Defining Soil and Water Assessment Tool(SWAT)hydrologic responseunits(HRUs)by field boundaries

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.

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Hydrological modeling plays a crucial role in efficiently managing water resources and understanding the hydrologic behavior of watersheds. This study aims to simulate daily streamflow in the Godavari River Basin in Maharashtra using the Soil and Water Assessment Tool (SWAT). SWAT is a process-based hydrological model used to predict water balance components, sediment levels, and nutrient contamination. In this research, we used integrated remote sensing and GIS data, including Digital Elevation Models (DEM), land use and land cover (LULC) maps, soil maps, and observed precipitation and temperature data, as input for developing the SWAT model to assess surface runoff in this large river basin. The Godavari River Basin under study was divided into 25 sub-basins, comprising 151 hydrological response units categorized by unique land cover, soil, and slope characteristics using the SWAT model. The model was calibrated and validated against observed runoff data for two time periods: 2003-2006 and 2007-2010 respectively. Model performance was assessed using the Nash-Sutcliffe efficiency (NSE) and the coefficient of determination (R2). The results show the effectiveness of the SWAT2012 model, with R2 value of 0.84 during calibration and 0.86 during validation. NSE values also ranged from 0.84 during calibration to 0.85 during validation. These findings enhance our understanding of surface runoff dynamics in the Godavari River Basin under study and highlight the suit-ability of the SWAT model for this region.

Simulation of hydrological processes of mountainous watersheds in inland river basins: taking the Heihe Mainstream River as an example

The hydrological processes of mountainous watersheds in inland river basins are complicated.It is absolutely significant to quantify mountainous runoff for social,economic and ecological purposes.This paper takes the mountainous watershed of the Heihe Mainstream River as a study area to simulate the hydrological processes of mountainous watersheds in inland river basins by using the soil and water assessment tool(SWAT)model.SWAT simulation results show that both the Nash–Sutcliffe efficiency and the determination coefficient values of the calibration period(January 1995 to December 2002)and validation period(January 2002 to December 2009)are higher than 0.90,and the percent bias is controlled within±5%,indicating that the simulation results are satisfactory.According to the SWAT performance,we discussed the yearly and monthly variation trends of the mountainous runoff and the runoff components.The results show that from 1996 to 2009,an indistinctive rising trend was observed for the yearly mountainous runoff,which is mainly recharged by lateral flow,and followed by shallow groundwater runoff and surface runoff.The monthly variation demonstrates that the mountainous runoff decreases slightly from May to July,contrary to other months.The mountainous runoff is mainly recharged by shallow groundwater runoff in January,February,and from October to December,by surface runoff in March and April,and by lateral flow from May to September.

Analysis of the Relationship between Landuse and Non-point Source Pollution in Ashi River Basin

Landuse is one of the most influential factors of non-point source pollution. Based on the three-year landuse data( 2000,2005 and 2008),Arc GIS and Fragstat were used to analyze the landuse type and the change of landscape pattern. The relationships between landuse and non-point source-total nitrogen( NPS-TN) and nonpoint source-total phosphorus( NPS-TP) were discussed with the methods of spatially statistical analysis,landscape pattern analysis and principal component analysis. The study results conveyed that agricultural land and forestland,which accounted for over 92% of the study area,were the major landuse type of Ashi River Basin.Meanwhile,the NPS pollution had close connections with landuse type and landscape pattern. When it comes to landuse type,the export risks of NPS-TN and NPS-TP were agricultural land > urban land > grassland > forestland. As for landscape pattern,NPS-TN and NPS-TP were positively related to SHDI and SHEI, while negatively connected with LPI,AI and COHESION. Therefore,the study could reach the conclusion that the more fragmented and complicated the landscape patterns were,the more serious the NPS pollution was.

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology

Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used approach.Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the YHHRB.Nine descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological processes.The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in turn.Then,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments.Furthermore,the similaritybased regionalization scheme was used for comparison with the regression-based approach.The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments.Compared with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships.The performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid regions.When the study area contained nested catchments,the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance.The new findings could improve flood forecasting and water resources planning in regions that lack observed data.

A comparison of single- and multi-site calibration and validation： a case study of SWAT in the Miyun Reservoir watershed, China

An essential task in evaluating global water resource and pollution problems is to obtain the optimum set of parameters in hydrological models through calibra- tion and validation. For a large-scale watershed, single-site calibration and validation may ignore spatial heterogeneity and may not meet the needs of the entire watershed. The goal of this study is to apply a multi-site calibration and validation of the Soil and Water Assessment Tool （SWAT）, using the observed flow data at three monitoring sites within the Baihe watershed of the Miyun Reservoir watershed, China. Our results indicate that the multi-site calibration parameter values are more reasonable than those obtained from single-site calibrations. These results are mainly due to significant differences in the topographic factors over the large-scale area, human activities and climate variability. The multi-site method involves the division of the large watershed into smaller watersheds, and applying the calibrated parameters of the multi-site calibration to the entire watershed. It was anticipated that this case study could provide experience of multi-site calibration in a large-scale basin, and provide a good foundation for the simulation of other pollutants in follow- up work in the Miyun Reservoir watershed and other similar large areas.

Application of Spatially Distributed Calibrated Hydrological Model in Evapotranspiration Simulation of Three Gorges Reservoir Area of China:A Case Study in the Madu River Basin

Evapotranspiration(ET)is the key to the water cycle process and an important factor for studying near-surface water and heat balance.Accurately estimating ET is significant for hydrology,meteorology,ecology,agriculture,etc..This paper simulates ET in the Madu River Basin of Three Gorges Reservoir Area of China during 2009-2018 based on the Soil and Water Assessment Tool(SWAT)model,which was calibrated and validated using the MODIS(Moderate-resolution Imaging Spectroradiometer)/Terra Net ET 8-Day L4 Global 500 m SIN Grid(MOD16A2)dataset and measured ET.Two calibration strategies(lumped calibration(LC)and spatially distributed calibration(SDC))were used.The basin was divided into 34 sub-basins,and the coefficient of determination(R^(2))and NashSutcliffe efficiency coefficient(NSE)of each sub-basin were greater than 0.6 in both the calibration and validation periods.The R2 and NSE were higher in the validation period than those in the calibration period.Compared with the measured ET,the accuracy of the model on the daily scale is:R^(2)=0.704 and NSE=0.759(SDC results).The model simulation accuracy of LC and SDC for the sub-basin scale was R^(2)=0.857,R^(2)=0.862(monthly)and R^(2)=0.227,R^(2)=0.404(annually),respectively;for the whole basin scale was R^(2)=0.902,R^(2)=0.900(monthly)and R^(2)=0.507 and R^(2)=0.519(annually),respectively.The model performed acceptably,and SDC performed the best,indicating that remote sensing data can be used for SWAT model calibration.During 2009-2018,ET generally increased in the Madu River Basin(SDC results,7.21 mm/yr),with a multiyear average value of 734.37 mm/yr.The annual ET change rate for the sub-basin was relatively low upstream and downstream.The linear correlation analysis between ET and meteorological factors shows that on the monthly scale,precipitation,solar radiation and daily maximum and minimum temperature were significantly correlated with ET;annually,solar radiation and wind speed had a moderate correlation with ET.The correlation between maximum temperature and ET is best on the monthly scale(Pearson correlation coefficient R=0.945),which may means that the increasing ET originating from increasing temperature(global warming).However,the sub-basins near Shennongjia Nature Reserve that are in upstream have a negative ET change rate,which means that ET decreases in these sub-basins,indicating that the’Evaporation Paradox’exists in these sub-basins.This study explored the potential of remote-sensing-based ET data for hydrological model calibration and provides a decision-making reference for water resource management in the Madu River Basin.

Evaluation of non-point source pollution reduction by applying Best Management Practices using a SWAT model and QuickBird high resolution satellite imagery

This study evaluated the reduction effect of non-point source pollution by applying best management practices （BMPs） to a 1.21 km^2 small agricultural watershed using a SWAT （Soil and Water Assessment Tool） model. Two meter QuickBird land use data were prepared for the watershed. The SWAT was calibrated and validated using dally streamflow and monthly water quality （total phosphorus （TP）, total nitrogen （TN）, and suspended solids （SS）） records from 1999 to 2000 and from 2001 to 2002. The average Nash and Sutcliffe model efficiency was 0.63 for the streamflow and the coefficients of determination were 0.88, 0.72, and 0.68 for SS, TN, and TP, respectively. Four BMP scenarios viz. the application of vegetation filter strip and riparian buffer system, the regulation of Universal Soil Loss Equation P factor, and the fertilizing control amount for crops were applied and analyzed.

Runoff variation and its response to climate change in the Three Rivers Source Region

Runoff at the three time scales （non-flooding season, flooding season and annual period） was simulated and tested from 1958 to 2005 at Tangnaihai （Yellow River Source Region： YeSR）, Zhimenda （Yangtze River Source Region： YaSR） and Changdu （Lancang River Source Region： LcSR） by hydrological modeling, trend detection and comparative analysis. Also, future runoff variations from 2010 to 2039 at the three outlets were analyzed in A1B and B1 scenarios of CSIRO and NCAR climate model and the impact of climate change was tested. The results showed that the annual and non-flooding season runoff decreased significantly in YeSR, which decreased the water discharge to the midstream and down- stream of the Yellow River, and intensified the water shortage in the Yellow River Basin, but the other two regions were not statistically significant in the last 48 years. Compared with the runoff in baseline （1990s）, the runoff in YeSR would decrease in the following 30 years （2010-2039）, especially in the non-flooding season. Thus the water shortage in the mid- stream and downstream of the Yellow River Basin would be serious continuously. The runoff in YaSR would increase, especially in the flooding season, thus the flood control situation would be severe. The runoff in LcSR would also be greater than the current runoff, and the annual and flooding season runoff would not change significantly, while the runoff variation in the non-flooding season is uncertain. It would increase significantly in the B1 scenario of CSIRO model but decrease significantly in B1 scenario of NCAR model. Furlhermore, the most sensitive region to climate change is YaSR, followed by YeSR and LcSR.

IJABE SWAT Special Issue:Innovative modeling solutions for water resource problems

The Soil and Water Assessment Tool(SWAT)ecohydrological model is used worldwide to evaluate hydrological and water quality concerns across a plethora of watershed scales and environmental conditions.The ten studies featured in this special issue confirm the global utility of SWAT,which include applications of the model in Brazil,China,Ethiopia and the United States.The range of applications reported in the special issue mirror broader trends in the extensive existing SWAT literature and provide valuable insights regarding input data sensitivity,testing,scenario analysis,software development and other important SWAT-related advancements.Brief summaries of these ten studies in this SWAT special issue are provided,highlighting key procedures and findings for each application.A brief description of SWAT structure and historical development is also given including a complete listing of key documentation and enhancements for every major release of the model between the early 1990s to the present time.This overview will serve as a guide to better reading and understanding of the ten research papers in this SWAT special issue of IJABE.

Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China

Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool(SWAT) model(CMADS)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs;i.e., Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis 3B42 Version 7(TMPA 3B42V7), Climate Prediction Center(CPC) morphing technique satellite–gauge blended product(CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data(CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record(PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that:(1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient(CC) and rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias(BIAS;-22.72%);(2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation;(3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency(NSE) values(0.85 and 0.75 for calibration period and validation period, respectively);and(4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in datascarce regions with similar climates and topography in the Global Precipitation Measurement(GPM) era.

Generation of a long-term daily gridded precipitation dataset for the rTjSj-Upper Indus Basin(UIB)through temporal Reconstruction,Correction I sag&Informed Regionalization-“ReCIR”

This study attempted to generate a long-term(1961-2010)daily gridded precipitation dataset for the Upper Indus Basin(UIB)with orographic adjustments so as to generate realistic precipitation estimates,enabling hydrological and water resource investigations that can close the water balance,that is difficult,if not impossible to achieve with the currently available precipitation data products for the basin.The procedure includes temporal reconstruction of precipitation series at points where data were not recorded prior to the mid-nineties,followed by a regionalization of the precipitation series to a smaller scale across the basin(0.125°x 0.125°),while introducing adjustments for the orographic effect and changes in glacier storage.The reconstruction process involves interpolation of the precipitation at virtual locations of the current(1995-)dense observational network,followed by corrections for frequency and intensity and adjustments for temporal trends at these virtual locations.The data generated in this way were further validated for temporal and spatial representativeness through evaluation of SWAT-modelled streamflow responses against observed flows across the UIB.The results show that the calibrated SWAT-simulated daily discharge at the basin outlet as well as at different sub-basin outlets,when forcing the model with the reconstructed precipitation of years 1973—1996,is almost identical to that when forcing it with the reference precipitation data(1997-2008).Finally,the spatial distribution pattern of the reconstructed(1961—1996)and reference(1997—2008)precipitation were also found consistent across the UIB,reflecting well the large-scale atmospheric-circulation pattern in the region.

Evaluation of alternative surface runoff accounting procedures using SWAT model

For surface runoff estimation in the Soil and Water Assessment Tool(SWAT)model,the curve number(CN)procedure is commonly adopted to calculate surface runoff by dynamically updating CN values based on antecedent soil moisture condition(SCSI)in field.From SWAT2005 and onward,an alternative approach has become available to apply the CN method by relating the runoff potential to daily evapotranspiration(SCSII).While improved runoff prediction with SCSII has been reported in several case studies,few investigations have been made on its influence to water quality output or on the model uncertainty associated with the SCSII method.The objectives of the research were:(1)to quantify the improvements in hydrologic and water quality predictions obtained through different surface runoff estimation techniques;and(2)to examine how model uncertainty is affected by combining different surface runoff estimation techniques within SWAT using Bayesian model averaging(BMA).Applications of BMA provide an alternative approach to investigate the nature of structural uncertainty associated with both CN methods.Results showed that SCSII and BMA associated approaches exhibit improved performance in both discharge and total NO3 predictions compared to SCSI.In addition,the application of BMA has a positive effect on finding well performed solutions in the multi-dimensional parameter space,but the predictive uncertainty is not evidently reduced or enhanced.Therefore,we recommend additional future SWAT calibration/validation research with an emphasis on the impact of SCSII on the prediction of other pollutants.