Seasonal and Hydroclimatic Dynamics in Wetlands Using Spectral Indices and Water Balance in Different Geomorphological Units

Seasonal and Hydroclimatic Dynamics in Wetlands Using Spectral Indices and Water Balance in Different Geomorphological Units | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Seasonal and Hydroclimatic Dynamics in Wetlands Using Spectral Indices and Water Balance in Different Geomorphological Units Christhian Santana, Laurindo Antônio Guasselli¹, Carina Cristiane, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6331096/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Wetlands perform essential ecological functions and are sensitive to hydroclimatic variations. This study analyzed the hydrological dynamics of wetlands located in two geomorphological units in the far south of Brazil. We integrated time series (2016–2024) of precipitation (CHIRPS), evapotranspiration (MODIS MOD16A2), and Sentinel-2 images processed in Google Earth Engine, and applied the NDWI, NDMI, and MNDWI indices to map water surfaces. The results indicate that MNDWI was more effective in identifying flooded areas, especially in environments containing vegetation and sediments. The water balance analysis (P-ET) revealed distinct seasonal patterns: (1) greater stability and extensive flooded areas; and (2) irregular pulses and prolonged periods of water surface retreat. Evapotranspiration displayed seasonality, peaking in summer, while precipitation had an irregular pattern with greater amplitude during extreme events. Wetland responses to climatic variability were directly tied to local geomorphology and water storage capacity. These findings underscore the importance of using time-series and remote sensing in wetland monitoring, especially in the face of climate change. The proposed methodology is replicable and supports wetland conservation and mapping through the interplay between spectral indices and water balance. Wetlands Water Balance Time Series and Water Surface Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Wetlands perform fundamental environmental roles in biodiversity conservation, hydrological and geochemical cycles (GOMES; JUNIOR, 2018; SIMIONI; GUASSELLI, 2024). In this context, understanding the seasonal dynamics of wetlands (WDs) is essential for assessing and monitoring water availability, conservation, and management of these ecosystems (DRENKHAN et al., 2022). Furthermore, wetlands, characterized by the transition between terrestrial and aquatic environments, are crucial for maintaining fauna and flora species (CUNHA et al., 2023). In this sense, it is important to understand that the relationship between terrestrial and aquatic environments in wetlands is directly influenced by hydrological connectivity, a key characteristic for maintaining ecosystem functions in these transitional environments (ZIQI et al., 2021). According to Nhamo, Magidi, and Dickens (2017), wetlands can only be properly managed if their spatial location and extent are accurately documented, and their dynamics are well understood, as their type and morphology are highly variable. In the context of wetlands, understanding the hydrological components, the relationship with seasonal changes, the response to flood pulses, and the connectivity between environments (SIMIONI; GUASSELLI, 2017) are important aspects. In summary, ecological connectivity between rivers and floodplains can be classified into (i) landscape connectivity; (ii) hydrological connectivity; and (iii) sedimentological connectivity (BRACKEN; CROKE, 2007). From another perspective, considering that the surface and water layer in wetlands are directly influenced by hydroclimatic aspects, precipitation (P), and evapotranspiration (ET), understanding the land surface and its relationship with the atmosphere enables a better understanding of the water cycle, hydroclimatic relationships, surface water dynamics, and the occurrence of droughts and floods (MOREIRA et al., 2019). Furthermore, according to Cristobal et al. (2024), evapotranspiration (ET) plays a significant role in the hydrological cycle due to surface-atmosphere exchanges, along with precipitation (P). In this context, the surface water balance (P-ET) is a key parameter for the management and conservation of wetland ecosystems. However, in situ measurement of variables such as precipitation and evapotranspiration, for instance, can be challenging and involve high costs, complicating the monitoring of these variables. Gao et al. (2010) conducted water balance analyses using satellite images and remote sensing techniques, estimating surface water runoff based on the relationship between precipitation, evaporation, and terrestrial water storage change. Moreira et al. (2019) achieved satisfactory results with high correlation percentages and acceptable mean error for water balance calculations using different sensor systems, such as the Gravity Recovery and Climate Experiment (GRACE), Tropical Rainfall Measuring Mission (TRMM), Multi-Source Weighted-Ensemble Precipitation (MSWEP), MODIS Global Evapotranspiration Project (MOD16), and the Global Land Evaporation Amsterdam Model (GLEAM). Trabelsi and Abida (2024) estimated water balance in wetlands based on Sentinel-2B, Global Precipitation Measurements (GPM), and the Famine Early Warning System Network (FEWS-NET). They observed that annual, seasonal, and monthly water balance simulations showed agreement between remote sensing-based analyses and hydrological modeling. Regarding the characterization of wetlands, according to Cunha et al. (2023) and Semeniuk; Semeniuk (1995; 2011), geomorphology and hydrology are important variables for the classification and understanding of wetlands. These are structuring factors that tend to be less dynamic and mutable over time compared to biological factors, allowing wetlands to be categorized into more stable classes, even when substantially altered by vegetation or soil removal (GOMES; JUNIOR, 2018). Understanding the relationship between precipitation, evapotranspiration (water balance), and spectral indices allows for the evaluation of the impact of changes and seasonal variability, as well as the interpretation of water availability (APOPEI et al., 2023). However, according to Karaman et al. (2015) and Tahsin, Medeiros, and Singh (2020), the variation in the water layer of wetlands, analyzed through spectral indices and hydrometeorological data derived from satellite images, can be influenced by climatic seasonality and the greater or lesser incidence of rainfall. For wetland monitoring and classification, Sadiq et al. (2022) used the Normalized Difference Moisture Index (NDMI) derived from two infrared bands. The authors mention that the index is sensitive to vegetation moisture content but can also be used to distinguish basic land cover classes in wetlands. Areas with an NDMI greater than zero are used to delineate wetlands. Chowdhary and Vyas (2022) applied spectral indices over different periods to map seasonal changes in water levels in wetlands, resulting in structural and functional alterations. They observed that 50% of the mapped wetlands contribute to water retention during dry periods. According to Ziqi et al. (2021), seasonal and interannual variations in wetlands influence the connectivity processes of these environments, considering the increase in emergent plants and water levels. Fawang et al. (2018) used satellite imagery and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to calculate the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Vegetation Index (NDVI) for analyzing the seasonal dynamics of wetland ecosystems, vegetation responses, and the delineation of water layers. Abílio (2018) employed time-series satellite images in wetlands and floodplains to assess changes, risk factors, and potential in natural or anthropized environments, aiming to adapt land use and occupation, exploitation, conservation, mitigation, and prioritization of responses. A comparative hydrology approach involving multiple wetlands emerges as a promising framework for understanding ET in wet landscapes across various climates and biomes, as highlighted by Fleischmann et al. (2023). Moreover, the authors emphasize that, by doing so, this framework has the potential to facilitate a consistent understanding of the role of various environmental factors (e.g., precipitation, flooding, available energy, and vapor pressure deficit) and enable predictions about how these areas respond to ongoing environmental changes. Although the use of time series and spectral indices to analyze seasonal dynamics in wetlands is addressed in various studies, the context that integrates water balance and water layer variation considering geomorphological units is rarely discussed and analyzed. This indicates that analyses must take into account the importance of wetland ecosystems, the hydrological connectivity between terrestrial and aquatic environments, and the influence of seasonal and hydrometeorological variables on vegetation cover and the water layer in these environments. In this context, this study aims to analyze the hydroclimatic dynamics of wetlands in southern Brazil using time series data from different sensor systems processed in the cloud computing environment of Google Earth Engine. The study seeks to identify similar hydrological patterns among the wetlands, understand the influence of water balance on apparent water layer pulses, and evaluate the role of geomorphological units in this context. Materials and Methods Study area The study area (Figure 1) encompasses wetlands located in two geomorphological units: the Coastal Plain and the Central Depression, in the state of Rio Grande do Sul, Brazil. Regarding climate, according to Moreno (1961) and Rossato (2011), geomorphological compartments significantly influence the state's climate. The Central Depression region has higher altitudes, while maritime influences predominantly affect the Coastal Plain. These geomorphological differences result in distinct distributions of precipitation and temperature. In the Coastal Plain, the wetlands include the São Gonçalo Channel, Taim Ecological Station, and Lagoa do Peixe National Park. This region covers approximately 33,000 km² in the outer portion of the Coastal Plain. It consists of a mosaic of ecosystems dominated by pioneer vegetation under marine and fluvial influence, as well as grassland and forest formations (SCHAFER, 2013). Differentiated mosaics defined by landscape morphology and water availability occur, with sparse vegetation in dry grasslands and dune areas and lush vegetation in wetlands in depressions and dune fields (SCHAFER, 2013). In the Central Depression, located in the central portion of Rio Grande do Sul, the wetlands analyzed were Banhado Grande and Banhado do Inhatium. This geomorphological unit corresponds to a low-altitude area, represented by Mesozoic sediments of the Paraná Basin, shaped by peripheral erosion processes from the Late Mesozoic and Cenozoic. The geomorphology is characterized by a surface with differentiated patterns of hills with flat or convex tops (SUERTEGARAY; GUASSELLI, 2012). It is predominantly composed of grasslands and deforested pastures, with an intensive summer agricultural zone and a diversified crop agricultural zone (SPGM, 2021). According to Brubacher et al. (2021), the state of Rio Grande do Sul is located in a transitional climatic zone cyclically influenced by pressure centers and atmospheric systems active in southern South America, including Extratropical Atmospheric Systems (polar masses and fronts) and Intertropical Systems (tropical masses and disturbed currents). The climate in Rio Grande do Sul is Temperate Subtropical, classified as humid Mesothermal (Köppen classification). According to Rossato (2011) and Maluf (2000), the Central Depression has a Subtropical and Subtemperate climate with temperatures ranging from 16°C to 22°C, while the Coastal Plain has a Subtemperate climate with average temperatures between 16°C and 22°C. In terms of precipitation, Rio Grande do Sul experiences annual volumes ranging from 1,200 mm to 2,000 mm in the rainiest regions. According to Rossato (2011), the study area exhibits significant seasonal variability in precipitation, often masked when only annual totals are considered. Furthermore, Brubacher et al. (2021) emphasize that the rainfall regime in Rio Grande do Sul is not homogeneous. The specific characteristics of the state’s relief result in varying precipitation behaviors, with rainfall being primarily influenced by atmospheric dynamics in relation to the terrain, whose compartmentalization promotes the spatial distribution of precipitation. Database, Sensors and Indexes For floodplain and wetland mapping and climatic characterization, images from the Harmonized Sentinel-2 MSI: MultiSpectral Instrument collection, CHIRPS Daily: Climate Hazards Center InfraRed Precipitation With Station Data (Version 2.0 Final), and MODIS Evapotranspiration (MOD16A2 Version 6) satellite images were used, covering the period from 2016 to 2024 (Table 1). Table 1 . Datasets available in Google Earth Engine. Sensor Resolution Use Acquisition Source (m) (days) Harmonized Sentinel 2A e 2B 20 Surface Reflectance 5 to 10 https://developers.google.com/earth-engine/datasets/catalog/sentinel-2 CHIRPS 5000 Precipitation 1 https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY MOD16A2 500 Total Evapotranspiration (ET), 8 https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2 Potential Total Evapotranspiration (PET) https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2 Elaboration: The authors (2022) From Sentinel-2 images, the following indices were calculated: the Normalized Difference Water Index (NDWI), the Normalized Difference Moisture Index (NDMI), and the Modified Normalized Difference Water Index (MNDWI) for characterization associated with vegetation, water, and soil moisture (Table 2). To estimate the evapotranspiration of wetlands and characterize the water balance, we selected the MOD16A2 Version 6 product. This dataset is an 8-day composite product with a spatial resolution of 500 m. The algorithm used to generate the MOD16 data product is based on the logic of the Penman-Monteith equation, which incorporates daily meteorological reanalysis data along with MODIS remote sensing data products, such as vegetation property dynamics, albedo, and land cover (CUNHA et al., 2023). The ET variable is important for wetland mapping and characterization due to its ability to identify seasonal variations over historical series (CERON et al., 2015) and to contextualize hydroclimatic aspects (MOREIRA et al., 2019). CHIRPS Daily data were used to obtain precipitation information and to calculate the simplified water balance (P-ET). Water flow and water balance simulations using CHIRPS have shown satisfactory results when compared to other precipitation products (DHANESH et al., 2020). Workflow, Data Processing, and Time Series Analysis The database was standardized according to the temporal scale of the data, adjusting for monthly totals and averages and filtering data for the study areas. Data aggregation to a monthly scale aimed to standardize temporal scales and reduce the likelihood of missing data. Sentinel-2 images were filtered for cloud coverage below 35%, and area values were calculated in hectares. Cloud masks were applied using Sentinel-2 image properties to remove cloud pixels and optimize mosaics without the presence of clouds and shadows. According to Al-Maliki et al. (2022), wetland characterization is typically achieved by analyzing the primary spectral characteristics of land cover units based on their reflectance in the visible and infrared ranges. The separation of open water, vegetation, and soils is best accomplished using red (0.60-0.69 μm) and near-infrared (0.70-1.30 μm) wavelengths. From monthly images between 2016 and 2023, the NDVI, NDMI, NDWI, and MNDWI indices were calculated to delineate water layers in the time series and compare them with the simplified water balance (P-ET) and the respective water layers generated by applying a threshold. To define thresholds, pixels corresponding to water were inspected, and these values were subsequently used to segment areas with the presence of apparent water. Figure 2 illustrates the relationship between the indices and the water layers generated. In this context, a water layer was created through segmentation by selecting only pixels with values greater than 0.01 for MNDWI. This definition was based on the visual interpretation of the images and the analysis of partial results of the water layer areas delineated by each index, which showed the best adjustments for MNDWI values. Monthly water layer masks were then created, and the area was calculated in hectares. To analyze the results of the relationship between water layer variations and water balances (Figure 3), a database was structured containing wetlands, year, month, water layer area values, precipitation, evapotranspiration, and effective precipitation (P-ET balance). Results The analysis of the results (Figure 4) shows that the NDMI index presented the highest values for delineated wet or apparent water layer areas across all wetlands analyzed. The MNDWI values recorded larger areas for water delineation compared to NDWI, which showed the smallest results for water layer area. The results highlight the effect of seasonality, defining periods of maximum, minimum, or transitional areas over months and years in all wetlands. The Inhatium and Banhado Grande wetlands, located in the Central Depression, exhibited lower water peaks throughout the historical series when compared to other wetlands in the Coastal Plain. In the Coastal Plain, fluctuations in maximum and minimum values were more consistent, with smaller variations. The MNDWI values showed a better fit for detecting the presence of water compared to the other indices, which underestimated (NDWI) or overestimated (NDMI) water values. In the historical water layer series the São Gonçalo Channel System shows the largest water layer area compared to the other wetlands analyzed. This result can be observed considering the amplitude and persistence of the water layer area values obtained from the historical MNDWI series, indicating more extensive and permanent water bodies. The Taim Ecological Station and Lagoa do Peixe National Park wetlands have smaller areas, although they exhibit significant fluctuations related to seasonality. The largest precipitation amplitudes are observed in Banhado do Inhatium, with a record of 466 mm/month in September 2023, and in Taim Ecological Station, with 431 mm/month in April 2016. Lagoa do Peixe National Park shows the lowest precipitation values compared to the others, with smaller precipitation peaks. In terms of evapotranspiration, Banhado Grande exhibits the highest indices, with peaks nearing 150 mm/month. The other wetlands display similar evapotranspiration amplitudes. In the São Gonçalo Channel System and Taim Ecological Station, there was a significant reduction in the water layer between November 2019 and August 2022, reflecting lower water balance results during this period. The reduction in rainfall during this time may have led to greater water loss through evapotranspiration (Figure 6). The wetland in Lagoa do Peixe National Park exhibited less interannual variability in water layer areas and water balance results, potentially indicating greater hydrological resilience even during deficit periods. The Inhatium wetland showed a significant reduction in the water layer between September 2018 and September 2023 (Figure 6). Although the corresponding values showed a positive water balance (P-ET) as indicated in Figure 7, it is estimated that the water retention capacity of this wetland, combined with soil characteristics and surrounding land use, resulted in a longer dry period compared to other wetlands. As a result, the apparent water layer values remained close to zero, differing from the other wetlands. Periods in the historical series were observed in which MNDWI did not directly reflect the water balance. This suggests the influence of other factors, such as vegetation cover, land use, anthropogenic changes, or the absence of images for data extraction. It is estimated that smaller areas like the Inhatium wetland exhibit lower amplitude due to the seasonality of water balances and water layers, resulting from a reduced water storage capacity. Banhado Grande, like the others, recorded the highest water layer values in July and September. Unlike the behavior observed in Inhatium, Banhado Grande maintained a stable apparent water layer over the years, with small pulses caused by accumulated rainfall. The results of the historical series (Figure 6) reveal distinct seasonal variations, showing increases and decreases in the apparent water layer identified by MNDWI. These areas (Figure 7) are compared with the results of the simplified water balance (P-ET), highlighting the water balance throughout the months. In the historical series for each wetland, regarding water layer area and water balance (Figure 7), only the Inhatium wetland exhibited water layers close to zero even during periods of water surplus (P-ET). Between 2017 and 2023, the highest water layer values were recorded, but these were not associated with the highest P-ET balances. The Inhatium wetland region experienced few months of water deficit; however, between 2020 and 2023, trends of water reduction in the system were observed, although recovery of the system was already noticeable by 2023. A similar behavior was observed in Banhado Grande, where peaks in water layers were recorded in the historical series between 2020 and 2022. However, the water area in July 2020 was the result of elevated precipitation levels. In other months, even with P-ET balances greater than 600 mm/month, water layer areas did not exceed the average. In the São Gonçalo Channel and Lagoa do Peixe wetlands, between 2020 and 2022, the water layer values directly responded to the monthly water balance. During periods of water deficit, the water layer areas in these wetlands decreased, and when the water balance (P-ET) increased, the flooded area was reflected in the historical series. The seasonal variations (Figure 8) in water balance (in mm/month) for the different wetlands throughout the year illustrate the dispersion of monthly values, allowing for the identification of seasonal patterns, outliers, and the amplitude of variations within the study areas. All wetlands exhibit lower water balance values during the summer months, from January to February, due to lower rainfall, higher temperatures, and increased evapotranspiration in southern Brazil. However, the highest water balance values consistently appear between September and November (spring in the Southern Hemisphere). Although the amplitude of maximum and minimum values varies across wetlands, a pattern of greater dispersion is noticeable in October and November, indicating that these months may be subject to extreme rainfall events with high interannual variability. Regarding variability, the São Gonçalo Channel shows the greatest dispersion among values, representing potential sensitivity to extreme climatic events, such as intense rainfall or severe drought periods. Conversely, the Taim Ecological Station exhibits a more consistent hydrological pattern with lower dispersion, representing greater environmental stability. The Inhatium wetland exhibits greater variability during the drier months, with higher dispersions in September and October, suggesting a stronger influence of rainfall. Between November and December, the variability decreases, similar to the behavior observed in other wetlands. Banhado Grande shows significant concentrations of negative values during the drier months, indicating a reduction in water within the system and potentially reflecting periods of water deficit. The concentration of water balance values remains stable throughout the months, with the greatest dispersions occurring during both wet and dry periods. In the analysis of the areas calculated using MNDWI (Figure 9), most wetlands show larger areas between September and November and significant reductions between January and February. This relationship is also observed in the water balances, except for Banhado Grande and Lagoa do Peixe, which also exhibit high values in July. Furthermore, the seasonal dynamics of wetlands can be observed in the variation of water layer areas mapped using MNDWI for both geomorphological regions. The distance between the upper and lower quartiles increases significantly in the months with the greatest floods, indicating greater variability in the hydrological response in these periods in different wetlands. In contrast, during drier months (January and February), the dispersion of results is smaller and more stable. The water layer area in Banhado Grande shows low concentrations between January and April, with an increase in water layer area starting in May and peaking between July and September. Similarly, the same behavior is observed in Banhado do Inhatium, where small water layer areas are concentrated between November and April, while larger dispersions with high amplitudes occur between July and September. Wetlands in Lagoa do Peixe National Park, Taim Ecological Station, and São Gonçalo Channel exhibit constant transitions, with a continuous increase in water layer area between March and October, peaking between August and October. It is evident that all wetlands have smaller areas during summer in hotter periods and larger flooded areas in wet months between winter and spring. Moreover, the wetlands exhibit differences in the scales of hydrological dynamics, with those in the Coastal Plain being larger than those in the Central Depression. Discussion Performance of Water-Mapping Indices The use of satellite image collections has made it possible to analyze, through time series, how wetlands respond to changes in land use and cover, flooding pulses, and water stress resulting from extreme events associated with El Niño and La Niña. Employing spectral indices enhances the classification of these areas, demonstrating efficiency and feasibility (CAVALLO et al., 2021), and is valuable for the continuous monitoring of wetland environments. Our study utilized images from 2016 to 2024 to calculate water indices derived from Sentinel-2 MSI, employing threshold-based segmentation. According to Kordelas et al. (2018), using thresholds to define the apparent water surface in flooded areas presents high performance metrics for various indices. To identify the most suitable index for detecting variations in the apparent water surface area, we evaluated the performance of NDMI, NDWI, and MNDWI. These indices have proven effective, offering solid support for surface water management and informing discussions on which index, methods, and processes are the most efficient (ALBERTINI et al., 2022). The results obtained with NDMI indicated overestimated values for the water surface in wetlands. In addition to the water surface, pixels corresponding to vegetation with higher moisture content were also mapped. NDMI is widely used to understand vegetation moisture and measure water stress. Because it is sensitive to moisture in vegetation, it can cause confusion between highly humid areas and water bodies (GAO, 1996; AL-MALIKI et al., 2022). Al-Maliki et al. (2022) emphasize that NDMI is particularly suitable for classifying different vegetation types; however, in our study, it did not delineate water areas as expected. The tendency to underestimate water surface areas using NDWI was identified by Menon et al. (2015) when comparing the areas obtained through other indices via statistical analyses and visual inspections. MNDWI showed values that were more consistent with inspections carried out in the watercourses of both geomorphological units, confirming the visual analysis when compared with the other indices. However, we observed that, in mapping water surface areas, water pixels more strongly influenced by sediments or vegetation were not classified. Singh et al. (2015), when analyzing the NDWI and MNDWI indices for flood detection, found that NDWI tends to highlight water surface areas less effectively, especially when there is interference from built structures located nearby or mixed with the water. MNDWI performed better, being more sensitive to the presence of water even when mixed with vegetation, resulting in positive values that facilitate identification. Mehmood et al. (2021) used MNDWI in Google Earth Engine to map and classify water by implementing the Flood Mapping Algorithm (FMA), filtering out areas of shadow, vegetation, and HAND maps. The literature on mapping areas with a water surface or flooded wetlands using various sensor systems indicates that MNDWI achieves the best performance for identifying flooded areas, thanks to its capacity for recognizing mixed pixels and turbid water (containing algae and vegetation). In terms of detecting surface water, both MNDWI and NDWI performed well; however, few outliers were detected (ALBERTINI et al., 2022). We therefore employed MNDWI to integrate the spectral indices and water balance, aiming to establish a methodology that facilitates monitoring hydrological dynamics in wetlands. Relationship Between the Water Surface and Water Balance (P-ET) By combining data from Sentinel-2 MSI, CHIRPS, and MODIS, it was possible to identify distinct behaviors in wetlands due to variations in water surface area. These variations are associated with hydrological pulses, as well as periods of water deficit (Schwerdtfeger et al., 2015) within each geomorphological unit. The presence of water in wetlands is a key factor controlling the dynamics of these systems (KGABO et al., 2021). Furthermore, hydrological pulses triggered by frequent rainfall or by extreme climatic events play a fundamental role in connecting terrestrial and aquatic systems (SIMIONI; GUASSELLI, 2017; ZIQI et al., 2021). Regarding the climatic variables and the analyzed time series, precipitation and evapotranspiration were crucial for characterizing the wetlands, enabling an understanding of each system’s inputs and outputs. Kuppel et al. (2015) and Fleischmann et al. (2023) used precipitation, evapotranspiration, and water storage data to examine the influence of climatic factors on wetlands in different regions. According to Hesslerová et al. (2019), evapotranspiration plays a key role, acting like a natural “air conditioner” in the landscape because of water phase changes. These authors highlight that permanent wetland vegetation functions as an active agent that, through evapotranspiration, directly influences the climate. This occurs because moist vegetation converts solar radiation into the latent heat of water vapor, reducing local warming. Schwerdtfeger et al. (2015) employed spectral indices to evaluate the dynamics of dry and rainy seasons in wetland areas and to determine the water available for evaporation in these environments. For those authors, evaporation is the primary component of the water balance in wetlands, linking the flood pulse to the ecosystem. To grasp the importance of these climatic variables in the context of wetland ecosystems, we analyzed the relationship between Precipitation and Evapotranspiration (Figure 5). Seasonal effects were identified in the wetlands of both geomorphological units, with precipitation peaking during the wettest months. These higher precipitation levels are paired with evapotranspiration responses that reflect wetland hydrological behavior (DREXLER et al., 2004). The results for the Coastal Plain and Central Depression units showed that evapotranspiration exhibits both intra- and interannual seasonality in both regions, with higher values in summer and lower values in winter. However, despite following the same seasonal pattern, the absolute values of evapotranspiration differed among the wetlands in the two geomorphological units. In the Coastal Plain, peaks ranged from 100 to 135 mm/month, whereas in the Central Depression, they were lower. The exception was the Banhado Grande wetland, which reached higher values of around 120 mm/month. Thus, while the seasonality is similar, the intensity and amplitude of absolute evapotranspiration values vary among the wetlands. According to Fleischmann et al. (2023), evapotranspiration (ET) plays an essential role in linking surface and atmospheric energy balances. Moreover, it is the primary process responsible for water and energy consumption in wetlands. For this reason, ET is a fundamental variable for characterizing these ecosystems, as it can reflect different hydrological behaviors depending on local physical conditions and vegetation. An analysis of the hydrographs shows that precipitation volumes exhibited significant peaks only in certain months throughout the time series (2016, 2019, 2020, 2022, and 2024). These peaks are tied to intense rainfall events concentrated over short periods, rather than a regular distribution over the year. A striking example occurred in April 2016, when Taim Ecological Station recorded more than 400 mm of precipitation in a single month. Between 2019 and 2020, similar high-intensity events affected not only Taim but also other important wetlands such as Banhado Grande and Lagoa do Peixe. These elevated accumulated values contrast with lower ones, resulting in an irregular rainfall pattern throughout the year, which influences wetland pulses. The precipitation and evapotranspiration characteristics described above are corroborated by the hydroclimatic characterization of Rossato (2011) and Brubacher et al. (2021), highlighting how these variables behave in Rio Grande do Sul State. Because these wetlands are found in two different geomorphological units and feature distinct vegetation cover, it is possible to understand the variations between their minimum and maximum evapotranspiration values. According to Lu et al. (2024), land use and land cover influence actual evapotranspiration in plains and wetlands. The historical precipitation data show spaced rainfall periods with no clear seasonal pattern. This behavior suggests that the evapotranspiration regime is strongly linked to temperature, given that water availability does not remain consistent throughout the year. According to Sun et al. (2024), climatic variables such as temperature and precipitation anomalies affect wetland vegetation composition and coverage, including aspects like leaf area and plant greenness, which can in turn influence the volume of water lost through evapotranspiration. Wang (2025) highlights that evapotranspiration (ET) represents the total water loss from the wetland surface to the atmosphere via evaporation and transpiration, and it is typically the largest component of the wetland hydrological cycle. Moreover, Wang (2025) suggests that understanding how potential seasonal variations might influence ET in wetlands is essential. Integrating spectral indices (MNDWI) with water balance (P-ET) provided a robust tool for monitoring wetland water dynamics. The results underscore the importance of management strategies that consider seasonality and extreme events for the conservation of these areas. According to Trabelsi and Abida (2024), understanding the hydrological processes associated with wetland dynamics through remote sensing and hydrological modeling is fundamental, enabling further research on the impacts of climate change and the global water cycle. The geomorphological features and vegetation composition (Marchetti et al., 2013) in both geomorphological units directly respond to flooding pulses caused by extreme precipitation events (GOMES; MAGALHÃES, 2017). Marchetti et al. (2013) investigated the relationship between geomorphology and vegetation in floodplain regions and observed that the main variations in seasonal vegetation dynamics depend more on the hydrological cycle than on other variables. They also found that flooding dynamics are regulated by geomorphological architecture, such that vegetation is influenced by the geomorphological unit in which it occurs and by water pulses during both dry and flood periods. According to Gomes and Magalhães (2017), wetlands are permanently or temporarily saturated, flooded, and/or waterlogged systems, formed in landforms and substrates that allow for a greater accumulation of surface and/or subsurface water. Additionally, they highlight the roles of geomorphological, hydrological, vegetation, and anthropogenic factors in the formation of wetlands. Although it was possible to map water surface areas in wetlands and identify their relationship with climatic variables and flood pulses, we acknowledge the need for more rigorous analyses to achieve greater accuracy. Di Vittorio and Georgakakos (2018) state that none of these techniques have been entirely precise, and the visual inspection of water surfaces overlaid on true-color band compositions can aid in this assessment. In this regard, Kgabo et al. (2021) note that the spatial extent of wetland or flood water surface areas depends on upstream precipitation, environmental trends in evapotranspiration, and local groundwater infiltration or recharge. Furthermore, Luo et al. (2024) used remote sensing to evaluate wetland responses to extreme precipitation events, employing the MNDWI index in GEE to delineate water bodies. They concluded that precipitation influences the results of environmental indicator indices. Understanding spatiotemporal variation in water levels and flooding in wetlands, considering the hydrological connectivity between environments via multi-source remote sensing data, as well as assessing ecosystem responses to change, has been the focus of numerous studies (ZHENG et al., 2024). By integrating remote sensing with time-series analysis and Sentinel-2, Landsat, and spectral indices, Zheng et al. (2024) examined the interannual variation of flood peaks, drainage networks, and rainfall impacts to infer floodplain flooding patterns and river–floodplain–wetland connectivity. Finally, we can say that our study made it possible to correlate rainfall volumes, evapotranspiration, and water surfaces mapped with MNDWI. As illustrated in Figures 7, 8, and 9, higher precipitation periods resulted in more extensive water surfaces. However, during certain intervals of intense rainfall, it was not possible to record water surface areas because of excessive cloud cover (above the established threshold), which prevented the acquisition of suitable images for mapping. Seasonal and Interannual Dynamics of Wetlands With respect to seasonal and interannual dynamics, the wetlands in the São Gonçalo Channel System and at the Taim Ecological Station showed greater fluctuations, marked by more frequent flooding periods followed by significant retreats, underscoring their seasonality. The highest peaks in water surface extent at the Taim Ecological Station occur at intervals of one to two years, ranging from 4,491 to 5,400 hectares, and exhibit a downward trend that may be associated with more severe La Niña events (KORB et al., 2024). In the wetlands of the Central Depression, the water surfaces identified using the MNDWI threshold were consistently smaller. The pulses were more sporadic and did not display a defined seasonality when compared with wetlands in the Coastal Plain. The reduction in the apparent water surface in these systems may be directly related to lower total precipitation and decreased monthly rainfall, along with minimal influence from groundwater storage (SCHWERDTFEGER et al., 2015; HEIDARZADEH et al., 2024). These findings are significant because they take into account the interplay of local geomorphological and climatological conditions, which revealed a clear seasonality in evapotranspiration, while precipitation totals remained dispersed and lacked a well-defined seasonal pattern. In other words, the wetlands presented seasonal similarities tied to the geomorphological units, such as increased water balance in spring (peaking in October/November), followed by a decrease in summer. This pattern suggests that the wetlands are strongly influenced by the same climatic regime, with local or regional variations stemming from factors like topography, vegetation, or human management. According to Zhang et al. (2024), in seeking to understand the spatial and temporal variation of water levels using data from multiple remote sensors, increased precipitation is the key climatic factor that raises water levels in wetlands. Simioni and Guasselli (2017) observed that after high precipitation volumes, an expansion in water surface area occurs, resulting in connectivity between wetlands and the river floodplain. These variations in water surface area and volume (SIOMINI; GUASSELLI, 2017; ZHANG et al., 2024) highlight the importance of precipitation and how wetlands respond to these anomalies and extreme events. Ribeiro et al. (2024), using Sentinel-2, CHIRPS, and spectral indices in the Brazilian Pantanal, noted stable precipitation patterns throughout the region and that phenology responds positively to these patterns. They emphasize that flooding is regionally asynchronous and, although the correlation between phenology and flooding is predominantly negative, areas showing positive responses indicate that flood seasonality is an important driver of local vegetation variability. Our analysis, however, showed that precipitation in the wetlands across the two geomorphological units does not follow a defined pattern. This finding reinforces the importance of time-series data and an understanding of the wetlands’ climatic context as essential factors for accurate mapping and delineation. Conclusion This study aimed to understand the influence of water balance on flood pulses and the variation of water surfaces in wetlands located in two geomorphological units. We demonstrated the potential of integrating different sensor systems, spectral indices, climatic data, and time series to monitor the hydrological dynamics of wetlands. By analyzing Sentinel-2 images (2016–2024) and applying NDWI, NDMI, and MNDWI, we identified seasonal and interannual patterns in the variation of wetland water surfaces and correlated these with precipitation and evapotranspiration data. MNDWI showed the best results for detecting water surfaces, mainly due to its sensitivity to water even when mixed with vegetation and sediments, conditions typically found in these environments. The other indices overestimated or underestimated results (NDMI tended to overestimate, while NDWI underestimated). Wetlands in the Coastal Plain presented more extensive flooded areas, with pulses occurring in periods similar to those observed in the water balance (P-ET). However, in the Central Depression, flood pulses produced smaller water surfaces, with irregular hydrological cycles and longer periods of reduced surface water, as noted at the Banhado do Inhatium site. Regarding the Water Balance (P-ET), we underscore the role of evapotranspiration in regulating water availability, especially during higher temperatures and lower precipitation. We noted seasonality in evapotranspiration time series, whereas precipitation did not show a clear seasonal pattern, displaying irregular peaks concentrated in certain months and years and reflecting extreme events. By combining these variables, we revealed that wetlands respond directly to climatic variations and to the geomorphology of the units in which they are found. The hydrological dynamics of wetlands depend on geomorphological traits, vegetation cover, and climatic variability. Hydrological pulses caused by extreme events play an important role in these ecosystems, helping maintain connectivity between aquatic and terrestrial systems. Our methodology proved to be effective and replicable for mapping wetlands and supporting their monitoring. Nonetheless, we recommend combining these findings with visual inspections and true-color imagery to validate and refine delineations. Additionally, factors such as cloud cover and missing data in certain periods posed occasional limitations to the analysis. As future studies, (a) projecting scenarios that consider the historical P-ET series and water surface data, including possible gap-filling, may be evaluated, as it offers a potential solution for verifying immediate responses to extreme events; and (b) improving mapping thresholds for waterbody segmentation, testing methods such as principal component classification, clustering, or supervised classification, could further enhance the results obtained. Finally, the findings underscore the importance of time-series climatic and remote sensing data in analyzing hydrological variability, as well as in mapping and monitoring wetlands, particularly in the context of climate change and extreme events. Declarations Acknowledgements The authors would like to thank the Postgraduate Program in Remote Sensing, of the State Center for Research in Remote Sensing and Meteorology/UFRGS. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—finance code 001, award 88887.801261/2023-00, and the Rio Grande do Sul State Foundation for Research Support (FAPERGS). Author contributions All authors contributed to the study conception and design. Christhian Santana Cunha writing—original draft, conceptualization, methodology, investigation, formal analysis, visualization. Laurindo Antônio Guasselli.: conceptualization; writing—review and editing, supervision, funding acquisition. Carina Cristiane Korb. and Tássia Fraga Belloli.: writing—review and editing. Funding This study was funded in part by the Coordination for the Improvement of Higher Education Personnel—Brazil (CAPES)—funding code 001, grant 88887.801261/2023-00, and by the Rio Grande do Sul Research Foundation (FAPERGS). Data availability The datasets analyzed in this study can be found on Dataset in Google Earth Engine. Further inquiries about the data can be directed to the corresponding author. Conflicts of Interest The authors declare no conflicts of interest. References Albertini C, Gioia A, Iacobellis V, Manfreda S (2022) Detection of Surface Water and Floods with Multispectral Satellites. Remote Sensing 14:6005. https://doi.org/10.3390/rs14236005 Al-Maliki S, Ibrahim TIM, Jakab G, Masoudi M, Makki JS, Vekerdy Z (2022) An Approach for Monitoring and Classifying Marshlands Using Multispectral Remote Sensing Imagery in Arid and Semi-Arid Regions. 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Remote Sensing 7(8):9769–9795 Simioni JPD, Guasselli LA, Etchelar CB (2017) Connectivity among wetlands of the Banhado Grande EPA, RS. Revista Brasileira de Recursos Hídricos 22 Simioni JPD, Guasselli LA (2024) Dual-season comparison of OBIA and pixel-based approaches for coastal wetland classification. Revista Brasileira de Recursos Hídricos 29:e5 Singh KV, Setia R, Sahoo S, Prasad A, Pateriya B (2014) Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level. Geocarto International 30(6):650–661. https://doi.org/10.1080/10106049.2014.965757 Sun H, Wang WJ, Liu Z, Ballantyne AP, Yu K, Bao SG, He HS (2024) Enhanced productivity and evapotranspiration dominated by woody plant encroachment-induced vegetation greening in boreal wetland ecosystems. GIScience & Remote Sensing 61(1). https://doi.org/10.1080/15481603.2024.2391144 Trabelsi R, Abida H (2024) Estimation of the water balance of wetlands in an arid region using remote sensing technology and hydrological modeling. Hydrological Sciences Journal 1–18 Wang M, Gu Q, Liu G, Shen J, Tang X (2019) Hydrological condition constrains vegetation dynamics for wintering waterfowl in China’s East Dongting Lake Wetland. Sustainability 11:4936 Ward JV, Stanford JA (1995) Ecological connectivity in alluvial river ecosystems and its disruption by flow regulation. Regulated Rivers: Research & Management 11:105–119 Xue D, Rongrong W, Guishan Y, Xiaolong W, Ligang X, Yanyan L, Bing L (2019) Impact of seasonal water-level fluctuations on autumn vegetation in Poyang Lake wetland, China. Frontiers in Earth Science . https://doi.org/10.1007/S11707-018-0731-Y Zheng H, Tetzlaff D, Freymüller J, Chmieleski J, Okujeni A, Soulsby C (2024) Quantifying intra- and inter-annual dynamics of river-floodplain connectivity and wetland inundation with remote sensing and wavelet analysis. Hydrological Processes 38(4):e15137 Ziqi L, Wenchao S, Haiyang C, Baolin X, Jingshan Y, Zaifeng T (2021) Interannual and Seasonal Variations of Hydrological Connectivity in a Large Shallow Wetland of North China Estimated from Landsat 8 Images. Remote Sensing 13(6):1214. https://doi.org/10.3390/RS13061214 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6331096","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435810261,"identity":"e54f744f-25a0-4f48-8fc0-fba671a91335","order_by":0,"name":"Christhian 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23:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6331096/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6331096/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80292712,"identity":"fefed99e-bac7-426a-a43f-1772029b6da9","added_by":"auto","created_at":"2025-04-10 08:09:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2378869,"visible":true,"origin":"","legend":"\u003cp\u003eStudy areas, wetlands and geomorphological units\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6331096/v1/458f763110088b26e0195fb4.png"},{"id":80291519,"identity":"7009a5b4-e751-4a59-8cf9-95b0bcd057dc","added_by":"auto","created_at":"2025-04-10 08:01:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2141731,"visible":true,"origin":"","legend":"\u003cp\u003eDelimitation of Water Surfaces Using NDMI, NDWI, and MNDWI Indexes\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6331096/v1/9970cfe5c52cdf01e0fc9ad6.png"},{"id":80291512,"identity":"8f645c9d-d60d-454b-a473-c4ae75acf981","added_by":"auto","created_at":"2025-04-10 08:01:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":298579,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow and processing\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6331096/v1/cdc846055952ca672d30d6a1.png"},{"id":80291511,"identity":"fd063495-e03d-4222-ae50-bb6adb18d0c5","added_by":"auto","created_at":"2025-04-10 08:01:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":973707,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical Water Layer Series Based on MNDWI, NDMI, and NDWI Indices for 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Wetlands\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6331096/v1/3dce658b0d32427f19cdc8cb.png"},{"id":80291513,"identity":"feab9b01-7d74-47f2-b67b-a7ec68609840","added_by":"auto","created_at":"2025-04-10 08:01:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":696969,"visible":true,"origin":"","legend":"\u003cp\u003eWater Balance and comparative area flood in time series.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6331096/v1/335a59b6e5b8ec9b5df7a7b3.png"},{"id":80293340,"identity":"cb491927-2117-447e-aa86-f91db22984c3","added_by":"auto","created_at":"2025-04-10 08:17:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":234964,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Distribution of Water Balance by 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08:25:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10862493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6331096/v1/5dc08264-1c63-44fa-8750-659f4d7dc8ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal and Hydroclimatic Dynamics in Wetlands Using Spectral Indices and Water Balance in Different Geomorphological Units","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWetlands perform fundamental environmental roles in biodiversity conservation, hydrological and geochemical cycles (GOMES; JUNIOR, 2018; SIMIONI; GUASSELLI, 2024). In this context, understanding the seasonal dynamics of wetlands (WDs) is essential for assessing and monitoring water availability, conservation, and management of these ecosystems (DRENKHAN et al., 2022). Furthermore, wetlands, characterized by the transition between terrestrial and aquatic environments, are crucial for maintaining fauna and flora species (CUNHA et al., 2023).\u003c/p\u003e\n\u003cp\u003eIn this sense, it is important to understand that the relationship between terrestrial and aquatic environments in wetlands is directly influenced by hydrological connectivity, a key characteristic for maintaining ecosystem functions in these transitional environments (ZIQI et al., 2021). According to Nhamo, Magidi, and Dickens (2017), wetlands can only be properly managed if their spatial location and extent are accurately documented, and their dynamics are well understood, as their type and morphology are highly variable.\u003c/p\u003e\n\u003cp\u003eIn the context of wetlands, understanding the hydrological components, the relationship with seasonal changes, the response to flood pulses, and the connectivity between environments (SIMIONI; GUASSELLI, 2017) are important aspects. In summary, ecological connectivity between rivers and floodplains can be classified into (i) landscape connectivity; (ii) hydrological connectivity; and (iii) sedimentological connectivity (BRACKEN; CROKE, 2007).\u003c/p\u003e\n\u003cp\u003eFrom another perspective, considering that the surface and water layer in wetlands are directly influenced by hydroclimatic aspects, precipitation (P), and evapotranspiration (ET), understanding the land surface and its relationship with the atmosphere enables a better understanding of the water cycle, hydroclimatic relationships, surface water dynamics, and the occurrence of droughts and floods (MOREIRA et al., 2019). Furthermore, according to Cristobal et al. (2024), evapotranspiration (ET) plays a significant role in the hydrological cycle due to surface-atmosphere exchanges, along with precipitation (P). In this context, the surface water balance (P-ET) is a key parameter for the management and conservation of wetland ecosystems. However, in situ measurement of variables such as precipitation and evapotranspiration, for instance, can be challenging and involve high costs, complicating the monitoring of these variables.\u003c/p\u003e\n\u003cp\u003eGao et al. (2010) conducted water balance analyses using satellite images and remote sensing techniques, estimating surface water runoff based on the relationship between precipitation, evaporation, and terrestrial water storage change. Moreira et al. (2019) achieved satisfactory results with high correlation percentages and acceptable mean error for water balance calculations using different sensor systems, such as the Gravity Recovery and Climate Experiment (GRACE), Tropical Rainfall Measuring Mission (TRMM), Multi-Source Weighted-Ensemble Precipitation (MSWEP), MODIS Global Evapotranspiration Project (MOD16), and the Global Land Evaporation Amsterdam Model (GLEAM). Trabelsi and Abida (2024) estimated water balance in wetlands based on Sentinel-2B, Global Precipitation Measurements (GPM), and the Famine Early Warning System Network (FEWS-NET). They observed that annual, seasonal, and monthly water balance simulations showed agreement between remote sensing-based analyses and hydrological modeling.\u003c/p\u003e\n\u003cp\u003eRegarding the characterization of wetlands, according to Cunha et al. (2023) and Semeniuk; Semeniuk (1995; 2011), geomorphology and hydrology are important variables for the classification and understanding of wetlands. These are structuring factors that tend to be less dynamic and mutable over time compared to biological factors, allowing wetlands to be categorized into more stable classes, even when substantially altered by vegetation or soil removal (GOMES; JUNIOR, 2018).\u003c/p\u003e\n\u003cp\u003eUnderstanding the relationship between precipitation, evapotranspiration (water balance), and spectral indices allows for the evaluation of the impact of changes and seasonal variability, as well as the interpretation of water availability (APOPEI et al., 2023). However, according to Karaman et al. (2015) and Tahsin, Medeiros, and Singh (2020), the variation in the water layer of wetlands, analyzed through spectral indices and hydrometeorological data derived from satellite images, can be influenced by climatic seasonality and the greater or lesser incidence of rainfall.\u003c/p\u003e\n\u003cp\u003eFor wetland monitoring and classification, Sadiq et al. (2022) used the Normalized Difference Moisture Index (NDMI) derived from two infrared bands. The authors mention that the index is sensitive to vegetation moisture content but can also be used to distinguish basic land cover classes in wetlands. Areas with an NDMI greater than zero are used to delineate wetlands. Chowdhary and Vyas (2022) applied spectral indices over different periods to map seasonal changes in water levels in wetlands, resulting in structural and functional alterations. They observed that 50% of the mapped wetlands contribute to water retention during dry periods.\u003c/p\u003e\n\u003cp\u003eAccording to Ziqi et al. (2021), seasonal and interannual variations in wetlands influence the connectivity processes of these environments, considering the increase in emergent plants and water levels. Fawang et al. (2018) used satellite imagery and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to calculate the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Vegetation Index (NDVI) for analyzing the seasonal dynamics of wetland ecosystems, vegetation responses, and the delineation of water layers. Ab\u0026iacute;lio (2018) employed time-series satellite images in wetlands and floodplains to assess changes, risk factors, and potential in natural or anthropized environments, aiming to adapt land use and occupation, exploitation, conservation, mitigation, and prioritization of responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA comparative hydrology approach involving multiple wetlands emerges as a promising framework for understanding ET in wet landscapes across various climates and biomes, as highlighted by Fleischmann et al. (2023). Moreover, the authors emphasize that, by doing so, this framework has the potential to facilitate a consistent understanding of the role of various environmental factors (e.g., precipitation, flooding, available energy, and vapor pressure deficit) and enable predictions about how these areas respond to ongoing environmental changes.\u003c/p\u003e\n\u003cp\u003eAlthough the use of time series and spectral indices to analyze seasonal dynamics in wetlands is addressed in various studies, the context that integrates water balance and water layer variation considering geomorphological units is rarely discussed and analyzed. This indicates that analyses must take into account the importance of wetland ecosystems, the hydrological connectivity between terrestrial and aquatic environments, and the influence of seasonal and hydrometeorological variables on vegetation cover and the water layer in these environments.\u003c/p\u003e\n\u003cp\u003eIn this context, this study aims to analyze the hydroclimatic dynamics of wetlands in southern Brazil using time series data from different sensor systems processed in the cloud computing environment of Google Earth Engine. The study seeks to identify similar hydrological patterns among the wetlands, understand the influence of water balance on apparent water layer pulses, and evaluate the role of geomorphological units in this context.\u003c/p\u003e\n"},{"header":"Materials and Methods ","content":"\u003ch2\u003eStudy area \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe study area (Figure 1) encompasses wetlands located in two geomorphological units: the Coastal Plain and the Central Depression, in the state of Rio Grande do Sul, Brazil. Regarding climate, according to Moreno (1961) and Rossato (2011), geomorphological compartments significantly influence the state\u0026apos;s climate. The Central Depression region has higher altitudes, while maritime influences predominantly affect the Coastal Plain. These geomorphological differences result in distinct distributions of precipitation and temperature.\u003c/p\u003e\n\u003cp\u003eIn the Coastal Plain, the wetlands include the S\u0026atilde;o Gon\u0026ccedil;alo Channel, Taim Ecological Station, and Lagoa do Peixe National Park. This region covers approximately 33,000 km\u0026sup2; in the outer portion of the Coastal Plain. It consists of a mosaic of ecosystems dominated by pioneer vegetation under marine and fluvial influence, as well as grassland and forest formations (SCHAFER, 2013). Differentiated mosaics defined by landscape morphology and water availability occur, with sparse vegetation in dry grasslands and dune areas and lush vegetation in wetlands in depressions and dune fields (SCHAFER, 2013).\u003c/p\u003e\n\u003cp\u003eIn the Central Depression, located in the central portion of Rio Grande do Sul, the wetlands analyzed were Banhado Grande and Banhado do Inhatium. This geomorphological unit corresponds to a low-altitude area, represented by Mesozoic sediments of the Paran\u0026aacute; Basin, shaped by peripheral erosion processes from the Late Mesozoic and Cenozoic. The geomorphology is characterized by a surface with differentiated patterns of hills with flat or convex tops (SUERTEGARAY; GUASSELLI, 2012). It is predominantly composed of grasslands and deforested pastures, with an intensive summer agricultural zone and a diversified crop agricultural zone (SPGM, 2021).\u003c/p\u003e\n\u003cp\u003eAccording to Brubacher et al. (2021), the state of Rio Grande do Sul is located in a transitional climatic zone cyclically influenced by pressure centers and atmospheric systems active in southern South America, including Extratropical Atmospheric Systems (polar masses and fronts) and Intertropical Systems (tropical masses and disturbed currents). The climate in Rio Grande do Sul is Temperate Subtropical, classified as humid Mesothermal (K\u0026ouml;ppen classification). According to Rossato (2011) and Maluf (2000), the Central Depression has a Subtropical and Subtemperate climate with temperatures ranging from 16\u0026deg;C to 22\u0026deg;C, while the Coastal Plain has a Subtemperate climate with average temperatures between 16\u0026deg;C and 22\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eIn terms of precipitation, Rio Grande do Sul experiences annual volumes ranging from 1,200 mm to 2,000 mm in the rainiest regions. According to Rossato (2011), the study area exhibits significant seasonal variability in precipitation, often masked when only annual totals are considered. Furthermore, Brubacher et al. (2021) emphasize that the rainfall regime in Rio Grande do Sul is not homogeneous. The specific characteristics of the state\u0026rsquo;s relief result in varying precipitation behaviors, with rainfall being primarily influenced by atmospheric dynamics in relation to the terrain, whose compartmentalization promotes the spatial distribution of precipitation.\u003c/p\u003e\u003ch2\u003eDatabase, Sensors and Indexes\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;For floodplain and wetland mapping and climatic characterization, images from the Harmonized Sentinel-2 MSI: MultiSpectral Instrument collection, CHIRPS Daily: Climate Hazards Center InfraRed Precipitation With Station Data (Version 2.0 Final), and MODIS Evapotranspiration (MOD16A2 Version 6) satellite images were used, covering the period from 2016 to 2024 (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eDatasets available in Google Earth Engine.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResolution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8168%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcquisition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47.1197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8168%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eHarmonized Sentinel 2A\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ee 2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSurface Reflectance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8168%;\"\u003e\n \u003cp\u003e5 to 10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.1197%;\"\u003e\n \u003cp\u003ehttps://developers.google.com/earth-engine/datasets/catalog/sentinel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eCHIRPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ePrecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8168%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.1197%;\"\u003e\n \u003cp\u003ehttps://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003eMOD16A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTotal Evapotranspiration (ET),\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.8168%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.1197%;\"\u003e\n \u003cp\u003ehttps://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;Potential Total Evapotranspiration (PET)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.1197%;\"\u003e\n \u003cp\u003ehttps://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eElaboration: The authors (2022)\u003c/p\u003e\n\u003cp\u003eFrom Sentinel-2 images, the following indices were calculated: the Normalized Difference Water Index (NDWI), the Normalized Difference Moisture Index (NDMI), and the Modified Normalized Difference Water Index (MNDWI) for characterization associated with vegetation, water, and soil moisture (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate the evapotranspiration of wetlands and characterize the water balance, we selected the MOD16A2 Version 6 product. This dataset is an 8-day composite product with a spatial resolution of 500 m. The algorithm used to generate the MOD16 data product is based on the logic of the Penman-Monteith equation, which incorporates daily meteorological reanalysis data along with MODIS remote sensing data products, such as vegetation property dynamics, albedo, and land cover (CUNHA et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe ET variable is important for wetland mapping and characterization due to its ability to identify seasonal variations over historical series (CERON et al., 2015) and to contextualize hydroclimatic aspects (MOREIRA et al., 2019). CHIRPS Daily data were used to obtain precipitation information and to calculate the simplified water balance (P-ET). Water flow and water balance simulations using CHIRPS have shown satisfactory results when compared to other precipitation products (DHANESH et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eWorkflow, Data Processing, and Time Series Analysis\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;The database was standardized according to the temporal scale of the data, adjusting for monthly totals and averages and filtering data for the study areas. Data aggregation to a monthly scale aimed to standardize temporal scales and reduce the likelihood of missing data. Sentinel-2 images were filtered for cloud coverage below 35%, and area values were calculated in hectares. Cloud masks were applied using Sentinel-2 image properties to remove cloud pixels and optimize mosaics without the presence of clouds and shadows.\u003c/p\u003e\n\u003cp\u003eAccording to Al-Maliki et al. (2022), wetland characterization is typically achieved by analyzing the primary spectral characteristics of land cover units based on their reflectance in the visible and infrared ranges. The separation of open water, vegetation, and soils is best accomplished using red (0.60-0.69 \u0026mu;m) and near-infrared (0.70-1.30 \u0026mu;m) wavelengths.\u003c/p\u003e\n\u003cp\u003eFrom monthly images between 2016 and 2023, the NDVI, NDMI, NDWI, and MNDWI indices were calculated to delineate water layers in the time series and compare them with the simplified water balance (P-ET) and the respective water layers generated by applying a threshold. To define thresholds, pixels corresponding to water were inspected, and these values were subsequently used to segment areas with the presence of apparent water. Figure 2 illustrates the relationship between the indices and the water layers generated.\u003c/p\u003e\n\u003cp\u003eIn this context, a water layer was created through segmentation by selecting only pixels with values greater than 0.01 for MNDWI. This definition was based on the visual interpretation of the images and the analysis of partial results of the water layer areas delineated by each index, which showed the best adjustments for MNDWI values. Monthly water layer masks were then created, and the area was calculated in hectares.\u003c/p\u003e\n\u003cp\u003eTo analyze the results of the relationship between water layer variations and water balances (Figure 3), a database was structured containing wetlands, year, month, water layer area values, precipitation, evapotranspiration, and effective precipitation (P-ET balance).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis of the results (Figure 4) shows that the NDMI index presented the highest values for delineated wet or apparent water layer areas across all wetlands analyzed. The MNDWI values recorded larger areas for water delineation compared to NDWI, which showed the smallest results for water layer area.\u003c/p\u003e\n\u003cp\u003eThe results highlight the effect of seasonality, defining periods of maximum, minimum, or transitional areas over months and years in all wetlands. The Inhatium and Banhado Grande wetlands, located in the Central Depression, exhibited lower water peaks throughout the historical series when compared to other wetlands in the Coastal Plain. In the Coastal Plain, fluctuations in maximum and minimum values were more consistent, with smaller variations. The MNDWI values showed a better fit for detecting the presence of water compared to the other indices, which underestimated (NDWI) or overestimated (NDMI) water values.\u003c/p\u003e\n\u003cp\u003eIn the historical water layer series the S\u0026atilde;o Gon\u0026ccedil;alo Channel System shows the largest water layer area compared to the other wetlands analyzed. This result can be observed considering the amplitude and persistence of the water layer area values obtained from the historical MNDWI series, indicating more extensive and permanent water bodies. The Taim Ecological Station and Lagoa do Peixe National Park wetlands have smaller areas, although they exhibit significant fluctuations related to seasonality.\u003c/p\u003e\n\u003cp\u003eThe largest precipitation amplitudes are observed in Banhado do Inhatium, with a record of 466 mm/month in September 2023, and in Taim Ecological Station, with 431 mm/month in April 2016. Lagoa do Peixe National Park shows the lowest precipitation values compared to the others, with smaller precipitation peaks.\u003c/p\u003e\n\u003cp\u003eIn terms of evapotranspiration, Banhado Grande exhibits the highest indices, with peaks nearing 150 mm/month. The other wetlands display similar evapotranspiration amplitudes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In the S\u0026atilde;o Gon\u0026ccedil;alo Channel System and Taim Ecological Station, there was a significant reduction in the water layer between November 2019 and August 2022, reflecting lower water balance results during this period. The reduction in rainfall during this time may have led to greater water loss through evapotranspiration (Figure 6). The wetland in Lagoa do Peixe National Park exhibited less interannual variability in water layer areas and water balance results, potentially indicating greater hydrological resilience even during deficit periods.\u003c/p\u003e\n\u003cp\u003eThe Inhatium wetland showed a significant reduction in the water layer between September 2018 and September 2023 (Figure 6). Although the corresponding values showed a positive water balance (P-ET) as indicated in Figure 7, it is estimated that the water retention capacity of this wetland, combined with soil characteristics and surrounding land use, resulted in a longer dry period compared to other wetlands. As a result, the apparent water layer values remained close to zero, differing from the other wetlands.\u003c/p\u003e\n\u003cp\u003ePeriods in the historical series were observed in which MNDWI did not directly reflect the water balance. This suggests the influence of other factors, such as vegetation cover, land use, anthropogenic changes, or the absence of images for data extraction. It is estimated that smaller areas like the Inhatium wetland exhibit lower amplitude due to the seasonality of water balances and water layers, resulting from a reduced water storage capacity. Banhado Grande, like the others, recorded the highest water layer values in July and September. Unlike the behavior observed in Inhatium, Banhado Grande maintained a stable apparent water layer over the years, with small pulses caused by accumulated rainfall.\u003c/p\u003e\n\u003cp\u003eThe results of the historical series (Figure 6) reveal distinct seasonal variations, showing increases and decreases in the apparent water layer identified by MNDWI. These areas (Figure 7) are compared with the results of the simplified water balance (P-ET), highlighting the water balance throughout the months.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In the historical series for each wetland, regarding water layer area and water balance (Figure 7), only the Inhatium wetland exhibited water layers close to zero even during periods of water surplus (P-ET). Between 2017 and 2023, the highest water layer values were recorded, but these were not associated with the highest P-ET balances. The Inhatium wetland region experienced few months of water deficit; however, between 2020 and 2023, trends of water reduction in the system were observed, although recovery of the system was already noticeable by 2023.\u003c/p\u003e\n\u003cp\u003eA similar behavior was observed in Banhado Grande, where peaks in water layers were recorded in the historical series between 2020 and 2022. However, the water area in July 2020 was the result of elevated precipitation levels. In other months, even with P-ET balances greater than 600 mm/month, water layer areas did not exceed the average.\u003c/p\u003e\n\u003cp\u003eIn the S\u0026atilde;o Gon\u0026ccedil;alo Channel and Lagoa do Peixe wetlands, between 2020 and 2022, the water layer values directly responded to the monthly water balance. During periods of water deficit, the water layer areas in these wetlands decreased, and when the water balance (P-ET) increased, the flooded area was reflected in the historical series.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The seasonal variations (Figure 8) in water balance (in mm/month) for the different wetlands throughout the year illustrate the dispersion of monthly values, allowing for the identification of seasonal patterns, outliers, and the amplitude of variations within the study areas. All wetlands exhibit lower water balance values during the summer months, from January to February, due to lower rainfall, higher temperatures, and increased evapotranspiration in southern Brazil. However, the highest water balance values consistently appear between September and November (spring in the Southern Hemisphere).\u003c/p\u003e\n\u003cp\u003eAlthough the amplitude of maximum and minimum values varies across wetlands, a pattern of greater dispersion is noticeable in October and November, indicating that these months may be subject to extreme rainfall events with high interannual variability.\u003c/p\u003e\n\u003cp\u003eRegarding variability, the S\u0026atilde;o Gon\u0026ccedil;alo Channel shows the greatest dispersion among values, representing potential sensitivity to extreme climatic events, such as intense rainfall or severe drought periods. Conversely, the Taim Ecological Station exhibits a more consistent hydrological pattern with lower dispersion, representing greater environmental stability.\u003c/p\u003e\n\u003cp\u003eThe Inhatium wetland exhibits greater variability during the drier months, with higher dispersions in September and October, suggesting a stronger influence of rainfall. Between November and December, the variability decreases, similar to the behavior observed in other wetlands.\u003c/p\u003e\n\u003cp\u003eBanhado Grande shows significant concentrations of negative values during the drier months, indicating a reduction in water within the system and potentially reflecting periods of water deficit. The concentration of water balance values remains stable throughout the months, with the greatest dispersions occurring during both wet and dry periods.\u003c/p\u003e\n\u003cp\u003eIn the analysis of the areas calculated using MNDWI (Figure 9), most wetlands show larger areas between September and November and significant reductions between January and February. This relationship is also observed in the water balances, except for Banhado Grande and Lagoa do Peixe, which also exhibit high values in July. Furthermore, the seasonal dynamics of wetlands can be observed in the variation of water layer areas mapped using MNDWI for both geomorphological regions.\u003c/p\u003e\n\u003cp\u003eThe distance between the upper and lower quartiles increases significantly in the months with the greatest floods, indicating greater variability in the hydrological response in these periods in different wetlands.\u0026nbsp;In contrast, during drier months (January and February), the dispersion of results is smaller and more stable.\u003c/p\u003e\n\u003cp\u003eThe water layer area in Banhado Grande shows low concentrations between January and April, with an increase in water layer area starting in May and peaking between July and September. Similarly, the same behavior is observed in Banhado do Inhatium, where small water layer areas are concentrated between November and April, while larger dispersions with high amplitudes occur between July and September.\u003c/p\u003e\n\u003cp\u003eWetlands in Lagoa do Peixe National Park, Taim Ecological Station, and S\u0026atilde;o Gon\u0026ccedil;alo Channel exhibit constant transitions, with a continuous increase in water layer area between March and October, peaking between August and October.\u003c/p\u003e\n\u003cp\u003eIt is evident that all wetlands have smaller areas during summer in hotter periods and larger flooded areas in wet months between winter and spring. Moreover, the wetlands exhibit differences in the scales of hydrological dynamics, with those in the Coastal Plain being larger than those in the Central Depression.\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003ePerformance of Water-Mapping Indices\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe use of satellite image collections has made it possible to analyze, through time series, how wetlands respond to changes in land use and cover, flooding pulses, and water stress resulting from extreme events associated with El Ni\u0026ntilde;o and La Ni\u0026ntilde;a. Employing spectral indices enhances the classification of these areas, demonstrating efficiency and feasibility (CAVALLO et al., 2021), and is valuable for the continuous monitoring of wetland environments.\u003c/p\u003e\n\u003cp\u003eOur study utilized images from 2016 to 2024 to calculate water indices derived from Sentinel-2 MSI, employing threshold-based segmentation. According to Kordelas et al. (2018), using thresholds to define the apparent water surface in flooded areas presents high performance metrics for various indices.\u003c/p\u003e\n\u003cp\u003eTo identify the most suitable index for detecting variations in the apparent water surface area, we evaluated the performance of NDMI, NDWI, and MNDWI. These indices have proven effective, offering solid support for surface water management and informing discussions on which index, methods, and processes are the most efficient (ALBERTINI et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe results obtained with NDMI indicated overestimated values for the water surface in wetlands. In addition to the water surface, pixels corresponding to vegetation with higher moisture content were also mapped. NDMI is widely used to understand vegetation moisture and measure water stress. Because it is sensitive to moisture in vegetation, it can cause confusion between highly humid areas and water bodies (GAO, 1996; AL-MALIKI et al., 2022). Al-Maliki et al. (2022) emphasize that NDMI is particularly suitable for classifying different vegetation types; however, in our study, it did not delineate water areas as expected. The tendency to underestimate water surface areas using NDWI was identified by Menon et al. (2015) when comparing the areas obtained through other indices via statistical analyses and visual inspections.\u003c/p\u003e\n\u003cp\u003eMNDWI showed values that were more consistent with inspections carried out in the watercourses of both geomorphological units, confirming the visual analysis when compared with the other indices. However, we observed that, in mapping water surface areas, water pixels more strongly influenced by sediments or vegetation were not classified.\u003c/p\u003e\n\u003cp\u003eSingh et al. (2015), when analyzing the NDWI and MNDWI indices for flood detection, found that NDWI tends to highlight water surface areas less effectively, especially when there is interference from built structures located nearby or mixed with the water. MNDWI performed better, being more sensitive to the presence of water even when mixed with vegetation, resulting in positive values that facilitate identification. Mehmood et al. (2021) used MNDWI in Google Earth Engine to map and classify water by implementing the Flood Mapping Algorithm (FMA), filtering out areas of shadow, vegetation, and HAND maps.\u003c/p\u003e\n\u003cp\u003eThe literature on mapping areas with a water surface or flooded wetlands using various sensor systems indicates that MNDWI achieves the best performance for identifying flooded areas, thanks to its capacity for recognizing mixed pixels and turbid water (containing algae and vegetation). In terms of detecting surface water, both MNDWI and NDWI performed well; however, few outliers were detected (ALBERTINI et al., 2022).\u003c/p\u003e\n\u003cp\u003eWe therefore employed MNDWI to integrate the spectral indices and water balance, aiming to establish a methodology that facilitates monitoring hydrological dynamics in wetlands.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Relationship Between the Water Surface and Water Balance (P-ET)\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eBy combining data from Sentinel-2 MSI, CHIRPS, and MODIS, it was possible to identify distinct behaviors in wetlands due to variations in water surface area. These variations are associated with hydrological pulses, as well as periods of water deficit (Schwerdtfeger et al., 2015) within each geomorphological unit.\u003c/p\u003e\n\u003cp\u003eThe presence of water in wetlands is a key factor controlling the dynamics of these systems (KGABO et al., 2021). Furthermore, hydrological pulses triggered by frequent rainfall or by extreme climatic events play a fundamental role in connecting terrestrial and aquatic systems (SIMIONI; GUASSELLI, 2017; ZIQI et al., 2021).\u003c/p\u003e\n\u003cp\u003eRegarding the climatic variables and the analyzed time series, precipitation and evapotranspiration were crucial for characterizing the wetlands, enabling an understanding of each system\u0026rsquo;s inputs and outputs. Kuppel et al. (2015) and Fleischmann et al. (2023) used precipitation, evapotranspiration, and water storage data to examine the influence of climatic factors on wetlands in different regions.\u003c/p\u003e\n\u003cp\u003eAccording to Hesslerov\u0026aacute; et al. (2019), evapotranspiration plays a key role, acting like a natural \u0026ldquo;air conditioner\u0026rdquo; in the landscape because of water phase changes. These authors highlight that permanent wetland vegetation functions as an active agent that, through evapotranspiration, directly influences the climate. This occurs because moist vegetation converts solar radiation into the latent heat of water vapor, reducing local warming.\u003c/p\u003e\n\u003cp\u003eSchwerdtfeger et al. (2015) employed spectral indices to evaluate the dynamics of dry and rainy seasons in wetland areas and to determine the water available for evaporation in these environments. For those authors, evaporation is the primary component of the water balance in wetlands, linking the flood pulse to the ecosystem.\u003c/p\u003e\n\u003cp\u003eTo grasp the importance of these climatic variables in the context of wetland ecosystems, we analyzed the relationship between Precipitation and Evapotranspiration (Figure 5). Seasonal effects were identified in the wetlands of both geomorphological units, with precipitation peaking during the wettest months. These higher precipitation levels are paired with evapotranspiration responses that reflect wetland hydrological behavior (DREXLER et al., 2004).\u003c/p\u003e\n\u003cp\u003eThe results for the Coastal Plain and Central Depression units showed that evapotranspiration exhibits both intra- and interannual seasonality in both regions, with higher values in summer and lower values in winter. However, despite following the same seasonal pattern, the absolute values of evapotranspiration differed among the wetlands in the two geomorphological units. In the Coastal Plain, peaks ranged from 100 to 135 mm/month, whereas in the Central Depression, they were lower. The exception was the Banhado Grande wetland, which reached higher values of around 120 mm/month. Thus, while the seasonality is similar, the intensity and amplitude of absolute evapotranspiration values vary among the wetlands.\u003c/p\u003e\n\u003cp\u003eAccording to Fleischmann et al. (2023), evapotranspiration (ET) plays an essential role in linking surface and atmospheric energy balances. Moreover, it is the primary process responsible for water and energy consumption in wetlands. For this reason, ET is a fundamental variable for characterizing these ecosystems, as it can reflect different hydrological behaviors depending on local physical conditions and vegetation.\u003c/p\u003e\n\u003cp\u003eAn analysis of the hydrographs shows that precipitation volumes exhibited significant peaks only in certain months throughout the time series (2016, 2019, 2020, 2022, and 2024). These peaks are tied to intense rainfall events concentrated over short periods, rather than a regular distribution over the year. A striking example occurred in April 2016, when Taim Ecological Station recorded more than 400 mm of precipitation in a single month. Between 2019 and 2020, similar high-intensity events affected not only Taim but also other important wetlands such as Banhado Grande and Lagoa do Peixe. These elevated accumulated values contrast with lower ones, resulting in an irregular rainfall pattern throughout the year, which influences wetland pulses.\u003c/p\u003e\n\u003cp\u003eThe precipitation and evapotranspiration characteristics described above are corroborated by the hydroclimatic characterization of Rossato (2011) and Brubacher et al. (2021), highlighting how these variables behave in Rio Grande do Sul State.\u003c/p\u003e\n\u003cp\u003eBecause these wetlands are found in two different geomorphological units and feature distinct vegetation cover, it is possible to understand the variations between their minimum and maximum evapotranspiration values. According to Lu et al. (2024), land use and land cover influence actual evapotranspiration in plains and wetlands.\u003c/p\u003e\n\u003cp\u003eThe historical precipitation data show spaced rainfall periods with no clear seasonal pattern. This behavior suggests that the evapotranspiration regime is strongly linked to temperature, given that water availability does not remain consistent throughout the year.\u003c/p\u003e\n\u003cp\u003eAccording to Sun et al. (2024), climatic variables such as temperature and precipitation anomalies affect wetland vegetation composition and coverage, including aspects like leaf area and plant greenness, which can in turn influence the volume of water lost through evapotranspiration. Wang (2025) highlights that evapotranspiration (ET) represents the total water loss from the wetland surface to the atmosphere via evaporation and transpiration, and it is typically the largest component of the wetland hydrological cycle. Moreover, Wang (2025) suggests that understanding how potential seasonal variations might influence ET in wetlands is essential.\u003c/p\u003e\n\u003cp\u003eIntegrating spectral indices (MNDWI) with water balance (P-ET) provided a robust tool for monitoring wetland water dynamics. The results underscore the importance of management strategies that consider seasonality and extreme events for the conservation of these areas. According to Trabelsi and Abida (2024), understanding the hydrological processes associated with wetland dynamics through remote sensing and hydrological modeling is fundamental, enabling further research on the impacts of climate change and the global water cycle.\u003c/p\u003e\n\u003cp\u003eThe geomorphological features and vegetation composition (Marchetti et al., 2013) in both geomorphological units directly respond to flooding pulses caused by extreme precipitation events (GOMES; MAGALH\u0026Atilde;ES, 2017).\u003c/p\u003e\n\u003cp\u003eMarchetti et al. (2013) investigated the relationship between geomorphology and vegetation in floodplain regions and observed that the main variations in seasonal vegetation dynamics depend more on the hydrological cycle than on other variables. They also found that flooding dynamics are regulated by geomorphological architecture, such that vegetation is influenced by the geomorphological unit in which it occurs and by water pulses during both dry and flood periods.\u003c/p\u003e\n\u003cp\u003eAccording to Gomes and Magalh\u0026atilde;es (2017), wetlands are permanently or temporarily saturated, flooded, and/or waterlogged systems, formed in landforms and substrates that allow for a greater accumulation of surface and/or subsurface water. Additionally, they highlight the roles of geomorphological, hydrological, vegetation, and anthropogenic factors in the formation of wetlands.\u003c/p\u003e\n\u003cp\u003eAlthough it was possible to map water surface areas in wetlands and identify their relationship with climatic variables and flood pulses, we acknowledge the need for more rigorous analyses to achieve greater accuracy. Di Vittorio and Georgakakos (2018) state that none of these techniques have been entirely precise, and the visual inspection of water surfaces overlaid on true-color band compositions can aid in this assessment.\u003c/p\u003e\n\u003cp\u003eIn this regard, Kgabo et al. (2021) note that the spatial extent of wetland or flood water surface areas depends on upstream precipitation, environmental trends in evapotranspiration, and local groundwater infiltration or recharge. Furthermore, Luo et al. (2024) used remote sensing to evaluate wetland responses to extreme precipitation events, employing the MNDWI index in GEE to delineate water bodies. They concluded that precipitation influences the results of environmental indicator indices.\u003c/p\u003e\n\u003cp\u003eUnderstanding spatiotemporal variation in water levels and flooding in wetlands, considering the hydrological connectivity between environments via multi-source remote sensing data, as well as assessing ecosystem responses to change, has been the focus of numerous studies (ZHENG et al., 2024). By integrating remote sensing with time-series analysis and Sentinel-2, Landsat, and spectral indices, Zheng et al. (2024) examined the interannual variation of flood peaks, drainage networks, and rainfall impacts to infer floodplain flooding patterns and river\u0026ndash;floodplain\u0026ndash;wetland connectivity.\u003c/p\u003e\n\u003cp\u003eFinally, we can say that our study made it possible to correlate rainfall volumes, evapotranspiration, and water surfaces mapped with MNDWI. As illustrated in Figures 7, 8, and 9, higher precipitation periods resulted in more extensive water surfaces. However, during certain intervals of intense rainfall, it was not possible to record water surface areas because of excessive cloud cover (above the established threshold), which prevented the acquisition of suitable images for mapping.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Seasonal and Interannual Dynamics of Wetlands\u003c/p\u003e\n\u003cp\u003eWith respect to seasonal and interannual dynamics, the wetlands in the S\u0026atilde;o Gon\u0026ccedil;alo Channel System and at the Taim Ecological Station showed greater fluctuations, marked by more frequent flooding periods followed by significant retreats, underscoring their seasonality. The highest peaks in water surface extent at the Taim Ecological Station occur at intervals of one to two years, ranging from 4,491 to 5,400 hectares, and exhibit a downward trend that may be associated with more severe La Ni\u0026ntilde;a events (KORB et al., 2024).\u003c/p\u003e\n\u003cp\u003eIn the wetlands of the Central Depression, the water surfaces identified using the MNDWI threshold were consistently smaller. The pulses were more sporadic and did not display a defined seasonality when compared with wetlands in the Coastal Plain. The reduction in the apparent water surface in these systems may be directly related to lower total precipitation and decreased monthly rainfall, along with minimal influence from groundwater storage (SCHWERDTFEGER et al., 2015; HEIDARZADEH et al., 2024).\u003c/p\u003e\n\u003cp\u003eThese findings are significant because they take into account the interplay of local geomorphological and climatological conditions, which revealed a clear seasonality in evapotranspiration, while precipitation totals remained dispersed and lacked a well-defined seasonal pattern. In other words, the wetlands presented seasonal similarities tied to the geomorphological units, such as increased water balance in spring (peaking in October/November), followed by a decrease in summer. This pattern suggests that the wetlands are strongly influenced by the same climatic regime, with local or regional variations stemming from factors like topography, vegetation, or human management.\u003c/p\u003e\n\u003cp\u003eAccording to Zhang et al. (2024), in seeking to understand the spatial and temporal variation of water levels using data from multiple remote sensors, increased precipitation is the key climatic factor that raises water levels in wetlands.\u003c/p\u003e\n\u003cp\u003eSimioni and Guasselli (2017) observed that after high precipitation volumes, an expansion in water surface area occurs, resulting in connectivity between wetlands and the river floodplain. These variations in water surface area and volume (SIOMINI; GUASSELLI, 2017; ZHANG et al., 2024) highlight the importance of precipitation and how wetlands respond to these anomalies and extreme events.\u003c/p\u003e\n\u003cp\u003eRibeiro et al. (2024), using Sentinel-2, CHIRPS, and spectral indices in the Brazilian Pantanal, noted stable precipitation patterns throughout the region and that phenology responds positively to these patterns. They emphasize that flooding is regionally asynchronous and, although the correlation between phenology and flooding is predominantly negative, areas showing positive responses indicate that flood seasonality is an important driver of local vegetation variability.\u003c/p\u003e\n\u003cp\u003eOur analysis, however, showed that precipitation in the wetlands across the two geomorphological units does not follow a defined pattern. This finding reinforces the importance of time-series data and an understanding of the wetlands\u0026rsquo; climatic context as essential factors for accurate mapping and delineation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to understand the influence of water balance on flood pulses and the variation of water surfaces in wetlands located in two geomorphological units. We demonstrated the potential of integrating different sensor systems, spectral indices, climatic data, and time series to monitor the hydrological dynamics of wetlands.\u003c/p\u003e\n\u003cp\u003eBy analyzing Sentinel-2 images (2016\u0026ndash;2024) and applying NDWI, NDMI, and MNDWI, we identified seasonal and interannual patterns in the variation of wetland water surfaces and correlated these with precipitation and evapotranspiration data.\u003c/p\u003e\n\u003cp\u003eMNDWI showed the best results for detecting water surfaces, mainly due to its sensitivity to water even when mixed with vegetation and sediments, conditions typically found in these environments. The other indices overestimated or underestimated results (NDMI tended to overestimate, while NDWI underestimated).\u003c/p\u003e\n\u003cp\u003eWetlands in the Coastal Plain presented more extensive flooded areas, with pulses occurring in periods similar to those observed in the water balance (P-ET). However, in the Central Depression, flood pulses produced smaller water surfaces, with irregular hydrological cycles and longer periods of reduced surface water, as noted at the Banhado do Inhatium site.\u003c/p\u003e\n\u003cp\u003eRegarding the Water Balance (P-ET), we underscore the role of evapotranspiration in regulating water availability, especially during higher temperatures and lower precipitation. We noted seasonality in evapotranspiration time series, whereas precipitation did not show a clear seasonal pattern, displaying irregular peaks concentrated in certain months and years and reflecting extreme events. By combining these variables, we revealed that wetlands respond directly to climatic variations and to the geomorphology of the units in which they are found.\u003c/p\u003e\n\u003cp\u003eThe hydrological dynamics of wetlands depend on geomorphological traits, vegetation cover, and climatic variability. Hydrological pulses caused by extreme events play an important role in these ecosystems, helping maintain connectivity between aquatic and terrestrial systems.\u003c/p\u003e\n\u003cp\u003eOur methodology proved to be effective and replicable for mapping wetlands and supporting their monitoring. Nonetheless, we recommend combining these findings with visual inspections and true-color imagery to validate and refine delineations. Additionally, factors such as cloud cover and missing data in certain periods posed occasional limitations to the analysis.\u003c/p\u003e\n\u003cp\u003eAs future studies, (a) projecting scenarios that consider the historical P-ET series and water surface data, including possible gap-filling, may be evaluated, as it offers a potential solution for verifying immediate responses to extreme events; and (b) improving mapping thresholds for waterbody segmentation, testing methods such as principal component classification, clustering, or supervised classification, could further enhance the results obtained.\u003c/p\u003e\n\u003cp\u003eFinally, the findings underscore the importance of time-series climatic and remote sensing data in analyzing hydrological variability, as well as in mapping and monitoring wetlands, particularly in the context of climate change and extreme events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Postgraduate Program in Remote Sensing, of the State Center for Research in Remote Sensing and Meteorology/UFRGS. This study was financed in part by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior\u0026mdash;Brasil (CAPES)\u0026mdash;finance code 001, award 88887.801261/2023-00, and the Rio Grande do Sul State Foundation for Research Support (FAPERGS).\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Christhian Santana Cunha writing\u0026mdash;original draft, conceptualization, methodology, investigation, formal analysis, visualization. Laurindo Ant\u0026ocirc;nio Guasselli.: conceptualization; writing\u0026mdash;review and editing, supervision, funding acquisition. Carina Cristiane Korb. and T\u0026aacute;ssia Fraga Belloli.: writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was funded in part by the Coordination for the Improvement of Higher Education Personnel\u0026mdash;Brazil (CAPES)\u0026mdash;funding code 001, grant 88887.801261/2023-00, and by the Rio Grande do Sul Research Foundation (FAPERGS).\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study can be found on Dataset in Google Earth Engine. Further inquiries about the data can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbertini C, Gioia A, Iacobellis V, Manfreda S (2022) Detection of Surface Water and Floods with Multispectral Satellites. \u003cem\u003eRemote Sensing\u003c/em\u003e 14:6005. https://doi.org/10.3390/rs14236005\u003c/li\u003e\n\u003cli\u003eAl-Maliki S, Ibrahim TIM, Jakab G, Masoudi M, Makki JS, Vekerdy Z (2022) An Approach for Monitoring and Classifying Marshlands Using Multispectral Remote Sensing Imagery in Arid and Semi-Arid Regions. \u003cem\u003eWater\u003c/em\u003e 14:1523. https://doi.org/10.3390/w14101523\u003c/li\u003e\n\u003cli\u003eApopei LM, Mihăilă D, Lazurca LG, Bistricean PI, Mihăilă EV, Horodnic VD, Emandi ME (2024) Precipitation variation and water balance evaluation using different indices. \u003cem\u003eActa Geographica Slovenica\u003c/em\u003e 64(1):41\u0026ndash;60\u003c/li\u003e\n\u003cli\u003eBracken LJ, Croke J (2007) The concept of hydrological connectivity and its contribution to understanding runoff-dominated geomorphic systems. \u003cem\u003eHydrological Processes\u003c/em\u003e 21:1749\u0026ndash;1763\u003c/li\u003e\n\u003cli\u003eBrubacher JP, Oliveira GG, Guasselli LA (2021) Spatial precipitation database of the state of Rio Grande do Sul. \u003cem\u003eRevista Brasileira de Meteorologia\u003c/em\u003e 36(3):471\u0026ndash;493. https://doi.org/10.1590/0102-77863630009\u003c/li\u003e\n\u003cli\u003eCarvalho J\u0026uacute;nior AO (2018) Remote sensing applications and perspectives for floodable area mapping. \u003cem\u003eRevista de Geografia\u003c/em\u003e 35(4):412\u0026ndash;431\u003c/li\u003e\n\u003cli\u003eChowdhary A, Vyas S (2022) Seasonal change detection of wetlands using remote sensing and GIS. 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This study analyzed the hydrological dynamics of wetlands located in two geomorphological units in the far south of Brazil. We integrated time series (2016–2024) of precipitation (CHIRPS), evapotranspiration (MODIS MOD16A2), and Sentinel-2 images processed in Google Earth Engine, and applied the NDWI, NDMI, and MNDWI indices to map water surfaces. The results indicate that MNDWI was more effective in identifying flooded areas, especially in environments containing vegetation and sediments. The water balance analysis (P-ET) revealed distinct seasonal patterns: (1) greater stability and extensive flooded areas; and (2) irregular pulses and prolonged periods of water surface retreat. Evapotranspiration displayed seasonality, peaking in summer, while precipitation had an irregular pattern with greater amplitude during extreme events. Wetland responses to climatic variability were directly tied to local geomorphology and water storage capacity. These findings underscore the importance of using time-series and remote sensing in wetland monitoring, especially in the face of climate change. The proposed methodology is replicable and supports wetland conservation and mapping through the interplay between spectral indices and water balance.","manuscriptTitle":"Seasonal and Hydroclimatic Dynamics in Wetlands Using Spectral Indices and Water Balance in Different Geomorphological Units","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 08:01:35","doi":"10.21203/rs.3.rs-6331096/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4860d385-0f0c-4dcc-bfcb-d55756dd3d65","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-10T08:01:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-10 08:01:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6331096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6331096","identity":"rs-6331096","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}