Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

The study has shown the integration of Arc SWAT with Arc GIS and remote sensing tool are helpful analyze and evaluate both spatial and temporal land use/cover dynamics. It has also shown that Arc SWAT is an effective tool in analyzing the impacts of land use/cover changes on stream flow in areas with limited readily available data. This study is mainly focusing on the investigation of the impacts of land use / land cover changes on the stream flow of Muga watershed which is located in the East Choke Mountains watersheds, Upper Abbay Basin, East Gojjam Zone, Amhara Regional state, Ethiopia. Soil and Water Assessment Tool (SWAT) model were used it investigate the impact of land cover change on the stream flow. For this study SWAT Simulation is used in identifying the most vulnerable sub basins to the stream flow and sediment load changes of Muga watershed. The model was calibrated and validated using historic Stream flow data. The model was calibrated using stream flow data from 1993 to1998, validated from 1999 to2002. The R 2 and NSE values were used to examine model performance and the result indicates 0.81 and 0.87 to R 2 and 0.80 and 0.86 to NSE during calibration and validation respectively. The result of this analysis indicated that the mean monthly stream flow for wet months had increased by 17.75m 3 /s while the dry season decreased by 12.76m 3 /s during the 1995-2013 period due to the land use and land cover change. The highest annual surface runoff was attributed by sub basin 5 whereas sub basin 6 contributes the highest ground water respectively for 1995, 2003 and 2013 land cover maps. In terms of sediment yield, sub basin 1 contributes a maximum load for the study periods. Keywords : Arc SWAT, Arc GIS, Land Use /land cover change, Muga Watershed DOI: 10.7176/CER/13-2-01 Publication date: April 30 th 2021

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.

Improving hydrological simulation in the Upper Mississippi River Basin through enhanced freeze-thaw cycle representation

A simulation study using the Soil and Water Assessment Tool (SWAT) model has been initiated to assess current and alternative nutrient, cropping, and management practices in the Sani River Basin in an effort to help mitigate climate change impact in the basin. The SWAT software system has been constructed to manage the input and output data for the SWAT simulations, and the execution of the model for each scenario. An overview of the modeling structure that has been developed for the basin is presented, including the spatial representation of the region within SWAT. Initial results for the baseline simulation are also presented for monthly stream flows near the outlet of the basin, and for annual stream flows for the basin. Overall, the model demonstrated good performance in capturing the patterns and trend of the observed flow series, which confirmed the appropriateness of the model for future scenario simulation. Therefore, SWAT model can be taken as a potential tool for simulation of the hydrology of watershed which behaves hydro-meteorologically similar with Sani basin. Future studies on Sani watershed modeling should address the issues related to water quality and evaluate best management practices.

Surface runoff is a critical input in watershed management. Runoff is a driving force for soil erosion which causes sedimentation of reservoirs located at downstream of watersheds. The present study aimed to model surface runoff using the Soil and Water Assessment Tools (SWAT) model in Ketar watershed, Ethiopia. Ketar river crosses mountainous steep slope areas. High surface runoff from the Ketar watershed flows to Lake Ziway. The Ketar watershed was delineated into 35 sub-basins and 147 Hydraulic Response Units (HRUs). In this work, surface runoff was simulated using 36 years (1980–2015) meteorological data as input. The model was calibrated and validated for streamflow using sequential uncertainty fitting-2 (SUFI_2) of the SWAT Calibration and Uncertainty Programs (SWAT_CUP). The model calibrated using 12-year measured streamflow data (1988–1997) and validated using 7-year streamflow data (1998–2004). The coefficient of determination (R2) and Nash–Sutcliffe (NSE) were used to measure the performance of the model. R2 and NSE were 0.82 and 0.7 during calibration and 0.78 and 0.71 during validation, respectively. The results show that there was an excellent relation between monthly observed and simulated streamflow during both calibration and validation. Simulated average monthly surface runoff of the watershed was 112.82 mm per month. The southwest part of the watershed, which was characterized by highest annual surface runoff, covers 27% of the total area.

Using satellite-based weather data as input to SWAT in a data poor catchment

The Citanduy Watershed is one of the largest watersheds on Java Island and is in critical condition. The Upper Citanduy Sub-watershed is the main sub-watershed in the Citanduy Watershed, which needs attention because it greatly affects the quantity and quality of its water. The Soil and Water Assessment Tool (SWAT) model, which is a hydrological model developed in the United States, can be used to predict the quantity of water. However, the water balance in the SWAT model uses the water balance for plants planted on dry land (up land) and is less suitable for watersheds that have rice fields (low land). For watersheds that have rice fields, pot hole and modified SWAT features have been tried and developed. This study aims to analyze the performance of the SWAT model with original features, pot hole features, and modified features in the QSWAT program to predict water discharge in the Upper Citanduy Sub-watershed. The results of the study indicate that the modified SWAT has the best performance compared to the original SWAT and pot-hole SWAT, as indicated by the NSE and R2 values. The NSE values of the original SWAT, pot hole SWAT, and modified SWAT were 0.70, 0.73, and 0.75, respectively. The R2 values of the modified SWAT, Original SWAT, and pot hole SWAT were 0.94, 0.93, and 0.94, respectively.

Abstract. SWAT (Soil and Water Assessment Tool) is a continuous-time, semi-distributed, river basin model widely used to evaluate the effects of alternative management decisions on water resources. This study examines the application of the SWAT model for streamflow simulation in an experimental basin with mixed-land-use characteristics (i.e., urban/peri-urban) using daily and hourly rainfall observations. The main objective of the present study was to investigate the influence of rainfall resolution on model performance to analyze the mechanisms governing surface runoff at the catchment scale. The model was calibrated for 2018 and validated for 2019 using the Sequential Uncertainty Fitting (SUFI-2) algorithm in the SWAT-CUP program. Daily surface runoff was estimated using the Curve Number method, and hourly surface runoff was estimated using the Green–Ampt and Mein–Larson method. A sensitivity analysis conducted in this study showed that the parameters related to groundwater flow were more sensitive for daily time intervals, and channel-routing parameters were more influential for hourly time intervals. Model performance statistics and graphical techniques indicated that the daily model performed better than the subdaily model (daily model, with NSE = 0.86, R2 = 0.87, and PBIAS = 4.2 %; subdaily model with NSE = 0.6, R2 = 0.63, and PBIAS = 11.7 %). The Curve Number method produced higher discharge peaks than the Green–Ampt and Mein–Larson method and better estimated the observed values. Overall, the general agreement between observations and simulations in both models suggests that the SWAT model appears to be a reliable tool to predict discharge in a mixed-land-use basin with high complexity and spatial distribution of input data.

Improvement and testing of SWAT for multi-source irrigation systems with paddy rice

The soil and water assessment tool (SWAT) is a widely accepted semi-distributed agrohydrological model. In the present study, the SWAT has been calibrated for the agriculture-dominated Kantamal catchment of Mahanadi river basin, Odisha, India, to simulate streamflow at the Kantamal gauging station. The model was run for 19 years from 2000 to 2018 to simulate monthly streamflow. In addition, the model has been calibrated for the period 2004 to 2012, considering the initial 4 years (2000-2003) as a warm-up period and validated for 6 years from 2013 to 2018. Uncertainty analysis was carried out using the SUFI-2 algorithm in SWAT-CUP. The sensitivity of the parameters was determined using the t-stat and p-value. Fifteen distinguished parameters were selected for sensitivity analysis (SA). The performance of the model was evaluated satisfactorily on monthly time scale streamflow simulation using Nash- Sutcliffe Efficiency (NSE), coefficient of determination (R2), and percentage BIAS (PBIAS). The P- and R- factors were used to assess the degree of uncertainty. The values of NSE, R2, and PBIAS were found to be 0.91, 0.94, and -0.07 during the calibration period and 0.82, 0.87, and -0.16 during the validation period, respectively. The P- and R- factors values were 0.77 and 0.98 during calibration and 0.79 and 0.86 during the validation period, respectively. The simulated streamflow is well fitted within the 95% prediction uncertainty (95PPU) band of the SUFI-2 algorithm during the calibration and validation periods, indicating a satisfactory model performance under parameter uncertainty. Overall, it is depicted that the SWAT model can be successfully used for streamflow modeling and water resources assessment in agriculture-dominated catchments.

This study evaluated the performance of two water quality models in accordance to specific tasks designated in the USDA Agricultural Research Service Conservation Effects Assessment Project. The Soil and Water Assessment Tool (SWAT) and the Annualized Agricultural Non-Point Source (AnnAGNPS) models were applied uncalibrated to the Cedar Creek watershed within the St. Joseph River watershed in northeastern Indiana to predict streamflow and atrazine losses. In order to ultimately assess the benefits of conservation practices in agricultural watersheds (which is one of the major goals of the Conservation Effects Assessment Project), proper application of the SWAT and AnnAGNPS models is essential including baseline comparisons made in an uncalibrated mode aimed at eliminating bias due to parameter optimization. Streamflow prediction results show that SWAT model performance was superior to AnnAGNPS, with SWAT model efficiency values ranging from 0.66 to 0.25 and AnnAGNPS model efficiency values ranging from 0.13 to -2.06 for monthly and annual streamflow, respectively. For uncalibrated conditions, neither model was able to adequately simulate atrazine loss concentrations. Overall results suggest that for Conservation Effects Assessment Project modeling applications at the Cedar Creek watershed scale in this study, the use of the SWAT model would be preferable to AnnAGNPS in terms of overall model performance and model support technology (e.g., model interface and documentation).

The Soil and Water Assessment Tool (SWAT) model is a continuation of nearly 30 years of modeling efforts conducted by the U.S. Department of Agriculture (USDA), Agricultural Research Service. SWAT has gained international acceptance as a robust interdisciplinary watershed modeling tool, as evidenced by international SWAT conferences, hundreds of SWAT-related papers presented at numerous scientific meetings, and dozens of articles published in peer-reviewed journals. The model has also been adopted as part of the U.S. Environmental Protection Agency's BASINS (Better Assessment Science Integrating Point & Nonpoint Sources) software package and is being used by many U.S. federal and state agencies, including the USDA within the Conservation Effects Assessment Project. At present, over 250 peer-reviewed, published articles have been identified that report SWAT applications, reviews of SWAT components, or other research that includes SWAT. Many of these peer-reviewed articles are summarized here according to relevant application categories such as streamflow calibration and related hydrologic analyses, climate change impacts on hydrology, pollutant load assessments, comparisons with other models, and sensitivity analyses and calibration techniques. Strengths and weaknesses of the model are presented, and recommended research needs for SWAT are provided.

Modelling the hydrology of North American Prairie watersheds is complicated because of the existence of numerous landscape depressions that vary in storage capacity. The Soil and Water Assessment Tool (SWAT) is a widely applied model for long‐term hydrological simulations in watersheds dominated by agricultural land uses. However, several studies show that the SWAT model has had limited success in handling prairie watersheds. In past works using SWAT, landscape depression storage heterogeneity has largely been neglected or lumped. In this study, a probability distributed model of depression storage is introduced into the SWAT model to better handle landscape storage heterogeneity. The work utilizes a probability density function to describe the spatial heterogeneity of the landscape depression storages that was developed from topographic characteristics. The integrated SWAT–PDLD model is tested using datasets for two prairie depression dominated watersheds in Canada: the Moose Jaw River watershed, Saskatchewan; and the Assiniboine River watershed, Saskatchewan. Simulation results were compared to observed streamflow using graphical and multiple statistical criterions. Representation of landscape depressions within SWAT using a probability distribution (SWAT–PDLD) provides improved estimations of streamflow for large prairie watersheds in comparison to results using a lumped, single storage approach. Copyright © 2016 John Wiley & Sons, Ltd.

<b><sc>Abstract.</sc></b> The current available rainfall-runoff models vary from the simple lumped data-driven models that purely depend on the observed inputs and outputs of a watershed to the more complex physically-based models representing a system using mathematical equations that describes important physical laws of conservation of mass, energy, and momentum. In this study, the accuracy of streamflow estimated by a data-driven Artificial Neural Network (ANN) and the physically-based Soil and Water Assessment Tool (SWAT) are compared. The models were applied in two small watersheds, one highly urbanized and the other primarily covered with forest and shrub, in the San Antonio Region of central south Texas, where karst geologic features are prevalent. Both models were found to perform very well in the prediction of daily streamflow in the urbanized watershed. However, both models poorly predicted streamflow in the nonurban watershed. Additionally, the ANN model performance significantly improved when a time series autoregressive model structure using historical streamflow data was implemented. The SWAT model achieved very limited improvement through model calibration with the current model structure. This result suggests that the ANN model is more suitable for short-term streamflow forecasting in watersheds heavily affected by karst features where surface water flow is strongly influenced by the complex processes of rapid groundwater recharge and discharge.

The Soil and Water Assessment Tool (SWAT) model has been successfully used to predict alterations in streamflow, evapotranspiration and soil water; however, it is not clear how effective or accurate SWAT is at predicting crop growth. Previous research suggests that while the hydrologic balance in each watershed is accurately simulated with SWAT, the SWAT model over or under predicts crop yield relative to fertilizer inputs. The SWAT model now has three alternative N simulation options: 1) SWAT model with an added flush of N (SWAT-flush); 2) N routines derived from the CENTURY model (SWAT-C); and 3) a one-pool C and N model (SWAT-One). The objective of this study was to evaluate the performance of SWAT-flush, SWAT-C, and SWAT-One as they affect wheat yield prediction. Simulated yields were compared to wheat yields in a 28-year fertilizer/wheat yield study in Lahoma, OK. Simulated yields were correlated with actual 28-year mean yield; however, none of the available N cycling models predicted yearly yields. SWAT-C simulated average yields were closer than other N sub-models to average actual yield. Annually there was a stronger correlation between SWAT-flush and actual yields than the other submodels. However, none of the N-cycling routines were able to accurately predict annual variability in yield at any fertilizer rate. We found that SWAT-C or SWAT-flush are the most viable choices for accurately simulating long-term average wheat yields although annual variations in yield prediction should be taken into consideration. Further research is needed to determine the effectiveness of SWAT-C and SWAT-flush in determining average and annual yield in various farming regions and with numerous agronomic crops.