Calibration Of Soil And Water Assessment Tool Model Research Articles

Improving rainfall-runoff modeling in the Mekong river basin using bias-corrected satellite precipitation products by convolutional neural networks

Accurate rainfall-runoff (RR) modeling is crucial for effective Mekong River Basin (MRB) water resource management. Satellite precipitation products (SPPs) can offer valuable data for such modeling; however, these products often exhibit biases that may adversely affect hydrological simulations. This study aimed to improve RR modeling using bias-corrected SPPs and the Soil and Water Assessment Tool (SWAT) model for MRB. A convolutional neural network-based deep learning framework was employed to correct biases in four SPPs (TRMM, PERSIANN-CDR, CHIRPS, and CMORPH), resulting in four respective bias-corrected SPPs (ADJ_TRMM, ADJ_CDR, ADJ_CHIR, and ADJ_CMOR). The bias-corrected products were compared against a gauge-based dataset in terms of rainfall analysis, and their performance within the SWAT model was assessed over calibration (2004–2013) and validation (2014–2015). Bias-corrected products demonstrated superior performance in rainfall analysis, with ADJ_TRMM outperforming other products. The SWAT model calibration results showed satisfactory performance across all stations, with a Nash-Sutcliffe Efficiency (NSE) ranging from [0.76–0.87]. Integrating bias-corrected SPPs into the SWAT model significantly increased the RR simulations in the MRB, indicated by higher NSE values [0.72–0.85] compared to uncorrected SPPs [-0.37 to 0.85] at the Kratie station. Besides, the inconsistent performance of bias-corrected products between rainfall analysis and RR modeling was observed, with ADJ_CDR outperforming ADJ_TRMM in the SWAT model. These results highlight the significance of using bias-corrected SPPs in hydrological modeling applications, especially in areas with limited ground-based precipitation data, and highlight the need for further research to refine bias correction methods and address the limitations of the SWAT model.

Land Use and Land Cover Change Modulates Hydrological Flows and Water Supply to Gaborone Dam Catchment, Botswana

Identifying the mechanism through which changes in land use and land cover (LULC) modulate hydrological flows is vital for water resource planning and management. To examine the impact of LULC change on the hydrology of the Gaborone Dam catchment within the upper Limpopo basin, where Notwane river is the major river within the catchment, three LULC maps for the years 1997, 2008, and 2017 were established based on a mosaic of Landsat 5 for 1997 and 2008 and Landsat 8 for 2017. The 10 m-resolution Version 200 ESA World Land Cover Map for 2021 is used as a ground truth to train the random forest (RF) classifier to identify land cover classes from Landsat 8 imageries of 2021 using the Google Earth Engine (GEE) Python API. The overall accuracy/kappa coefficient of the RF classifier is 0.99/0.99 for the training and 0.73/0.68 for the validation data sets, which indicate excellent and substantial agreements with the ground truth, respectively. With this confidence in the LULC classification, the impact of LULC change on the hydrological flow within the catchment was estimated by employing the Soil and Water Assessment Tool (SWAT) and indicator of hydrological alteration (IHA). The SWAT model calibration and validation were first performed, and the ability of the model to capture the observed stream flow was found to be good. The LULC maps from Landsat images during the 1997–2017 period show a decrease in forests and shrubland in contrast to an increase in pasture land. The expansion of pasture and cropland and the reduction in forests and shrubland led to a decline in the amount of evapotranspiration and groundwater recharge. Furthermore, the LULC change also caused a reduction in low flow during dry periods and an increase in high flow during the rainy season. The findings clearly demonstrate that LULC changes can affect the water table by altering soil water recharge capacity. The study highlighted the importance of LULC for catchment water resource management through land use planning to regulate the water level in the Gaborone Dam against the impact of climate change and growing water demands by the city of Gaborone due to population growth.

Monitoring, Modeling and Planning Best Management Practices (BMPs) in the Atwood and Tappan Lake Watersheds with Stakeholders Engagements

This study was conducted in the Atwood and Tappan Lakes watersheds of the Tuscarawas basin of Ohio. The flow, total nitrogen (TN), and total phosphorus (TP) loadings were monitored with the help of local stakeholders for a few years at various locations of the watershed to develop the Soil and Water Assessment Tool (SWAT). The multi-site SWAT model calibration and validation were accomplished with a reasonable model performance. In the next step, the scenario analysis was conducted in the SWAT model using various BMPs, including vegetative filter strips, grass waterways, fertilizer reduction, crop rotation, and cover crops to evaluate their performance in reducing TN and TP from the watershed. While BMPS in many studies are decided based on researchers’ intuition, these BMPs were selected based on active consultation with the local stakeholders, who were engaged in the reduction of TN and TP loadings from the watersheds. Since the SWAT model calibration for TN and TP was not as good as the hydrologic model calibration, various scenarios of TN and TP reduction using BMPs were investigated for several years using both calibrated and uncalibrated SWAT models. We examined all the BMPs in 12 sub-watersheds of the Atwood and 10 sub-watersheds of the Tappan Lake watershed. The analysis indicated that the management practices of cover crops (rye) in combination with grass waterways with a 10% fertilizer reduction could minimize the TN and TP loading by as much as 88%, without significantly compromising the agricultural yield. However, a 10% fertilizer reduction without any BMPs could reduce TN and TP by just 9%. The cover crop (rye) including 10% fertilizer reduction with grass waterways seemed to be the most effective in reducing TN and TP, whereas the implementation of a filter strip led to a 70% reduction and was the next effective BMPs in reducing TN and TP loadings. In general, TN losses were reduced by 8% to 53%, while TP losses were reduced by 7% to 88%, depending on the BMPs used. By and large, the TN and TP reduction achieved through the calibrated model was not significantly different from the uncalibrated model, even though the reduction using the calibrated model was slightly higher for all scenarios than that of the uncalibrated model. The TN and TP loadings were highly sensitive to cattle grazing. When just 50% of the cattle were permitted to graze, the model predicted that there would be a 40% increase in total nitrogen and a 70% increase in total phosphorus in both watersheds. Our investigation revealed that monitoring the watershed at a small sub-watershed scale and calibrating the SWAT model for nitrogen and phosphorus is delicate.

Land use/land cover change impact on streamflow using Arc-SWAT model, in case of Fetam watershed, Abbay Basin, Ethiopia

Changes in Land Use Land Cover (LULC) are currently one of the greatest pressing issues facing the watershed, its hydrological properties of soil, and water management in catchment areas. One of the most important elements impacting streamflow in watersheds is LULC change. The main objective of this study was to evaluate the effect and future predication of LULC change on streamflow of the Fetam watershed by using Cellular Automata (CA)-Markov in IDRISI software. To analyze the impact of land use/cover change on streamflow, the Soil and Water Assessment Tool (SWAT) calibration and validation model was used. LULC map was developed by using Cellular Automata (CA)-Markov in IDRISI software, and the coverage of LULCs was including parameters of cropland, vegetation, grassland, Built-up area/Urban and water body. The findings of this study showed that the major challenges of land use/cove changes were rapid population increase, farming, and industrial activity. During the study period (2000–2020), most portions of the water body, vegetation, and grassland were changed into cropland and constructed by building. Cropland and construction areas increased by 15% and 46.95%, respectively, whereas water bodies, vegetation, and grassland decreased by 62.7%, 70.02%, and 38.1%, respectively. According to the forecasted results for the period of 2030–2040, cropland and built-up areas are increased, while vegetation, grassland, and water bodies were decreased. The SWAT model's calibration and validation performance was evaluated using the streamflow of the most sensitive parameters. For the years 2000–2004, and 2005–2012, the models were calibrated and validated, and the results showed good agreement between observed and simulated streamflow, with NSE and R2 values of 0.88 and 0.72 and 0.9 and 0.85, respectively. The results of this study indicated that the seasonal streamflow was decreased from 2000 to 2010 and 2010–2020 years during the dry and rainy seasons. In general, the impacts of land use/cover change on streamflow are significant considerations for planning and implementing water resource projects. In order to address the risks, effective land-use planning and climate-resilient water management strategies will be improved.

Scalable deep learning for watershed model calibration

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.

Multi-Variable SWAT Model Calibration Using Satellite-Based Evapotranspiration Data and Streamflow

In this study, monthly streamflow and satellite-based actual evapotranspiration data (AET) were used to evaluate the Soil and Water Assessment Tool (SWAT) model for the calibration of an experimental sub-basin with mixed land-use characteristics in Athens, Greece. Three calibration scenarios were performed using streamflow (i.e., single variable), AET (i.e., single variable), and streamflow–AET data together (i.e., multi-variable) to provide insights into how different calibration scenarios affect the hydrological processes of a catchment with complex land use characteristics. The actual evapotranspiration data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration was achieved with the use of the SUFI-2 algorithm in the SWAT-CUP program. The results suggested that the single variable calibrations showed moderately better performance than the multi-variable calibration. However, the multi-variable calibration scenario displayed acceptable outcomes for both streamflow and actual evapotranspiration and indicated reasonably good streamflow estimations (NSE = 0.70; R2 = 0.86; PBIAS = 6.1%). The model under-predicted AET in all calibration scenarios during the dry season compared to MODIS satellite-based AET. Overall, this study demonstrated that satellite-based AET data, together with streamflow data, can enhance model performance and be a good choice for watersheds lacking sufficient spatial data and observations.

Impacts of Land-Use Change on the Hydrology of Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia.

Human activities impact hydrology through changes in land use and land cover. This study examins the effects of changing land use on hydrological processes using the soil and water assessment tool (SWAT) model. The data is acquired from Landsat 4‐5 Thematic Mapper (TM) in 1989, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in 2005, and Landsat 8 Operational Land Inventory (OLI) in 2019. Image preprocessing, which includes georeferencing, radiometric and atmospheric correction, image enhancement, band composite, mosaicking, and sub‐setting, are performed. After that, supervised classification, accuracy assessment, and change detection are carried out. The hydrological changes in 1989, 2005, and 2019 are analyzed using land‐use maps. The SWAT model's calibration, validation, and sensitivity analysis are performed using the Integrated Parameter Estimation and Uncertainty Analysis Tool in the four main rivers of the basin. Farmlands and built‐up lands are found to have steadily increased in the basin, while shrublands, grasslands, and bare lands declined. Due to an expansion of agricultural and built‐up lands and a decrease in shrublands and grasslands, the basin's mean annual water yield and surface runoff increased in 2019, while evapotranspiration and lateral flow decreased compared to 1989 and 2005. Therefore, future watershed and basin management shall consider changing land use.

Calibration and Validation of SWAT Model by Using Hydrological Remote Sensing Observables in the Lake Chad Basin

Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm–version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and determination coefficient (R2). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R2 > 0.9, NSE > 0.8, and KGE > 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010–2015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R2 = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R2 and NSE greater than 0.85.

Applications of the SWAT Model for Coastal Watersheds: Review and Recommendations

HighlightsA systematic review was performed of SWAT applications in coastal watersheds.Three percent of SWAT applications have occurred in coastal watersheds.SWAT performed better at a monthly time step versus a daily time step.Nash-Sutcliffe efficiency (NSE) was the most common metric used for evaluating simulations.More research should be conducted on coupling SWAT with hydrodynamic models in tidal systems.Abstract. The Soil and Water Assessment Tool (SWAT) is a watershed to river basin scale model widely used to simulate the quality and quantity of surface water and groundwater. SWAT has been applied in a wide variety of geographical landscapes around the world. This review presents a comprehensive summary of SWAT applications for coastal watersheds. Thirty-four articles were identified as coastal applications of SWAT, which account for 3% of the total published studies using SWAT. Nash-Sutcliffe efficiency (NSE) was the most common metric used to evaluate SWAT simulations. The SWAT model calibration and validation studies in coastal watersheds reported higher NSE values for monthly flow simulation (NSE up to 0.95) than for daily flow simulation (NSE up to 0.89). Among all the studies, 34% of the reported NSE values (flow and water quality combined) were >0.75. The majority (58%) of flow values were reported daily, while the majority (81%) of water quality values were reported monthly. Only two studies combined SWAT with a hydrodynamic model to account for tide-storm surge processes. SWAT may be applied more readily and successfully to coastal watersheds if a user-friendly method is developed for coupling SWAT with hydrodynamic models to simulate the tidal influence. Keywords: Bay, Coast, Estuary, Gulf, Hydrologic model, Soil and Water Assessment Tool, Water quality model, Watershed model.

Performance of HEC-HMS and SWAT to simulate streamflow in the sub-humid tropical Hemavathi catchment

Abstract The present study was conducted to examine the accuracy and applicability of the hydrological models Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center (HEC)- Hydrologic Modeling System (HMS) to simulate streamflows. Models combined with the ArcGIS interface have been used for hydrological study in the humid tropical Hemavathi catchment (5,427 square kilometer). The critical focus of the streamflow analysis was to determine the efficiency of the models when the models were calibrated and optimized using observed flows in the simulation of streamflows. Daily weather gauge stations data were used as inputs for the models from the 2014–2020 period. Other data inputs required to run the models included land use/land cover (LU/LC) classes resulting from remote sensing satellite imagery, soil map and digital elevation model (DEM). For evaluating the model performance and calibration, daily stream discharge from the catchment outlet data were used. For the SWAT model calibration, available water holding capacity by soil (SOL_AWC), curve number (CN) and soil evaporation compensation factor (ESCO) are identified as the sensitive parameters. Initial abstraction (Ia) and lag time (Tlag) are the significant parameters identified for the HEC-HMS model calibration. The models were subsequently adjusted by autocalibration for 2014–2017 to minimize the variations in simulated and observed streamflow values at the catchment outlet (Akkihebbal). The hydrological models were validated for the 2018–2020 period by using the calibrated models. For evaluating the simulating daily streamflows during calibration and validation phases, performances of the models were conducted by using the Nash-Sutcliffe model efficiency (NSE) and coefficient of determination (R2). The SWAT model yielded high R2 and NSE values of 0.85 and 0.82 for daily streamflow comparisons for the catchment outlet at the validation time, suggesting that the SWAT model showed relatively good results compared to the HEC-HMS model. Also, under modified LU/LC and ungauged streamflow conditions, the calibrated models can be later used to simulate streamflows for future predictions. Overall, the SWAT model seems to have done well in streamflow analysis for hydrological studies.

Assessment of the Impact of Land Cover Type on the Water Quality in Lake Tondano Using a SWAT Model

Lake Tondano is the largest natural lake in North Sulawesi, Indonesia, which functions as a provider of clean water, hydroelectric power, rice field irrigation, inland fisheries, and tourism. This research aims to determine the effect of land cover types from the Tondano watershed on the lake water quality. The Soil and Water Assessment Tool (SWAT) model was used to evaluate the rate of soil erosion and the pollutant load of various land types in the watershed during the last ten years. Rainfall data is obtained from two rainfall stations, namely Paleloan Station and Noonan Station. The model is calibrated and validated before being used for analysis. We use climatological data from 2014 to 2019. The process of the SWAT model calibration and validation was carried out with the statistical formulas of the coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE). The results show that the potential for pollution load from the Tondao watershed is organic N of 0.039 kg/ha and organic P of 0.006 kg/ha coming from the agricultural land. The results of this study conclude that the fertility conditions of Lake Tondano are at the eutrophic level, where the pollutant inflow is collected in the lake waters, especially for the parameters of total N (1503697.44kg/year) and total P (144831.36kg/year). The SWAT simulation results show deviation between the modeling and field data collected with the value of R2 = 0.9303, and the significant level ≤ 10.

Impact of climate change on surface runoff: a case study of the Darabad River, northeast of Iran

Abstract Climate change is one of the major challenges affecting natural ecosystems and various aspects of human life. The effects of global warming on the hydrology and water cycle in nature are very serious, and the quantitative recognition of these effects creates more readiness to deal with its consequences. In the present study, the 2006–2100 period is predicted based on the statistical downscaling model (SDSM). Finally, the effects of climate change on the hydrological conditions in the Darabad watershed are simulated using the Soil and Water Assessment Tool (SWAT) model. The SWAT model calibration is done based on the SUFI-2 algorithm, and the effective and optimal parameter is identified. The results of the study, while confirming the efficiency of both SDSM in climate simulations and SWAT in hydrological simulation, showed that the increase in precipitation and temperature is probably in future climate conditions for the 2010–2040 period. The surface flow and runoff at the watershed area during the observation period (1970–2010) is 0.29 m3/s, but this value for the predicted period with regard to climate change in the RCP 2.6, RCP 4.5, and RCP 8.5 scenarios is equal to 0.43, 0.44, and 0.45 m3/s. The results of research, while highlighting the importance of effects of climate change, make it essential to apply them for proper management in order to adapt to climate change in the future policies of the Darabad watershed management.

Enhancing Soil and Water Assessment Tool Snow Prediction Reliability with Remote-Sensing-Based Snow Water Equivalent Reconstruction Product for Upland Watersheds in a Multi-Objective Calibration Process

Precipitation occurs in two basic forms defined as liquid state and solid state. Different from rain-fed watershed, modeling snow processes is of vital importance in snow-dominated watersheds. The seasonal snowpack is a natural water reservoir, which stores snow water in winter and releases it in spring and summer. The warmer climate in recent decades has led to earlier snowmelt, a decline in snowpack, and change in the seasonality of river flows. The Soil and Water Assessment Tool (SWAT) could be applied in the snow-influenced watershed because of its ability to simultaneously predict the streamflow generated from rainfall and from the melting of snow. The choice of parameters, reference data, and calibration strategy could significantly affect the SWAT model calibration outcome and further affect the prediction accuracy. In this study, SWAT models are implemented in four upland watersheds in the Tulare Lake Basin (TLB) located across the Southern Sierra Nevada Mountains. Three calibration scenarios considering different calibration parameters and reference datasets are applied to investigate the impact of the Parallel Energy Balance Model (ParBal) snow reconstruction data and snow parameters on the streamflow and snow water-equivalent (SWE) prediction accuracy. In addition, the watershed parameters and lapse rate parameters-led equifinality is also evaluated. The results indicate that calibration of the SWAT model with respect to both streamflow and SWE reference data could improve the model SWE prediction reliability in general. Comparatively, the streamflow predictions are not significantly affected by differently lumped calibration schemes. The default snow parameter values capture the extreme high flows better than the other two calibration scenarios, whereas there is no remarkable difference among the three calibration schemes for capturing the extreme low flows. The watershed and lapse rate parameters-induced equifinality affects the flow prediction more (Nash-Sutcliffe Efficiency (NSE) varies between 0.2–0.3) than the SWE prediction (NSE varies less than 0.1). This study points out the remote-sensing-based SWE reconstruction product as a promising alternative choice for model calibration in ungauged snow-influenced watersheds. The streamflow-reconstructed SWE bi-objective calibrated model could improve the prediction reliability of surface water supply change for the downstream agricultural region under the changing climate.

Pemodelan Perubahan Tataguna Lahan Terhadap Debit Banjir DAS Tanggul, Jember Menggunakan Model SWAT (Soil and Water Assessment Tool)

Population growth is one of the causes of land-use change that can increase runoff. Tanggul watershed is one of the watersheds which often overflows during the rainy season. This study purpose to analyze the effect of land-use changes on runoff in Tanggul watershed using SWAT (Soil and Water Assessment Tool) model. To make sure the performance of SWAT model calibration and classified by the value of NSE and R2. The result of calibration included in a good category and validation included in a very good category. This study was modeling forest land-use change in 2004-2017 to determine the effect of land-use change on runoff. The result in this model of forest land-use change can increase runoff.

Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models

Kangsabati basin located in tropical plateau region faces multiple problems of soil erosion susceptibility (SES), soil fertility deterioration, and sedimentation in reservoirs. Hence, identification of SES zones in thirty-eight sub-basins (SB) for basin prioritization is necessary. The present research addressed the issue by using four multi-criteria decision-making (MCDM) models: VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), technique for order preference by similarity to ideal solution (TOPSIS), simple additive weighing (SAW), compound factor (CF). To determine the best fitted method from MCDM for erosion susceptibility (ES), a comparison has been made with Soil and Water Assessment Tool (SWAT), where fifteen morphometric parameters were considered for MCDM, and meteorological data, soil, slope and land use land cover (LULC) were considered for SWAT model. Two validation indices of percentage change and intensity change were used for evaluation and comparison of MCDM results. With SWAT model performance, SWAT calibration and uncertainty analysis programs (CUP) was used for sensitive analysis of SWAT parameters on flow discharge and sediment load simulation. The results showed that 23, 16, 18 SB have high ES; therefore they were given 1 to 3 ranks, whereas 31, 37, 21SB have low ES, hence given 38 to 36 rank as predicted by MCDM methods and SWAT. MCDM validation results depict that VIKOR and CF methods are more acceptable than TOPSIS and SAW. Calibration (flow discharge R2 0.86, NSE 0.75; sediment load R2 0.87, NSE 0.69) and validation (flow discharge R2 0.79, NSE 0.55; sediment load R2 0.79, NSE 0.76) of SWAT model indicated that simulated results are well fitted with observed data. Therefore, VIKOR reflects the significant role of morphometric parameters on ES, whereas SWAT reflects the significant role of LULC, slope, and soil on ES. However, it could be concluded that VIKOR is more effective MCDM method in comparison to SWAT prediction.

Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow

This paper couples the Soil and Water Assessment Tool (SWAT) model and the chloride mass balance (CMB) method to improve the modeling of streamflow in high-permeability bedrock basins receiving interbasin groundwater flow (IGF). IGF refers to the naturally occurring groundwater flow beneath a topographic divide, which indicates that baseflow simulated by standard hydrological models may be substantially less than its actual magnitude. Identification and quantification of IGF is so difficult that most hydrological models use convenient simplifications to ignore it, leaving us with minimal knowledge of strategies to quantify it. The Castril River basin (CRB) was chosen to show this problematic and to propose the CMB method to assess the magnitude of the IGF contribution to baseflow. In this headwater area, which has null groundwater exploitation, the CMB method shows that yearly IGF hardly varies and represents about 51% of mean yearly baseflow. Based on this external IGF appraisal, simulated streamflow was corrected to obtain a reduction in the percent bias of the SWAT model, from 52.29 to 22.40. Corrected simulated streamflow was used during the SWAT model calibration and validation phases. The Nash–Sutcliffe Efficiency (NSE) coefficient and the logarithmic values of NSE (lnNSE) were used for overall SWAT model performance. For calibration and validation, monthly NSE was 0.77 and 0.80, respectively, whereas daily lnNSE was 0.81 and 0.64, respectively. This methodological framework, which includes initial system conceptualization and a new formulation, provides a reproducible way to deal with similar basins, the baseflow component of which is strongly determined by IGF.

Hydrological Modelling of Deo River Sub-Basin using SWAT Model and Performance Evaluation using SWAT-CUP

The current study analyses the runoff response using Soil and Water Assessment Tool (SWAT) during rainfall incidents over the sub-basin of Deo River, Panch Mahal, Gujarat, India. The SWAT model is developed for the Deo river sub-basin having catchment area of 194.36 km2, with 7 sub-basins comprising of 94 Hydrological Response Units (HRUs). Two rain gauge stations present in the study area (viz., Deo dam and Shivrajpur) werechosen to evaluate the efficiency of the SWAT model. To conduct SWAT model Calibration and Validation, the Soil and Water Assessment Tool-Calibration Uncertainty Program (SWAT-CUP) with Sequential Uncertainty Fitting (SUFI-2) algorithm has been used. The model was run for the period from 2000 to 2017 considering 2 years (2000-2001) warm up period with a calibration period of 2002 to 2012 and a validation period of 2013 to 2017. The sensitivity of the basin parameters was evaluated and found Curve Number as the most sensitive parameter, hence, it can be considered to improve the model's runoff simulation efficiency. The study found that the model performed good with a Coefficient of Determination (R2) and Nash–Sutcliffe Efficiency (NSE) as 0.89 and 0.87 during calibration and 0.88 and 0.81 during validation respectively giving data at daily scale. The findings of this study revealed that SWAT model is helpful for runoff prediction and flood forecasting for extreme rainfall occurrences in Deo river basin.

Identifying erosion hotspots in Lake Tana Basin from a multisite Soil and Water Assessment Tool validation: Opportunity for land managers

AbstractExtensive catchment degradation throughout the Ethiopian Highlands induced by long‐term intensified land use, erosion‐prone topography, and climate causes substantial soil erosion that limits agricultural productivity and results in lake sedimentation. However, before taking soil conservation measures, management of the soil loss problem essentially needs catchment‐level modelling to estimate the geographic distribution of erosion hotspots. With the increasing availability of sediment and spatial data and development of physically based models, this study aims multisite calibration of Soil and Water Assessment Tool (SWAT) to map erosion hotspot areas and to assess the effect of well‐known land management alternatives in sediment reduction in the Lake Tana Basin. The SWAT simulations indicated that the goodness of fit between predicted and observed data was satisfactory for all gauge stations except for one, and the model performance was within acceptable evaluation ratings. Simulated average sediment yield (SY) for the period 2001–2016 at subbasin level varies from negligible to about 169 Mg ha−1 yr−1 (basin average 32 Mg ha−1 yr−1). High potential SY (>50 Mg ha−1 yr−1) was simulated for 23% of the subbasins in Megech, upper Rib, upper Gumara, and Gilgel Abay catchments due to steep slope topography, aggressive rainfall, croplands dominance, and low rock fragment cover. The differences in level of erosion risk among subbasins help to prioritize and target specific areas of the basin that need urgent soil conservation activities. Scenario analysis also showed that implementing stone bunds, Acacia decurrens‐based crop rotation, reforestation, and grass contour strips reduces the existing SY by 51–61% at basin level. The potential sediment production could reach tolerable levels by implementing stone bunds, tree‐based crop rotation, reforestation in steep slope areas, and grass contour strips on gentle slopes. Overall, the multisite calibration of SWAT model using the measured run‐off and sediment data produces reasonable results that may support decision makers and planners to implement relevant land management measures and thereby reduce the alarming problems of soil loss in the basin and sedimentation of Lake Tana.

Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria

Abstract. The main objective of this study was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data from the Global Land Evaporation Amsterdam Model (GLEAM_v3.0a) and from the Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) for the Ogun River Basin (20 292 km2) located in southwestern Nigeria. Three potential evapotranspiration (PET) equations (Hargreaves, Priestley–Taylor and Penman–Monteith) were used for the SWAT simulation of AET. The reference simulations were the three AET variables simulated with SWAT before model calibration took place. The sequential uncertainty fitting technique (SUFI-2) was used for the SWAT model sensitivity analysis, calibration, validation and uncertainty analysis. The GLEAM_v3.0a and MOD16 products were subsequently used to calibrate the three SWAT-simulated AET variables, thereby obtaining six calibrations–validations at a monthly timescale. The model performance for the three SWAT model runs was evaluated for each of the 53 subbasins against the GLEAM_v3.0a and MOD16 products, which enabled the best model run with the highest-performing satellite-based AET product to be chosen. A verification of the simulated AET variable was carried out by (i) comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, which is a product that has different forcing data than the version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95 % uncertainty of the SWAT-simulated variable bracketed most of the satellite-based AET data in each subbasin. A validation of the simulated soil moisture dynamics for GS1 was carried out using satellite-retrieved soil moisture data, which revealed good agreement. The SWAT model (GS1) also captured the seasonal variability of the water balance components at the outlet of the watershed. This study demonstrated the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged large river basin with reasonable accuracy. The novelty of the study is the use of these freely available satellite-derived AET datasets to effectively calibrate and validate an eco-hydrological model for a data-scarce catchment.

Modeling Pesticide and Sediment Transport in the Malewa River Basin (Kenya) Using SWAT

Understanding the dynamics of pesticide transport in the Malewa River and Lake Naivasha, a major fresh water resource, is critical to safeguard water quality in the basin. In this study, the soil and water assessment tool (SWAT) model was used to simulate the discharge of sediment and pesticides (notably the organochlorine residues of lindane, methoxychlor and endosulfan) into the Malewa River Basin. Model sensitivity analysis, calibration and validation were performed for both daily and monthly time steps using the sequential uncertainty fitting version 2 (SUFI-2) algorithm of the SWAT-CUP tool. Water level gauge data as well as a digital turbidity sensor (DTS-12) for suspended sediment transport were used for the SWAT calibration. Pesticide residues were measured at Upper and Down Malewa locations using a passive sampling technique and their quantity was determined using laboratory gas chromatography. The sensitivity analysis results showed that curve number (CN2), universal soil loss equation erodibility factor (USLE-K) and pesticide application efficiency (AP_EF) formed the most sensitive parameters for discharge, sediment and pesticide simulations, respectively. In addition, SWAT model calibration and validation showed better results for monthly discharge simulations than for daily discharge simulations. Similarly, the results obtained for the monthly sediment calibration demonstrated more match between measured and simulated data as compared to the simulation at daily steps. Comparison between the simulated and measured pesticide concentrations at upper Malewa and down Malewa locations demonstrated that although the model mostly overestimated pesticide loadings, there was a positive association between the pesticide measurements and the simulations. Higher concentrations of pesticides were found between May and mid-July. The similarity between measured and simulated pesticides shows the potential of the SWAT model as initial evaluation modelling tool for upstream to downstream suspended sediment and pesticide transport in catchments.