APPLICATION OF THE SWAT MODEL FOR SPATIAL ANALYSIS OF SEDIMENTATION RATES IN THE TANRALILI SUB-WATERSHED, MAROS WATERSHED

. This paper discusses spatial pattern of Hydrologic Response Unit (HRU), which is a unit formed of hydrological analysis, including geology and soil type, elevation and slope, and also land cover in 2009. This paper also discusses the impact of HRU on streamflow of Ci Rasea watershed, West Java. Ci Rasea watershed is located at the upstream part of Ci Tarum watersheds in West Java Province, Indonesia. This research used SWAT (Soil and Water Assessment Tool) model to obtain spatial HRU and river flow. The method used Landsat TM data for land cover and daily rainfall for river flow modeling. The results have shown spatial pattern of HRU which was affected by land cover, soil type and slope. In 2009, accumulated surface runoff and streamflow changes were spatially affected by HRU changes. The large amount accumulation of river flow discharge happened in HRU with landcover paddy field, silty clay soil, and flat slope. While the low discharge of river flow happened in HRU with plantation, clay soil, and slightly steep slopes as HRU dominant. It was found that accumulation of surface runoff in Ci Rasea watershed can be reduced by changing the land cover type in some areas with clay and slightly steep slope to become plantation area and the areas with sandy loam soil and flat slope can be used for paddy fields. Beside affected by HRU, the river flow discharge was also affected by the distance of sub watershed to the outlet. By using NS model and statistical t-student for calibration and validation, it was obtained that the accuracy of river flow models with HRU was 70%. It meant that the model could better simulate water flows of the Ci Rasea watershed.

<abstract> The Soil and Water Assessment Tool (SWAT) uses hydrologic response units (HRUs) as the basic unit of all model calculations. ArcSWAT, the ArcGIS interface for SWAT, allows users to specify thresholds of land cover, soil, and slope in defining HRUs to improve the computational efficiency of simulations while keeping key landscape features of a watershed in the hydrologic modeling. However, this study found that applying commonly used thresholds in defining HRUs may lead to considerable loss of information about the watershed landscape, emphasizing larger soil types on smaller land covers once the land covers meet a threshold for land cover, and potentially changing average slopes. These changes often have a minor effect on water yield and streamflow simulations by SWAT but a larger effect on sediment and nutrient load simulations, which are more sensitive to slope and soil type and are more influential on outputs at the subwatershed than at the watershed outlet. Study results can help modelers improve their understanding of the HRU strategy for simplifying watershed representation while maintaining major landscape features and make decisions in the HRU delineation process to minimize the chance of biased simulations.

Climate change is increasingly threatening watersheds worldwide (Harris et al. 2024; Mankar et al. 2020; Tran and Lakshmi 2024a; Tran et al. 2024; Yin et al. 2024), leading to prolonged droughts (Sharma et al. 2022; Tapas, Kumar, et al. 2022; Do et al. 2024) and more frequent floods (Prabha and Tapas 2020) that endanger ecosystem health (Mishra et al. 2023). Hydrological modelling has become an essential tool for assessing the severity of these impacts (Tapas et al. 2024c; Murumkar et al. 2025; Marshall et al. 2025a, 2025b; Tran and Lakshmi 2024b). Over the past decade, technological advancements have significantly enhanced hydrological modelling capabilities (Brookfield et al. 2023; Jehanzaib et al. 2022). The Soil and Water Assessment Tool (SWAT) has evolved into SWAT+, offering a more user-friendly environment and improved process representations (SWAT+ 2020; Tran et al. 2023). While recent studies have utilised SWAT+, there remains considerable potential to further explore its effectiveness in nutrient modelling. In this study, we employed the SWAT+ model for the Tar-Pamlico River Basin, developed by Tapas (2024a, 2024b), to provide a detailed depiction of flow and nitrate distributions across the basin (Tapas et al. 2024c). The model was constructed using an array of datasets, including the Digital Elevation Model (DEM), observed flow data from the United States Geological Survey (USGS), land cover data from the National Land Cover Database (NLCD), soil data from the Soil Survey Geographic Database (SSURGO), weather data from the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG), and water quality data from the North Carolina Department of Environmental Quality (NCDEQ) (Tapas, Etheridge, et al. 2022; Tapas et al. 2023, Tapas 2024b, Tapas, Etheridge, et al. 2025, Tapas, Howard, et al. 2025). Tapas et al. (2024) optimised the SWAT+ model for monthly flow and monthly nitrate load at Washington, North Carolina, using a two-year warm-up period (January 2001 to December 2003), a calibration period from January 2003 to December 2011, and a validation period from January 2012 to December 2019. The model was also soft-calibrated for annual average hydrological response unit (HRU) scale flow, nitrate loss, yield, and denitrification. Lastly, the model was cross-validated for monthly flow at two additional upstream locations in the watershed (Greenville and Tarboro, NC) (Video 1). To effectively communicate the model results, dynamic animations illustrate the spatiotemporal dynamics of flow and nitrate load throughout the Tar-Pamlico River Basin. These visualisations capture the variability of streamflow and nitrate transport over time, highlighting seasonal and interannual trends. Additionally, a comparative analysis of observed and simulated nitrate loads is presented using graphical representations and statistical indices (NSE and PBIAS) to assess model performance. The integration of remote sensing datasets and SWAT+ simulations provides a comprehensive depiction of hydrological and water quality patterns in the basin. The data supporting this study will be made available upon reasonable request from the corresponding author, Mahesh Tapas ([email protected]).

The Soil and Water Assessment Tool (SWAT) is widely used to relate farm management practices to their impacts on surface waters at the watershed scale, yet its smallest spatial unit is not generally defined by physically meaningful boundaries. The hydrologic response unit (HRU) is the smallest spatial unit of the model, and the standard HRU definition approach lumps all similar land uses, soils, and slopes within a subbasin based upon user-defined thresholds. This standard method provides an efficient way to discretize large watersheds where simulation at the field scale may not be computationally feasible. In relatively smaller watersheds, however, defining HRUs to specific spatial locations bounded by property lines or field borders would often be advantageous, yet this is not currently possible within the ArcSWAT interface. In this study, a simple approach is demonstrated that defines HRUs by field boundaries through addition of uniquely named soils to the SWAT user soil database and creation of a field boundary layer with majority land use and soil attributes. Predictions of nitrogen, phosphorus, and sediment losses were compared in a case study watershed where SWAT was set up using both the standard HRU definition and field boundary approach. Watershed-scale results were reasonable and similar for both methods, but aggregating fields by majority soil type masked extremely high soil erosion predicted for a few soils. Results from field-based HRU delineation may be quite different from the standard approach due to choosing a majority soil type in each farm field. This approach is flexible such that any land use and soil data prepared for SWAT can be used and any shapefile boundary can divide HRUs. Keywords: watershed, modeling, Soil and Water Assessment Tool (SWAT), hydrologic response units, field boundaries, common land units, landuse management DOI: 10.3965/j.ijabe.20150803.951 Online first on [2015-03-03] Citation: Kalcic M M, Chaubey I, Frankenberger J. Defining Soil and Water Assessment Tool (SWAT) hydrologic response units (HRUs) by field boundaries. Int J Agric & Biol Eng, 2015; 8(3): 69－80.

Hydrologic Response Unit (HRU) is a unit formed of hydrological analysis based on geology and soil type, slope, and land cover. This paper discussed the spatial pattern of Hydrologic Response Unit (HRU) in 1997-2009 and its impact on flow Ci Rasea watershed temporally. In this study, SWAT (Soil and Water Assessment Tool) model, based on land cover changed, was used to get HRU and flow in spatially and temporally. This method used Landsat TM 1997, 2003 and 2009 data for land cover and daily rainfall 1997-2009 for flow modeling. The results showed the spatial pattern of HRU in temporally was affected by landcover based on the changing of HRU. The majority of HRU spatial pattern at Ci Rasea watershed were clustered. During 1997-2009, accumulated surface runoff and the changing of flow discharge were affected by changes of HRU spatial pattern. The biggest accumulated surface runoff in Ci Rasea watershed influenced by HRU of agricultural cropland in area of clay soil type with slope slightly obliquely. While the smallest accumulated surface runoff in Ci Rasea watershed influenced by HRU of paddy field in the area of sandy loam soil type with a gentle slope. The changes of HRU agriculture cropland become HRU mixed cropland in area clay soil type with slope at a slight angle and HRU agriculture cropland become HRU paddy field in area, sandy loam soil type with a gentle slope could be decreasing the accumulation of surface runoff in Ci Rasea watershed.

Spatial discretization in hydrological models has a strong impact on computation times. This study investigates its effect on the performance of the Soil and Water Assessment Tool (SWAT) applied to a French Mediterranean watershed. It quantifies how spatial discretization (the number of sub-basins and hydrological response units (HRUs)) affects the SWAT model’s performance in simulating daily streamflow and whether this effect depends on the choice of soil and land use input datasets. Sixty-eight SWAT model configurations were created using various soil and land use datasets and 17 discretization setups, evaluated from 2001 to 2021 with the Kling–Gupta efficiency (KGE) metric. The key findings include (1) while the number of sub-basins does not impact model performance, increasing HRUs significantly degrades it (KGE loss of 0.13 to 0.26) regardless of the number of sub-basins or input datasets. (2) SWAT is found to be more sensitive to variations in soil datasets than in land use datasets, but the observed performance decline with more HRUs is attributed to the calibration process and the increased heterogeneity in soil types rather than input dataset spatial resolution. (3) Minimizing the number of HRUs may improve both the accuracy of streamflow simulations and the computational efficiency of the SWAT model.

Palaeogeographic heterogeneity of large-amplitude changes in marine sedimentation rates during the Carnian Pluvial Episode (Late Triassic)

Multivariate data assimilation (DA), a novel way to couple big data with land surface models, was extensively employed in forecasting‐reanalyzing systems (FRSs), for example, ECMWF and GLDAS. Meanwhile, most (distributed) hydrological models, like soil and water assessment tool (SWAT), have not been equipped with straightforward ways to link to DA algorithms. Therefore, it is one of the main barriers to utilizing such hydrological models in FRSs. This paper deals with multivariate DA into SWAT (DA‐SWAT), which is complicated since the original model does not provide full access to the models' initial conditions (ICs) at the hydrologic response unit (HRU) scale. The preceding DA‐SWAT works commonly used an integrated approach in which the DA and SWAT codes were implemented in the same programming environment. We discuss how this approach complicates and prevents the application of DA‐SWAT in multivariate, multimodel, and multisensor systems. Accordingly, we proposed a new approach for DA‐SWAT by which SWAT can be perfectly linked with any DA algorithm of interest coded in any desired programming environment. Our framework utilizes input/output text files to access ICs and to link DA with SWAT. Moreover, we designed some univariate and multivariate scenarios for assimilating in situ streamflow measurement and MODIS's snow cover fraction (SCF) data, which has not yet been focused on in the SWAT calibration context. Results show that compared to the univariate assimilation of streamflow (SCF), the multivariate assimilation mitigates the equifinality problem and more accurately estimates SCF (streamflow) by improving NS and PBIAS measures with the differences of 0.4 (0.86), 12% (64%), respectively.

The Illinois State Water Survey has been developing hydrologic and hydraulic models for watersheds in the Illinois River basin as part of the Illinois Rivers Decision Support System (ELRDSS). The hydrologic model is based on the U.S. Environmental Protection Agency's BASINS 3.1 modeling system. The Soil and Water Assessment Tool (SWAT) and Hydrologic Simulation Program — Fortran (HSPF), which are part of the BASINS system, were used to simulate the hydrology of watersheds in the Illinois River basin. Both SWAT and HSPF are comprehensive watershed models that also have the capability to simulate sediment transport. Based on the topographic, and hydrographic, land use, and soil types data, hydrologic models (SWAT and HSPF) were developed for the Court Creek watershed. The Court Creek watershed was divided into 35 subwatersheds and 346 HRUs. Two outlets specified in the models correspond to the streamflow gaging/sediment monitoring stations in the Court Creek watershed. Both models use the same precipitation and temperature data, and the potential evapotranspiration (PEVT), potential surface evaporation (EVAP), and other climate data. The simulation flow and sediments from the HSPF and SWAT models were compared graphically and statistically. Overall relative errors of simulated flow to the observed flow are –0.2 and 3.8% for HSPF and SWAT, respectively, and the relative errors of sediment load are –15 and –47%, respectively. Based on the correlation and the Nash-Sutcliffe Efficiency coefficients of simulated flows, the HSPF model outperformed the SWAT model for daily and monthly flow. However, the models performed almost equally well on the annual average. As for the suspended sediment load, the HSPF model performed slightly better than the SWAT model.

Effects of land-use data resolution on hydrologic modelling, a case study in the upper reach of the Heihe River, Northwest China

Modeling hydrologic processes for depression-dominated areas such as the North American Prairie Pothole Region is complex and reliant on a clear understanding of dynamic filling-spilling-merging-splitting processes of numerous depressions over the surface. Puddles are spatially distributed over a watershed and their sizes, storages, and interactions vary over time. However, most hydrologic models fail to account for these dynamic processes. Like other traditional methods, depressions are filled as a required preprocessing step in the Soil and Water Assessment Tool (SWAT). The objective of this study was to facilitate hydrologic modeling for depression-dominated areas by coupling SWAT with a Puddle Delineation (PD) algorithm. In the coupled PD-SWAT model, the PD algorithm was utilized to quantify topographic details, including the characteristics, distribution, and hierarchical relationships of depressions, which were incorporated into SWAT at the hydrologic response unit (HRU) scale. The new PD-SWAT model was tested for a large watershed in North Dakota under real precipitation events. In addition, hydrologic modeling of a small watershed was conducted under two extreme high and low synthetic precipitation conditions. In particular, the PD-SWAT was compared against the regular SWAT based on depressionless DEMs. The impact of depressions on the hydrologic modeling of the large and small watersheds was evaluated. The simulation results for the large watershed indicated that SWAT systematically overestimated the outlet discharge, which can be attributed to the failure to account for the hydrologic effects of depressions. It was found from the PD-SWAT modeling results that at the HRU scale surface runoff initiation was significantly delayed due to the threshold control of depressions. Under the high precipitation scenario, depressions increased the surface runoff peak. However, the low precipitation scenario could not fully fill depressions to reach the overflow thresholds in the selected sub-basins. These results suggest the importance of depressions as gatekeepers in watershed modeling.

&amp;lt;p&amp;gt;Land Use Land Cover (LULC) change is widely recognised as one of the most important factors impacting river basin hydrology.&amp;amp;#160; It is therefore imperative that the hydrological impacts of various LULC changes are considered for effective flood management strategies and future infrastructure decisions within a catchment. &amp;amp;#160;The Soil and Water assessment Tool (SWAT) has been used extensively to assess the hydrological impacts of LULC change.&amp;amp;#160; Areas with assumed homogeneous hydrologic properties, based on their LULC, soil type and slope, make up the basic computational units of SWAT known as the Hydrologic Response Units (HRUs).&amp;amp;#160; LULC changes in a catchment are typically modelled by SWAT through alterations to the input files that define the properties of these HRUs. &amp;amp;#160;However, to our knowledge at least, the process of making such changes to the SWAT input files is often cumbersome and non-intuitive.&amp;amp;#160; This affects the useability of SWAT as a decision support tool amongst a wider pool of applied users (e.g., engineering teams in environmental regulatory agencies and local authorities).&amp;amp;#160; In this study, we seek to address this issue by developing a user-friendly toolkit that will: (1) allow the end user to specify, through a Graphical User Interface (GUI), various types of LULC changes at multiple locations within their study catchment, (2) run the SWAT+ model (the latest version of SWAT) with the specified LULC changes, and (3) enable interactive visualisation of the different SWAT+ output variables to quantify the hydrological impacts of these scenarios.&amp;amp;#160; Importantly, our toolkit does not require the end user to have any operational knowledge of the SWAT+ model to use it as a decision support tool.&amp;amp;#160; Our toolkit will be trialled at 15 catchments in Gwynedd county, Wales, which has experienced multiple occurrences of high flood events, and consequent economic damage, in the recent past.&amp;amp;#160; We anticipate this toolkit to be a valuable addition to the decision-making processes of Gwynedd County Council for the planning and development of future flood alleviation schemes as well as other infrastructure projects.&amp;lt;/p&amp;gt;

In the global strive for improved quality of life, sustainable and clean agricultural production is among one of the priorities. Mauritius, being a small island country, where sugarcane production predominates in the agricultural landscape, is aware of the delicate balance of preserving its fresh water supply, lagoon ecology, and the economic progress. Every year 11,000 tonnes of N is being applied to the sugarcane fields, which occupy 43 % of total land area. The perception that sugarcane production is a potential pollutant to the rivers and the lagoon is felt strongly. Though the findings from samples collected in River La Chaux showed that the N detected was not of agronomical significance, and was just within the threshold value for environmental concerns, refinement to these initial findings was necessary to verify possible undetected peak values, which were not revealed by the simplicity of a lumped model. The objectives of this study are: 1. To define a conceptual model for River La Chaux catchment hydrology, taking into account the water balance, and hence the transport of different forms of N within the catchment’s sources and sinks. 2. To identify the sources and sinks of N, the key parameters which influence their transport in the catchment, through the integration of surface and subsurface hydrology model, where a conventional hydrology model would be limiting because of the complex volcanic geology of the island. The project study focus is to understand the processes of catchment hydrology and nutrient transport in River La Chaux catchment through a conceptual model, and to translate it to a catchment model that corresponds most to the conceptual model. Soil and Water Assessment Tool (SWAT) was chosen and tested for this simulation exercise. It is a physically distributed catchment model that caters for surface and subsurface hydrology interaction, including simulation of crop growth, the transformation processes of N nutrient, and its transport within the catchment. GIS processing facilities were used to define the sub-basins of the catchment into hydrologic response units (HRU), based on topography, land use, and soil type characteristics. The defined HRUs were coupled with the daily time series meteorological data to establish the hydrology components of the flow. This rainfall-runoff model enables simultaneous calculation of lateral and vertical flow through soil profile, in addition to the surface runoff. A commercial crop calendar was also linked to the model to represent the standard cropping practice of cane growers in terms of planting and harvest dates, land preparation, N input and expected crop yield. Since the sugarcane crop occupies 87 % of the catchment, the hydrology model hinges on realistic crop growth simulation in terms of water and nutrient uptake by the crop, and its subsequent influence to other hydrology components like run-off, lateral flow and percolation. Modifications to parameters in SWAT crop database with local values and recent findings in the sugarcane crop modeling improved the simulated biomass and evaporation values considerably. With the problem of incomplete climate datasets, two methods for potential evapotranspiration calculation were tested. The Hargreave method was found to give better potential evapotranspiration (PET) but lower biomass estimate than that of Penman- Monteith method, as it uses only the two predominant parameters: solar radiation and temperature, reducing parameters errors. However, for longer time series simulation Penman-Monteith method performed slightly better, giving satisfactory evapotranspiration and biomass simulation. Calibration of the catchment hydrology was done, using historical data of rainfall-runoff records, and the hydrology components were evaluated for the catchment. The results compared well with those obtained from the lumped model, Hydrology Simulation Program-Fortran (HSPF). It was found that rainfall in the catchment contributed to 14% runoff, 16% lateral flow, and 37% percolation, 31% of evapotranspiration, and 2 % as deep drainage loss. The surface run-off however was much more important in SWAT, and insignificant the deep aquifer loss, as opposed to the findings of HSPF. Comparison of simulated flow with measured flow, using the Nash-Sutcliffe model efficiency evaluation method, gave values between - 0.39 to 0.14, and the R2 value 0.7 - 0.8, with a systematic overestimation of 30 to 40 % of measured flow. Dilution of the model’s efficiency was exhibited with long term historical data, indicating inherent measurement errors in these data. The model also showed that the upstream catchment boundary does not reflect the groundwater boundary. However despite the unaccounted transmission loss in geological faults and collapsed lava tunnel in the catchment, the majority of the fast moving base flow exits at River La Chaux estuary. The study highlighted the preponderant role of sugarcane crop in the hydrology of the catchment in attenuating run-off, and in ensuring the replenishment of the aquifer. Nutrient transport is mainly by base and lateral flows, which prevail even in dry period. Detailed water balance for each sub-basin revealed the difference in N transport pathways in the sub-basins: Percolation predominates in young lava soils, whereas lateral flow in intermediate lava soils. Erosion is not a problem in the catchment. Simulation results ranked the agronomic importance of N loss as follow: Leached N <17.5%, lateral flow N < 15%, and surface runoff N < 2% of applied N fertilizer. Though the simulated N budget for sugarcane cropping system reproduced relevant N transformation rates, and showed the N sources and sinks in a predominantly sugarcane growing catchment. Insufficient representation of N immobilization by the model was noted for long cycle crop like sugarcane. The findings showed that ‘wet season’ nitrate-N loss could be quite high in sub-basins where lateral flow and preferential pathways like drains were found. During the ‘dry’ season, nitrate transport is negligible. From the ‘snapshot’ sampling, insignificant N contribution of sugar industry to stream water is confirmed, whereas other activities done at the expense of the riparian vegetation seemed to increase N pollution considerably in the downstream area even during ‘dry season’. However, the hot spots of point source and non-point source N contribution in the catchment went undetected at next sampling point, as the nitrate was well assimilated by the stream, thus pointing to the necessity of research into ‘untouched areas’ like stream ecology and the role of riparian zone, to complete the picture of catchment N dynamics in the future. The model showed how much gone down the ditch, but did not clarify the fate of N in the ditch. On the whole the objectives have been achieved. Better understanding of the flow and N transport pathways of this geologically complex catchment was allowed with the study. The underlying conceptual model of SWAT has been tested for non-point source pollution of the catchment, and can be adapted to Mauritian catchment for hydrology modeling.

Applications of the Soil and Water Assessment Tool (SWAT) model typically involve delineation of a watershed into subwatersheds/subbasins that are then further subdivided into hydrologic response units (HRUs) which are homogeneous areas of aggregated soil, landuse, and slope and are the smallest modeling units used within the model. In a given standard SWAT application, multiple potential HRUs (farm fields) in a subbasin are usually aggregated into a single HRU feature. In other words, the standard version of the model combines multiple potential HRUs (farm fields) with the same landuse/landcover, soil, and slope, but located at different places of a subbasin (spatially non-unique), and considers them as one HRU. In this study, ArcGIS pre-processing procedures were developed to spatially define a one-to-one match between farm fields and HRUs (spatially unique HRUs) within a subbasin prior to SWAT simulations to facilitate input processing, input/output mapping, and further analysis at the individual farm field level. Model input data such as landuse/landcover (LULC), soil, crop rotation, and other management data were processed through these HRUs. The SWAT model was then calibrated/validated for Raccoon River watershed in Iowa for 2002–2010 and Big Creek River watershed in Illinois for 2000–2003. SWAT was able to replicate annual, monthly, and daily streamflow, as well as sediment, nitrate and mineral phosphorous within recommended accuracy in most cases. The one-to-one match between farm fields and HRUs created and used in this study is a first step in performing LULC change, climate change impact, and other analyses in a more spatially explicit manner.

Sediment yield refers to the amount of sediment exported by a basin over a period of time, which is also the amount that will enter a reservoir located at the downstream limit of the basin (Morris and Fan, 1998). The subject of sediment yield modelling has attracted the attention of many scientists but lack of data, resources and widely accepted methods to predict/estimate sediment yields are some of the barriers against this direction of research (Summer et al., 1992; Wasson 2002; Lawrence et al., 2004; Ndomba, 2007; Ndomba et al., 2005, 2008b, 2009; Shimelis et al., 2010). The sediment yield model evaluated in this paper is the Soil and Water Assessment Tool (SWAT). It is hypothesized in the presented study cases that distributed and process based mathematical models such as SWAT could be a potential tool in predicting and estimating sediment yield especially at a catchment scale. Application of the distributed and processbased models could minimize the uncertainty resulting from assuming lumped, stationary and linear systems. Besides, the SWAT model has particular advantages for the study of basin change impacts and applications to basins with limited records (Bathurst, 2002; Ndomba, 2007). In principle, their parameters have a physical meaning and can be measured in the field, and therefore model validation can be concluded on the basis of a short field survey and a short time series of meteorological and hydrological data (Bathurst, 2002). SWAT was originally developed by the United States Department of Agriculture (USDA) to predict the impact of land management practices on water, sediment and agricultural chemical yields in large ungauged basins (Arnold et al., 1995). The SWAT model has a long modelling history since it incorporates features of several Agriculture Research Service (ARS) models (Neitsch et al., 2005). The SWAT model is a catchment-scale continuous time model that operates on a daily time step with up to monthly/annual output frequency. The major components of the model include weather, hydrology, erosion, soil temperature, plant growth, nutrients, pesticides, land management, channel and reservoir routing. It divides a catchment into subcatchments. Each subcatchment is connected through a stream channel and further divided into a Hydrologic Response Unit (HRU). The HRU is a unique combination of a soil and vegetation types within the subcatchment. Sediment yield is estimated for each HRU with the Modified Universal Soil Loss Equation (MUSLE) (Williams, 1975) (Equation 1).