Soil And Water Assessment Tool Simulation Research Articles

Multi-gauge calibration comparison for simulating streamflow across the Major River Basins in Madagascar: SWAT + Toolbox, R-SWAT, and SWAT + Editor Hard calibration

Abstract This paper aims to improve the Soil and Water Assessment Tool (SWAT) model performance across the Major River Basins in Madagascar (MRBM), specifically for SWAT simulation in the Manambolo, Onilahy, Mananara, and Mandrare basins. A multi-gauge calibration was carried out to compare the performance of SWAT+ Toolbox, and R-SWAT, SWAT+ Editor Hard calibration on a monthly time step for the periods 1982–1999. We found that the SWAT+ model generated greater surface runoff, while the SWAT model resulted in higher groundwater flow in both CSFR and CHIRPS datasets. It has been demonstrated that the SWAT+ Toolbox had more potential in calibrating runoff across the MRBM compared to R-SWAT. Calibration in both methods led to a reduction in surface runoff, percolation, water yield, and curve number but increased the lateral flow, evapotranspiration (ET), and groundwater flow. The results showed that the multi-gauge calibrations did not significantly enhance simulation performance in the MRBM compared to single-site calibration. The performance of the SWAT+ model for runoff simulation within the SWAT+ Toolbox and R-SWAT was unsatisfactory for most basins (NSE < 0) except for Betsiboka, Mahavavy, Tsiribihina, Mangoro, and Mangoky basins (NSE = 0.40–0.70; R2 = 0.45–0.80, PBIAS≤ ±25), whether considering the CHIRPS or CSFR datasets. Further study is still required to address this issue.

Groundwater Recharge and Surface Runoff Modeling Response to Land Use and Land Cover Dynamics in a Mae Wong Watershed of Thailand

This study aims to develop efficient management strategies and assess the spatiotemporal dynamics of land use and land cover (LULC) on surface runoff modeling response dynamics for the long-term sustainability of watersheds. The soil and water assessment tool (SWAT) model was used to evaluate the LULC dynamics on GRSR in the Mae Wong Watershed (MWW) of Thailand. Using Landsat images, three different LULC maps (2011, 2021, and 2031) were created using the cellular automata markov chain (CA-Markov) model, and TerrSet 2020 geospatial monitoring and modeling software. In the overall MWW, the forestland has undergone deforestation and decreased by 2.10% of the total area and 2.72% of the total area has been transformed into agricultural lands due to human activity and population growth. The soil, LULC, weather, and the digital elevation model (DEM) were all used in the SWAT simulation procedure. To understand the groundwater recharge and surface runoff (GRSR) responses of each hydrologic response units (HRUs), the SWAT model was calibrated and verified using streamflow and the sequential uncertainty fitting (SUFI-2) technique from the SWAT calibration and uncertainty program (SWAT-CUP). The results indicate that there is a good agreement for both the calibration and validation phases of all LULC simulations. The study indicated that groundwater recharge has decreased over the last two decades while surface runoff has increased due to the forest area being converted to agricultural land. Thus, the study can support maximizing water management and strategies for systematically attaining sustainability.

Coupling of SWAT and EPIC Models to Investigate the Mutual Feedback Relationship between Vegetation and Soil Erosion, a Case Study in the Huangfuchuan Watershed, China

Identifying the feedback relationship between soil erosion and vegetation growth would contribute to sustainable watershed management. In order to study the long-term interaction between soil erosion and vegetation change, a comprehensive modeling framework was proposed by combining the Soil and Water Assessment Tool (SWAT) and the Environmental Policy Integrated Climate (EPIC) model. The Huangfuchuan Watershed was taken as an example area due to serious erosion and large-scale conversion of farmland to forest. Based on long-term variation analyses from 1956 to 2020, the effect of land cover change on runoff and sediment discharge was quantified using SWAT to create scenario simulations, and then environmental stresses factors (i.e., soil water content, nitrogen, and phosphorus contents) output by SWAT were input into EPIC to evaluate effects of soil erosion on potential biomass of vegetation. Results showed that the annual runoff reduction was 32.5 million m3 and the annual sediment reduction was 15 million t during the past 65 years. The scenario we created using the SWAT simulation showed that both forest and grassland reduced water yield, while bare land increased water yield by 10%. In addition, grassland and forest reduced soil erosion by 20% and 18%, respectively, while bare land increased sand production by 210%. The EPIC model results exhibited a negative correlation between the potential for vegetation biomass and erosion intensity. The average annual potential biomass of forest and grass under micro-erosion was 585.7 kg/ha and 485.9 kg/ha, respectively, and was 297.9 kg/ha and 154.6 kg/ha, respectively, under the extremely strong erosion. The results of this study add to the body of information regarding how soil erosion and vegetation biomass interact with each other. The proposed coupled SWAT-EPIC strategy may provide a way for further investigating the quantitative relationship between soil erosion and vegetation cover.

Integrating ACPF and SWAT to Assess Potential Phosphorus Loading Reductions to Lake Erie: A Case Study.

Lake Erie is threatened by eutrophication and harmful algal blooms due to excess nutrient loading from agricultural sources. To reduce nutrient loading to Lake Erie, widespread adoption of agricultural conservation practices (ACPs) has been proposed. However, identifying appropriate and effective locations for ACP placement has been challenging. Another challenge is understanding how effective the ACPs are in reducing nutrient loading and achieving water quality goals. Therefore, identifying the most effective ACPs, as well as spatially optimal placement of ACPs to achieve the maximum environmental benefit, is of paramount importance. The main objective of this study was to integrate the Agricultural Conservation Planning Framework (ACPF) with the Soil and Water Assessment Tool (SWAT) to assess the potential effectiveness of ACPs developed by ACPF in reducing phosphorous losses from an agriculturally dominated small watershed within the Western Lake Erie Basin. ACPF was used to develop a series of ACP opportunity plans, which were then integrated into a calibrated SWAT model. SWAT simulation of ACPF developed ACP opportunity plans for grassed waterways (GWs), contour buffer strips (CBSs), water and sediment control basins (WASCOBs), nutrient removal wetlands (NRWs), and farm ponds (FPs) revealed various reductions in sediment, soluble reactive phosphorus (SRP), and total phosphorus (TP) at the watershed-scale. The simulation of individual ACP opportunity plans revealed that GW resulted in the greatest annual average SRP and TP reductions (19% and 30%, respectively), followed by CBS (16% and 22%), and WASCOB (13% and 16%); NRWs were the most effective at reducing sediment (56%) but increased SRP (27%). Combined GW, CBS, and WASCOB opportunity plans resulted in the greatest reduction of SRP (34%), while the combination of all ACP opportunity plans resulted in the greatest reduction of TP (49%) and sediment (78%).

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.

Improvement of evapotranspiration estimates for grasslands in the southern Great Plains: Comparing a biophysical model (SWAT) and remote sensing (MODIS)

Study regionMixed- and tall-grass prairies in the southern Great Plains, USA Study focusEstimates of evapotranspiration (ET) are widely available from remote sensing and commonly used for water management. However, this approach is limited by prolonged satellite revisit periods and prominent algorithm-induced bias. We compared ET estimates from MODIS (Moderate Resolution Imaging Spectroradiometer) products and those based on the biophysically-based Soil and Water Assessment Tool (SWAT) to measurements using the eddy covariance (EC) technique for a subhumid, tall-grass prairie site near Stillwater, Oklahoma (OK) and a semiarid, mixed-grass prairie site near Clinton, OK in the southern Great Plains, USA. New hydrological insights for the regionSWAT and MODIS produced ET estimates closer to EC measurements for the tall-grass prairie (calibration site) than for the mixed-grass prairie (validation site), with a better performance from SWAT. For the tall-grass prairie, the R2 values were relatively high and comparable (0.77 and 0.87), and the biases were relatively small (−0.40% and 5.04%) for the SWAT and MODIS comparisons to EC. SWAT performed much better than MODIS for the mixed-grass prairie with R2 values of 0.68 vs. 0.13 and bias of − 1.87% vs. − 45.71%, respectively. The SWAT simulation also reproduced better estimates of aboveground net primary productivity than the MODIS products. This study suggests that site-specific SWAT simulations can produce better ET estimates than MODIS products, especially in the water-limited mixed-grass prairie.

Modeling Non-Point Source Nutrient Loads with Different Cropping Systems in an Agricultural Lake Watershed in Southwestern China: From Field to Watershed Scale

Understanding the influence of cropping systems on non-point source pollution (NPSP) is crucial, since NPSP has become the major nutrient source of lake eutrophication. How to identify the characteristics of the N and P balance at different spatial and temporal scales remains a challenge in pollution control and decision-making. In this study, we built a soil and water assessment tool (SWAT) model coupled with an export coefficient model for a NPSP simulation in the North of Erhai Lake Basin (NELB). A method was proposed to study the N and P transport from fields and the individual sub-basins to Erhai Lake using SWAT simulation. The results showed that the N and P loss fields were mainly situated in the vicinity of the Fengyu river and along the mainstream of the Miju and Mici rivers. N and P loss fields were mainly occupied by rice–broad bean/rice–rapeseed crops and vegetables. While the critical N and P load contribution areas were situated in the vicinity of downstream of the Miju, Yong’an, and Luoshi rivers. The effects of different cropping systems on the N and P export to the watershed were insignificant in the NELB and decreased by 4–9% when changing cropping system compared to the original crops. The NPSP discharged from the critical areas was retained and purified by the flow and the reservoirs scattered along the rivers, and it was noticed that the N and P loss was mainly from the critical pollution discharge areas located downstream of Miju river. This study can provide an important simulation method for understanding NPSPs and, therefore, can help authorities improve agricultural land use and reduce lake pollution.

Hydrological evaluation of gridded climate datasets in a texas urban watershed using soil and water assessment tool and artificial neural network

Precipitation is a vital component of the hydrologic cycle, and successful hydrological modeling largely depends on the quality of precipitation input. Gridded precipitation datasets are gaining popularity as a convenient alternative for hydrological modeling. However, many of the gridded precipitation data have not been adequately assessed across a range of conditions. This study compared three gridded precipitation datasets, Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), and Parameter-elevation Relationships on Independent Slopes Model (PRISM). This study used the conventional gauge observation as reference data and evaluated the suitability of the three sources of gridded rainfall data to drive rainfall-runoff simulations. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) were used to create daily streamflow simulations in the Leon Creek Watershed (LCW) in San Antonio, Texas, with the TRMM, CFSR, PRISM, and gauge rainfall data used as inputs. A direct comparison of the gridded data sources showed that the TRMM data underestimates the volume of rainfall, while PRISM data most closely matches the volume of rainfall when compared to the gauge rainfall observations. The hydrological simulation results showed that the PRISM and TRMM rainfall data driven models had preferable results to the CFSR and gauge driven models, in terms of both graphical comparison and goodness-of-fit indicator values. Additionally, no significant discrepancy was found between SWAT and ANN simulation results when the same precipitation data source was used, while SWAT and ANN simulation results varied in an identical pattern when different precipitation data sources were applied.

Using SWAT Model to Assess the Impacts of Land Use and Climate Changes on Flood in the Upper Weihe River, China

Flood disasters have occurred frequently in recent years, but there is no consensus on the mechanism and influencing factors. Taking the upper reaches of Weihe River Basin as a case in Western China, a soil and water assessment tool (SWAT) model was established to quantitatively simulate the impact of land use and climate change on runoff changes, while 4 extreme land-use scenarios and 24 temperature and precipitation scenarios assumptions were proposed to simulate the response of runoff to land use and climate changes. The SWAT simulation results showed that the sensitivity parameters affecting the model simulation were the CANMX, CN2, SOL_K, CH_N2, and SOL_AWC. The correlation index R2 and the efficiency coefficient ENS of the upper Weihe River were both in the range of 0.75–0.78, the relative error PS between the simulated results and the measured runoff was below 10%, suggesting the good applicability of the SWAT model in this study area. Using the improved SWAT model to simulate the peak runoff (flood) simulation value is generally smaller than the measured value, and the absolute value of the error is less than 6%. The expansion of wasteland increased the runoff by over 90% on average, the expansion of cultivated land increased the runoff by 8% on average, and the expansion of woodland and grassland increased the surface runoff by 6% on average. When the precipitation decreased by 25% and the temperature increased by 22%, the smallest runoff was obtained in the simulation. Accordingly, when the precipitation increased by 25% and the temperature decreased by 22%, the maximum annual runoff was obtained. By decomposing the contribution rate of human activities and climate change to runoff, it showed that the contribution rate of human activities to the reduction of runoff was greater than that of climate change. This study can provide scientific reference for the simulation and prediction of future floods.

Impacts and Implications of Land Use Land Cover Dynamics on Groundwater Recharge and Surface Runoff in East African Watershed

Assessing the spatiotemporal dynamics of land use land cover (LULC) change on water resources is vital for watershed sustainability and developing proper management strategies. Evaluating LULC scenarios synergistically with hydrologic modeling affords substantial evidence of factors that govern hydrologic processes. Hence, this study assessed the spatiotemporal effects and implications of LULC dynamics on groundwater recharge and surface runoff in Gilgel Gibe, an East African watershed, using the Soil and Water Assessment Tool (SWAT) model. Three different LULC maps (2000, 2010, and 2020) were derived from Landsat images, and the comparisons pointed out that the land-use pattern had changed significantly. The agricultural land and grassland cover increased by 3.76% and 1.36%, respectively, from 2000 to 2020. The implications acquired for 2000 show that forested land covers decreased by 5.49% in 2020. The SWAT simulation process was executed using a digital elevation model, soil, LULC, and weather data. The model was calibrated and validated using streamflow data to understand the surface runoff and groundwater recharge responses of each Hydrologic Response Units on reference simulation periods using the Calibration and Uncertainty Program (SWAT-CUP), Sequential Uncertainty Fitting (SUFI-2) algorithm. The observed and simulated streamflows were checked for performance indices of coefficient of determination (R2), Nash–Sutcliffe model efficiency (NSE), and percent bias (PBIAS) on monthly time steps. The results show that there is good agreement for all LULC simulations, both calibration and validation periods (R2 & NSE ≥ 0.84, −15 < PBIAS < +15). This reveals that for the LULC assessment of any hydrological modeling, the simulation of each reference period should be calibrated to have reasonable outputs. The study indicated that surface runoff has increased while groundwater decreased over the last two decades. The temporal variation revealed that the highest recharge and runoff occurred during the wet seasons. Thus, the study can support maximizing water management strategies and reducing adverse driving environmental forces.

Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)

Lakes are major surface water resource in semi-arid regions, providing water for agriculture and domestic use. Prediction of future water availability in lakes of semi-arid regions is important as they are highly sensitive to climate variability. This study is to examine the water level fluctuations in Pakhal Lake, Telangana, India using a combination of a process-based hydrological model and machine learning technique under climate change scenarios. Pakhal is an artificial lake built to meet the irrigation requirements of the region. Predictions of lake level can help with effective planning and management of water resources. In this study, an integrated approach is adopted to predict future water level fluctuations in Pakhal Lake in response to potential climate change. This study makes use of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset which contains 21 Global Climate Models (GCMs) at a resolution of 0.25 × 0.25° is used for the study. The Reliability Ensemble Averaging (REA) method is applied to the 21 models to create an ensemble model. The hydrological model outputs from Soil and Water Assessment Tool (SWAT) are used to develop the machine-learning based Support Vector Regression (ν-SVR) model for predicting future water levels in Pakhal Lake. The scores of the three metrics, correlation coefficient (R2), RMSE and MEA are 0.79, 0.018 m, and 0.13 m, respectively for the training period. The values for the validation periods are 0.72, 0.6, and 0.25 m, indicating that the model captures the observed lake water level trends satisfactorily. The SWAT simulation results showed a decrease in surface runoff in the Representative Concentration Pathways (RCP) 4.5 scenario and an increase in the RCP 8.5 scenario. Further, the results from ν-SVR model for the future time period indicate a decrease in future lake levels during crop growth seasons. This study aids in planning of necessary water management options for Pakhal Lake under climate change scenarios. With limited observed datasets, this study can be easily extended to the other lake systems.

Comparison of the shallow groundwater storage change estimated by a distributed hydrological model and GRACE satellite gravimetry in a well-irrigated plain of the Haihe River basin, China

Groundwater is a key component of the global hydrological cycle. Quantitative approaches to estimate the spatial and temporal changes in groundwater storage (GWS) could provide solid foundation for sustainable management. This study made a comprehensive comparison of GWS changes based on two independent approaches: the Soil and Water Assessment Tool (SWAT) hydrological model and Gravity Recovery and Climate Experiment (GRACE) satellite mission data. The feasibility of using these two estimation methods to quantify shallow groundwater variations in the Haihe River basin of China, a globally representative overexploited area, was assessed. The GRACE-derived GWS changes were calculated based on independent component analysis and a joint inversion method. The results showed that the GRACE-estimated shallow GWS anomalies during 2003–2012 agree well with the SWAT-simulated data. For the variations in the long-term GWS depletion rate, we found that heterogeneity at 22 subbasin (approximately 500 to 1500 km2) scales was detected by the GRACE data, and these results were spatially coherent with the SWAT simulations based on the distributed modeling units and multiple inputs. The GRACE estimates could adequately replicate the general trends of GWS depletion in the winter wheat (Triticum aestivum L.) growth period and recovery in the summer maize (Zea mays L.) growth period. Specifically, comparisons between GRACE-based and SWAT-simulated results indicated that the signals of pumping for winter wheat irrigation in several key water requirement periods in April, May and November were identified by GRACE, as were seasonal changes in shallow GWS (e.g., an obvious depletion in spring). Despite an overestimation of the scope of the fluctuations in GRACE estimates compared with those of SWAT simulations from the perspective of the water flux, the effective identification of the impact of anthropogenic exploitation on shallow GWS in GRACE suggests that this monitoring tool could be applied in the groundwater management of this plain. This study demonstrated a good agreement between SWAT-simulated and GRACE-based shallow GWS changes from the perspectives of long-term trends and variations in crop seasons. Additionally, satisfactory consistency was observed from the perspective of the water flux in this semiarid and semihumid study region considering the influence of human activities.

Crop improvement influences on water quantity and quality processes in an agricultural watershed

Field crop traits have and are experiencing significant changes due to genetic and agronomic improvements. How these changes affect regional water quantity and quality processes has not been clarified. The St. Joseph River Watershed (SJRW) located in the U.S. Corn Belt was selected as a case study area. Crop (corn and soybean) trait improvements in the past decades were reviewed and summarized and include changes of growing degree days (GDD), leaf area index (LAI), light utilization (LU), drought tolerance (DT), nutrient content (NC), and harvest index (HI). Based on a calibrated 9-year (from 2011 to 2019) SWAT (Soil and Water Assessment Tool) simulation in SJRW, sensitivities of the above crop traits to yield, ETa, stream flow, tile flow, surface runoff, and nutrient loads (NO3N, TN, soluble-P, and TP) were analyzed. Crop traits and their corresponding SWAT parameters for the 2010s were obtained from model calibration and used as the baseline/current scenario; for the 1980s, they were summarized from literature review and used as an historical scenario, while those for the 2040s were determined by assuming crop traits are changing linearly with time and projected as the future scenario. Water quantity and quality changes under the historical and future crop scenarios were compared with the baseline/current simulation. Results showed LU and DT were the most sensitive crop traits to water quantity (i.e., ETa, stream flow, tile flow, and surface runoff), while HI was the most sensitive to nutrient loads. The impacts of crop improvements on nutrient loads were more significant than on water budgets. Compared with the baseline, the historical and future scenarios resulted in 1.5 − 2.0% changes of stream flow, 6.8 − 18.6% changes of nitrogen loads (NO3N and TN) and 2.6 − 3.9% changes of phosphorus loads (soluble-P, and TP) in the stream flow, annually. Moreover, in certain months, these changes can reach about 12% for stream flow, 42% for nitrogen loads, and 12% for phosphorus loads. Nitrogen losses by tile drainage and percolation, and phosphorus losses by surface runoff and tile drainage were most significantly affected by the crop improvements. Future work should consider expected crop improvements when studying long-term hydrology and nutrient cycles in agricultural watersheds.

Using a regionalisation approach to evaluate streamflow simulated by an ecohydrological model calibrated with global land surface evaporation from remote sensing

Study regionWest Africa. Study focusThe objective of this study was to quantitively evaluate streamflow previously simulated by the Soil and Water Assessment Tool (SWAT) model in an ungauged basin (Ogun River) that was calibrated/validated using GLEAM (v3.0a) AET data (Odusanya et al., 2019). To evaluate this simulated streamflow in the ungauged Ogun River Basin, a gauged basin (Ouémé River) was selected based on proximity and on similar catchment attributes. Firstly, the non-unique parameter sets from the SWAT set-up in the ungauged Ogun River Basin were transferred to SWAT that was setup in the neighbouring gauged Ouémé River Basin (referred to as the regionalisation method). Secondly, the SWAT model in the Ouémé River Basin was setup by calibrating the model using the observed streamflow data (referred to as the conventional method). New hydrological insights for the regionIn the gauged Ouémé River Basin, using the conventional method, a higher streamflow performance was obtained for all non-unique SWAT simulations, compared with the regionalisation method. However, a satisfactory streamflow performance was obtained with the regionalisation method in the south of Ouémé River Basin (Save gauge: highest NSE = 0.79, R2 = 0.81, PBIAS = 2.1, KGE = 0.85) and at the Ouémé River Basin outlet (Bonou gauge: highest NSE = 0.79, R2 = 0.84, PBIAS = −4.5, KGE = 0.84) that had the most similar attributes to the Ogun River Basin. The study findings show that using satellite-based AET data to calibrate a hydrological model can lead to an acceptable model performance in ungauged basins. The regionalisation method was useful for assessing the simulated streamflow in the ungauged basin, especially when catchment similarity is ensured, and parameter non-uniqueness is applied. This research contributes to advancement in alternative means of modelling and evaluation of hydrologic model simulations in an ungauged basin.

Hydrological Response of the Wami–Ruvu Basin to Land-Use and Land-Cover Changes and Its Impacts for the Future

The evaluation of the hydrological responses of river basins to land-use and land-cover (LULC) changes is crucial for sustaining water resources. We assessed the impact of LULC changes (1990–2018) on three hydrological components (water yield (WYLD), evapotranspiration (ET), and sediment yield (SYLD)) of the Wami–Ruvu Basin (WRB) in Tanzania, using the Soil and Water Assessment Tool (SWAT). The 1990 LULC imagery was used for SWAT simulation, and imagery from 2000, 2010, and 2018 was used for comparison with modelled hydrological parameters. The model was calibrated (1993–2008) and validated (2009–2018) in the SWAT-CUP after allowing three years (1990–1992) for the warm-up period. The results showed a decrease in WYLD (3.11 mm) and an increase in ET (29.71 mm) and SYLD (from 0.12 t/h to 1.5 t/h). The impact of LULC changes on WYLD, ET, and SYLD showed that the increase in agriculture and built-up areas and bushland, and the contraction of forest led to the hydrological instability of the WRB. These results were further assessed with climatic factors, which revealed a decrease in precipitation and an increase in temperature by 1 °C. This situation seems to look more adverse in the future, based on the LULC of the year 2036 as predicted by the CA–Markov model. Our study calls for urgent intervention by re-planning LULC and re-assessing hydrological changes timely.

Evaluating the Hydrologic and Water Quality Impacts of the Revised SCS CN Method and its Implications on Decision-Making

Highlights The revised CN equation uses decreased CN values, causing decreased initial abstraction values and increased runoff. SWAT’s water balance approach simulated increased runoff to decrease groundwater flow and associated baseflow with the revised SCS CN method. Landscape analysis showed minor changes in runoff and nutrient loadings, except in row cropping areas in high runoff potential soils. Reduced mitigation occurs if post-development runoff increases by a greater margin than the pre-development runoff. Abstract. The Soil Conservation Service (SCS) Curve Number (CN) method is a widely used model developed by the Natural Resources Conservation Service (NRCS) that estimates surface runoff generated from a land area using factors such as land cover, soil type, and precipitation depth. However, because this empirical model was developed over 60 years ago with limited data, the NRCS is considering revising it with the help of an ASCE-ASABE task group. The proposed revisions include updating the initial abstraction (Ia) ratio in the CN equation and modifying the curve number (CN) for all land use and soil hydrologic group combinations. The proposed revisions are expected to alter the hydrologic response estimation, especially for low flow events. Since surface runoff is driving sediment, nutrients, and the transport of chemicals in natural systems, this study aims to understand how predicted hydrologic and water quality variables vary between the original and the proposed revised versions of the SCS CN method using the Soil and Water Assessment Tool (SWAT). Additionally, a case study site was selected to understand how the revisions alter the design storm runoff analysis. Results from the simulation case study on a 36,900 ha mixed land-use watershed located in central Pennsylvania, USA indicate a 5% to 14% increase in annual runoff predictions with the revised equation compared to the original equation. Though most average monthly runoff depths increased by 1% to 25% using the revised equation, winter months generally simulated runoff depths that were 1% to 5% lower than the original equation. Average daily loadings at the watershed outlet increased by 11% to 19% and 4% to 11% for nitrate and mineral phosphorus, respectively. The SWAT simulation results suggest that current agricultural and urban best management practices (BMPs) will experience higher runoff volumes on a yearly time scale due to the decrease in the Ia term, while the design storm case study results suggest that urban BMPs can be smaller in size when rural lands are urbanized due to the reduction in runoff estimation from the initial land use with the revised equation. Additional detailed evaluations of the proposed SCS CN method are required for water quality simulations, stormwater management, and BMP designs. Keywords: Hydrology, Revised curve number method, SCS Curve Number Method, Soil and Water Assessment Tool, Water quality.

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.

Long-term water quality assessments under changing land use in a large semi-arid catchment in South Africa

Increasing nutrient loads from land use and land cover (LULC) change degrade water quality through eutrophication of aquatic ecosystems globally. The Vaal River Catchment in South Africa is an agriculturally and economically important area where eutrophication has been a problem for decades. Effective mitigation strategies of eutrophication in this region require an understanding of the relationship between LULC change and water quality. This study assessed the long-term impacts of LULC changes on nitrate (NO3-N) and orthophosphate (PO4-P) pollution in the lower Vaal River Catchment between 1980 and 2018. Multi-year LULC was mapped from Landsat imagery and changes were determined. Long-term trends in NO3-N and PO4-P loads and concentrations in river water samples were analysed, while multi-year LULC data were ingested into the Soil and Water Assessment Tool (SWAT) to simulate the impacts of LULC changes in NO3-N and PO4-P loads. Main LULC changes included an increase in the irrigated area by 262% and in built-up area by 33%. This occurred at the expense of cultivated dryland fields and rangelands. In situ data analysis showed that at the catchment inlet, PO4-P concentration and loads significantly increased, while NO3-N concentration and loads decreased between 1980 and 2018. At the catchment outlet, only PO4-P loads increased, while NO3-N loads and concentrations remained the same. SWAT simulations at the Hydrologic Response Unit scale showed that irrigated land was the largest contributor to NO3-N leaching per ha. Aggregation of nutrient loads by LULC type showed increased nutrient loads from irrigated and built-up areas over time, while loads from dryland areas decreased. At catchment scale, dryland remained an important contributor of the annual nutrient loads total because of its large area. In future, research efforts should focus on crop management practices to reduce nutrient loads.

Impacts of forestation on the annual and seasonal water balance of a tropical catchment under climate change

BackgroundThis study aims to assess the effects of a forestation program and climate change on the annual and seasonal water balance of the Bogowonto catchment (597 km2) in Java, Indonesia. The catchment study is rare example in Indonesia where forestation has been applied at the catchment level. However, since the forestation program has been initiated, evaluations of the program only focus on the planting area targets, while the environmental success e.g., impacts on the hydrological processes have never been assessed. This study used a calibrated Soil and Water Assessment Tool (SWAT) model to diagnose the isolated and combined effects of forestation and climate change on five water balance components, namely streamflow (Q), evapotranspiration (ET), surface runoff (Qs), lateral flow (Ql) and base flow (Qb).ResultsThe results show that from 2006 to 2019, forest cover has increased from 2.7% to 12.8% of the total area, while in the same period there was an increase in the mean annual and seasonal temperature, rainfall, and streamflow. Results of SWAT simulations show that changes in the mean annual and seasonal water balance under the forestation only scenario were relatively minor, while changes were more pronounced under the climate change only scenario. Based on the combined impacts scenario, it was observed that the effects of a larger forest area on the water balance were smaller than the effects of climate change.ConclusionsAlthough we found that forestation program has minor impacts compared to that of climate change on the hydrological processes in the Bogowonto catchment, seasonally, forestation activity has decreased the streamflow and surface runoff during the wet season which may reduce the risk of moderate floods. However, much attention should be paid to the way how forestation may result in severe drought events during the dry season. Finally, we urge the importance of accounting for the positive and negative effects in future forestation programs.

Evaluation of Evapotranspiration in Brazilian Cerrado Biome Simulated with the SWAT Model

Evapotranspiration represents a significant part on the water balance and, thus, the correct evaluation of this hydrological parcel is relevant when modeling a watershed. The objective of this work is to evaluate the Soil and Water Assessment Tool (SWAT) model’s capability in adequately simulating evapotranspiration in a watershed with predominance of the Brazilian Cerrado biome. Hydrological modeling of the Gama watershed located in the Federal District, which has 57.5% of its total area covered by pristine Cerrado, was conducted. Hydrometeorological and turbulent flow variables have been monitored in weather station and Eddy Covariance (EC) tower, respectively. SWAT simulations were performed for potential evapotranspiration methods: Hargreaves (H), Priestley–Taylor (PT) and Penman–Monteith (PM). Modified versions of SWAT for estimating actual (ET) by Strauch and Volk (2013) (SV) and Arroio Junior (2016) (AR) were also tested. The calibration and verification of the SWAT model, in terms of daily flow, were carried out using a Particle Swarm Optimization algorithm, and fair results were obtained with all the methods evaluated. The actual evapotranspiration (ET) simulated with SWAT (ETsim) using the PM, PT, H, SV and AR methods for a Cerrado hydrological response unit (HRU) were evaluated and compared with the ET obtained using the turbulent flow (Eddy Covariance) method (ETobs). Comparing ETobs and ETsim results, the PM method showed the best fitness and the H and PT methods showed better fit for the dry and the rainy periods, respectively. Although representing an advance on ET modeling, the SV and AR modifications did not improve the response in terms of simulation of the studied area.