angarita-Sierra T (2021) Watershed-scale ecosystem service accounting to support river basin management in Colombia. Ecosyst Serv 48:101250. https://doi.org/10.1016/j.ecoser.2021.101250 Suarez E (2016) Estimation of the regional GDP using VIIRS nighttime lights. UNEP/GRID-Geneva Sutton PC, Costanza R (2002) Global estimates of market and non-market values derived from nighttime satellite imagery, land cover, and ecosystem service valuation. Ecol Econ 41(3):509. https://doi.org/10.1016/s0921-8009(02)00097-6 United Nations E, Commission F, Organization A, Bank OECD (2014) System of Environmental-Economic Accounting 2012: Central Framework. United Nations. https://doi.org/10.18356/4c8ffc5a-en United Nations E, Commission F, Organization A, World Bank (2021) OECD, and. System of Environmental-Economic Accounting,Ecosystem Accounting (SEEA EA). United Nations. https://seea.un.org/ecosystem-accounting Vallecillo S, La Notte A, Zuccarini G, Ferrini S, Acosta L, Maes J (2022) How to align monetary ecosystem services accounts with the SEEA EA: A proposal for a combined framework. Ecol Econ 196:107402. https://doi.org/10.1016/j.ecolecon.2022.107402 Yu T, Ye Y, Fan Z (2023) Estimating GDP growth using VIIRS night-time light data. Int J Remote Sens 45(21):7939. https://doi.org/10.1080/01431161.2023.2203345 Zhao N, Liu Y, Cao G, Samson E, Zhang J (2017) Forecasting China’s GDP at the pixel level using nighttime lights time series and population images. GIScience Remote Sens 54(3):407. https://doi.org/10.1080/15481603.2016.1276705 Zheng Q, Seto KC, Zhou Y, You S, Weng Q (2023) Nighttime light remote sensing for urban applications: Progress, challenges, and prospects. ISPRS J Photogrammetry Remote Sens 202:125. https://doi.org/10.1016/j.isprsjprs.2023.05.028 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9452018","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630860170,"identity":"6fef8ff9-8256-4fcc-b5c5-aaa027fe586d","order_by":0,"name":"Paulina Reyes","email":"","orcid":"","institution":"Universidad Rafael Landívar","correspondingAuthor":false,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Reyes","suffix":""},{"id":630860172,"identity":"c34ce6c8-4111-4985-9ab8-dd4d4c2da328","order_by":1,"name":"Juan Miguel Goyzueta","email":"","orcid":"","institution":"Fideres Partners LLC","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Miguel","lastName":"Goyzueta","suffix":""},{"id":630860173,"identity":"dd4fc8d9-7e91-4840-a78a-3e912bdd48fd","order_by":2,"name":"Juan-Pablo Castaneda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACxgYkjgRDBZBkRxPFpUUCQpxhYOBhJqAFYQGIYGwjQgvzjNyDH37U1NXxz24+eOPnPJvE/czMBx/OYLCT08Whj3FGXrJkz7HDEhJ3jiVb9m5LS+xhZks23MCQbGx2AJeWHANpBrYDEgw3cswkeLcdBmrhMZN8wHAgcRtuLca/Gf7VScjfyP8m+XcOcVrMpBnbmCUMbuSwSfM2QLVswKel512aZW/fYcmNN9KMrWWOpRn3HAb6ZYYBbr8YtucevvHjWx2/3I3khzff1NjItrc3H3zYU2Enh1NLAw9WcQPsykFAngG7llEwCkbBKBgFCAAA7bdcNSNl+nIAAAAASUVORK5CYII=","orcid":"","institution":"Universidad del Valle de Guatemala","correspondingAuthor":true,"prefix":"","firstName":"Juan-Pablo","middleName":"","lastName":"Castaneda","suffix":""}],"badges":[],"createdAt":"2026-04-17 18:24:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9452018/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9452018/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108389391,"identity":"2a85d5db-1c3c-4342-a6be-ccacad08b91c","added_by":"auto","created_at":"2026-05-04 06:49:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodology for GDP Disaggregation Using Nighttime Lights and Land Use Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes. \u003c/em\u003eElaborated by the authors based on the University of Colorado framework (Ghosh et al., 2010).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9452018/v1/c1c6c3e714bd035b2a11d450.png"},{"id":108804066,"identity":"eee356a2-7dbf-4f39-b364-447b89946fc9","added_by":"auto","created_at":"2026-05-08 15:15:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":522100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of Nighttime Light Intensity across Guatemala, 2020\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes.\u003c/em\u003e Own elaboration based on VIIRS Day/Night Band annual composite imagery (NASA/NOAA, 2020).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9452018/v1/0f1e6fe5de26e7d7d18cf77e.png"},{"id":108493231,"identity":"922c2726-3930-4fef-acc7-c9c57758cd81","added_by":"auto","created_at":"2026-05-05 09:59:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1327777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of agricultural and non-agricultural economic activities in the territory (2020).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes.\u003c/em\u003e Based on own calculations\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9452018/v1/fa64bbb197e2254264741e81.png"},{"id":108809208,"identity":"48097dae-2438-4488-a4a8-3b2324a7deba","added_by":"auto","created_at":"2026-05-08 15:50:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2349333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9452018/v1/bce4eac6-667b-4e12-97c1-db72ea3f7d76.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Watershed Income Accounting for SEEA-EA Implementation: Mapping Economic Activity in Guatemala Using Satellite Imagery","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe spatial disaggregation of Gross Domestic Product (GDP) at subnational levels has become an increasingly important tool for understanding territorial economic inequalities and for informing evidence-based public policy. The geographic distribution of economic activity enables researchers and planners to analyze urbanization patterns, industry concentration, and regional development dynamics within a single country (Liang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The capacity to disaggregate GDP at subnational scales enables the identification of the structure of goods and services at the territorial level. It quantifies the influence of economic activities at the spatial unit level, an aspect of considerable relevance for public planning and national development strategies (CEPAL, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In developing countries where official subnational accounts are absent, satellite-based methods have emerged as the primary alternative, leveraging the global coverage and temporal consistency of remote sensing data to approximate economic activity across administrative and ecological units alike.\u003c/p\u003e \u003cp\u003eThis study is specifically motivated by the requirements of watershed-level economic accounting as a cornerstone of ecosystem accounting implementation in Guatemala. Watersheds, defined as the geographic areas that drain to a common point in a river or water body, are among the most widely used spatial units for environmental management and natural resource governance. They are also the primary spatial organizational units adopted in Guatemala's institutional framework for environmental management, as reflected in the activities of the Instituto Nacional de Bosques (INAB), the Universidad Rafael Land\u0026iacute;var's Institute for Environment and Natural Resources (IARNA-URL), and the regulatory framework governing water resources and land use. The System of Environmental-Economic Accounting, Ecosystem Accounting (SEEA-EA), adopted as an international statistical standard by the United Nations Statistical Commission in 2021, organizes accounts by ecosystem type and spatial extent rather than by administrative or political units, with the explicit goal of linking economic flows to the natural assets and ecosystem services that generate or sustain them (United Nations et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Watersheds serve as natural ecosystem accounting units. They have well-defined boundaries, they integrate multiple ecosystem types within a coherent hydrological system, and they are the scale at which many critical regulating ecosystem services, water regulation, flood attenuation, sediment retention, and groundwater recharge, are most meaningfully quantified and managed.\u003c/p\u003e \u003cp\u003eNational-level GDP figures are insufficient for SEEA-EA purposes. They can quantify an economy's aggregate output. However, they cannot identify which economic activities occur within or depend upon ecosystems, or what share of output is generated at the cost of natural capital depletion (Edens \u0026amp; Hein, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Subnational GDP disaggregated to watershed and sub-watershed scales, as produced here, fills this gap by providing the economic counterpart to the spatially explicit ecosystem extent and condition accounts compiled by SEEA-EA. This alignment between economic and ecological spatial units is essential for computing ecosystem service flow accounts, for attributing environmental pressure to its territorial source, and for constructing spatially explicit adjusted GDP indicators that reflect natural capital consumption (Hein et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The paper contributes directly to this broader agenda by generating watershed-level GDP estimates for Guatemala that are immediately compatible with the country\u0026rsquo;s ongoing SEEA-EA implementation efforts.\u003c/p\u003e \u003cp\u003eGuatemala's System of National Accounts follows the 2008 System of National Accounts (SNA 2008), an international methodological framework that regulates the measurement of economic activities through various aggregates, including GDP, in accordance with economic principles. Within the SNA framework, the Banco de Guatemala produces macroeconomic account tables including the Supply and Use Tables (SUT), which register the monetary values of transactions involving goods and services, covering production, exports, imports, intermediate consumption, and final consumption, annually and by economic activity (Banco de Guatemala, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Guatemala currently has official GDP data produced at the national level, derived from the sum of total monetary values generated by each product and economic activity annually. However, official subnational GDP estimates at the municipality or watershed level do not exist. This gap limits policymakers' and researchers' ability to make spatially informed decisions on regional development, resource allocation, and inequality reduction. It also prevents Guatemala from advancing toward full SEEA-EA implementation, as the latter requires monetary economic data georeferenced to ecosystem accounting units, such as watersheds and land cover classes (United Nations et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA sizeable literature has responded to this methodological gap by using satellite-derived nighttime light (NTL) data to estimate economic activity at subnational scales. Nighttime light emissions captured through satellite imagery have been used across a wide range of geophysical and socioeconomic investigations (Cao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The main data sources are the Day Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS), onboard the Joint Polar-orbiting Satellite System operated by NASA and NOAA from 2012 onward, and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) of NOAA, which covers 1992 to 2014. Nighttime lights are a well-established proxy for economic activity (Henderson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and for the spatial intensity of production in a given area. NTL data are generated by the artificial electricity present in most buildings and urban infrastructure, providing a means to analyze the socioeconomic characteristics of urban dynamics in a given area (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent applications have demonstrated the viability of NTL-based approaches for subnational GDP estimation across diverse geographic and institutional contexts (Hu \u0026amp; Yao, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gibson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper disaggregates Guatemala's 2020 GDP across departments, municipalities, watersheds, and sub-watersheds, drawing on VIIRS nighttime light imagery and on land cover and land use data from the Ministry of Agriculture, Livestock, and Food (MAGA). The combined approach is well-suited to countries like Guatemala, where agricultural GDP is poorly captured by satellite luminosity yet accounts for a sizeable share of economic output. The methodology builds on the international literature (Ghosh et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Keola et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; McCord \u0026amp; Rodriguez-Heredia, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and adapts it to the spatial and economic context of Central America. The outputs are designed to be directly compatible with the ongoing and future SEEA-EA efforts in Guatemala. Watershed-level results are emphasized because hydrologically bound economic accounts are not only a methodological contribution but also a concrete step toward SEEA-EA implementation in a country with rich ecological endowments and wide gaps in spatially explicit environmental-economic statistics. Following Rincon-Patino et al. (2021), who developed a watershed-scale ecosystem service accounting framework for Colombia, the exercise shows that watershed GDP accounting is both technically feasible and institutionally relevant in comparable Latin American settings.\u003c/p\u003e \u003cp\u003eThe paper is organized into five further sections. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the theoretical and policy framework that links watershed accounting to the implementation of SEEA-EA. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e sets out the methodology and data inputs. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports results at four spatial scales with emphasis on watershed and sub-watershed outcomes. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses the findings relative to existing estimates and the broader academic literature and draws out implications for environmental-economic accounting. Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e6\u003c/span\u003e closes with conclusions and future research directions.\u003c/p\u003e"},{"header":"2. Theoretical and Policy Framework: Watershed Accounting and the SEEA-EA","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ecosystem Accounting Units\u003c/h2\u003e \u003cp\u003eThe System of Environmental-Economic Accounting, Ecosystem Accounting (SEEA-EA), represents a fundamental evolution in the conceptual architecture of national accounting. Adopted by the United Nations Statistical Commission in March 2021 as an international statistical standard, the SEEA-EA provides a coherent framework for measuring the extent and condition of ecosystems, the services they provide to the economy and human well-being, and the monetary value of those services as a component of natural capital (United Nations et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A defining characteristic of the SEEA-EA framework is its spatial organization. All accounts are structured around ecosystem spatial units, geographically defined areas of land and water that share common ecological characteristics, rather than around administrative or political boundaries (Vallecillo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This spatial structure reflects the insight that economic flows and ecological processes are both geographically embedded, and that understanding their interactions requires aligning the spatial units at which each is measured.\u003c/p\u003e \u003cp\u003eWithin the SEEA-EA architecture, ecosystem accounts are compiled at multiple spatial aggregation levels. These include individual ecosystem types (e.g., tropical moist forest, cultivated land), ecosystem accounting areas, and larger spatial units such as watersheds or administrative regions. The monetary supply and use accounts for ecosystem services, which quantify the value of services such as water purification, carbon sequestration, pollination, and flood regulation flowing from ecosystems to economic units, require spatially disaggregated economic data as the receiving end of the supply chain. Without knowing how much economic output is generated within or adjacent to a given watershed, it is impossible to compute meaningful ratios of ecosystem service contribution to GDP, or to assess the degree to which territorial economic activity depends on the ecosystem services supplied by that watershed. This data gap is what the present analysis is designed to close.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Watersheds as Ecosystem Accounting Units\u003c/h2\u003e \u003cp\u003eWatersheds occupy a central place in environmental governance and ecosystem accounting for four interrelated reasons. They are ecologically coherent because water, sediments, nutrients, and pollutants move through them under the physical laws of hydrology, which makes the watershed boundary a meaningful delineation of functional ecosystem interactions. They also integrate multiple ecosystem types within a single accounting unit, including forests, wetlands, agricultural lands, rivers, and urban areas, allowing analyses of how different land uses interact to produce combined ecological outcomes, such as water yield or erosion rates. Watersheds are, moreover, the scale at which many regulating and provisioning ecosystem services are most meaningfully quantified, including water regulation, flood attenuation, groundwater recharge, sediment retention, and freshwater supply (Rincon-Patino et al., 2021). Finally, watersheds are institutionally salient in many countries, including Guatemala, where river basin management plans and integrated water resource management frameworks explicitly use hydrological boundaries as the unit of planning and governance.\u003c/p\u003e \u003cp\u003eFor SEEA-EA implementation, computing economic output at the watershed scale is therefore both theoretically appropriate and practically necessary. The watershed-level GDP estimates produced here open up four concrete applications. They provide the denominator for computing ecosystem service dependency ratios, which capture the fraction of territorial economic output that relies on specific services such as freshwater supply or flood regulation. They also allow the calibration of monetary supply accounts for ecosystem services by situating those services in their economic context. They support the computation of spatially explicit natural capital depletion indicators, linking economic output to physical measures of ecosystem degradation such as deforestation, aquifer drawdown, or soil loss within the same watershed. Moreover, they permit an assessment of environmental pressure by relating the scale of local economic activity to the basin's ecological carrying capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Guatemala's Institutional Context for SEEA-EA Implementation\u003c/h2\u003e \u003cp\u003eGuatemala is among the Latin American countries with the most advanced institutional infrastructure for environmental-economic accounting. The Guatemala Central Bank and IARNA-URL have collaborated since 2011, following the SEEA Central Framework (United Nations et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and have produced a series of physical and monetary accounts covering land, water, forests, and biodiversity (Banco de Guatemala \u0026amp; IARNA-URL, 2011). The adoption of the SEEA-EA as an international standard in 2021 opens a new chapter in this institutional trajectory, requiring the development of ecosystem extent and condition accounts and monetary ecosystem service flow accounts that go beyond the asset-stock orientation of the SEEA Central Framework.\u003c/p\u003e \u003cp\u003eA critical constraint on Guatemala's SEEA-EA implementation pathway is the absence of subnational GDP data at scales compatible with ecosystem spatial units. While the country has developed extensive spatial datasets on ecosystem extent, land cover change, and watershed condition, linking these to economic flows has not been possible without corresponding sub-national economic data. The watershed GDP estimates reported here directly address this constraint. By generating monetized economic output data at the watershed scale, and using the same hydrological boundaries as the ecosystem extent accounts, the exercise creates the conditions for integrating physical ecosystem accounts with monetary economic accounts in the manner envisioned by the SEEA-EA framework. This represents a concrete step toward the production of full SEEA-EA accounts for Guatemala, including ecosystem service flow accounts for water regulation, agricultural water use, and urban ecosystem services.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods and Data","content":"\u003cp\u003eThe methodology for disaggregating Guatemala's GDP using nighttime light emissions followed a four-phase approach informed by the existing literature (Ghosh et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McCord \u0026amp; Rodriguez-Heredia, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Suarez, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The procedure integrates two principal spatial data layers, nighttime light satellite imagery and land cover classification, with national economic statistics to produce spatially explicit estimates of economic output at multiple subnational scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMethodology for GDP Disaggregation Using Nighttime Lights and Land Use Data\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNotes.\u003c/em\u003e Elaborated by the authors based on the University of Colorado framework (Ghosh et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Phase 1: Satellite Image Preparation\u003c/h2\u003e \u003cp\u003eA global satellite raster image captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) of NASA/NOAA was used. VIIRS imagery contains radiance data in nanoWatts per square centimeter per steradian (nW/cm\u0026sup2;/sr) (Suarez, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with values ranging from 0 to 1,223 nW/cm\u0026sup2;/sr for Guatemala in 2020. The nighttime light emission data were obtained in TIFF format, from which the national territory was extracted by clipping the satellite imagery with a shapefile of the Republic of Guatemala (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The VIIRS Day/Night Band (DNB) product used in this study is an annual composite derived from monthly averages, minimizing the influence of ephemeral light sources such as fires and reducing cloud contamination through temporal averaging. Annual composites have been shown to produce more stable and spatially consistent proxies for underlying economic activity than shorter-period composites (Zheng et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe raster was then vectorized into shapefile format to enable visualization and extraction of radiance values. A new shapefile was generated by joining the nighttime light layer with the departmental boundaries from the National Geographic Institute (IGN) and the 2020 land use layer from MAGA (MAGA, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to calculate departmental GDP using both nighttime lights and land use. The same procedure was applied with watershed, sub-watershed, and municipal layers from IARNA.\u003c/p\u003e \u003cp\u003eAlthough the original methodological framework from the University of Colorado (Ghosh et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) incorporated a population layer for GDP regionalization, prior research has demonstrated a direct correlation between nighttime light emissions and population density, meaning that luminosity data already implicitly incorporate the population factor (Zheng et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, a separate population layer was not added in this study. This approach is consistent with the methodology applied by McCord and Rodriguez-Heredia (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for Paraguay and by P\u0026eacute;rez-Sind\u0026iacute;n et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Colombia, both of which achieved strong correspondence between NTL-based estimates and available reference data without an additional population covariate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDistribution of Nighttime Light Intensity across Guatemala, 2020\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNotes.\u003c/em\u003e Own elaboration based on VIIRS Day/Night Band annual composite imagery (NASA/NOAA, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Phase 2: Calculation of Agricultural Value Added Using MAGA Land Use Data\u003c/h2\u003e \u003cp\u003eAll spatial processing was performed in a Geographic Information System (GIS) environment using vector overlay operations to intersect the nighttime light raster with the respective administrative and hydrological boundary layers. The VIIRS DNB product has a native spatial resolution of approximately 500 meters, which is sufficient for disaggregation to the departmental and watershed scales used in this study but introduces some imprecision at the boundaries of small municipalities or narrow sub-watersheds. At polygon boundaries that straddle raster pixels, radiance values were allocated in proportion to the area of each pixel falling within the respective polygon, using an area-weighted zonal statistics approach. This procedure ensures that light emissions near unit boundaries are not arbitrarily assigned to a single spatial unit, thereby minimizing systematic edge effects in the luminosity shares used for GDP allocation. The watershed and sub-watershed boundary layers provided by IARNA were derived from the national hydrological network at a 1:50,000 scale, consistent with the spatial resolution of the MAGA land use map, ensuring geometric compatibility between the two principal input layers. Prior to the overlay operations, all spatial layers were projected to a common coordinate reference system (WGS 84 / UTM Zone 15N) to preserve areal accuracy across the national territory. The resulting polygon dataset, containing both luminosity values and land-use attributes for each spatial unit, served as the integrated input database for the value-added calculations conducted in Phases 2 and 3.\u003c/p\u003e \u003cp\u003eThe procedure for calculating GDP at the department, watershed, and sub-watershed levels was consistent across spatial units. Three separate databases were built, one by department, one by municipality, and one by watershed and sub-watershed, because of differences in the input layers used. To calculate agricultural GDP, the land-use polygons generated in Phase 1 were classified according to MAGA's Level 2 categories into two groups of economic activity, agricultural and non-agricultural.\u003c/p\u003e \u003cp\u003eAfter categorizing land uses as agricultural and non-agricultural, the agricultural land uses were associated with the agricultural economic activities listed in the 2020 Supply and Use Table (SUT) produced by the Banco de Guatemala under the SNA 2008 framework. The total area in hectares was calculated for each activity, and the proportional share of each polygon was used to allocate the monetary value added according to the following formula:\u003c/p\u003e \u003cp\u003e \u003cem\u003eVApa\u0026thinsp;=\u0026thinsp;Ap \u0026times; VAt\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eVApa\u003c/em\u003e represents the value added of the polygon for agricultural activity (millions of Quetzales). \u003cem\u003eAp\u003c/em\u003e is the percentage share of the polygon area within the total area of the economic activity. \u003cem\u003eVAt\u003c/em\u003e is the total national value added of the economic activity (millions of Quetzales). This area-proportional approach assumes that the productivity of a given agricultural activity is spatially homogeneous within land-use polygons of the same type, a simplifying assumption discussed in the limitations section.\u003c/p\u003e \u003cp\u003eThe crosswalk between MAGA Level 2 land use categories and the agricultural activities in the Supply and Use Table required a series of expert judgments about the correspondence between land cover classes and National Accounts categories. For example, the MAGA category \u0026ldquo;annual crops\u0026rdquo; was mapped to activities such as maize, bean, and other basic grains production in the SUT. In contrast, \u0026ldquo;perennial export crops\u0026rdquo; were linked to coffee, sugarcane, cardamom, banana, and palm oil activities. Pasture and grazing land were associated with livestock and dairy activities. Where a land use class corresponds to multiple SUT activities, as in the case of mixed agroforestry systems, the national value added of those activities was combined and allocated proportionally to the total area of the aggregated land use class. This aggregation introduces some imprecision, but it is necessary given that the MAGA classification does not distinguish between specific crop types at the sub-category level with sufficient spatial resolution for one-to-one SUT matching. The agricultural value-added estimates are therefore best interpreted as broad agricultural category estimates rather than precise sectoral measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Phase 3: Calculation of Non-Agricultural Value Added Using Nighttime Lights\u003c/h2\u003e \u003cp\u003eTo territorialize non-agricultural GDP, the total nighttime light intensity of all polygons within each spatial unit, department, municipality, watershed, and sub-watershed, was summed. Each polygon's luminosity share was calculated as a percentage of the national total. These luminosity shares were then combined with the total and activity-level value-added data from the Banco de Guatemala for 2020. A coefficient was derived for each economic activity:\u003c/p\u003e \u003cp\u003e \u003cem\u003eC\u0026thinsp;=\u0026thinsp;VAe / VAt\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhere C is the economic activity coefficient, VAe is the value added of the economic activity (millions of Quetzales), and \u003cem\u003eVAt\u003c/em\u003e is the total national value added (millions of Quetzales). The non-agricultural value added for each polygon was then calculated as:\u003c/p\u003e \u003cp\u003e \u003cem\u003eVApna\u0026thinsp;=\u0026thinsp;NTLp \u0026times; C \u0026times; VAt\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eVApna\u003c/em\u003e is the non-agricultural value added of the polygon (millions of Quetzales). \u003cem\u003eNTLp\u003c/em\u003e is the percentage share of nighttime light intensity of the polygon. C is the economic activity coefficient. VA_t is the total national value added (millions of Quetzales). This formulation follows the luminosity-proportional allocation logic established by Ghosh et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and subsequently applied by Suarez (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), McCord and Rodriguez-Heredia (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and others. A key assumption is that the spatial distribution of non-agricultural economic activity within each sector is proportional to the spatial distribution of nighttime light intensity, a relationship supported by the extensive empirical literature on the NTL-GDP relationship (Henderson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hu \u0026amp; Yao, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe economic activity coefficients (C) were computed separately for each of the non-agricultural activity groups identified in the 2020 Guatemalan Supply and Use Table, which covers activities including manufacturing, construction, commerce, transportation, financial services, real estate, professional services, public administration, education, health, and other services. Each coefficient represents that activity\u0026rsquo;s share of total national non-agricultural value added. Multiplying the luminosity share of a polygon by the activity-specific coefficient and the corresponding national value added distributes each sector\u0026rsquo;s output across spatial units in proportion to their share of total luminosity. The underlying assumption is that all non-agricultural sectors follow the same proportional spatial pattern as nighttime light intensity. The assumption is a reasonable approximation for most service and commercial activities, which tend to co-locate with urban populations and infrastructure. It is less precise for activities such as manufacturing, utilities, or construction, which may cluster in industrial zones or peri-urban areas whose luminosity profiles differ from the broader urban core. The limitation is discussed further in Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e5.4\u003c/span\u003e. The non-agricultural value added computed in this phase was aggregated by economic activity sector before being summed to the polygon total, which preserves internal consistency with the SUT structure at the national level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Phase 4: Database Consolidation and GDP Calculation\u003c/h2\u003e \u003cp\u003eTo obtain the regionalized GDP values, the value-added data from all polygons, both agricultural and non-agricultural, were consolidated into a single database. Taxes were then allocated to each polygon proportionally to its share of total value added:\u003c/p\u003e \u003cp\u003e \u003cem\u003eIp\u0026thinsp;=\u0026thinsp;It \u0026times; VApp\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eIp\u003c/em\u003e is the taxes allocated to the polygon (millions of Quetzales). It is the total of national taxes (in millions of Quetzales). \u003cem\u003eVApp\u003c/em\u003e is the polygon's percentage share of value added. Finally, the GDP for each polygon was calculated by summing the value added and allocated taxes:\u003c/p\u003e \u003cp\u003e \u003cem\u003eGDPp\u0026thinsp;=\u0026thinsp;VAp\u0026thinsp;+\u0026thinsp;Ip\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe key result of a database of spatial distribution of agricultural and non-agricultural economic activity across Guatemala for 2020. The map in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e confirms the extraordinary concentration of non-agricultural output in the metropolitan core (in gray). Guatemala City, Mixco, Villa Nueva, and San Miguel Petapa emerge as the brightest nodes on the national surface, reflecting the dense luminosity recorded by VIIRS over the central departments. Secondary concentrations appear along the Pacific region, where Escuintla, Quetzaltenango, and Retalhuleu combine urban commercial activity with agro-industrial processing of sugarcane, palm oil, and coffee. Agricultural value added is distributed more broadly across the national territory, with dominant clusters on the southern coastal plain, the Verapaz uplands, and the banana and palm oil belt of Izabal. The northern lowlands of Pet\u0026eacute;n and the Western Highlands register low values on both layers despite covering extensive territory. The map reveals a sharp discontinuity between a small, highly productive metropolitan core and a vast, sparsely lit national periphery. These patterns anchor the quantitative results reported next.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDistribution of agricultural and non-agricultural economic activities in the territory (2020).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNotes.\u003c/em\u003e Based on own calculations\u003c/p\u003e \u003cp\u003eEscuintla's high contribution reflects its economic structure. The department hosts extensive sugarcane plantations, palm oil production, and several agro-industrial processing facilities, all of which generate significant agricultural and light industrial GDP. Quetzaltenango's contribution is consistent with its role as Guatemala's second city and the primary economic hub of the western highlands. Huehuetenango and Izabal's relatively higher contributions reflect, respectively, significant remittance-driven economic activity and the presence of export-oriented banana and palm oil production alongside important mining activity in Izabal.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEstimated GDP by Department, Guatemala 2020\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP (Q)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepartments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGDP (Q)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlta Verapaz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePet\u0026eacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23,336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaja Verapaz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuetzaltenango\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25,557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChimaltenango\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuich\u0026eacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChiquimula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetalhuleu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEl Progreso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSacatep\u0026eacute;quez\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEscuintla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34,445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSan Marcos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuatemala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e351,839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSanta Rosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuehuetenango\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolol\u0026aacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIzabal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuchitep\u0026eacute;quez\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJalapa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotonicap\u0026aacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJutiapa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZacapa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e600,727\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes.\u003c/em\u003e Own calculations\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe watershed-level results constitute the central analytical contribution of this study. At the watershed level, the Motagua watershed registered the highest GDP share (35.1% of the national total), followed by the Mar\u0026iacute;a Linda watershed (32.2%). These two watersheds together account for more than two-thirds of Guatemala's economic output. The Samal\u0026aacute; watershed ranked third (4.5%), followed by the Cahab\u0026oacute;n-Polochic-Lago Izabal (2.0%) and Ocosito-Naranjo (2.9%) watersheds. The lowest values were recorded for the Candelaria, Coat\u0026aacute;n, and Moh\u0026oacute; watersheds, each contributing less than 0.1% of the national GDP.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEstimated GDP by Watershed, Guatemala 2020\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWatershed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP Share (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGDP (Q millions)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDrainage Basin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotagua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMar\u0026iacute;a Linda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSamal\u0026aacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAchiguate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcosito-Naranjo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelegua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCahab\u0026oacute;n-Polochic-Lago Izabal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChixoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBah\u0026iacute;a de Amatique\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSan Pedro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLos Esclavos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic / Pacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLa Pasi\u0026oacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoyolate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOst\u0026uacute;a-G\u0026uuml;ija\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtitl\u0026aacute;n-Madre Vieja\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSis-Ic\u0026aacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNahualate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuchiate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMop\u0026aacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCuilco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaso Hondo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIxc\u0026aacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsumacinta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNent\u0026oacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarst\u0026uacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXaclbal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoj\u0026oacute;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoat\u0026aacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoh\u0026oacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCandelaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGulf of Mexico\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHondo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtlantic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e600,725\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e The top three watersheds (Motagua, Mar\u0026iacute;a Linda, and Samal\u0026aacute;) account for approximately 72% of the national GDP. Motagua and Mar\u0026iacute;a Linda combined represent 67.3%. Source: Authors\u0026rsquo; own calculations based on VIIRS nighttime lights (NASA/NOAA) and MAGA land use data (2021).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe dominance of the Motagua watershed is closely tied to the geographic extent of the department of Guatemala and the associated metropolitan municipalities within its drainage basin. The Motagua river basin is the largest in Guatemala and drains a wide swath of territory from the central highlands to the Caribbean coast through Izabal department. It encompasses the entire Guatemala metropolitan area, as well as significant portions of El Progreso, Zacapa, and Izabal, all of which contribute meaningfully to national economic output. The high NTL intensity in the metropolitan portion of the Motagua basin thus drives the watershed's dominant economic ranking.\u003c/p\u003e \u003cp\u003eThe high GDP share of the Mar\u0026iacute;a Linda watershed (32.2%) is similarly driven by the geography of economic concentration. The basin takes in the southern and southwestern portions of the Guatemala metropolitan area, including the heavily urbanized and industrially active municipalities of Villa Nueva and San Miguel Petapa, parts of Guatemala City itself, and the agro-industrial corridor of Escuintla. The Mar\u0026iacute;a Linda basin is thus at once one of the country\u0026rsquo;s most economically productive and most ecologically stressed hydrological units. This contrast has direct consequences for SEEA-EA ecosystem service accounting, as discussed in Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe relatively lower GDP shares of watersheds such as Cahab\u0026oacute;n-Polochic-Lago Izabal (2.0%), Chixoy (1.9%), and Usumacinta (0.3%) reflect their predominantly rural and forested character, with sparse urban centers and limited industrial activity. These watersheds, paradoxically, are likely to be among the most ecologically important in the country. The Chixoy and Usumacinta basins encompass large areas of humid tropical forest in the Pet\u0026eacute;n, Alta, and Baja Verapaz departments, yet they contribute a small fraction of national economic output. This contrast between ecological significance and economic weight is precisely the type of relationship that watershed-level SEEA-EA accounting is designed to make visible and quantifiable.\u003c/p\u003e \u003cp\u003eBelow the top-ranked watersheds sits a second tier of economically significant hydrological units. The Samal\u0026aacute; watershed (4.5%, Q.27,211\u0026nbsp;million), Achiguate (2.9%, Q.17,532\u0026nbsp;million), Selegua (2.9%, Q.17,261\u0026nbsp;million), and Ocosito-Naranjo (2.9%, Q.17,399\u0026nbsp;million) each contribute between 2.9% and 4.5% of national GDP and together account for roughly 11.2% of total output. These watersheds share a common structural feature. All four drain the Pacific slope of the Western Highlands and the agro-industrial piedmont, and they combine moderate urban economic activity with significant agricultural production in sugarcane, palm oil, and coffee. The Cahab\u0026oacute;n-Polochic-Lago Izabal watershed (2.0%, Q.12,195\u0026nbsp;million) and the Chixoy watershed (1.9%, Q.11,263\u0026nbsp;million) form a second group of watersheds with meaningful but more modest GDP contributions, a pattern reflecting their mixed rural and agricultural character, combined with urban nodes in Cob\u0026aacute;n and Salam\u0026aacute;. The five lowest-ranked watersheds, Candelaria (0.0%, Q.48\u0026nbsp;million), Moh\u0026oacute; (0.0%, Q.215\u0026nbsp;million), Hondo (0.0%, Q.1\u0026nbsp;million), Coat\u0026aacute;n (0.0%, Q.295\u0026nbsp;million), and Poj\u0026oacute;m (0.1%, Q.348\u0026nbsp;million), generate a combined total of less than Q.908\u0026nbsp;million, equivalent to just 0.15% of national GDP. The spatial concentration of Guatemala\u0026rsquo;s economic output is therefore extreme. The two leading watersheds alone (Motagua and Mar\u0026iacute;a Linda) account for 67.3% of national GDP, while the ten lowest-ranked watersheds together account for less than 2%.\u003c/p\u003e \u003cp\u003eThe Pacific-slope watersheds deserve particular attention, given their hybrid economic character. Watersheds such as Achiguate (2.9%), Ocosito-Naranjo (2.9%), and Coyolate (1.5%, Q.8,737\u0026nbsp;million) generate GDP shares that are substantially larger than their populations might suggest, reflecting the high land productivity of the Pacific piedmont, one of the most fertile agricultural zones in Central America, and the presence of large agro-industrial complexes processing sugarcane and palm oil for export. By contrast, the northern watersheds draining into the Gulf of Mexico, including the Usumacinta (0.3%, Q.1,636\u0026nbsp;million), La Pasi\u0026oacute;n (1.5%, Q.9,088\u0026nbsp;million), and Ixc\u0026aacute;n (0.3%, Q.1,642\u0026nbsp;million), generate relatively modest economic output despite encompassing large portions of the Pet\u0026eacute;n department, Guatemala\u0026rsquo;s most biodiverse and ecologically critical region. This spatial pattern has direct implications for environmental-economic accounting. The watersheds generating the least economic output are often those providing the most substantial ecosystem services, including carbon sequestration, biodiversity habitat, and freshwater regulation, pointing to a systematic undervaluation of ecological contributions to national welfare in conventional GDP accounting.\u003c/p\u003e \u003cp\u003eThe Mar\u0026iacute;a Linda sub-watershed recorded the highest GDP share at 32.2% of the national total. Since this is the only sub-watershed in the Mar\u0026iacute;a Linda watershed, the results at both levels are equivalent, making it the second-highest watershed overall. The Las Vacas sub-watershed ranked second (28.6%) and formed part of the Motagua watershed, which ranked highest at the watershed level. The sub-watershed concentration of the Motagua basin's economic output in the Las Vacas unit reflects the geographic concentration of metropolitan Guatemala within the Las Vacas tributary system, which drains the urban core of Guatemala City northward to its confluence with the Motagua River. The Samal\u0026aacute; sub-watershed ranked third (4.5%), being the only sub-watershed of the Samal\u0026aacute; watershed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEstimated GDP by Sub-Watershed: Top 15, Guatemala 2020\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-Watershed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP Share (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSub-Watershed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGDP Share (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNahualate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLos Esclavos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotagua bajo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePuerto Barrios\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOst\u0026uacute;a-G\u0026uuml;ija\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAchiguate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCahab\u0026oacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelegua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoyolate-Acom\u0026eacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSamal\u0026aacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrande\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLas Vacas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaranjo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMar\u0026iacute;a Linda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePixcay\u0026aacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Source: Authors\u0026rsquo; own calculations based on VIIRS nighttime lights (NASA/NOAA) and MAGA land use data (2021).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExamining the full sub-watershed distribution reveals a more granular picture of economic geography within Guatemala\u0026rsquo;s major basins. Within the Motagua watershed, which contributes 35.1% of the national GDP, economic output is highly unequally distributed across its constituent sub-watersheds. The Las Vacas sub-watershed (28.6%, Q.171,584\u0026nbsp;million) accounts for the overwhelming majority of the watershed\u0026rsquo;s total (Q.211,059\u0026nbsp;million). In contrast, the remaining Motagua sub-watersheds, Pixcay\u0026aacute; (1.7%, Q.10,238\u0026nbsp;million), Motagua bajo (1.0%, Q.5,971\u0026nbsp;million), El Tambor (0.4%, Q.2,322\u0026nbsp;million), Motagua alto (0.8%, Q.4,958\u0026nbsp;million), Chuac\u0026uacute;s-Uy\u0026uacute;s (0.7%, Q.3,989\u0026nbsp;million), and Teculut\u0026aacute;n Huit\u0026eacute; (0.4%, Q.2,461\u0026nbsp;million), collectively contribute only 5.0% of national GDP despite spanning a vast lowland territory. This intra-watershed inequality reflects the extraordinary concentration of economic activity in the Las Vacas headwaters region, which drains Guatemala City and its immediate metropolitan fringe, contrasted with the largely rural and sparsely populated middle and lower Motagua valley. The Pixcay\u0026aacute; sub-watershed\u0026rsquo;s relatively higher share (1.7%) reflects the economic activity of the Chimaltenango department and parts of the Guatemala City commuter belt that fall within its drainage area.\u003c/p\u003e \u003cp\u003eA further set of sub-watersheds warrants discussion for their intermediate economic weight. The Los Esclavos sub-watershed (1.7%, Q.10,337\u0026nbsp;million), which drains parts of Jalapa and Santa Rosa departments, and the Naranjo sub-watershed (1.6%, Q.9,376\u0026nbsp;million) and Grande sub-watershed (1.5%, Q.9,094\u0026nbsp;million), both part of the Pacific coastal system, rank among the top ten nationally, reflecting the agricultural productivity of their drainage areas. The Coyolate-Acom\u0026eacute; sub-watershed (1.5%, Q.8,737\u0026nbsp;million) captures a comparable share of Pacific piedmont agro-industrial output. At the lower end of the distribution, several sub-watersheds of the northern lowland systems produce extremely low GDP values. The Livingston sub-watershed (0.0%, Q.29\u0026nbsp;million), Hondo (0.0%, Q.1\u0026nbsp;million), and Las Pozas (0.0%, Q.237\u0026nbsp;million) represent hydrological units with minimal human economic activity but potentially high ecological value as part of the Caribbean coastal and lowland forest ecosystem. The Cahab\u0026oacute;n sub-watershed (1.4%, Q.8,490\u0026nbsp;million) deserves special mention as the most economically significant unit within the larger Cahab\u0026oacute;n-Polochic-Lago Izabal watershed, driven by coffee and cardamom production in Alta Verapaz alongside the growing economic activity of Cob\u0026aacute;n city. In total, the dataset covers 62 named sub-watersheds whose GDP values span five orders of magnitude, from Q.1\u0026nbsp;million for Hondo to Q.193,590\u0026nbsp;million for Mar\u0026iacute;a Linda, illustrating the spatial heterogeneity of economic activity across Guatemala\u0026rsquo;s hydrological landscape.\u003c/p\u003e \u003cp\u003eThe results at the sub-watershed scale reveal a marked concentration of economic activity in a small number of spatial units. Mar\u0026iacute;a Linda, Las Vacas, and Samal\u0026aacute; record substantially higher GDP shares than the remaining 59 sub-watersheds. This disparity reflects the dominance of non-agricultural economic activity in Guatemala's GDP and its geographic concentration in densely populated metropolitan and peri-urban areas. The three highest-ranking sub-watersheds are located in departments that rank among the most populous in the 2018 national census (INE, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Mar\u0026iacute;a Linda sub-watershed spans Guatemala, Sacatep\u0026eacute;quez, Santa Rosa, and Escuintla. The Las Vacas sub-watershed lies within the departments of Guatemala and El Progreso. The Samal\u0026aacute; sub-watershed covers Totonicap\u0026aacute;n and Quetzaltenango, which rank eighth and ninth in national population. These findings are consistent with the well-documented relationship between nighttime luminosity, population density, and economic output.\u003c/p\u003e \u003cp\u003eThe departmental results presented above can now be compared against the recently published official subnational accounts for Guatemala. In 2025, the Banco de Guatemala published its Regional and Departmental Gross Domestic Product estimates covering the period 2013\u0026ndash;2023 (Banco de Guatemala, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the first official disaggregation of GDP below the national level in the country\u0026rsquo;s statistical history. This publication constitutes an important benchmark for evaluating the estimates generated in the present study. A comparison of the departmental shares reported in the official accounts with those produced by this NTL-based methodology for 2020 reveals a high degree of correspondence in both relative rankings and orders of magnitude. The department of Guatemala consistently emerges as the dominant economic unit in both datasets, accounting for approximately 55\u0026ndash;60% of national GDP, while the ranking of subsequent departments, Escuintla, Quetzaltenango, Huehuetenango, is broadly preserved across the two methodologies. Departments at the lower end of the distribution, such as Baja Verapaz, El Progreso, and Totonicap\u0026aacute;n, also occupy comparable positions in both sets of estimates. These convergences lend credibility to the NTL-based approach adopted here, demonstrating that satellite-derived luminosity combined with land-use data can approximate the spatial structure of departmental economic activity with reasonable accuracy, even in the absence of official subnational accounts.\u003c/p\u003e \u003cp\u003eThe exercise reported here extends the official Banco de Guatemala estimates in two ways that are directly relevant to planning and to ecosystem accounting. The official regional accounts are published only at the departmental level, leaving the municipal scale, at which many local development decisions and natural resource management actions are taken, without any GDP reference. The municipal estimates produced here fill that gap for 2020, attributing economic output to Guatemala\u0026rsquo;s 340 municipalities and providing a spatially granular baseline for subnational investment planning and inequality analysis. A second, more consequential extension concerns the spatial logic of the accounts. The Banco de Guatemala regional accounts are organized around administrative units and cannot be linked directly to ecosystem spatial boundaries. Extending the disaggregation to the watershed and sub-watershed scales generates hydrologically organized economic data that SEEA-EA implementation requires,a dimension absent from the official departmental accounts. The two datasets are therefore best understood as complementary. The official accounts provide a validated reference for departmental magnitudes, while the NTL-watershed methodology developed here provides the spatial granularity needed for environmental-economic accounting and municipal-level policy analysis that the official statistics do not yet cover.\u003c/p\u003e \u003cp\u003eAt the municipal scale, the four highest-GDP municipalities all sit within the department of Guatemala. These are Guatemala City (33.94%), Mixco (8.87%), Villa Nueva (5.54%), and San Miguel Petapa (2.66%). The two lowest-GDP municipalities are both in Solol\u0026aacute;, namely Santa Catarina Palop\u0026oacute; (0.001%) and Santa Cruz La Laguna (0.002%) (see Appendix 3). The results across the four spatial scales point to a consistent picture. Economic activity in Guatemala is extraordinarily concentrated in a small metropolitan core, and the gradient between the capital region and the peripheral rural departments is among the steepest in Latin America.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Validation Against Alternative Estimates\u003c/h2\u003e \u003cp\u003eThe absence of official subnational GDP statistics in Guatemala makes it difficult to directly validate the results of this exercise. Nevertheless, both FUNDESA (Fundaci\u0026oacute;n para el Desarrollo de Guatemala) and ICESH (Instituto de Investigaci\u0026oacute;n en Ciencias Socio Humanistas) have produced departmental GDP estimates for Guatemala using distinct methodologies, providing useful benchmarks for comparison.\u003c/p\u003e \u003cp\u003eFUNDESA estimated departmental GDP using disaggregated economic activity data from the Banco de Guatemala, combined with Principal Component Analysis to identify behavioral patterns among economic activities and their geographic clustering. Urban and rural concentration estimates from the National Statistics Institute (INE) and the geographic distribution of economic activities were then used to allocate GDP across departments (C\u0026oacute;rdova, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Unlike the present study, the FUNDESA methodology does not explicitly account for population density. In contrast, nighttime lights are directly correlated with population density as an indicator of human activity in a territory.\u003c/p\u003e \u003cp\u003eICESH used four indicators for its departmental GDP estimates: electricity consumption, exports, the number of firms, and the employed population (D\u0026iacute;az, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These indicators were averaged to allocate the national GDP across departments. The ICESH approach distributes total GDP without disaggregating by economic activity, whereas the method applied here disaggregates by economic activity using the MAGA land-use classification. Comparing the ICESH estimates with the results reported here reveals a high degree of convergence. The underlying variables drive the overlap. Employed population is embedded in nighttime light intensities, electricity consumption is directly tied to luminosity, and exports are partially captured through the MAGA land use map, which reflects the agricultural production that accounts for the bulk of Guatemalan exports (D\u0026iacute;az, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The departmental GDP results obtained here are broadly consistent with the existing estimates from ICESH and FUNDESA in relative rankings and shares.\u003c/p\u003e \u003cp\u003eThe results support the view that combining nighttime lights with land-use data is a methodologically sound approach for subnational GDP disaggregation in countries where agriculture still accounts for a meaningful share of output. Guatemala is a clear case, with 9.8% of GDP generated in agricultural activities spread across 47.6% of the national territory. The remaining 90.2% of GDP is concentrated in urban zones that cover less than 2% of the country.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Implications for SEEA Ecosystem Accounting at the Watershed Scale\u003c/h2\u003e \u003cp\u003eA key application of the spatial GDP estimates reported here lies in their compatibility with the SEEA-EA, particularly for constructing watershed-scale ecosystem service accounts. The SEEA-EA is explicitly designed to link monetary economic flows to their spatial origin in ecosystems and to the natural capital stocks that underpin them (United Nations et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This linkage is structurally impossible without subnational GDP data organized at scales that correspond to ecosystem boundaries, which is what the present exercise produces for Guatemala.\u003c/p\u003e \u003cp\u003eA central challenge in SEEA-EA implementation is matching the spatial scale of economic data to that of ecosystem accounting units. National accounts, by design, aggregate all economic activity without geographic reference, making it impossible to determine how much output is generated within or dependent upon a given watershed, forest, or coastal ecosystem. The watershed and sub-watershed GDP estimates produced here resolve this problem for Guatemala. By attributing economic value to the same hydrological units used in ecosystem extent and condition accounts, the results enable analysts to construct ecosystem service flow accounts that quantify the economic contribution of water regulation, flood control, sediment retention, and other provisioning and regulating services at the watershed scale. This application is particularly pertinent in Guatemala, where watershed-level management is already institutionalized through agencies such as INAB and IARNA, and where freshwater ecosystem services represent a critical but undervalued input to agricultural and urban economic activity (Banco de Guatemala \u0026amp; IARNA-URL, 2011).\u003c/p\u003e \u003cp\u003eSpatially disaggregated GDP also enables the computation of environmental pressure indicators tied to specific territories. The SEEA central framework (United Nations et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) distinguishes between the monetary value of economic output and the physical costs of environmental degradation, deforestation, aquifer depletion, and soil erosion that may accompany it. Without subnational GDP, it is possible to document that a given watershed lost X hectares of forest in a given year, but not to attribute that loss to identifiable economic activities generating quantifiable output. By overlaying the GDP estimates from this study with physical flow accounts for land cover change, water extraction, or biomass removal, it is possible to calculate spatially explicit natural capital depletion rates relative to local economic output, which are far more informative for governance than national averages (Edens \u0026amp; Hein, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Vallecillo et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have similarly argued that aligning economic and ecological spatial units is a necessary condition for meaningful natural capital accounting. That satellite-derived economic proxies represent a viable approach for bridging this gap in countries with limited statistical infrastructure.\u003c/p\u003e \u003cp\u003eFurthermore, the land-use classification approach used in this study, which separately estimates agricultural and non-agricultural value added, based on MAGA\u0026rsquo;s land-cover classifications, is structurally analogous to the ecosystem-type mapping that anchors SEEA-EA accounts. The land-use categories used here can be crosswalked to the ecosystem typologies used in national ecosystem extent accounts, enabling the GDP estimates to be directly integrated into a broader SEEA-EA framework. This is consistent with the recommendations of Hein et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who argue that integrating standard land use data with economic accounts is an essential and practical pathway for implementing SEEA-EA in countries with limited statistical infrastructure. Obst (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) similarly emphasizes that SEEA implementation in developing countries depends on the availability of spatially explicit economic data at scales compatible with ecosystem boundaries, a requirement that the present study helps to address for Guatemala. More recently, Bar\u0026oacute; et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated, for urban watersheds in Europe, that linking spatial economic data with ecosystem service accounts can reveal critical dependencies between urban economies and the regulating services provided by surrounding ecosystems, a framework directly applicable to Guatemala's highly urbanized watersheds, such as Las Vacas and Mar\u0026iacute;a Linda.\u003c/p\u003e \u003cp\u003eA further set of implications concerns the valuation of ecosystem services. The SEEA-EA monetary supply accounts require estimates of the economic value of ecosystem services flowing to economic units within a given territory. Knowing that the Motagua watershed generates 35.1% of national GDP, or that the Mar\u0026iacute;a Linda sub-watershed generates 32.2%, offers an order-of-magnitude estimate of the economic value at stake from ecosystem degradation in those units. It also grounds the computation of ecosystem service dependency ratios, the share of territorial GDP traceable to ecosystem service inputs such as water supply, pollination, or climate regulation, which are increasingly used as indicators for natural capital risk assessment and green finance (United Nations et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In a country like Guatemala, where ecosystem degradation through deforestation and watershed deterioration is spatially concentrated in the same sub-watersheds that generate the most economic output, such indicators carry immediate policy weight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Comparison with Academic Literature\u003c/h2\u003e \u003cp\u003eThe methodology used here falls within a substantial body of peer-reviewed research that uses nighttime satellite imagery to estimate subnational economic activity. That literature supports NTL data as a proxy for GDP and identifies methodological refinements relevant to the Guatemalan case.\u003c/p\u003e \u003cp\u003eHenderson et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) established the econometric foundation for treating nighttime lights as a measure of economic growth from space, showing that changes in light intensity correlate significantly with GDP growth rates, even in countries with poor national accounts data. Their framework has been widely adopted and extended for subnational applications worldwide. Nordhaus and Chen (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) then offered a rigorous assessment of the precision of nighttime light data as an economic proxy, finding that luminosity-based estimates achieve reasonable accuracy at subnational scales while suffering from systematic biases in densely populated areas. That bias is directly relevant to the Guatemala City metropolitan region, which dominates both light intensity and economic output in the present results.\u003c/p\u003e \u003cp\u003eKeola et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) conducted a particularly relevant study using nighttime light data combined with land cover information to estimate subnational GDP growth across a set of developing countries. Their integration of land cover data to account for agricultural economic activity not captured by luminosity closely parallels the approach adopted here, and their results confirmed that the combination of these two data sources substantially improves estimation accuracy compared to NTL data alone. Similarly, Zhao et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used VIIRS nighttime light time series combined with population data to produce pixel-level GDP estimates for China, demonstrating the utility of this approach for spatially granular economic mapping at a national scale. Their methodology showed strong alignment with official provincial GDP data, lending further credibility to satellite-based approaches in contexts like Guatemala, where official subnational data are absent.\u003c/p\u003e \u003cp\u003eDoll et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) demonstrated early on that nighttime satellite imagery could be used to map socioeconomic parameters globally, establishing important methodological groundwork for subsequent regional applications. Their findings underscored both the promise and the limitations of luminosity-based proxies, particularly in rural areas with low electrification rates, a concern equally applicable to Guatemala's more remote departments. More recently, Sutton and Costanza (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) estimated market and non-market values at the global level by combining nighttime imagery with land cover data and ecosystem service valuation, illustrating how NTL data can be integrated with additional spatial layers to produce more comprehensive economic assessments. This finding anticipates and reinforces the SEEA-EA application dimension of the present study.\u003c/p\u003e \u003cp\u003eSubsequent contributions have refined the NTL-GDP methodology and extended its applications. Hu and Yao (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) developed an econometric framework to illuminate economic growth using panel data across countries and subnational regions, and they found that VIIRS data provide substantially more accurate economic proxies than the older DMSP-OLS sensor, particularly in rapidly growing economies where light saturation in dense urban areas distorts DMSP-based estimates. Yu et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used VIIRS nighttime light data to estimate subnational GDP growth, confirming the method\u0026rsquo;s temporal stability and demonstrating its utility for monitoring spatial economic dynamics over time, which is a potential extension for the Guatemalan application reported here. Gibson et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) systematically evaluated which NTL data sources and preprocessing approaches are most appropriate for different economic applications, and their finding that annual composites from VIIRS outperform monthly or shorter-period products for GDP estimation is consistent with the data selection made here.\u003c/p\u003e \u003cp\u003eAt the regional scale, McCord and Rodriguez-Heredia (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) estimated departmental GDP in Paraguay using nighttime lights, reporting results that closely matched alternative subnational estimates and confirming the methodology's transferability across Latin American countries. P\u0026eacute;rez-Sind\u0026iacute;n et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the use of nighttime lights as a proxy for economic activity in rural areas of Colombia, finding moderate but meaningful correlations at the municipal level, particularly where agricultural activity is relatively minor, suggesting that the NTL-land use combination approach adopted here is especially well suited to countries like Guatemala. Addison and Stewart (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) provided an extensive review of the conditions under which NTL data serve as reliable proxies for economic variables, noting that the proxy performs best when complemented by additional spatial covariates that capture economic dimensions not reflected in luminosity, which is exactly the role played by the MAGA land use data in the present exercise. Liang et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) extended the NTL-GDP methodology to incorporate machine learning via random forest regression to produce higher-resolution GDP estimates in Ningbo, China, suggesting a possible avenue for future methodological refinement in the Guatemalan context as higher-resolution spatial datasets become available.\u003c/p\u003e \u003cp\u003eThese studies form a comparative frame within which the Guatemalan watershed GDP accounting exercise can be situated, drawing on an established international literature on satellite-based subnational economic estimation. The watershed focus and the explicit SEEA-EA orientation of the present work add a dimension that has not been previously explored in the Central American context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Limitations\u003c/h2\u003e \u003cp\u003eThe GDP estimates generated in this study are an academic exercise and should not be taken as official statistics. A key limitation is the reliance on available land-use information. Guatemala lacks a continuous multi-year time series of land cover and land-use maps, and the most recent available map is from MAGA (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for 2020. The area-proportional allocation of agricultural value added assumes spatial homogeneity in productivity across land-use classes. This assumption does not hold in practice, given substantial variation in soil quality, water availability, and farming technology across Guatemala's diverse agroecological zones. Future work should explore weighting agricultural value added by agro-ecological productivity gradients or by yield data where available.\u003c/p\u003e \u003cp\u003eAnother limitation concerns the spatial allocation of non-agricultural economic activities, which had to be distributed proportionally based on light intensity, as precise geolocated data on the distribution of non-agricultural businesses and industries were unavailable. The results should therefore be interpreted as approximations that reflect broad spatial economic patterns rather than precise sectoral measurements at fine spatial scales. Light saturation in the Guatemala City metropolitan area, a known limitation of NTL data in high-density urban environments documented by Nordhaus and Chen (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), may slightly underestimate the economic output of the metropolitan core compared to that of peri-urban areas. For SEEA-EA applications specifically, these estimates would need to be complemented by physical flow accounts and ecosystem condition data before full ecosystem service valuations or natural capital depletion indicators could be computed.\u003c/p\u003e \u003cp\u003eFinally, the study covers a single year (2020), which coincides with the COVID-19 pandemic and associated economic disruptions. The unusual economic conditions of 2020, including contractions in service-sector output, reduced nighttime light intensity in commercial zones, and disruptions to agricultural supply chains, may affect the representativeness of the results relative to a typical pre-pandemic year. Extending the analysis to include 2018 or 2019 data would help assess the robustness of the watershed GDP distribution to inter-annual variability.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe paper has disaggregated Guatemala\u0026rsquo;s 2020 GDP across departments, municipalities, watersheds, and sub-watersheds using nighttime light maps and land use data, with particular emphasis on the watershed as the unit most relevant to SEEA-EA ecosystem accounting. The three departments with the highest GDP shares were Guatemala (58.6%), Escuintla (5.7%), and Quetzaltenango (4.3%). At the watershed level, Motagua (35.1%) and Mar\u0026iacute;a Linda (32.2%) accounted for more than two-thirds of national output. At the sub-watershed scale, Mar\u0026iacute;a Linda (32.2%) and Las Vacas (28.6%) accounted for the largest share. The four highest-GDP municipalities, Guatemala City (33.94%), Mixco (8.87%), Villa Nueva (5.54%), and San Miguel Petapa (2.66%), are all concentrated in the metropolitan department. The results also show that 9.8% of GDP is generated in agricultural activities spread across 47.6% of the national territory, while urban areas covering less than 2% of the country produce the remaining 90.2%.\u003c/p\u003e \u003cp\u003eThe watershed-level GDP estimates produced here are a building block for SEEA-EA implementation in Guatemala. Aligning economic output data with the hydrological spatial units used in ecosystem accounting supplies the monetary counterpart needed to construct ecosystem service flow accounts, compute natural capital depletion rates by watershed, and assess ecosystem service dependencies for the country\u0026rsquo;s most economically productive territories. The contrast between watersheds with high economic output and limited ecological cover, such as Mar\u0026iacute;a Linda and Las Vacas, and those with low economic output but high ecological significance, such as Chixoy, Polochic, and Usumacinta, is exactly the kind of spatial tension between economic activity and natural capital that SEEA-EA is designed to make visible and governable.\u003c/p\u003e \u003cp\u003eThe combination of VIIRS nighttime lights with land-use classification offers a viable and reproducible approach for countries like Guatemala, where official subnational GDP estimates do not exist, and agricultural production must be accounted for separately because it is poorly detected by satellite luminosity. The method travels well to other Central American and Latin American countries with similar data constraints that are advancing SEEA-EA implementation, as comparable applications in Paraguay (McCord \u0026amp; Rodriguez-Heredia, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Colombia (P\u0026eacute;rez-Sind\u0026iacute;n et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) illustrate. The approach is also well-suited to temporal extension. VIIRS nighttime light composites are available every year from 2012 onward, so the same procedure can generate a multi-year time series of watershed-level GDP estimates for the analysis of spatial economic dynamics and regional growth patterns.\u003c/p\u003e \u003cp\u003eFuture extensions of this work could draw on the updated MAGA land cover map, scheduled for publication in 2025 (MAGA, 2024), which will sharpen the allocation of agricultural value added and open up analysis of how land-use change affects watershed economic output. The spatial precision of the non-agricultural GDP estimates could also improve if geolocated data on non-agricultural economic activities became available from business registries or tax records. The most consequential next step, however, is to integrate these GDP estimates with Guatemala\u0026rsquo;s ongoing work on physical ecosystem accounting, particularly land cover change monitoring and water resource accounts. Such integration would move the country toward full SEEA-EA implementation and toward green GDP indicators at the subnational scale. It would also offer a methodological and policy contribution of value not only to Guatemala but also to the wider set of developing countries working to operationalize the SEEA-EA framework in conditions of statistical scarcity and ecological richness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. All three authors were affiliated with Universidad Rafael Land\u0026iacute;var at the time the research was conducted. At the time of manuscript preparation and submission, the authors\u0026rsquo; affiliations had changed to those indicated in the paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePaulina Reyes: Methodology, Formal analysis, Investigation, Data curation, Visualization, Writing \u0026ndash; original draft. Juan Pablo Casta\u0026ntilde;eda S\u0026aacute;nchez: Conceptualization, Supervision, Writing \u0026ndash; review and editing. Juan Miguel Goyzueta: Data curation, Resources, Writing \u0026ndash; review and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAddison D, Stewart B (2015) Nighttime Lights Revisited: The Use of Nighttime Lights Data as a Proxy for Economic Variables. In the \u003cem\u003eWorld Bank policy research working paper\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1596/1813-9450-7496\u003c/span\u003e\u003cspan address=\"10.1596/1813-9450-7496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanco de Guatemala y IARNA-URL (2011) Sistema de Contabilidad Ambiental y Econ\u0026oacute;mica Integrada de Guatemala (SCAEI): S\u0026iacute;ntesis explicativa. Autor\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanco de Guatemala (2019) Cuentas nacionales de Guatemala. Banco de Guatemala\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanco de Guatemala (2025) \u003cem\u003eProducto Interno Bruto Regional y Departamental 2013\u0026ndash;2023\u003c/em\u003e. Banco de Guatemala. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://banguat.gob.gt/page/producto-interno-bruto-regional-y-departamental-0\u003c/span\u003e\u003cspan address=\"https://banguat.gob.gt/page/producto-interno-bruto-regional-y-departamental-0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBar\u0026oacute; F, Palomo I, Zulian G, Vizca\u0026iacute;no P, Haase D, G\u0026oacute;mez-Baggethun E (2021) Mapping ecosystem service capacity, flow, and demand for landscape and urban planning: A conceptual framework and case study for urban watershed accounting. Landsc Urban Plann 208:104031. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landurbplan.2020.104031\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2020.104031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao C, Zhang B, Xia F, Bai Y (2022) Exploring VIIRS Night Light Long-Term Time Series with CNN/SI for Urban Change Detection and Aerosol Monitoring. Remote Sens 14(13):3126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs14133126\u003c/span\u003e\u003cspan address=\"10.3390/rs14133126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCEPAL (2023) Producto Interno Bruto subnacional por enfoque de producci\u0026oacute;n. Naciones Unidas. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cepal.org/es/notas/producto-interno-bruto-subnacional-enfoque-la-produccion\u003c/span\u003e\u003cspan address=\"https://www.cepal.org/es/notas/producto-interno-bruto-subnacional-enfoque-la-produccion\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Yu B, Yang C, Zhou Y, Yao S, Qian X, Wang C, Wu B, Wu J (2021) An extended time series (2000\u0026ndash;2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth Syst Sci Data 13(3):889. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/essd-13-889-2021\u003c/span\u003e\u003cspan address=\"10.5194/essd-13-889-2021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC\u0026oacute;rdova E (2019) Diagn\u0026oacute;stico del Corredor Econ\u0026oacute;mico Quetzaltenango-Huehuetenango/La Mesilla para el Proyecto Creando Oportunidades Econ\u0026oacute;micas. USAID\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az G (2023) Estimaci\u0026oacute;n del PIB por departamento. ICESH\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoll CNH, M\u0026uuml;ller J, Elvidge CD (2000) Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions. Ambio 29(3):157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1579/0044-7447-29.3.157\u003c/span\u003e\u003cspan address=\"10.1579/0044-7447-29.3.157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdens B, Hein L (2013) Towards a consistent approach for ecosystem accounting. Ecol Econ 90:41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolecon.2013.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolecon.2013.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh T, Powell R, Elvidge CD, Baugh K, Sutton PC, Anderson S (2010) Shedding Light on the Global Distribution of Economic Activity. Open Geogr J, \u003cem\u003e3\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://benthamopen.com/ABSTRACT/TOGEOGJ-3-147\u003c/span\u003e\u003cspan address=\"https://benthamopen.com/ABSTRACT/TOGEOGJ-3-147\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibson J, Olivia S, Boe-Gibson G, Li C (2020) Which night lights data should we use in economics, and where? J Dev Econ 149:102602. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jdeveco.2020.102602\u003c/span\u003e\u003cspan address=\"10.1016/j.jdeveco.2020.102602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHein L, Bagstad KJ, Obst C, Edens B, Schenau S, Castillo GB, Soulard F, Brown C, Driver A, Bordt M, Steurer A, Harris R, Caparr\u0026oacute;s A (2020) Progress in natural capital accounting for ecosystems. Science 367(6477):514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aaz8901\u003c/span\u003e\u003cspan address=\"10.1126/science.aaz8901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenderson JV, Storeygard A, Weil DN (2012) Measuring economic growth from outer space. Am Econ Rev 102(2):994\u0026ndash;1028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1257/aer.102.2.994\u003c/span\u003e\u003cspan address=\"10.1257/aer.102.2.994\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Yao J (2022) Illuminating economic growth. J Econ 228(2):359\u0026ndash;378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jeconom.2021.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jeconom.2021.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINE (2018) Caracter\u0026iacute;sticas generales de la poblaci\u0026oacute;n. Instituto Nacional de Estad\u0026iacute;stica, Guatemala. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.censopoblacion.gt/censo2018/poblacion.php\u003c/span\u003e\u003cspan address=\"https://www.censopoblacion.gt/censo2018/poblacion.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeola S, Andersson M, Hall O (2015) World Dev 66:322\u0026ndash;334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.worlddev.2014.08.017\u003c/span\u003e\u003cspan address=\"10.1016/j.worlddev.2014.08.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Monitoring economic development from space: Using nighttime light and land cover data to measure economic growth\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang H, Guo Z, Wu J, Chen Z (2020) Adv Space Res 65(1):481\u0026ndash;493. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.asr.2019.09.035\u003c/span\u003e\u003cspan address=\"10.1016/j.asr.2019.09.035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMAGA (2021) Determinaci\u0026oacute;n de la cobertura vegetal y del uso de la tierra a escala 1:50.000 en la Rep\u0026uacute;blica de Guatemala, a\u0026ntilde;o 2020. Serviprenta, S.A.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMAGA. (2024), April 1 Actualizar\u0026aacute;n el Mapa de Cobertura Vegetal y de Uso de la Tierra. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maga.gob.gt/actualizaran-mapa-de-cobertura-vegetal-y-uso-de-la-tierra/\u003c/span\u003e\u003cspan address=\"https://www.maga.gob.gt/actualizaran-mapa-de-cobertura-vegetal-y-uso-de-la-tierra/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCord GC, Rodriguez-Heredia M (2022) Nightlights and Subnational Economic Activity: Estimating Departmental GDP in Paraguay. Remote Sens 14(5):1150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs14051150\u003c/span\u003e\u003cspan address=\"10.3390/rs14051150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNordhaus WD, Chen X (2015) A sharper image? Estimates of the precision of nighttime lights as a proxy for economic statistics. J Econ Geogr 15(1):217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jeg/lbu035\u003c/span\u003e\u003cspan address=\"10.1093/jeg/lbu035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObst C (2015) Reflections on natural capital accounting at the national level: The state of play for ecosystem accounting. Sustain Acc Manage Policy J 6(3):406\u0026ndash;429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/SAMPJ-04-2015-0034\u003c/span\u003e\u003cspan address=\"10.1108/SAMPJ-04-2015-0034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Sind\u0026iacute;n X, Chen TK, Prishchepov AV (2021) Are night-time lights a good proxy for economic activity in rural areas of middle- and low-income countries? Examining the empirical evidence from Colombia. Remote Sens Appl Soc Environ 24:100647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2021.100647\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2021.100647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRinc\u0026oacute;n-Patino J, Ramos-Poli D, Angarita-Sierra T (2021) Watershed-scale ecosystem service accounting to support river basin management in Colombia. Ecosyst Serv 48:101250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoser.2021.101250\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoser.2021.101250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuarez E (2016) Estimation of the regional GDP using VIIRS nighttime lights. UNEP/GRID-Geneva\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutton PC, Costanza R (2002) Global estimates of market and non-market values derived from nighttime satellite imagery, land cover, and ecosystem service valuation. Ecol Econ 41(3):509. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0921-8009(02)00097-6\u003c/span\u003e\u003cspan address=\"10.1016/s0921-8009(02)00097-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations E, Commission F, Organization A, Bank OECD (2014) System of Environmental-Economic Accounting 2012: Central Framework. United Nations. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18356/4c8ffc5a-en\u003c/span\u003e\u003cspan address=\"10.18356/4c8ffc5a-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations E, Commission F, Organization A, World Bank (2021) OECD, and. System of Environmental-Economic Accounting,Ecosystem Accounting (SEEA EA). United Nations. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seea.un.org/ecosystem-accounting\u003c/span\u003e\u003cspan address=\"https://seea.un.org/ecosystem-accounting\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVallecillo S, La Notte A, Zuccarini G, Ferrini S, Acosta L, Maes J (2022) How to align monetary ecosystem services accounts with the SEEA EA: A proposal for a combined framework. Ecol Econ 196:107402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolecon.2022.107402\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolecon.2022.107402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu T, Ye Y, Fan Z (2023) Estimating GDP growth using VIIRS night-time light data. Int J Remote Sens 45(21):7939. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01431161.2023.2203345\u003c/span\u003e\u003cspan address=\"10.1080/01431161.2023.2203345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao N, Liu Y, Cao G, Samson E, Zhang J (2017) Forecasting China\u0026rsquo;s GDP at the pixel level using nighttime lights time series and population images. GIScience Remote Sens 54(3):407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15481603.2016.1276705\u003c/span\u003e\u003cspan address=\"10.1080/15481603.2016.1276705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Q, Seto KC, Zhou Y, You S, Weng Q (2023) Nighttime light remote sensing for urban applications: Progress, challenges, and prospects. ISPRS J Photogrammetry Remote Sens 202:125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isprsjprs.2023.05.028\u003c/span\u003e\u003cspan address=\"10.1016/j.isprsjprs.2023.05.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"sn-business-and-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"43546","submissionUrl":"https://submission.nature.com/new-submission/43546/3","title":"SN Business \u0026 Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"watershed accounting, GDP regionalization, SEEA ecosystem accounting, satellite remote sensing, natural capital","lastPublishedDoi":"10.21203/rs.3.rs-9452018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9452018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a spatial disaggregation of Guatemala's Gross Domestic Product (GDP) at the watershed and sub-watershed levels using Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) satellite imagery and land cover and land use national data. The central motivation is to generate spatially explicit economic data aligned with hydrological units, a foundational requirement for implementing the System of Environmental-Economic Accounting, Ecosystem Accounting (SEEA-EA), adopted by the United Nations Statistical Commission in 2021. By integrating satellite-derived luminosity with National Accounts statistics from the Guatemalan Central Bank, the country's 2020 Gross Domestic Product (GDP) was disaggregated to the department, municipality, watershed, and sub-watershed levels. Non-agricultural value added was allocated using nighttime light intensity as a proxy for economic activity. In contrast, agricultural value added was distributed using a land cover map from the Ministry of Agriculture (MAGA). Results reveal that the Motagua watershed (35.1%) and the Mar\u0026iacute;a Linda watershed (32.2%) generated the largest shares of national GDP, followed by Samal\u0026aacute; (4.5%) and Cahab\u0026oacute;n-Polochic-Lago Izabal (2.0%). The findings demonstrate that combining VIIRS nighttime lights with land-use classification constitutes a valid and reproducible methodology for subnational GDP estimation in data-scarce environments. Beyond its direct policy relevance for regional planning and inequality analysis, this study contributes to Guatemala's SEEA-EA implementation pathway by providing the monetary economic counterpart to spatially explicit ecosystem extent and condition accounts. Watershed-level GDP estimates could enable the computation of ecosystem service flow accounts.\u003c/p\u003e","manuscriptTitle":"Watershed Income Accounting for SEEA-EA Implementation: Mapping Economic Activity in Guatemala Using Satellite Imagery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:49:16","doi":"10.21203/rs.3.rs-9452018/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T06:07:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145280312820228014380733203614694383883","date":"2026-05-02T15:41:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T05:18:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T05:02:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-20T05:01:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"SN Business \u0026 Economics","date":"2026-04-17T18:18:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"sn-business-and-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"43546","submissionUrl":"https://submission.nature.com/new-submission/43546/3","title":"SN Business \u0026 Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"55a73c20-a305-4e62-a831-5dc5e54cb7db","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T06:07:34+00:00","index":26,"fulltext":""},{"type":"reviewerAgreed","content":"145280312820228014380733203614694383883","date":"2026-05-02T15:41:15+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:49:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:49:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9452018","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9452018","identity":"rs-9452018","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}