Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model

Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model | 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 Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model Aditi Majumdar, Kirti Avishek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3995791/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 Riverine ecosystems supply humans with a variety of ecosystem services (ESs), but anthropogenic activities endanger their availability worldwide. Understanding the spatiotemporal characteristics of riverine ESs and identifying the primary driving forces behind various ESs are crucial for preserving regional ecological security and achieving ecosystem sustainability. The study examines the spatio-temporal changes from 2000 to 2022 in the Danro River Basin in Jharkhand in four essential Ecosystem Services (ES): Sediment Delivery Ratio (SDR), Nutrient Delivery Ratio (NDR), Habitat Quality Monitoring (HQM) and Carbon Storage (CS), using InVEST model, Land Use Dynamics Index and Correspondence analysis. Danro River is a tributary of the Ganges River basin affected by sand mining. Key results were: ( 1 ) A rise in soil erosion was observed due to the transformation of agricultural land into urban areas; ( 2 ) The phosphorous and nitrogen retention was higher in agricultural land as compared to forest areas; ( 3 ) The habitat quality of the Danro River body showed degradation during 2000 to 2020; ( 4 ) The study area can sequester 2128304.92 Mg of Carbon; ( 5 ) The land use dynamic index (K) indicated that bare ground experienced the greatest impact, with a value of -0.021. The study uncovered complex relationships between ecosystem services and land use changes, emphasizing tradeoffs and synergies and laying stress on the holistic management strategies to balance tradeoffs and leverage synergies. The findings provide valuable insights for decision-making in socio-environmental processes. Other regions missing meteorological, hydrological, and geological data may also benefit from applying the InVEST model with localized parameters. InVEST Model Ecosystem Services LULC Tradeoff Synergy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction River ecosystems worldwide support a wide variety of biodiversity (Elosegi et al., 2010) and are crucial for sustaining human livelihoods and traditions by offering various benefits such as food, water, and recreational spaces (Gowda et al., 2015; Hanna et al., 2018). These benefits derived from ecosystems are termed ecosystem services, which can be categorized into cultural, providing, regulating, and supporting services (Assessment, 2005). As the demand for riverine ecosystem services rises, there is a risk to their sustainable provision (Durance et al., 2016; Small et al., 2017). Research indicates that riverine ecosystems are crucial for livelihoods but are also facing significant threats (Beck et al., 2012; Dudgeon et al., 2006; A. J. Reid et al., 2019). The concept of ecosystem services offers a comprehensive approach to assessing how ecosystems contribute to human well-being, presenting a promising strategy for effective riverine management (Brauman et al., 2014; Bunch et al., 2011). The Danro River, a tributary of the Ganges River basin serves as one instance where unlawful sand extraction occurs frequently, resulting in environmental deterioration throughout several sections of the river. Illegal sand mining exacerbates the adverse effects on riverine ecosystems compared to legal operations, primarily because it often occurs without regard for environmental safeguards and regulatory oversight (Ghosh & Jana, 2021). Some specific impacts include the disruption of carbon storage mechanisms in riparian zones, contributing to greenhouse gas emissions and climate change (Qin et al., 2020). Channel incision, bank collapse, and loss of vegetation resulting from illegal sand mining reduce habitat quality for aquatic organisms and contribute to declines in biodiversity (Ghosh & Jana, 2021; Koehnken & Rintoul, 2018). Altered hydrology and sediment distribution following illegal sand mining can disrupt nutrient cycles, leading to shifts in community composition and productivity (Koehnken et al., 2020). Unregulated sand mining causes excessive sediment loading, leading to increased turbidity and smothering of aquatic life (Ghosh & Jana, 2021; Koehnken & Rintoul, 2018). Other consequences of sand mining include land degradation, loss of agricultural lands, biodiversity decline, and increased poverty among affected communities. Additionally, sand mining contributes to increased shoreline erosion, reduced protection from storms, and economic losses through tourism abandonment and aesthetic damage. Regulating and controlling illegal sand mining is crucial to preserving the integrity of riverine ecosystems and ensuring sustainable development (Ghosh & Jana, 2021). Few studies have systematically quantified the ecosystem service potential of whole river ecosystems. A number have targeted only single services, such as nitrogen retention (Basak et al., 2021; Burgin et al., 2013; Vermaat et al., 2016); water quality (Gilvear et al., 2019; Keele et al., 2019; Stammel et al., 2021); water provision (Notter et al., 2012) and flood regulation (Asbjornsen et al., 2022; Hill et al., 2023; Liu et al., 2021). There remains a paucity of tools to assess and quantify the ecosystem services generated by often complex river reaches and stream networks (Palmer & Ruhi, 2019). The majority of studies on the assessment of ecosystem services (Colson & Cooke, 2018; Daniel et al., 2012; Johnston & Bauer, 2020; Martínez-Harms & Balvanera, 2012) combine mathematical techniques with measured data, however, some studies struggle to finish the task due to a dearth of measured data (Koschke et al., 2012). Through the advancement of Geographic Information Systems (GIS) and Remote Sensing (RS), models such as the Soil and Water Assessment Tool (SWAT) (Douglas-Mankin et al., 2010), Hydrological Simulation Programme Fortran (HSPF) (Donigian Jr et al., 1995), and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) (Cong et al., 2020) have been built and utilised to evaluate ecosystem services in recent years. This research uses the InVEST model, created by the Natural Capital Project, a collaborative effort between the University of Minnesota, The Nature Conservancy, Stanford University, and the World Wildlife Fund. The InVEST model is notably preferred due to its straightforward, swift, and robust spatial portrayal, particularly in situations with limited data when evaluating ecosystem services. InVEST uses a gridded map and an average annual time step, in contrast to other hydrological models. It is suitable for assessing the consequences of alterations in land use on an array of ecosystem services because it only requires a small amount of data and knowledge. These services include water yield, carbon retention, and habitat quality (Lüke & Hack, 2018; Vigerstol & Aukema, 2011). Furthermore, it has mapping and spatial analysis features under ArcGIS, and the outputs can be presented as shapefiles, tables, or gridded maps (Ashkezari et al., 2018; Moreira et al., 2018; Yang et al., 2019). The review of the literature revealed that no studies have been done in the region under consideration or in most parts of the Indian subcontinent regarding the impact of LULC on ESs. To address these concerns, we applied the InVEST model with localized tailored settings to simulate and estimate changes in ecosystem services of the Danro River Basin in the Garhwa-Palamu districts of Jharkhand from 2000 to 2022. Danro River Basin lies in the semi-arid region and is vulnerable to significant soil loss risk due to several variables such as changing land use and rainfall. Additionally, fertilizer inputs have caused the trophic state of the Danro River Basin in Jharkhand to continuously shift from mesotrophic to eutrophic and thus the river is at risk of sedimentation. According to (Cai et al., 2023; Goshu et al., 2017), toxic cyanobacteria and faeces are present in rivers' mouths and shorelines. Sediment, fertilizers, animal manure, and manufacturing waste are the primary sources of nitrogen and phosphorus (J. Zhang et al., 2022). Consequently, the key objectives of this study were to: (i) estimate the LULC change in the Danro River Basin between the years 2000 and 2022; (ii) to analyse ES changes in response to LULC; and (iii) to investigate the complex relationships between ecosystem services and land use changes, emphasizing tradeoffs and synergies. 2. Methodology 2.1 Study Area This study focuses on the Danro River Basin, shared by the Garhwa and Palamu districts in Jharkhand (Fig. 1 ). Situated near Palamu Division's southwest edge, Garhwa covers latitudes 23°60’–24°39’ N and longitudes 83°22’–84°00’ E; Palamu extends across 23°50’–24°8’ N and 83°55’–84°30’ E. Neighbours include the Sone River, Sarguja district (Chh.), and Sonebhadra district (UP); they share roughly 1,200 ft.-1,110 ft. elevation above MSL. South-to-north draining Koyal and Sone rivers define the region's arid, cool climate with ~ 52.55" annual rainfall (Korisettar, 2007). Summers can hit 47°C, and both districts grapple with drought due to being part of a rain shadow zone (Jha, 2009). Dominant rocks consist of granite gneiss and related migmatites, revealing the gneissic structure and pale colouring. Soils vary from lateritic clay or clayey loam on plateaus to coarse sandy loam stemming from granite gneiss, quartzite, or gneiss elsewhere. Alluvial soil exists solely in the valley floors of the Koel, Kanhar, Tahuan, and Danro rivers. 2.2 Analysis of land use changes This research examined alterations in land utilization within the Danro River Basin using a land use dynamic index. The evaluation involved comparing the extent of each land use category during different time periods (F. Hao et al., 2012; Zhang et al., 2015). The expression formula can be assessed as follows: $$K= \frac{{S}_{bi}-{S}_{ai}}{{S}_{ai}}\times \frac{1}{T} \times 100\%$$ 1 The variables S ai and S bi denote the initial land area (in hectares) of type I at the beginning and conclusion of the study, respectively, T stands for the study's duration, and K is the land use dynamic index for a certain land use type during T. 2.3 The InVEST model InVEST, known as Integrated Valuation of Ecosystem Services and Tradeoffs, is a software tool crafted by the Natural Capital Project (Hamel et al., 2021) (version 3.9.0 - InVEST 3.9.0). This tool was employed to quantify the responses of ecosystem services (ES) to modifications in land use and land cover spanning the years 2000 to 2022. Utilizing a gridded map, the InVEST model operates with a minimal set of distinct datasets that are easily accessible. Additionally, the model is designed to be user-friendly and cost-effective (Harichandan et al., 2023). The depiction of the research framework can be observed in Fig. 2 . 2.4 Selection of Ecosystem Service Function Evaluating Ecosystem Services (ESs) requires selecting suitable indicators, as highlighted by (Wong et al., 2015) and (Bai et al., 2018). This study used four criteria to choose ES indicators for the Danro River Basin: ( 1 ) Utilizing indicators from the Millennium Ecosystem Assessment (W. V. Reid et al., 2005) and the Common International Classification of Ecosystem Services (CICES) (Haines-Young & Potschin-Young, 2018). ( 2 ) Selecting ES indicators addressing stakeholder concerns from governments, enterprises, and residents (J. Chen et al., 2019). ( 3 ) Focusing on ES indicators linked to human well-being, in line with recommendations from (Wong et al., 2015). ( 4 ) Ensuring robust data availability. These criteria led to identifying four key ES indicators for the Danro River Basin: 1) SDR services for assessing soil erosion factors, land use changes, topography, vegetation cover, and management practices affecting sediment-reaching water bodies. 2) NDR services for nonpoint source pollution reduction involving nitrogen and phosphorous retention. 3) Habitat quality services as biodiversity indicators using land use and threats data to map habitat quality. 4) Carbon capture services depicting land use maps and quantifying carbon stocks in aboveground biomass, belowground biomass, soil, and dead organic matter. SDR assesses soil erosion levels, NDR aids in understanding nutrient cycling capacity, habitat evaluation tracks biodiversity changes, and carbon stock estimation helps assess forested areas. ES evaluation spanned 2000–2022 at a 30m × 30m grid scale to capture recent developments in the Danro River Basin while considering data availability and policy adaptability. 2.5 Ecosystem Service Assessment 2.5.1 Sediment Delivery Ratio (SDR) The amount of sediment that was added to the water body was calculated using InVEST's SDR model. The model is based on the computation of the annual soil loss rate for individual pixels, coupled with the sediment delivery rate (SDR). The SDR is a measure of the amount that soil erosion ends up in aquatic habitats and settles there. This approach was formulated using research conducted by (Hamel et al., 2015). The model overlooks processes taking place within the stream itself and assumes that any sediment reaching the watercourse is transported to the hydrographic basin's exit. The Revised Universal Soil Loss Equation (RUSLE) (Eq. (2)) is used in the SDR model to predict the annual soil loss per pixel (measured in tons/ha/year). In comparison to earlier hydrological models, the InVEST model offers greater flexibility and demands less extensive data (Hamel et al., 2015). It can assess soil loss and the export of sediment across various land use categories. Among other functions, it can also estimate the amount of silt reaching water bodies (Aneseyee et al., 2020; Sarkar et al., 2022). RUSLE = R* K * LS * C * P ( 2 ) In the formula, R represents the erosive power of rainfall (measured in MJ mm/ha/h/year), K represents the soil's susceptibility to erosion (measured in t/ha/MJ/mm), LS represents the factor related to slope length and gradient (dimensionless), C represents the factor linked to land cover and management (dimensionless), and P represents the factor tied to support practices (dimensionless). The process for determining the RUSLE parameters is outlined in Table 1 . Figure 3 shows the regional distribution of input parameters for the watershed. Table 1 RUSLE input parameters Factor Input Data Equation(s) Used Rainfall erosivity factor (R); MJ mm/ha/h/year India Meteorological Department (IMD) R = 81.5 + 0.38P P: annual precipitation (mm) Soil erodibility factor (K); t/ha/MJ/mm FAO Soil Data K RUSLE = f csand * f ci−si * f orgc * f hisand (f csand = (0.2 + 0.3 * exp[-0.256*m s *(1-m sil /100)]) f cl−si = (m silt /m c +m silt ) 0.3 f org = (1- 0.25*org c /org c +exp [3.27–2.95*org c ] f hisand = (1- 0.7*(1-m s /100)/(1-m s /100) + exp[-5.51 + 22.9*(1-m s /100)]) where, f csand denotes a factor that assigns lower erodibility values to soils rich in coarse sand and higher values to soils with minimal sand content; f cl−si signifies a factor that bestows decreased soil erodibility values to soils with elevated clay-to-silt ratios; f orgC represents a factor that diminishes soil erodibility for soils with substantial organic carbon content; and f hisand stands for a factor that reduces soil erodibility in instances of exceedingly high sand content. Furthermore, the variables are defined as follows: m s corresponds to the sand fraction content (with a diameter of 0.05-2.00 mm) [%]; m silt refers to the silt fraction content (with a diameter of 0.002–0.05 mm) [%]; m c designates the clay fraction content (with a diameter of < 0.002 mm) [%]; and org C signifies the percentage of organic carbon (SOC) content [%]. Slope length factor (L); dimensionless SRTM 90 m digital elevation data L= (Flow Accumulation*Cell size / 22.13) m where m is an exponent that depends on slope steepness and assumes a value between 0.2 and 0.5 Slope steepness factor (S); dimensionless SRTM 90 m digital elevation data S = 0.065 + 0.045S + 0.065S 2 Land cover management factor (C); dimensionless NDVI from Landsat TM and ETM+ C = 0.431–0.805 * NDVI Support practice factor (P); dimensionless Land cover map Literature Review To determine the sediment delivery rate in Eq. ( 3 ), the first step entails computing the hydrological connectivity index (IC) using Eq. ( 4 ). This index functions as a gauge of the hydrological linkage between sources of sediment spread throughout the landscape and the watercourses. There is a greater chance of sediment entering the stream as the pixel's IC value rises. Each pixel's (D up ) upstream characteristics, such as land cover, slope, and drainage area (Eq. ( 5 )), as well as the features of the flow path that crosses the pixel and the watercourse (D dn ), which include distance, land cover, and gradient (Eq. ( 6 )), interact to determine the IC. $$SDR=\frac{SDRmax}{1+\text{e}\text{x}\text{p}(ICo-\frac{ICi}{kb})}$$ 3 where SDR max is the theoretical maximum SDR, with a mean value of 0.8 (Gashaw et al., 2021), and IC o and kb are calibration factors. $$IC= {\text{log}}_{10}\left(\frac{Dup}{Ddn}\right)$$ 4 $$Dup=CS\sqrt{A}$$ 5 $$Ddn= \sum _{i}\frac{di}{CiSi}$$ 6 In this context, C represents the mean value of factor C across the catchment area upstream, while S denotes the average of slope gradients upstream (measured in meters per meter). A represents the upstream contributing area, measured in square meters, whereas di represents the length of the flow path following the steepest downward slope direction, measured in meters. C i represents the C factor associated with each pixel, and Si corresponds to the specific slope gradient of the pixel. Equation (7) computes the sediment load exported per pixel in tons/ha/year. The sediment discharge from the basin is calculated by considering the total number of pixels that constitute the watershed. E = RUSLE × SDR ( 7 ) 2.5.2 Nutrient Delivery Ratio (NDR) The Nutrient Delivery Ratio (NDR) module within InVEST—a widely applied tool—tracks nutrient origins and routes toward waterways, simplifying the measurement of vegetation's nutrient retention capabilities (He et al., 2023; Tikuye et al., 2023). NDR estimates nutrient transport (nitrogen and phosphorus) to water streams. To function, NDR relies upon spatial data plus supplementary parameters, including land use/cover (LULC) data, DEM data, and precipitation data (Fang et al., 2022). The model's hydrologic connectivity, gauged via the Borselli kb parameter, is refined to optimize performance (Das et al., 2022; R. Hao et al., 2022). Due to varying outcomes in diverse catchments, (Redhead et al., 2016) conducted a sensitivity analysis, concluding that this link is locale-specific. Our focus, though, centred on modifying spatial data instead of parameters, adopting (Griffin et al., 2020) suggestion to utilize the default value of 2. Additionally, a detailed biophysical table must be prepared for each unique LULC dataset, containing nuanced info about nutrient loads connected to every LULC class, comprising the critical distance at which a LULC patch retains nutrients maximally (tailored to pixel resolution specific to each LULC dataset), the ratio of nutrients delivered through surface and subsurface pathways (default value of zero), and nutrient loads associated with each LULC class (kg/ha/yr). The table incorporates details pertinent to both nitrogen and phosphorus. Two extra parameters, subsurface maximum retention efficiency and subsurface critical length, are added when the final parameter is set to a value other than zero. Published studies (Anjinho et al., 2022; Benez-Secanho & Dwivedi, 2019; Han et al., 2021; Tran et al., 2022; C. Wang et al., 2017) provided nutrient loads and efficiency coefficients (Table 2 ). Table 2 Biophysical parameters utilized in the Nutrient Delivery Ratio (NDR) module of InVEST Description LULC_Code Nutrient Load_P (kg/ha/yr.) Max. Retention Efficiency _P Max. Retention Efficiency _N Water 1 0 0.4 0.02 Vegetation 2 1.36 0.67 0.4 Shrubs 4 1.4 0.6 0.4 Crops 5 3.57 0.48 0.25 Built up 7 2.1 0.26 0.05 Bareground 8 0.79 0.26 0.05 Rangeland 11 0.93 0.6 0.35 To adjust nutrient loads according to the basin's surface runoff potential, the model uses Equations 8 and 9 . modified.load xi = load xi ×RPI xi ( 8 ) RPI xi =RP i RP av ( 9 ) Finally, similar to the SDR concept, the model calculates the NDR factor (Eq. 10 ) for each pixel based on the IC and retention properties of neighbouring pixels within the same flow channel. $${NDR}_{i}= {NDR}_{0,i} \left(1+\left(ICi-\frac{ICo}{k}\right)\right)-1$$ 10 Lastly, Eq. 11 computes the nutrient load exported by each pixel in kg/ha/yr, summed to obtain the overall nutrient export figure. Since InVEST's nutrient export model considers just nonpoint sources, it was crucial to include total nitrogen and total phosphorus point sources. X expi = modified. load xi * NDR i ( 11 ) 2.5.3 Habitat Quality Monitoring (HQM) Biodiversity indicators rely on habitat quality, with higher-quality areas supporting greater biodiversity (Aznarez et al., 2022; Sun et al., 2019). Modelling the interaction of biodiversity and ecosystem services allows for scrutinizing their spatial arrangements, identifying areas where conservation efforts can benefit both natural systems and human economies, and highlighting where these goals do not align. To evaluate the Danro River's riparian zone habitat quality, InVEST's habitat quality module was used, with Eq. (12) computing the habitat quality in grid x for habitat type (land cover) j. Q xj = H j [1-(D 2 xj / (D 2 xj + k z ))] ( 12 ) Each habitat type is assigned a relative rating of habitat appropriateness (Hj) between 0 and 1, with 1 being the highest value for the target species. Threats to habitats are depicted as a raster, with values normalized within a range of 0 to 1, where 0 signifies the least threat and 1 represents the highest. The influence of threats on habitats in each grid cell is determined by several factors, including the distance between the cell and the source of the threat, the proportionate significance of the threat, and the relative susceptibility of habitat types to the threats (Terrado et al., 2016). Parameter values were established to compute the spatial arrangement of habitat quality across the research region, including hazards to habitats (Table 3 ) and the vulnerabilities of various land categories to these hazards (Table 4 ), referencing pertinent literature specific to the study area and consulting the InVEST user manual (Lai & Leone, 2017; Qiao et al., 2023; B. Wang & Cheng, 2022). Table 3 Habitat threat factors. Max_Dist Weight Threat Decay 3 1 Roads Linear 9 1 Urbanization Exponential 4 0.6 Soil Erosion Exponential 2.8 0.5 Agricultural Runoff Linear 2 0.3 Water Abstraction Linear Table 4 Sensitivity of land use types to the threat factors. LULC NAME HABITAT Roads Urbanization Soil Erosion Agricultural Runoff Water Abstraction 1 Water 1 0.9 0.9 0.9 1 0 2 Vegetation 1 0.7 0.7 0.8 0.2 0.2 4 Shrubs 1 0.8 0.8 0.6 0.2 0.3 5 Crops 1 0.9 0.9 0.7 0.2 0.6 7 Built up 1 0.1 0 0.2 0 0.6 8 Bareground 0 0.9 1 1 0.5 0.9 11 Rangeland 0 0.8 0.8 0.7 0.2 0.7 2.5.4 Carbon Storage (CS) One of the most significant ecosystem services, carbon storage and sequestration, is crucial for maintaining ecological balance, reducing atmospheric concentrations of greenhouse gases like CO 2 , regulating regional microclimate, and mitigating global climate change (Mbow et al., 2014; Smith et al., 2013). The estimation of carbon storage was done using the InVEST programme. It involved the following calculation: C total = C above + C below + C soil + C dead ( 13 ) where C total stood for total carbon storage (Mg), C above for above-ground biomass storage (Mg), C below for below-ground biological storage (Mg), C soil for below-ground soil storage (Mg), and C dead for above-ground dead organic storage (Mg). The total carbon storage of the study area is then calculated by the model software based on Eq. (14) . C total = ∑n i C i + A i ( 14 ) where C total signifies the total carbon amount stored within the investigated area (measured in tons), n represents the count of different land use and land cover (LULC) types present, and A i denotes the area of each specific LULC type (measured in hectares). As indicated by (Sharp et al., 2018), the primary data required for executing the InVEST carbon storage and sequestration model comprised LULC data for the study area and carbon density data for each LULC category within that area. The carbon storage figures for both aboveground and belowground carbon reservoirs across various LULC types were sourced from the IPCC 2006 report (Dida et al., 2021; Rajbanshi & Das, 2021), recognized as one of the foremost global repositories of carbon data. Data for the present investigation was gathered from the Ministry of Environment and Forest Assessment, the Forest Survey of India, and the Carbon Pool Data for Forest Classifications (as shown in Table 5 ). For biomass values about non-forest classifications, the biomass guidelines outlined in the IPCC's 2006 directives for constructing greenhouse gas inventories within the Agriculture, Forestry, and Other Land Use (AFOLU) Sectors were utilized. Table 5 Formulated carbon pool table LULC_Code LULC_Name C_above C_below C_soil C_dead 1 Water 0 0 0 0 2 Vegetation 180 120 120 55 4 Shrubs 90 60 110 30 5 Crops 3 2 8 1 7 Built up 15 10 60 1 8 Bareground 0 0 0 0 11 Rangeland 0 0 0 0 2.6 Correlation and Correspondence Analysis Pearson correlation analysis was employed to discover relationships among Ecosystem Services (ESs), land uses change metrics, and ES indicators throughout three time frames (2000, 2010, and 2022). The significance of the correlation coefficient (r) and potential tradeoffs was evaluated by carefully choosing variables for statistical analysis. Ecosystem services interactions fall into tradeoffs and synergies (Huang et al., 2023; Li et al., 2022; Sutherland et al., 2023; Zhou et al., 2023): tradeoffs occur when increasing one ES leads to decreased others, while synergies emerge when multiple ESs cooperatively enhance each other (Dade et al., 2019). Correspondence analysis, resembling correlations noted in previous studies, offers insightful clues about these links (Mouchet et al., 2014). Correspondence analysis's Euclidean distance between points in the diagram reflects differences between them; larger distances indicate higher dissimilarities. Angles created by arrowheads of two variables can suggest correlations: obtuse angles imply negative correlations (tradeoffs), while acute angles reveal positive relations (synergies) (Gao et al., 2019). SPSS software facilitated the execution of correspondence analysis. Across the entire river basin, the average value of each ES for each land use category was computed. Standardization ensued using Eq. ( 15 ) to ensure comparable average values in the contingency table where correspondence analysis occurred: $${x{\prime }}_{ij={x}_{ij}/\text{m}\text{a}\text{x}\left({x}_{j}\right)}$$ 15 Confidence in the accuracy of the information remains intact. 2.7 Validation of model results RUSLE and other models lacked data for validating model results, especially in data-scarce areas like Jharkhand. Comparing findings with those of similar or neighbouring watersheds can validate the results (Debie & Awoke, 2023; Degife et al., 2021). This study validated model outcomes by comparing them with results from investigations in a neighbouring watershed, the Upper Subarnarekha River Basin in Jharkhand, India, which shared similar topography and agroecological characteristics. The highest recorded soil erosion rate within the watershed for 2001 was 40 tons/ha/yr, increasing to 49.80 tons/ha/yr the following year (Samanta et al., 2016). Another study in the Karso Watershed of the Hazaribagh district, Jharkhand, India, using the USLE model, showed an average annual soil erosion of 3.66 tons/ha/yr, with 82.63% of the watershed area under slight erosion class and 9.6%, 5.93%, 1.27%, 0.42%, and 0.14% under moderate, high, very high, and severe erosion potential zones, respectively (Chowdary et al., 2004). Other models could not be tested accurately due to a lack of plot-based experimental investigations and long-term observed data in the study area. However, observations in different parts of the country showed that nitrogen at a rate of 2.9 Mg/ha and phosphate at a rate of 28.11 Mg/ha were preserved within mangrove sediments in Bhitarkanika, Orissa, India (Hussain & Badola, 2008). In Rajasthan's Keoladeo National Park, a study on habitat quality assessment found that between 2009 and 2015, the habitat's quality drastically declined (Chowdary et al., 2004). The InVEST model's carbon sequestration analysis in the Bidhalna micro-watershed (MWS) located in the Dehradun District of Uttarakhand State, India, found the entire MWS held a carbon stock of 697593.65 Mg in 2013 (Chowdary et al., 2004). 3. Result and discussion 3.1. Land Use Change in Danro River Body from 2000 to 2022 Between 2000 and 2022, there have been significant alterations in land use types within the study area (Fig. 4 ). Table 6 displays the frequency and spatiotemporal dynamics of each LULC type. With the use of the reference points gathered from the appropriate Google Earth Image, the overall accuracy and Kappa coefficient values of each of these maps were assessed, and they range from 90 to 93% and from 0.86 and 0.91, respectively. This showed that there was excellent agreement between the classified maps and the reference data (Monserud & Leemans, 1992). Significant transformations in land use types were observed, particularly in built-up areas, which expanded at the expense of agricultural land and bare grounds. According to Table 7 's land use dynamic index (K), bare ground experienced the most substantial change (K = -0.021). The extent of bare ground decreased from 1408.22 km 2 (0.12% of the total area) in 2000 to 1046.45 km 2 (0.08%) in 2022, and crop areas decreased from 425.39 km 2 (0.03%) in 2000 to 320.65 km 2 (0.02%) in 2022. Over the same period, water bodies decreased from 7982.43 km 2 in 2000 to 7228.82 km 2 in 2022 (K = -0.004). Meanwhile, built-up areas increased from 765.14 km 2 (0.06%) in 2000 to 2673.49 km 2 in 2022 (0.22%). The areas covered by vegetation, shrubs, and rangeland exhibited minimal changes between 2000 and 2022. Table 6 The areal extent of land use/cover classes Landscape Types 2000 (km 2 ) 2010 (km 2 ) 2022 (km 2 ) Water 7982.43 7423.9 7228.82 Vegetation 204.48 226.76 215.9 Shrubs 266.46 220.63 257.16 Crops 425.39 410.21 320.65 Built up 765.14 2031.03 2673.49 Bareground 1408.22 1426.95 1046.45 Rangeland 1.11 1.25 3.03 Table 7 The land use areas and their changes obtained in 1985, 1995, 2000 and 2007 for the polders in the study area 2000–2010 2010–2022 2000–2022 K% Landscape Types 2000 (km 2 ) 2010 (km 2 ) 2022 (km 2 ) Change area (km 2 ) Change area (km 2 ) Change area (km 2 ) T1 T2 T3 Water 7982.43 7423.9 7228.82 -558.53 -195.08 -753.61 -0.007 -0.002 -0.004 Vegetation 204.48 226.76 215.9 22.28 -10.86 11.42 0.011 -0.004 0.002 Shrubs 266.46 220.63 257.16 -45.83 36.53 -9.3 -0.017 0.013 -0.001 Crops 425.39 410.21 320.65 -15.18 -89.56 -104.74 -0.004 -0.017 -0.013 Built up 765.14 2031.03 2673.49 1265.89 642.46 1908.35 0.165 0.025 0.029 Bareground 1408.22 1426.95 1046.45 18.73 -380.5 -361.77 0.001 -0.021 -0.014 Rangeland 1.11 1.25 3.03 0.14 1.78 1.92 0.013 0.114 0.025 K refers to the land use dynamic index in Eq. ( 1 ) T1 refers to the period between 2000 and 2010, T2 refers to the period between 2010 and 2022, and T3 refers to the period between 2000 and 2022 3.2 Sediment Delivery Ratio (SDR) Model In the research region, the RUSLE model calculated annual soil loss at 20.50 t/ha/year, which is evident in the outcomes. There was potential soil loss ranging from 3.99 tons/ha/year (2000) to 20.50 tons/ha/year (2022) ( Fig. 5 ) . Following that, each land-use type's utilisation of these was examined. More than 0.90% of the study area was still considered to be at high risk for severe erosion. A very low rate of erosion occurs in roughly 15% of the studied area, mostly in the barren zones, with the areas at low and moderate risk of erosion being 2.80% and 1.90%, respectively. In agricultural areas, shrubs, grasslands, and woods, the mean erosion rate is highest. Steep slopes exhibit the highest rates of soil loss. This could be the result of a quicker conversion of forest cover to built-up land. Sediment export gives us how much sediment is eroded from each pixel and exported to the stream (Fig. 6 ). Meanwhile, the sediment retention index is calculated relative to bare ground. It gives us an idea of where the vegetation on the landscape is holding back (Fig. 7 ). The watershed's agricultural land or shrubland land use land cover types proved to be the majority of sediment export. Cropland, which is the predominant LULC, was where the greatest sediment outflow was found. Contrary to bare ground, farmland regions are typically characterized by frequent disruptions such as farming activities like ploughing and cropping. These actions involve clearing, fracturing, and turning over the soil, making it susceptible to erosion processes (Godron & Forman, 1983). Soil erosion results in the migration of vital nutrients including soil phosphorus and nitrogen in addition to the movement of soil particles, leaving the earth's surface fractured and with deteriorated plants (Ma et al., 2019). In the forested areas, the sediment retention rate was higher. 3.3 Nutrient Delivery Ratio (NDR) The NDR model is a crucial tool for evaluating nutrient transport efficiency, particularly nitrogen and phosphorus, from land to water bodies (Borrelli et al., 2020). A higher NDR value indicates elevated nutrient runoff, posing potential risks to water quality and ecosystem health, emphasizing the need for strategic land management interventions (Riahi et al., 2017). This underscores the significance of NDR in guiding effective environmental preservation strategies, especially in regions like the Garhwa district of Jharkhand, where agricultural practices contribute significantly to nutrient release in the Danro watershed (Borrelli et al., 2020). With population expansion, climate change, and changes in land usage, the issue is expected to worsen (Borrelli et al., 2020; Riahi et al., 2017). To compare nutrient contributions to water bodies across space, we utilized the NDR model within the InVEST ecosystem modelling framework. Nitrogen makes up the majority of the subsurface nutrient load in the Danro River body, while phosphorus plays a minor role. Nitrogen export was 1.665 kg/ha/yr. in 2022 compared to 1.692 kg/ha/yr. in 2000, while phosphorus export was 0.177 kg/ha/yr. in 2000 and 0.184 kg/ha/yr. in 2022, both were delivered to the waterbody (Figs. 8 , 9 ). The forest region releases substantially fewer nutrients per unit area than either the agriculture area or rangelands. Nitrogen and phosphorus hotspots are localized in agricultural areas, particularly in the south and west of the research area, as confirmed by the maps. Nutrient release into streams is less affected by other land use types, particularly forests. Phosphorus discharge is more sensitive in built-up areas than in undeveloped ones. The Danro River basin's rapidly expanding economy and population growth have encouraged the construction of built-up and agricultural areas, increasing the need for fertilizers, irrigation water, and domestic garbage production. Eutrophication in the Danro River has been indicated lately (Bouska et al., 2019), primarily attributed to phosphorus as the key limiting factor (Y. Chen et al., 2023). Dissolved oxygen levels in the water have been decreasing (Ni et al., 2019), simplifying the release of phosphorus from sediments into the water. 3.4 Habitat Quality The habitat quality index, ranging from 0 to 1, functions as an indicator of the quality of the habitat. The greater the value, the better and more comprehensive the environment, and the more favourable it is for the system's increased biodiversity (Terrado et al., 2016). The intensity of land use frequently has an impact on habitat quality. As land use intensity rises, so do the sources of habitat threat, which worsens the quality of the habitat around the threat sources. In this instance, the waterbody is recognized as being seriously threatened by agricultural land, roads, and built-up areas nearby. Water abstraction and soil erosion are additional dangers (Sun et al., 2018; Wolf et al., 2023). The average habitat quality in 2000 and 2022 was 0.7321 and 0.6095, respectively, according to the InVEST habitat quality module and Eq. (14) . The overall average habitat quality dropped significantly, and the average value of habitat quality displayed an "increased-decreased" trend. The computation results lack a consistent classification threshold, however, the widely used "Natural break approach" can pinpoint classification intervals, classify similar values most effectively, and emphasize differences across groups. As a result, the natural break approach was used in ArcMap 10.8 to classify the habitat quality index. The value was then divided into four categories: poor habitat (values between 0 and 0.5), general habitat (0.5 and 0.8), good habitat (0.8 and 0.9), and excellent habitat (1.0 and above). "General quality" predominated in terms of habitat quality overall. The geomorphic types indicated as preferred habitats for species were forests, rangelands, and shrubs, all of which had the best habitat quality. Figure 10 displays the InVEST-generated habitat quality map. 3.5 Carbon Storage The total carbon content across a landscape refers to the amount of carbon currently stored in megagrams (Mg) within each grid cell (Trentin et al., 2023). This accumulation encompasses all four carbon reservoirs (above ground, below ground, soil, and deceased matter) linked to the depicted land use and land cover (LULC) categories on the Danro River's map. Specifically, for the year 2022, the overall carbon content stood at 4.75 Mg for vegetated regions ( Fig. 11 ) , while the minimum was observed in aquatic bodies, representing non-forest categories devoid of vegetation (as seen in Fig. 11 ). Consequently, the total carbon content within the Danro River region, given the current situation, was established at 2128304.92 Mg. 3.6 Spatial Correlations Between Ecosystem Services Table 8 shows the correlation coefficients between four ecosystem services in the Danro River Basin: Phosphate Retention (P_Retention), Nitrogen Retention (N_Retention), Habitat Quality, and Carbon Pool. Here's a breakdown of the correlations: Table 8 Correlation between ecosystem services P_Retention N_Retention Habitat_Quality Carbon_Pool P_Retention 1 .933 0.109 0.626 N_Retention .933 1 -0.064 0.647 Habitat_Quality 0.109 -0.064 1 -0.161 Carbon_Pool 0.626 0.647 -0.161 1 P_Retention vs. N_Retention: This has a very strong positive correlation (0.933). This suggests that areas with high phosphate retention also tend to have high nitrogen retention. This could be due to common environmental factors or linked hydrology in these areas. P_Retention vs. Habitat Quality and Carbon Pool: These have moderate positive correlations (0.109 and 0.626, respectively). This means that there might be a slight tendency for areas with higher P_Retention to have better Habitat Quality and Carbon Pool. However, the correlation is not very strong, so this needs to be interpreted with caution. N_Retention vs. Habitat Quality: This has a weak negative correlation (-0.064). This suggests a very slight tendency for areas with higher N_Retention to have slightly lower Habitat Quality. However, the correlation is very weak, so it's not a definitive finding. N_Retention vs. Carbon Pool: This has a moderate positive correlation (0.647). Similar to P_Retention, areas with high N_Retention also tend to have a higher Carbon Pool. Habitat Quality vs. Carbon Pool: This has a weak negative correlation (-0.161). This suggests a very slight tendency for areas with better Habitat Quality to have a slightly lower Carbon Pool. Overall, the table highlights some interesting relationships between these ecosystem services in the Danro River Basin. The strong positive correlations between P_Retention and N_Retention, and between N_Retention and Carbon Pool, suggest potential synergies between these services. However, the weak negative correlations between some services indicate potential trade-offs that need to be considered in managing the basin. It's important to remember that these are correlations, not causations, and further research is needed to understand the underlying mechanisms behind these relationships. 3.7 Examining the relationship between the types of land use and the services offered by ecosystems through correspondence analysis Changes in land use can have a variety of effects on ecosystem services. These effects include many-to-one, many-to-many, and one-to-many relationships (Bryan, 2013). Thus, examining the relationship between different land-use types and ecosystem services can provide information about how modifications to one's land use affect the relationships between various ecosystem services (L. Chen et al., 2013; Lautenbach et al., 2011; Sawut et al., 2013; Schneiders et al., 2012; Su & Fu, 2013). In Table 9 , there are indications of both tradeoffs and synergies among land-use changes (LULC) and ecosystem services observed. The high positive correlations between LULC in the year 2000 and LULC in the year 2010 (0.983) and between LULC in the year 2010 and LULC in the year 2022 (0.993) suggest a strong synergy, indicating a consistent pattern in land-use changes over time. Conversely, the negative correlations between land-use changes and ecosystem services, such as P_Retention, N_Retention, and Carbon Pool, suggest potential tradeoffs. For instance, as land-use changes increase (-0.316 to -0.448), there is a corresponding decrease in nutrient retention and carbon storage. This implies that alterations in land use may adversely affect certain ecosystem services. The relationship between land-use changes and Habitat Quality shows mixed patterns (0.499 to 0.242), indicating that the impact on habitat quality is more complex and may depend on specific land-use transitions. Table 9 Correlation between land use land cover changes and ecosystem services LULC_2000 LULC_2010 LULC_2022 P_Retention N_Retention HabitatQuality CarbonPool LULC_2000 1 .983 .956 -0.316 -0.617 0.499 -0.348 LULC_2010 .983 1 .993 -0.429 -0.708 0.35 -0.37 LULC_2022 .956 .993 1 -0.448 -0.717 0.242 -0.354 P_Retention -0.316 -0.429 -0.448 1 .933 0.109 0.626 N_Retention -0.617 -0.708 -0.717 .933 1 -0.064 0.647 HabitatQuality 0.499 0.35 0.242 0.109 -0.064 1 -0.161 CarbonPool -0.348 -0.37 -0.354 0.626 0.647 -0.161 1 The two-dimensional correspondence analysis (CA) (Fig. 12 ) reveals insightful patterns and associations between land-use changes and ecosystem services. The high positive correlations between successive LULC periods (2000, 2010, 2022) are visually represented by their proximity on the CA plot, indicating a strong and consistent evolution in land-use transitions over time. The positioning of N_Retention points in the negative direction relative to LULC in the year 2010 suggesting a tradeoff scenario, that is, increasing land-use changes correspond with decreasing levels of this ecosystem services. This implies that alterations in land use may have adverse effects on nutrient retention. Conversely, the positive correlation between habitat quality and LULC in the year 2022, represented by its positive direction on the plot, suggests a potential positive influence of certain land-use transitions on habitat quality. The CA plot provides a visual representation of these intricate relationships, offering valuable insights into the dynamics between LULC and ecosystem services and highlighting areas for consideration in sustainable land management strategies. 3.8 Strengths and Limitations of the Study Strengths of the model include detailed assessments of soil loss proportions reaching streams, sensitivity to input variations, consideration of land-use suitability and biodiversity effects, and validations against regional and national datasets despite the lack of physical measurements at individual sites. However, the InVEST model focuses solely on rill and inter-rill erosion, excluding gully, bank, and mass erosion due to overlap with RUSLE's scope. Its SDR output indicates soil loss proportions reaching streams (Bouguerra & Jebari, 2017). The NDR model exhibits input sensitivity but may be affected by erroneous empirical load parameter settings (Sieber et al., 2021). Assuming nutrient impacts occur downstream, the model ignores internal stream dynamics. Habitat quality assessment considers land-use suitability while examining biodiversity effects via various land uses (Hassanzadeh et al., 2019). Carbon sequestration modelling faces limitations, including oversimplification of the carbon cycle and disregard for key biological factors (Sharma et al., 2023). Model validation against real data is essential to evaluate performance across diverse landscapes (Sharp et al., 2018). Notably, the lack of gauges did not hinder comparisons with regional and national datasets. 4. Conclusion This study investigated the impact of land-use changes on ecosystem services in the Danro River Basin from 2000 to 2022. The key findings are: Land-use changes: Significant transformations occurred, with built-up areas expanding at the expense of agricultural land and bare grounds. Soil erosion: Increased land-use changes led to higher potential soil loss, with agricultural areas and steep slopes exhibiting the highest rates. Nutrient delivery: Nitrogen and phosphorus export increased, primarily from agricultural areas, potentially impacting water quality. Habitat quality: The average habitat quality declined, suggesting a negative impact of land-use changes on biodiversity. Carbon storage: Forests showed the highest carbon storage, while the overall carbon content within the basin remains significant. Relationships between services: Trade-offs and synergies exist between ecosystem services. Strong positive correlations were observed between phosphorus retention and nitrogen retention, and between nitrogen retention and carbon pool, suggesting potential synergies. Conversely, negative correlations were found between land-use changes and some services, indicating trade-offs. Overall, land-use changes have significantly impacted the Danro River Basin's ecosystem services. While some services benefit from specific land-use transitions, others are negatively affected. Understanding these complex interactions is crucial for developing sustainable land management strategies that optimize the provision of multiple ecosystem services. Further research is needed to address the limitations of the models used in this study and to gain deeper insights into the specific impacts of different land-use transitions on individual ecosystem services. This knowledge can guide informed decision-making for the sustainable management of the Danro River Basin and similar ecosystems. Declarations Acknowledgements Authors acknowledge Environmental Engineering Lab and Remote Sensing and GIS Lab, BIT Mesra for performing analysis. Author contributions Aditi Majumdar is involved in the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. Kirti Avishek contributed to overall monitoring and manuscript editing. Data availability statement Satellite Data has been obtained from the USGS portal. Rest all is primary data collection and analysis. The data that support the findings of the study are available from the corresponding author upon reasonable request. Competing Interests and Funding This research was partially supported by the Institute Research Fellowship (Adm/Results/Ph.D. (MO 2021)/2020-21/4) awarded to AM. Conflict of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Aneseyee, A. B., Elias, E., Soromessa, T., & Feyisa, G. L. (2020). 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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-3995791","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283291739,"identity":"46236ee1-ad4d-4c7d-88bf-f5e514ee2bc6","order_by":0,"name":"Aditi Majumdar","email":"","orcid":"","institution":"Birla Institute of Technology, Mesra","correspondingAuthor":false,"prefix":"","firstName":"Aditi","middleName":"","lastName":"Majumdar","suffix":""},{"id":283291741,"identity":"6493a9d9-f72b-4c06-89c6-b263ca566f51","order_by":1,"name":"Kirti 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area\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-3995791/v1/841701c96757dbf4bde85bb1.png"},{"id":53416290,"identity":"888b6e41-5ec3-4a4a-a5ef-1cc928716520","added_by":"auto","created_at":"2024-03-25 17:53:51","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":271581,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon storage in the current scenario i.e., 2022\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-3995791/v1/292734b406c3c4e70290d018.png"},{"id":53416998,"identity":"e29a5f0a-4dcc-4b1b-8f7a-2d767fe900b6","added_by":"auto","created_at":"2024-03-25 18:01:51","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":18277,"visible":true,"origin":"","legend":"\u003cp\u003eThe closeness relationship between land use and ecosystem services by correspondence analysis (CA) (2000-2022)\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-3995791/v1/b145b97a18b211270113d485.png"},{"id":55844935,"identity":"8f0aa89b-4cba-4dca-9e69-8dc34ff8e06e","added_by":"auto","created_at":"2024-05-04 13:14:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5646961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3995791/v1/ff5ea3b2-42fb-4abb-965c-6ab665e88ca5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRiver ecosystems worldwide support a wide variety of biodiversity (Elosegi et al., 2010) and are crucial for sustaining human livelihoods and traditions by offering various benefits such as food, water, and recreational spaces (Gowda et al., 2015; Hanna et al., 2018). These benefits derived from ecosystems are termed ecosystem services, which can be categorized into cultural, providing, regulating, and supporting services (Assessment, 2005). As the demand for riverine ecosystem services rises, there is a risk to their sustainable provision (Durance et al., 2016; Small et al., 2017). Research indicates that riverine ecosystems are crucial for livelihoods but are also facing significant threats (Beck et al., 2012; Dudgeon et al., 2006; A. J. Reid et al., 2019). The concept of ecosystem services offers a comprehensive approach to assessing how ecosystems contribute to human well-being, presenting a promising strategy for effective riverine management (Brauman et al., 2014; Bunch et al., 2011).\u003c/p\u003e \u003cp\u003eThe Danro River, a tributary of the Ganges River basin serves as one instance where unlawful sand extraction occurs frequently, resulting in environmental deterioration throughout several sections of the river. Illegal sand mining exacerbates the adverse effects on riverine ecosystems compared to legal operations, primarily because it often occurs without regard for environmental safeguards and regulatory oversight (Ghosh \u0026amp; Jana, 2021). Some specific impacts include the disruption of carbon storage mechanisms in riparian zones, contributing to greenhouse gas emissions and climate change (Qin et al., 2020). Channel incision, bank collapse, and loss of vegetation resulting from illegal sand mining reduce habitat quality for aquatic organisms and contribute to declines in biodiversity (Ghosh \u0026amp; Jana, 2021; Koehnken \u0026amp; Rintoul, 2018). Altered hydrology and sediment distribution following illegal sand mining can disrupt nutrient cycles, leading to shifts in community composition and productivity (Koehnken et al., 2020). Unregulated sand mining causes excessive sediment loading, leading to increased turbidity and smothering of aquatic life (Ghosh \u0026amp; Jana, 2021; Koehnken \u0026amp; Rintoul, 2018). Other consequences of sand mining include land degradation, loss of agricultural lands, biodiversity decline, and increased poverty among affected communities. Additionally, sand mining contributes to increased shoreline erosion, reduced protection from storms, and economic losses through tourism abandonment and aesthetic damage. Regulating and controlling illegal sand mining is crucial to preserving the integrity of riverine ecosystems and ensuring sustainable development (Ghosh \u0026amp; Jana, 2021).\u003c/p\u003e \u003cp\u003eFew studies have systematically quantified the ecosystem service potential of whole river ecosystems. A number have targeted only single services, such as nitrogen retention (Basak et al., 2021; Burgin et al., 2013; Vermaat et al., 2016); water quality (Gilvear et al., 2019; Keele et al., 2019; Stammel et al., 2021); water provision (Notter et al., 2012) and flood regulation (Asbjornsen et al., 2022; Hill et al., 2023; Liu et al., 2021). There remains a paucity of tools to assess and quantify the ecosystem services generated by often complex river reaches and stream networks (Palmer \u0026amp; Ruhi, 2019).\u003c/p\u003e \u003cp\u003eThe majority of studies on the assessment of ecosystem services (Colson \u0026amp; Cooke, 2018; Daniel et al., 2012; Johnston \u0026amp; Bauer, 2020; Mart\u0026iacute;nez-Harms \u0026amp; Balvanera, 2012) combine mathematical techniques with measured data, however, some studies struggle to finish the task due to a dearth of measured data (Koschke et al., 2012). Through the advancement of Geographic Information Systems (GIS) and Remote Sensing (RS), models such as the Soil and Water Assessment Tool (SWAT) (Douglas-Mankin et al., 2010), Hydrological Simulation Programme Fortran (HSPF) (Donigian Jr et al., 1995), and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) (Cong et al., 2020) have been built and utilised to evaluate ecosystem services in recent years. This research uses the InVEST model, created by the Natural Capital Project, a collaborative effort between the University of Minnesota, The Nature Conservancy, Stanford University, and the World Wildlife Fund. The InVEST model is notably preferred due to its straightforward, swift, and robust spatial portrayal, particularly in situations with limited data when evaluating ecosystem services. InVEST uses a gridded map and an average annual time step, in contrast to other hydrological models. It is suitable for assessing the consequences of alterations in land use on an array of ecosystem services because it only requires a small amount of data and knowledge. These services include water yield, carbon retention, and habitat quality (L\u0026uuml;ke \u0026amp; Hack, 2018; Vigerstol \u0026amp; Aukema, 2011). Furthermore, it has mapping and spatial analysis features under ArcGIS, and the outputs can be presented as shapefiles, tables, or gridded maps (Ashkezari et al., 2018; Moreira et al., 2018; Yang et al., 2019).\u003c/p\u003e \u003cp\u003eThe review of the literature revealed that no studies have been done in the region under consideration or in most parts of the Indian subcontinent regarding the impact of LULC on ESs. To address these concerns, we applied the InVEST model with localized tailored settings to simulate and estimate changes in ecosystem services of the Danro River Basin in the Garhwa-Palamu districts of Jharkhand from 2000 to 2022. Danro River Basin lies in the semi-arid region and is vulnerable to significant soil loss risk due to several variables such as changing land use and rainfall. Additionally, fertilizer inputs have caused the trophic state of the Danro River Basin in Jharkhand to continuously shift from mesotrophic to eutrophic and thus the river is at risk of sedimentation. According to (Cai et al., 2023; Goshu et al., 2017), toxic cyanobacteria and faeces are present in rivers' mouths and shorelines. Sediment, fertilizers, animal manure, and manufacturing waste are the primary sources of nitrogen and phosphorus (J. Zhang et al., 2022). Consequently, the key objectives of this study were to: (i) estimate the LULC change in the Danro River Basin between the years 2000 and 2022; (ii) to analyse ES changes in response to LULC; and (iii) to investigate the complex relationships between ecosystem services and land use changes, emphasizing tradeoffs and synergies.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThis study focuses on the Danro River Basin, shared by the Garhwa and Palamu districts in Jharkhand (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Situated near Palamu Division's southwest edge, Garhwa covers latitudes 23\u0026deg;60\u0026rsquo;\u0026ndash;24\u0026deg;39\u0026rsquo; N and longitudes 83\u0026deg;22\u0026rsquo;\u0026ndash;84\u0026deg;00\u0026rsquo; E; Palamu extends across 23\u0026deg;50\u0026rsquo;\u0026ndash;24\u0026deg;8\u0026rsquo; N and 83\u0026deg;55\u0026rsquo;\u0026ndash;84\u0026deg;30\u0026rsquo; E. Neighbours include the Sone River, Sarguja district (Chh.), and Sonebhadra district (UP); they share roughly 1,200 ft.-1,110 ft. elevation above MSL. South-to-north draining Koyal and Sone rivers define the region's arid, cool climate with ~\u0026thinsp;52.55\" annual rainfall (Korisettar, 2007). Summers can hit 47\u0026deg;C, and both districts grapple with drought due to being part of a rain shadow zone (Jha, 2009). Dominant rocks consist of granite gneiss and related migmatites, revealing the gneissic structure and pale colouring. Soils vary from lateritic clay or clayey loam on plateaus to coarse sandy loam stemming from granite gneiss, quartzite, or gneiss elsewhere. Alluvial soil exists solely in the valley floors of the Koel, Kanhar, Tahuan, and Danro rivers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis of land use changes\u003c/h2\u003e \u003cp\u003eThis research examined alterations in land utilization within the Danro River Basin using a land use dynamic index. The evaluation involved comparing the extent of each land use category during different time periods (F. Hao et al., 2012; Zhang et al., 2015). The expression formula can be assessed as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$K= \\frac{{S}_{bi}-{S}_{ai}}{{S}_{ai}}\\times \\frac{1}{T} \\times 100\\%$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe variables S\u003csub\u003eai\u003c/sub\u003e and S\u003csub\u003ebi\u003c/sub\u003e denote the initial land area (in hectares) of type I at the beginning and conclusion of the study, respectively, T stands for the study's duration, and K is the land use dynamic index for a certain land use type during T.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The InVEST model\u003c/h2\u003e \u003cp\u003eInVEST, known as Integrated Valuation of Ecosystem Services and Tradeoffs, is a software tool crafted by the Natural Capital Project (Hamel et al., 2021) (version 3.9.0 - InVEST 3.9.0). This tool was employed to quantify the responses of ecosystem services (ES) to modifications in land use and land cover spanning the years 2000 to 2022. Utilizing a gridded map, the InVEST model operates with a minimal set of distinct datasets that are easily accessible. Additionally, the model is designed to be user-friendly and cost-effective (Harichandan et al., 2023). The depiction of the research framework can be observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Selection of Ecosystem Service Function\u003c/h2\u003e \u003cp\u003eEvaluating Ecosystem Services (ESs) requires selecting suitable indicators, as highlighted by (Wong et al., 2015) and (Bai et al., 2018). This study used four criteria to choose ES indicators for the Danro River Basin: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Utilizing indicators from the Millennium Ecosystem Assessment (W. V. Reid et al., 2005) and the Common International Classification of Ecosystem Services (CICES) (Haines-Young \u0026amp; Potschin-Young, 2018). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Selecting ES indicators addressing stakeholder concerns from governments, enterprises, and residents (J. Chen et al., 2019). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Focusing on ES indicators linked to human well-being, in line with recommendations from (Wong et al., 2015). (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Ensuring robust data availability. These criteria led to identifying four key ES indicators for the Danro River Basin: 1) SDR services for assessing soil erosion factors, land use changes, topography, vegetation cover, and management practices affecting sediment-reaching water bodies. 2) NDR services for nonpoint source pollution reduction involving nitrogen and phosphorous retention. 3) Habitat quality services as biodiversity indicators using land use and threats data to map habitat quality. 4) Carbon capture services depicting land use maps and quantifying carbon stocks in aboveground biomass, belowground biomass, soil, and dead organic matter. SDR assesses soil erosion levels, NDR aids in understanding nutrient cycling capacity, habitat evaluation tracks biodiversity changes, and carbon stock estimation helps assess forested areas. ES evaluation spanned 2000\u0026ndash;2022 at a 30m \u0026times; 30m grid scale to capture recent developments in the Danro River Basin while considering data availability and policy adaptability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Ecosystem Service Assessment\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Sediment Delivery Ratio (SDR)\u003c/h2\u003e \u003cp\u003eThe amount of sediment that was added to the water body was calculated using InVEST's SDR model. The model is based on the computation of the annual soil loss rate for individual pixels, coupled with the sediment delivery rate (SDR). The SDR is a measure of the amount that soil erosion ends up in aquatic habitats and settles there. This approach was formulated using research conducted by (Hamel et al., 2015). The model overlooks processes taking place within the stream itself and assumes that any sediment reaching the watercourse is transported to the hydrographic basin's exit.\u003c/p\u003e \u003cp\u003eThe Revised Universal Soil Loss Equation (RUSLE) \u003cb\u003e(Eq.\u0026nbsp;(2))\u003c/b\u003e is used in the SDR model to predict the annual soil loss per pixel (measured in tons/ha/year). In comparison to earlier hydrological models, the InVEST model offers greater flexibility and demands less extensive data (Hamel et al., 2015). It can assess soil loss and the export of sediment across various land use categories. Among other functions, it can also estimate the amount of silt reaching water bodies (Aneseyee et al., 2020; Sarkar et al., 2022).\u003c/p\u003e \u003cp\u003eRUSLE\u0026thinsp;=\u0026thinsp;R* K * LS * C * P (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn the formula, R represents the erosive power of rainfall (measured in MJ mm/ha/h/year), K represents the soil's susceptibility to erosion (measured in t/ha/MJ/mm), LS represents the factor related to slope length and gradient (dimensionless), C represents the factor linked to land cover and management (dimensionless), and P represents the factor tied to support practices (dimensionless). The process for determining the RUSLE parameters is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the regional distribution of input parameters for the watershed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRUSLE input parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInput Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquation(s) Used\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall erosivity factor (R); MJ mm/ha/h/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia Meteorological Department (IMD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026thinsp;=\u0026thinsp;81.5\u0026thinsp;+\u0026thinsp;0.38P\u003c/p\u003e \u003cp\u003eP: annual precipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil erodibility factor (K); t/ha/MJ/mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAO Soil Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003csub\u003eRUSLE\u003c/sub\u003e = f\u003csub\u003ecsand\u003c/sub\u003e * f\u003csub\u003eci\u0026minus;si\u003c/sub\u003e * f\u003csub\u003eorgc\u003c/sub\u003e * f\u003csub\u003ehisand\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(f\u003csub\u003ecsand\u003c/sub\u003e = (0.2\u0026thinsp;+\u0026thinsp;0.3 * exp[-0.256*m\u003csub\u003es\u003c/sub\u003e*(1-m\u003csub\u003esil\u003c/sub\u003e/100)])\u003c/p\u003e \u003cp\u003ef\u003csub\u003ecl\u0026minus;si\u003c/sub\u003e = (m\u003csub\u003esilt\u003c/sub\u003e/m\u003csub\u003ec\u003c/sub\u003e+m\u003csub\u003esilt\u003c/sub\u003e)\u003csup\u003e0.3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ef\u003csub\u003eorg\u003c/sub\u003e = (1- 0.25*org\u003csub\u003ec\u003c/sub\u003e/org\u003csub\u003ec\u003c/sub\u003e+exp [3.27\u0026ndash;2.95*org\u003csub\u003ec\u003c/sub\u003e]\u003c/p\u003e \u003cp\u003ef\u003csub\u003ehisand\u003c/sub\u003e = (1- 0.7*(1-m\u003csub\u003es\u003c/sub\u003e/100)/(1-m\u003csub\u003es\u003c/sub\u003e/100)\u0026thinsp;+\u0026thinsp;exp[-5.51\u0026thinsp;+\u0026thinsp;22.9*(1-m\u003csub\u003es\u003c/sub\u003e/100)])\u003c/p\u003e \u003cp\u003ewhere, f\u003csub\u003ecsand\u003c/sub\u003e denotes a factor that assigns lower erodibility values to soils rich in coarse sand and higher values to soils with minimal sand content; f\u003csub\u003ecl\u0026minus;si\u003c/sub\u003e signifies a factor that bestows decreased soil erodibility values to soils with elevated clay-to-silt ratios; f\u003csub\u003eorgC\u003c/sub\u003e represents a factor that diminishes soil erodibility for soils with substantial organic carbon content; and f\u003csub\u003ehisand\u003c/sub\u003e stands for a factor that reduces soil erodibility in instances of exceedingly high sand content. Furthermore, the variables are defined as follows: m\u003csub\u003es\u003c/sub\u003e corresponds to the sand fraction content (with a diameter of 0.05-2.00 mm) [%]; m\u003csub\u003esilt\u003c/sub\u003e refers to the silt fraction content (with a diameter of 0.002\u0026ndash;0.05 mm) [%]; m\u003csub\u003ec\u003c/sub\u003e designates the clay fraction content (with a diameter of \u0026lt;\u0026thinsp;0.002 mm) [%]; and org\u003csub\u003eC\u003c/sub\u003e signifies the percentage of organic carbon (SOC) content [%].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope length factor (L); dimensionless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRTM 90 m digital elevation data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL= (Flow Accumulation*Cell size / 22.13) \u003csup\u003em\u003c/sup\u003e where m is an exponent that depends on slope steepness and assumes a value between 0.2 and 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope steepness factor (S); dimensionless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRTM 90 m digital elevation data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026thinsp;=\u0026thinsp;0.065\u0026thinsp;+\u0026thinsp;0.045S\u0026thinsp;+\u0026thinsp;0.065S\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand cover management factor (C); dimensionless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDVI from Landsat TM and ETM+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u0026thinsp;=\u0026thinsp;0.431\u0026ndash;0.805 * NDVI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport practice factor (P); dimensionless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand cover map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiterature Review\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine the sediment delivery rate in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the first step entails computing the hydrological connectivity index (IC) using Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This index functions as a gauge of the hydrological linkage between sources of sediment spread throughout the landscape and the watercourses. There is a greater chance of sediment entering the stream as the pixel's IC value rises. Each pixel's (D\u003csub\u003eup\u003c/sub\u003e) upstream characteristics, such as land cover, slope, and drainage area (Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e5\u003c/span\u003e)), as well as the features of the flow path that crosses the pixel and the watercourse (D\u003csub\u003edn\u003c/sub\u003e), which include distance, land cover, and gradient (Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e6\u003c/span\u003e)), interact to determine the IC.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$SDR=\\frac{SDRmax}{1+\\text{e}\\text{x}\\text{p}(ICo-\\frac{ICi}{kb})}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere SDR\u003csub\u003emax\u003c/sub\u003e is the theoretical maximum SDR, with a mean value of 0.8 (Gashaw et al., 2021), and IC\u003csub\u003eo\u003c/sub\u003e and kb are calibration factors.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$IC= {\\text{log}}_{10}\\left(\\frac{Dup}{Ddn}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$Dup=CS\\sqrt{A}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$Ddn= \\sum _{i}\\frac{di}{CiSi}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this context, C represents the mean value of factor C across the catchment area upstream, while S denotes the average of slope gradients upstream (measured in meters per meter). A represents the upstream contributing area, measured in square meters, whereas di represents the length of the flow path following the steepest downward slope direction, measured in meters. C\u003csub\u003ei\u003c/sub\u003e represents the C factor associated with each pixel, and Si corresponds to the specific slope gradient of the pixel.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEquation (7)\u003c/b\u003e computes the sediment load exported per pixel in tons/ha/year. The sediment discharge from the basin is calculated by considering the total number of pixels that constitute the watershed.\u003c/p\u003e \u003cp\u003eE\u0026thinsp;=\u0026thinsp;RUSLE \u0026times; SDR (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Nutrient Delivery Ratio (NDR)\u003c/h2\u003e \u003cp\u003eThe Nutrient Delivery Ratio (NDR) module within InVEST\u0026mdash;a widely applied tool\u0026mdash;tracks nutrient origins and routes toward waterways, simplifying the measurement of vegetation's nutrient retention capabilities (He et al., 2023; Tikuye et al., 2023). NDR estimates nutrient transport (nitrogen and phosphorus) to water streams. To function, NDR relies upon spatial data plus supplementary parameters, including land use/cover (LULC) data, DEM data, and precipitation data (Fang et al., 2022). The model's hydrologic connectivity, gauged via the Borselli kb parameter, is refined to optimize performance (Das et al., 2022; R. Hao et al., 2022). Due to varying outcomes in diverse catchments, (Redhead et al., 2016) conducted a sensitivity analysis, concluding that this link is locale-specific. Our focus, though, centred on modifying spatial data instead of parameters, adopting (Griffin et al., 2020) suggestion to utilize the default value of 2. Additionally, a detailed biophysical table must be prepared for each unique LULC dataset, containing nuanced info about nutrient loads connected to every LULC class, comprising the critical distance at which a LULC patch retains nutrients maximally (tailored to pixel resolution specific to each LULC dataset), the ratio of nutrients delivered through surface and subsurface pathways (default value of zero), and nutrient loads associated with each LULC class (kg/ha/yr). The table incorporates details pertinent to both nitrogen and phosphorus. Two extra parameters, subsurface maximum retention efficiency and subsurface critical length, are added when the final parameter is set to a value other than zero. Published studies (Anjinho et al., 2022; Benez-Secanho \u0026amp; Dwivedi, 2019; Han et al., 2021; Tran et al., 2022; C. Wang et al., 2017) provided nutrient loads and efficiency coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiophysical parameters utilized in the Nutrient Delivery Ratio (NDR) module of InVEST\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC_Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNutrient Load_P (kg/ha/yr.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax. Retention Efficiency _P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax. Retention Efficiency _N\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShrubs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBareground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo adjust nutrient loads according to the basin's surface runoff potential, the model uses \u003cb\u003eEquations 8 and 9\u003c/b\u003e.\u003c/p\u003e \u003cp\u003emodified.load\u003csub\u003exi\u003c/sub\u003e= load\u003csub\u003exi\u003c/sub\u003e\u0026times;RPI\u003csub\u003exi\u003c/sub\u003e (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRPI\u003csub\u003exi\u003c/sub\u003e=RP\u003csub\u003ei\u003c/sub\u003eRP\u003csub\u003eav\u003c/sub\u003e (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFinally, similar to the SDR concept, the model calculates the NDR factor (Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e10\u003c/span\u003e) for each pixel based on the IC and retention properties of neighbouring pixels within the same flow channel.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${NDR}_{i}= {NDR}_{0,i} \\left(1+\\left(ICi-\\frac{ICo}{k}\\right)\\right)-1$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eLastly, \u003cb\u003eEq.\u0026nbsp;11\u003c/b\u003e computes the nutrient load exported by each pixel in kg/ha/yr, summed to obtain the overall nutrient export figure. Since InVEST's nutrient export model considers just nonpoint sources, it was crucial to include total nitrogen and total phosphorus point sources.\u003c/p\u003e \u003cp\u003eX\u003csub\u003eexpi\u003c/sub\u003e= modified. load\u003csub\u003exi\u003c/sub\u003e * NDR\u003csub\u003ei\u003c/sub\u003e (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Habitat Quality Monitoring (HQM)\u003c/h2\u003e \u003cp\u003eBiodiversity indicators rely on habitat quality, with higher-quality areas supporting greater biodiversity (Aznarez et al., 2022; Sun et al., 2019). Modelling the interaction of biodiversity and ecosystem services allows for scrutinizing their spatial arrangements, identifying areas where conservation efforts can benefit both natural systems and human economies, and highlighting where these goals do not align. To evaluate the Danro River's riparian zone habitat quality, InVEST's habitat quality module was used, with \u003cb\u003eEq.\u0026nbsp;(12)\u003c/b\u003e computing the habitat quality in grid x for habitat type (land cover) j.\u003c/p\u003e \u003cp\u003eQ\u003csub\u003exj\u003c/sub\u003e = H\u003csub\u003ej\u003c/sub\u003e [1-(D\u003csup\u003e2\u003c/sup\u003e\u003csub\u003exj\u003c/sub\u003e / (D\u003csup\u003e2\u003c/sup\u003e\u003csub\u003exj\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;k\u003csub\u003ez\u003c/sub\u003e))] (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eEach habitat type is assigned a relative rating of habitat appropriateness (Hj) between 0 and 1, with 1 being the highest value for the target species. Threats to habitats are depicted as a raster, with values normalized within a range of 0 to 1, where 0 signifies the least threat and 1 represents the highest. The influence of threats on habitats in each grid cell is determined by several factors, including the distance between the cell and the source of the threat, the proportionate significance of the threat, and the relative susceptibility of habitat types to the threats (Terrado et al., 2016). Parameter values were established to compute the spatial arrangement of habitat quality across the research region, including hazards to habitats (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and the vulnerabilities of various land categories to these hazards (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), referencing pertinent literature specific to the study area and consulting the InVEST user manual (Lai \u0026amp; Leone, 2017; Qiao et al., 2023; B. Wang \u0026amp; Cheng, 2022).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHabitat threat factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax_Dist\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThreat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecay\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrbanization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoil Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural Runoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater Abstraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity of land use types to the threat factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHABITAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrbanization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSoil Erosion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgricultural Runoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWater Abstraction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShrubs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBareground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Carbon Storage (CS)\u003c/h2\u003e \u003cp\u003eOne of the most significant ecosystem services, carbon storage and sequestration, is crucial for maintaining ecological balance, reducing atmospheric concentrations of greenhouse gases like CO\u003csub\u003e2\u003c/sub\u003e, regulating regional microclimate, and mitigating global climate change (Mbow et al., 2014; Smith et al., 2013). The estimation of carbon storage was done using the InVEST programme. It involved the following calculation:\u003c/p\u003e \u003cp\u003eC\u003csub\u003etotal\u003c/sub\u003e = C\u003csub\u003eabove\u003c/sub\u003e + C\u003csub\u003ebelow\u003c/sub\u003e + C\u003csub\u003esoil\u003c/sub\u003e + C\u003csub\u003edead\u003c/sub\u003e (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ewhere C\u003csub\u003etotal\u003c/sub\u003e stood for total carbon storage (Mg), C\u003csub\u003eabove\u003c/sub\u003e for above-ground biomass storage (Mg), C\u003csub\u003ebelow\u003c/sub\u003e for below-ground biological storage (Mg), C\u003csub\u003esoil\u003c/sub\u003e for below-ground soil storage (Mg), and C\u003csub\u003edead\u003c/sub\u003e for above-ground dead organic storage (Mg).\u003c/p\u003e \u003cp\u003eThe total carbon storage of the study area is then calculated by the model software based on \u003cb\u003eEq.\u0026nbsp;(14)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eC\u003csub\u003etotal\u003c/sub\u003e = \u0026sum;n\u003csub\u003ei\u003c/sub\u003eC\u003csub\u003ei\u003c/sub\u003e + A\u003csub\u003ei\u003c/sub\u003e (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ewhere C\u003csub\u003etotal\u003c/sub\u003e signifies the total carbon amount stored within the investigated area (measured in tons), n represents the count of different land use and land cover (LULC) types present, and A\u003csub\u003ei\u003c/sub\u003e denotes the area of each specific LULC type (measured in hectares). As indicated by (Sharp et al., 2018), the primary data required for executing the InVEST carbon storage and sequestration model comprised LULC data for the study area and carbon density data for each LULC category within that area. The carbon storage figures for both aboveground and belowground carbon reservoirs across various LULC types were sourced from the IPCC 2006 report (Dida et al., 2021; Rajbanshi \u0026amp; Das, 2021), recognized as one of the foremost global repositories of carbon data. Data for the present investigation was gathered from the Ministry of Environment and Forest Assessment, the Forest Survey of India, and the Carbon Pool Data for Forest Classifications (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For biomass values about non-forest classifications, the biomass guidelines outlined in the IPCC's 2006 directives for constructing greenhouse gas inventories within the Agriculture, Forestry, and Other Land Use (AFOLU) Sectors were utilized.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFormulated carbon pool table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC_Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC_Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC_above\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC_below\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC_soil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC_dead\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShrubs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBareground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Correlation and Correspondence Analysis\u003c/h2\u003e \u003cp\u003ePearson correlation analysis was employed to discover relationships among Ecosystem Services (ESs), land uses change metrics, and ES indicators throughout three time frames (2000, 2010, and 2022). The significance of the correlation coefficient (r) and potential tradeoffs was evaluated by carefully choosing variables for statistical analysis. Ecosystem services interactions fall into tradeoffs and synergies (Huang et al., 2023; Li et al., 2022; Sutherland et al., 2023; Zhou et al., 2023): tradeoffs occur when increasing one ES leads to decreased others, while synergies emerge when multiple ESs cooperatively enhance each other (Dade et al., 2019). Correspondence analysis, resembling correlations noted in previous studies, offers insightful clues about these links (Mouchet et al., 2014). Correspondence analysis's Euclidean distance between points in the diagram reflects differences between them; larger distances indicate higher dissimilarities. Angles created by arrowheads of two variables can suggest correlations: obtuse angles imply negative correlations (tradeoffs), while acute angles reveal positive relations (synergies) (Gao et al., 2019). SPSS software facilitated the execution of correspondence analysis. Across the entire river basin, the average value of each ES for each land use category was computed. Standardization ensued using Eq.\u0026nbsp;(\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e15\u003c/span\u003e) to ensure comparable average values in the contingency table where correspondence analysis occurred:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${x{\\prime }}_{ij={x}_{ij}/\\text{m}\\text{a}\\text{x}\\left({x}_{j}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e15\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eConfidence in the accuracy of the information remains intact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Validation of model results\u003c/h2\u003e \u003cp\u003eRUSLE and other models lacked data for validating model results, especially in data-scarce areas like Jharkhand. Comparing findings with those of similar or neighbouring watersheds can validate the results (Debie \u0026amp; Awoke, 2023; Degife et al., 2021). This study validated model outcomes by comparing them with results from investigations in a neighbouring watershed, the Upper Subarnarekha River Basin in Jharkhand, India, which shared similar topography and agroecological characteristics. The highest recorded soil erosion rate within the watershed for 2001 was 40 tons/ha/yr, increasing to 49.80 tons/ha/yr the following year (Samanta et al., 2016). Another study in the Karso Watershed of the Hazaribagh district, Jharkhand, India, using the USLE model, showed an average annual soil erosion of 3.66 tons/ha/yr, with 82.63% of the watershed area under slight erosion class and 9.6%, 5.93%, 1.27%, 0.42%, and 0.14% under moderate, high, very high, and severe erosion potential zones, respectively (Chowdary et al., 2004). Other models could not be tested accurately due to a lack of plot-based experimental investigations and long-term observed data in the study area. However, observations in different parts of the country showed that nitrogen at a rate of 2.9 Mg/ha and phosphate at a rate of 28.11 Mg/ha were preserved within mangrove sediments in Bhitarkanika, Orissa, India (Hussain \u0026amp; Badola, 2008). In Rajasthan's Keoladeo National Park, a study on habitat quality assessment found that between 2009 and 2015, the habitat's quality drastically declined (Chowdary et al., 2004). The InVEST model's carbon sequestration analysis in the Bidhalna micro-watershed (MWS) located in the Dehradun District of Uttarakhand State, India, found the entire MWS held a carbon stock of 697593.65 Mg in 2013 (Chowdary et al., 2004).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result and discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Land Use Change in Danro River Body from 2000 to 2022\u003c/h2\u003e \u003cp\u003eBetween 2000 and 2022, there have been significant alterations in land use types within the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the frequency and spatiotemporal dynamics of each LULC type. With the use of the reference points gathered from the appropriate Google Earth Image, the overall accuracy and Kappa coefficient values of each of these maps were assessed, and they range from 90 to 93% and from 0.86 and 0.91, respectively. This showed that there was excellent agreement between the classified maps and the reference data (Monserud \u0026amp; Leemans, 1992). Significant transformations in land use types were observed, particularly in built-up areas, which expanded at the expense of agricultural land and bare grounds. According to Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e's land use dynamic index (K), bare ground experienced the most substantial change (K = -0.021). The extent of bare ground decreased from 1408.22 km\u003csup\u003e2\u003c/sup\u003e (0.12% of the total area) in 2000 to 1046.45 km\u003csup\u003e2\u003c/sup\u003e (0.08%) in 2022, and crop areas decreased from 425.39 km\u003csup\u003e2\u003c/sup\u003e (0.03%) in 2000 to 320.65 km\u003csup\u003e2\u003c/sup\u003e (0.02%) in 2022. Over the same period, water bodies decreased from 7982.43 km\u003csup\u003e2\u003c/sup\u003e in 2000 to 7228.82 km\u003csup\u003e2\u003c/sup\u003e in 2022 (K = -0.004). Meanwhile, built-up areas increased from 765.14 km\u003csup\u003e2\u003c/sup\u003e (0.06%) in 2000 to 2673.49 km\u003csup\u003e2\u003c/sup\u003e in 2022 (0.22%). The areas covered by vegetation, shrubs, and rangeland exhibited minimal changes between 2000 and 2022.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe areal extent of land use/cover classes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7982.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7423.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7228.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e226.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e215.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShrubs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e266.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e257.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e425.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e410.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e320.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e765.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2031.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2673.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBareground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1408.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1426.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1046.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe land use areas and their changes obtained in 1985, 1995, 2000 and 2007 for the polders in the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2000\u0026ndash;2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2010\u0026ndash;2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2000\u0026ndash;2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eK%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2010 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChange area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChange area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7982.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7423.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7228.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-558.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-195.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-753.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e226.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e215.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShrubs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e266.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e257.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-45.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e425.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e410.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e320.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-15.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-89.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-104.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e765.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2031.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2673.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1265.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e642.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1908.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBareground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1408.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1426.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1046.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-380.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-361.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eK refers to the land use dynamic index in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eT1 refers to the period between 2000 and 2010, T2 refers to the period between 2010 and 2022, and T3 refers to the period between 2000 and 2022\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sediment Delivery Ratio (SDR) Model\u003c/h2\u003e \u003cp\u003eIn the research region, the RUSLE model calculated annual soil loss at 20.50 t/ha/year, which is evident in the outcomes. There was potential soil loss ranging from 3.99 tons/ha/year (2000) to 20.50 tons/ha/year (2022) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Following that, each land-use type's utilisation of these was examined. More than 0.90% of the study area was still considered to be at high risk for severe erosion. A very low rate of erosion occurs in roughly 15% of the studied area, mostly in the barren zones, with the areas at low and moderate risk of erosion being 2.80% and 1.90%, respectively. In agricultural areas, shrubs, grasslands, and woods, the mean erosion rate is highest. Steep slopes exhibit the highest rates of soil loss. This could be the result of a quicker conversion of forest cover to built-up land.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSediment export gives us how much sediment is eroded from each pixel and exported to the stream (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Meanwhile, the sediment retention index is calculated relative to bare ground. It gives us an idea of where the vegetation on the landscape is holding back (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The watershed's agricultural land or shrubland land use land cover types proved to be the majority of sediment export. Cropland, which is the predominant LULC, was where the greatest sediment outflow was found. Contrary to bare ground, farmland regions are typically characterized by frequent disruptions such as farming activities like ploughing and cropping. These actions involve clearing, fracturing, and turning over the soil, making it susceptible to erosion processes (Godron \u0026amp; Forman, 1983). Soil erosion results in the migration of vital nutrients including soil phosphorus and nitrogen in addition to the movement of soil particles, leaving the earth's surface fractured and with deteriorated plants (Ma et al., 2019). In the forested areas, the sediment retention rate was higher.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Nutrient Delivery Ratio (NDR)\u003c/h2\u003e \u003cp\u003eThe NDR model is a crucial tool for evaluating nutrient transport efficiency, particularly nitrogen and phosphorus, from land to water bodies (Borrelli et al., 2020). A higher NDR value indicates elevated nutrient runoff, posing potential risks to water quality and ecosystem health, emphasizing the need for strategic land management interventions (Riahi et al., 2017). This underscores the significance of NDR in guiding effective environmental preservation strategies, especially in regions like the Garhwa district of Jharkhand, where agricultural practices contribute significantly to nutrient release in the Danro watershed (Borrelli et al., 2020). With population expansion, climate change, and changes in land usage, the issue is expected to worsen (Borrelli et al., 2020; Riahi et al., 2017).\u003c/p\u003e \u003cp\u003eTo compare nutrient contributions to water bodies across space, we utilized the NDR model within the InVEST ecosystem modelling framework. Nitrogen makes up the majority of the subsurface nutrient load in the Danro River body, while phosphorus plays a minor role. Nitrogen export was 1.665 kg/ha/yr. in 2022 compared to 1.692 kg/ha/yr. in 2000, while phosphorus export was 0.177 kg/ha/yr. in 2000 and 0.184 kg/ha/yr. in 2022, both were delivered to the waterbody (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The forest region releases substantially fewer nutrients per unit area than either the agriculture area or rangelands. Nitrogen and phosphorus hotspots are localized in agricultural areas, particularly in the south and west of the research area, as confirmed by the maps. Nutrient release into streams is less affected by other land use types, particularly forests. Phosphorus discharge is more sensitive in built-up areas than in undeveloped ones. The Danro River basin's rapidly expanding economy and population growth have encouraged the construction of built-up and agricultural areas, increasing the need for fertilizers, irrigation water, and domestic garbage production. Eutrophication in the Danro River has been indicated lately (Bouska et al., 2019), primarily attributed to phosphorus as the key limiting factor (Y. Chen et al., 2023). Dissolved oxygen levels in the water have been decreasing (Ni et al., 2019), simplifying the release of phosphorus from sediments into the water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4 Habitat Quality\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe habitat quality index, ranging from 0 to 1, functions as an indicator of the quality of the habitat. The greater the value, the better and more comprehensive the environment, and the more favourable it is for the system's increased biodiversity (Terrado et al., 2016). The intensity of land use frequently has an impact on habitat quality. As land use intensity rises, so do the sources of habitat threat, which worsens the quality of the habitat around the threat sources. In this instance, the waterbody is recognized as being seriously threatened by agricultural land, roads, and built-up areas nearby. Water abstraction and soil erosion are additional dangers (Sun et al., 2018; Wolf et al., 2023).\u003c/p\u003e \u003cp\u003eThe average habitat quality in 2000 and 2022 was 0.7321 and 0.6095, respectively, according to the InVEST habitat quality module and \u003cb\u003eEq.\u0026nbsp;(14)\u003c/b\u003e. The overall average habitat quality dropped significantly, and the average value of habitat quality displayed an \"increased-decreased\" trend.\u003c/p\u003e \u003cp\u003eThe computation results lack a consistent classification threshold, however, the widely used \"Natural break approach\" can pinpoint classification intervals, classify similar values most effectively, and emphasize differences across groups. As a result, the natural break approach was used in ArcMap 10.8 to classify the habitat quality index. The value was then divided into four categories: poor habitat (values between 0 and 0.5), general habitat (0.5 and 0.8), good habitat (0.8 and 0.9), and excellent habitat (1.0 and above). \"General quality\" predominated in terms of habitat quality overall. The geomorphic types indicated as preferred habitats for species were forests, rangelands, and shrubs, all of which had the best habitat quality. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e displays the InVEST-generated habitat quality map.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Carbon Storage\u003c/h2\u003e \u003cp\u003eThe total carbon content across a landscape refers to the amount of carbon currently stored in megagrams (Mg) within each grid cell (Trentin et al., 2023). This accumulation encompasses all four carbon reservoirs (above ground, below ground, soil, and deceased matter) linked to the depicted land use and land cover (LULC) categories on the Danro River's map. Specifically, for the year 2022, the overall carbon content stood at 4.75 Mg for vegetated regions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, while the minimum was observed in aquatic bodies, representing non-forest categories devoid of vegetation (as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Consequently, the total carbon content within the Danro River region, given the current situation, was established at 2128304.92 Mg.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Spatial Correlations Between Ecosystem Services\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the correlation coefficients between four ecosystem services in the Danro River Basin: Phosphate Retention (P_Retention), Nitrogen Retention (N_Retention), Habitat Quality, and Carbon Pool. Here's a breakdown of the correlations:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between ecosystem services\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_Retention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN_Retention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHabitat_Quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCarbon_Pool\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP_Retention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN_Retention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHabitat_Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCarbon_Pool\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eP_Retention vs. N_Retention: This has a very strong positive correlation (0.933). This suggests that areas with high phosphate retention also tend to have high nitrogen retention. This could be due to common environmental factors or linked hydrology in these areas.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eP_Retention vs. Habitat Quality and Carbon Pool: These have moderate positive correlations (0.109 and 0.626, respectively). This means that there might be a slight tendency for areas with higher P_Retention to have better Habitat Quality and Carbon Pool. However, the correlation is not very strong, so this needs to be interpreted with caution.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eN_Retention vs. Habitat Quality: This has a weak negative correlation (-0.064). This suggests a very slight tendency for areas with higher N_Retention to have slightly lower Habitat Quality. However, the correlation is very weak, so it's not a definitive finding.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eN_Retention vs. Carbon Pool: This has a moderate positive correlation (0.647). Similar to P_Retention, areas with high N_Retention also tend to have a higher Carbon Pool.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHabitat Quality vs. Carbon Pool: This has a weak negative correlation (-0.161). This suggests a very slight tendency for areas with better Habitat Quality to have a slightly lower Carbon Pool.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eOverall, the table highlights some interesting relationships between these ecosystem services in the Danro River Basin. The strong positive correlations between P_Retention and N_Retention, and between N_Retention and Carbon Pool, suggest potential synergies between these services. However, the weak negative correlations between some services indicate potential trade-offs that need to be considered in managing the basin. It's important to remember that these are correlations, not causations, and further research is needed to understand the underlying mechanisms behind these relationships.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.7 Examining the relationship between the types of land use and the services offered by ecosystems through correspondence analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eChanges in land use can have a variety of effects on ecosystem services. These effects include many-to-one, many-to-many, and one-to-many relationships (Bryan, 2013). Thus, examining the relationship between different land-use types and ecosystem services can provide information about how modifications to one's land use affect the relationships between various ecosystem services (L. Chen et al., 2013; Lautenbach et al., 2011; Sawut et al., 2013; Schneiders et al., 2012; Su \u0026amp; Fu, 2013).\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, there are indications of both tradeoffs and synergies among land-use changes (LULC) and ecosystem services observed. The high positive correlations between LULC in the year 2000 and LULC in the year 2010 (0.983) and between LULC in the year 2010 and LULC in the year 2022 (0.993) suggest a strong synergy, indicating a consistent pattern in land-use changes over time. Conversely, the negative correlations between land-use changes and ecosystem services, such as P_Retention, N_Retention, and Carbon Pool, suggest potential tradeoffs. For instance, as land-use changes increase (-0.316 to -0.448), there is a corresponding decrease in nutrient retention and carbon storage. This implies that alterations in land use may adversely affect certain ecosystem services. The relationship between land-use changes and Habitat Quality shows mixed patterns (0.499 to 0.242), indicating that the impact on habitat quality is more complex and may depend on specific land-use transitions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between land use land cover changes and ecosystem services\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC_2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLULC_2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLULC_2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP_Retention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN_Retention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHabitatQuality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCarbonPool\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLULC_2000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLULC_2010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLULC_2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP_Retention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN_Retention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHabitatQuality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCarbonPool\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe two-dimensional correspondence analysis (CA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e) reveals insightful patterns and associations between land-use changes and ecosystem services. The high positive correlations between successive LULC periods (2000, 2010, 2022) are visually represented by their proximity on the CA plot, indicating a strong and consistent evolution in land-use transitions over time. The positioning of N_Retention points in the negative direction relative to LULC in the year 2010 suggesting a tradeoff scenario, that is, increasing land-use changes correspond with decreasing levels of this ecosystem services. This implies that alterations in land use may have adverse effects on nutrient retention. Conversely, the positive correlation between habitat quality and LULC in the year 2022, represented by its positive direction on the plot, suggests a potential positive influence of certain land-use transitions on habitat quality. The CA plot provides a visual representation of these intricate relationships, offering valuable insights into the dynamics between LULC and ecosystem services and highlighting areas for consideration in sustainable land management strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Strengths and Limitations of the Study\u003c/h2\u003e \u003cp\u003eStrengths of the model include detailed assessments of soil loss proportions reaching streams, sensitivity to input variations, consideration of land-use suitability and biodiversity effects, and validations against regional and national datasets despite the lack of physical measurements at individual sites. However, the InVEST model focuses solely on rill and inter-rill erosion, excluding gully, bank, and mass erosion due to overlap with RUSLE's scope. Its SDR output indicates soil loss proportions reaching streams (Bouguerra \u0026amp; Jebari, 2017). The NDR model exhibits input sensitivity but may be affected by erroneous empirical load parameter settings (Sieber et al., 2021). Assuming nutrient impacts occur downstream, the model ignores internal stream dynamics. Habitat quality assessment considers land-use suitability while examining biodiversity effects via various land uses (Hassanzadeh et al., 2019). Carbon sequestration modelling faces limitations, including oversimplification of the carbon cycle and disregard for key biological factors (Sharma et al., 2023). Model validation against real data is essential to evaluate performance across diverse landscapes (Sharp et al., 2018). Notably, the lack of gauges did not hinder comparisons with regional and national datasets.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study investigated the impact of land-use changes on ecosystem services in the Danro River Basin from 2000 to 2022. The key findings are:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLand-use changes: Significant transformations occurred, with built-up areas expanding at the expense of agricultural land and bare grounds.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSoil erosion: Increased land-use changes led to higher potential soil loss, with agricultural areas and steep slopes exhibiting the highest rates.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNutrient delivery: Nitrogen and phosphorus export increased, primarily from agricultural areas, potentially impacting water quality.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHabitat quality: The average habitat quality declined, suggesting a negative impact of land-use changes on biodiversity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCarbon storage: Forests showed the highest carbon storage, while the overall carbon content within the basin remains significant.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRelationships between services: Trade-offs and synergies exist between ecosystem services. Strong positive correlations were observed between phosphorus retention and nitrogen retention, and between nitrogen retention and carbon pool, suggesting potential synergies. Conversely, negative correlations were found between land-use changes and some services, indicating trade-offs.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eOverall, land-use changes have significantly impacted the Danro River Basin's ecosystem services. While some services benefit from specific land-use transitions, others are negatively affected. Understanding these complex interactions is crucial for developing sustainable land management strategies that optimize the provision of multiple ecosystem services.\u003c/p\u003e \u003cp\u003eFurther research is needed to address the limitations of the models used in this study and to gain deeper insights into the specific impacts of different land-use transitions on individual ecosystem services. This knowledge can guide informed decision-making for the sustainable management of the Danro River Basin and similar ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors acknowledge Environmental Engineering Lab and Remote Sensing and GIS Lab, BIT Mesra for performing analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAditi Majumdar is involved in the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. Kirti Avishek contributed to overall monitoring and manuscript editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSatellite Data has been obtained from the USGS portal. Rest all is primary data collection and analysis. The data that support the findings of the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests and Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was partially supported by the Institute Research Fellowship (Adm/Results/Ph.D. (MO 2021)/2020-21/4) awarded to AM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAneseyee, A. B., Elias, E., Soromessa, T., \u0026amp; Feyisa, G. L. (2020). 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Identifying synergies and hotspots of ecosystem services for the conservation priorities in the Asian Water Tower region. \u003cem\u003eRegional Environmental Change\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(4), 132. https://doi.org/10.1007/s10113-023-02129-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"InVEST Model, Ecosystem Services, LULC, Tradeoff, Synergy","lastPublishedDoi":"10.21203/rs.3.rs-3995791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3995791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRiverine ecosystems supply humans with a variety of ecosystem services (ESs), but anthropogenic activities endanger their availability worldwide. Understanding the spatiotemporal characteristics of riverine ESs and identifying the primary driving forces behind various ESs are crucial for preserving regional ecological security and achieving ecosystem sustainability. The study examines the spatio-temporal changes from 2000 to 2022 in the Danro River Basin in Jharkhand in four essential Ecosystem Services (ES): Sediment Delivery Ratio (SDR), Nutrient Delivery Ratio (NDR), Habitat Quality Monitoring (HQM) and Carbon Storage (CS), using InVEST model, Land Use Dynamics Index and Correspondence analysis. Danro River is a tributary of the Ganges River basin affected by sand mining. Key results were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) A rise in soil erosion was observed due to the transformation of agricultural land into urban areas; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The phosphorous and nitrogen retention was higher in agricultural land as compared to forest areas; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The habitat quality of the Danro River body showed degradation during 2000 to 2020; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The study area can sequester 2128304.92 Mg of Carbon; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) The land use dynamic index (K) indicated that bare ground experienced the greatest impact, with a value of -0.021. The study uncovered complex relationships between ecosystem services and land use changes, emphasizing tradeoffs and synergies and laying stress on the holistic management strategies to balance tradeoffs and leverage synergies. The findings provide valuable insights for decision-making in socio-environmental processes. Other regions missing meteorological, hydrological, and geological data may also benefit from applying the InVEST model with localized parameters.\u003c/p\u003e","manuscriptTitle":"Assessing Tradeoffs and Synergies between Land Use Land Cover Change and Ecosystem Services in River Ecosystem Using InVEST Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 17:53:46","doi":"10.21203/rs.3.rs-3995791/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":"439c3253-7237-48a1-a6b7-87e2123cf1e4","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-04T13:06:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-25 17:53:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3995791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3995791","identity":"rs-3995791","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}