Evaluation of the impact of hydrological changes on reservoir water management: A comparative analysis the CanESM5 model and the optimized SWAT-SVR-LSTM.

This research examines the impacts of climate change and socio-economic variables on the hydrological cycle, reservoir water management, and hydropower capacity at the Gezhouba Dam. The Gezhouba Dam serves as a crucial hydroelectric power station and dam, playing a vital role in regulating river flow and generating electricity. In this study, an innovative method is employed, combining the Soil and Water Assessment Tool (SWAT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) models. This model is optimized using the Developed Thermal Exchange Optimizer. This optimized combined model significantly enhances the reliability and precision of the forecasting inflow and reservoir levels. By utilizing the Canadian Earth System Model version 5 (CanESM5), we examine climate variables across various scenarios of Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP). Under the SSP5-RCP8.5 scenario, the most aggressive in terms of emissions, we project a temperature rise of 2.6 % and a precipitation decrease of 2.7 %. This scenario leads to the most substantial changes in the hydrological cycle and altered river flow patterns. The results show a direct correlation between precipitation and inflow (0.952) and a strong inverse correlation between temperature and inflow (0.893). The study predicts significant decreases in all flow metrics, with mean high flow (Q5) periods affecting hydropower generation, especially under the SSP5-RCP8.5 scenario. Additionally, the filling frequency rate (FFR) and mean filling level (MFL) are projected to decrease, with a more pronounced decline in the far future, indicating a potential compromise of the reservoir's water storage and power generation capabilities, especially under the SSP5-RCP8.5 scenario.

Wetland-mediated nitrate reductions attenuate downstream: Insights from a modeling study.

Connections between agricultural runoff and excess nitrogen in the Upper Mississippi River Basin are well-documented, as is the potential role of constructed wetlands in mitigating this surplus nitrogen. However, limited knowledge exists about the "best" placement of these wetlands for downstream nitrogen reductions within a whole watershed context as well as how far downstream these benefits are realized. In this study, we simulate the cumulative impacts of diverse wetland restoration scenarios on downstream nitrate reductions in different subbasins of the Raccoon River Watershed, Iowa, USA, and spatially trace their relative effects downstream. Our simulated results underscore previous work demonstrating that the total area of wetlands and the wetland-catchment-to-wetland area ratio are both significant factors for determining the nitrate load reduction benefits of wetlands at subbasin scales. Simulated wetland conservation scenarios resulted in nitrate load decreases ranging from 7.5 to 43.2% of our baseline model loads. However, we found these wetland-mediated nitrate reduction benefits are quickly attenuated downstream: load reductions were <1% at the watershed outlet across all model scenarios, despite the magnitude of the subbasin-scale nitrate decreases. The relatively rapid attenuation of wetland effects is largely due to downstream nitrate load contributions from untreated subbasins. However, higher subbasin-scale nitrate reductions from wetland-based conservation practices resulted in longer downstream distances prior to attenuation. This study highlights the importance of considering the spatial location of constructed or restored wetlands relative to the area within the watershed where nitrogen reductions are most needed.

How atmospheric deposition contribute to watershed heavy metals contamination in coastal watersheds in China: A case study of Laizhou Bay.

Atmospheric deposition is a significant source of heavy metal (HM) pollution. In order to understand the migration and transformation process of atmospheric HMs within the watershed and quantify the amount transported offshore by rivers, the Soil and Water Assessment Tool (SWAT) was developed to trace the migration of HMs from atmospheric deposition. The model simulates HMs in three forms: dissolved, adsorbed, and granular. It quantifies the movements of Cd, Cr, Cu, Hg, Pb, and Zn from atmospheric deposition into the sea via rivers in five coastal watersheds in East China and analyzes the effects of meteorological factors, vegetation cover, and slope on non-point pollution of these metals by Spearman correlation analysis. The results showed that the annual flux of HMs from atmospheric deposition to the sea through rivers accounted for 5 %-69 % of the total rivers flux. Among meteorological factors, precipitation demonstrated the strongest correlation with the monthly loads of HMs entering rivers from atmospheric deposition. Additionally, HMs loads entering rivers from atmospheric deposition were more closely related to vegetation cover than topographic slope. This model provides a new approach to distinguishing the flux of atmospheric HMs entering offshore waters through rivers. The findings will deepen our understanding of the migration and transformation of HMs from atmospheric deposition, enhance the ability to control offshore HMs pollution, and reduce the ecological risks associated by HMs.

Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland.

Modeling nitrate fate and transport in water sources is an essential component of predictive water quality management. Both mechanistic and data-driven models are currently in use. Mechanistic models, such as SWAT, simulate daily nitrate loads based on the results of simulating water flow. Data-driven models allow one to simulate nitrate loads and water flow independently. Performance of SWAT and deep learning model was evaluated in cases when deep learning model is used in (a) independent simulations of flow series and nitrate concentration series, and (b) in both flow rate and concentration simulations to obtain nitrate load values. The data were collected at the Tuckahoe Creek watershed in Maryland, United States. The data-driven deep learning model was built using long-short-term-memory (LSTM) and three-dimensional convolutional networks (3D Convolutional Networks) to simulate flow rate and nitrate concentration using weather data and imagery to derive leaf area index according to land use. Models were calibrated with data over training period 2014-2017 and validated with data over testing period. SWAT Nash-Sutcliffe efficiency (NSE) was 0.31 and 0.40 for flow rate and -0.26 and -0.18 for the nitrate load rate over training and testing periods, respectively. Three data-driven modeling scenarios were implemented: (1) using the observed flow rate and simulated nitrate concentration, (2) using the simulated flow rate and observed nitrate concentration, and (3) using the simulated flow rate and nitrate concentration. The deep learning model performed better than SWAT in all three scenarios with NSE from 0.49 to 0.58 for training and from 0.28 to 0.80 for testing periods with scenario 1 showing the best results. The difference in performance was most pronounced in fall and winter seasons. The deep learning modeling can be an efficient alternative to mechanistic watershed-scale water quality models provided the regular high-frequency data collection is implemented.

Altitude characteristics in the response of rain-on-snow flood risk to future climate change in a high-latitude water tower.

Global warming is profoundly impacting snowmelt runoff processes in seasonal freeze-thaw zones, thereby altering the risk of rain-on-snow (ROS) floods. These changes not only affect the frequency of floods but also alter the allocation of water resources, which has implications for agriculture and other key economic sectors. While these risks present a significant threat to our lives and economies, the risk of ROS floods triggered by climate change has not received the attention it deserves. Therefore, we chose Changbai Mountain, a water tower in a high-latitude cold zone, as a typical study area. The semi-distributed hydrological model SWAT is coupled with CMIP6 meteorological data, and four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) are selected after bias correction, thus quantifying the impacts of climate change on hydrological processes in the Changbai Mountain region as well as future evolution of the ROS flood risk. The results indicate that: (1) Under future climate change scenarios, snowmelt in most areas of the Changbai Mountains decreases. The annual average snowmelt under SSP126, SSP245, SSP370, and SSP585 is projected to be 148.65 mm, 135.63 mm, 123.44 mm, and 116.5 mm, respectively. The onset of snowmelt is projected to advance in the future. Specifically, in the Songhua River (SR) and Yalu River (YR) regions, the start of snowmelt is expected to advance by 1-11 days. Spatially, significant reductions in snowmelt were observed in both the central part of the watershed and the lower reaches of the river under SSP585 scenario. (2) In 2021-2060, the frequency of ROS floods decreases sequentially for different scenarios, with SSP 126 > SSP 245 > SSP 370 > SSP 585. The frequency increments of ROS floods in the source area for the four scenarios were 0.12 days/year, 0.1 d/yr, 0.13 days/year, and 0.15 days/year, respectively. The frequency of high-elevation ROS events increases in the YR in the low emission scenario. Conversely, in high emission scenarios, YR high-elevation ROS events will only increase in 2061-2100. This phenomenon is more pronounced in the Tumen River (TR), where floods become more frequent with increasing elevation.

Identifying and assessing pond best management practice under future climate scenarios.

Climate change can play important roles in the hydrological processes within watershed with ponds as the Best Management Practice (BMP). Unlike several other studies, this study integrated remote sensing technique with hydrological model to identify and simulate pond BMP. Limited studies have been carried out to evaluate pond BMP in relation to the climate change impacts on hydrology and water quality particularly in Mississippi watersheds. The objective of this study was to classify ponds on satellite imagery within the Big Sunflower River Watershed (BSRW) using Google Earth Engine (GEE) and incorporate this data with Soil and Water Assessment Tool (SWAT) model to evaluate future hydrological and water quality outputs. The SWAT model was calibrated and validated against streamflow (R2 and NSE values from 0.81 to 0.56) and sediment (R2 and NSE values from 0.91 to 0.40). Future climate data for the mid (2040-2060) and late (2079-2099) centuries were utilized to create climate change scenarios (e.g., RCP 4.5 and 8.5). Results of this study projected that the average annual flow and sediment load will increase by 26-46%, and 107-150% respectively by the late century compared to the baseline period (2002-2021). However, the projected sediment load with modified pond BMP data used in the SWAT model could decrease average annual sediment load by 44-46% under both RCP scenarios. Seasonal data analysis determined that spring, summer, and fall sediment loads were projected to decrease up to 42%, 52%, and 46% respectively under both RCP scenarios due to pond BMP. This study can be useful for the development of climate-smart management strategies in agricultural watersheds.

Patterns of nitrate load variability under surface water-groundwater interactions in agriculturally intensive valley watersheds.

Nitrate pollution is a significant environmental issue closely related to human activities, complicated hydrological interactions and nitrate fate in the valley watershed strongly affects nitrate load in hydrological systems. In this study, a nitrate reactive transport model by coupling SWAT-MODFLOW-RT3D between surface water and groundwater interactions at the watershed scale was developed, which was used to reproduce the interaction between surface water and groundwater in the basin from 2016 to 2019 and to reveal the nitrogen transformation process and the evolving trend of nitrate load within the hydrological system of the valley watershed. The results showed that the basin exhibited groundwater recharge to surface water in 2016-2019, particularly in the northwestern and northeastern mountainous regions of the valley watershed and the southern Beishan Reservoir vicinity. Groundwater recharge to surface water declined by 20.17 % from 2016 to 2019 due to precipitation. Nitrate loads in the hydrologic system of the watershed are primarily derived from human activities (including fertilizer application from agricultural activities and residential wastewater discharges) and the nitrogen cycle. Nitrate loads in surface water declined 16.05 % from 2016 to 2019. Nitrate levels are higher in agricultural farming and residential areas on the eastern and northern sides of the watershed. Additionally, hydrological interactions are usually accompanied by material accumulation and environmental changes. Nitrate levels tend to rise with converging water flows, a process that becomes more pronounced during precipitation events and cropping seasons in agriculturally intensive valley watersheds. However, environmental changes alter nitrogen transformation processes. Nitrogen fixation, nitrification, and ammonification intensify nitrogen inputs during river pooling, enhancing nitrogen cycling fluxes and elevating nitrate loads. These processes are further enhanced during groundwater recharge to surface water, leading to evaluated nitrate load. Enhanced denitrification, dissimilatory nitrate reduction to ammonium (DNRA), anaerobic ammonia oxidation, and assimilation promote the nitrogen export from the system and reduce the nitrate load during surface water recharge to groundwater.

Assessment of sediment yield and accumulation in reservoir: The case of Gibe One Reservoir, Southwestern Ethiopia.

Soil erosion and sediment buildup are the factors that speed up the decline in capacity and function of reservoirs, agricultural products, and water resources. In order to simulate sediment and runoff and map high sediment-yielding sub-basins in the Gibe Gojeb catchment in southwest Ethiopia, this study used the Soil and Water Assessment Tool (SWAT) model. Using data on sediment and river flow, calibration and validation were carried out. Between 2003 and 2016, the catchment produced an average annual sediment loading of 62.5 tons ha-1 yr-1, with loading fluctuations ranging from 0.2 to 108.4 tons ha-1 yr-1. The acceptable sediment yield threshold value ranges from 12.3 to 108.4 tons ha-1 yr-1 for 56 sub-basins, and from 0.2 to 10 tons ha-1 yr-1 for 5 sub-basins. The most significant sub-basins with very high to extremely severe sediment yields were sub-basins 1 to 30, 32 to 44, 47, 48, 50, 51, and 53 to 61. After thirteen years of operation, the yearly amount of 58,802 tons of sediment transferred from the catchment and deposited into Gibe One reservoir has decreased the capacity by 5.7 %. The accumulation of sediment in a reservoir has an impact on its functionality, power production, and capacity, affecting the safety of dams and the environment. The study's findings enhanced our comprehension of sediment accumulation in reservoirs and furnished us with the necessary information regarding reservoir safety, integrated soil, and water management.

A methodological framework for assessing sea level rise impacts on nitrate loading in coastal agricultural watersheds using SWAT+: A case study of the Tar-Pamlico River basin, North Carolina, USA.

This study addresses the urgent need to understand the impacts of climate change on coastal ecosystems by demonstrating how to use the SWAT+ model to assess the effects of sea level rise (SLR) on agricultural nitrate export in a coastal watershed. Our framework for incorporating SLR in the SWAT+ model includes: (1) reclassifying current land uses to water for areas with elevations below 0.3 m based on SLR projections for mid-century; (2) creating new SLR-influenced land uses, SLR-influenced crop database, and hydrological response units for areas with elevations below 2.4 m; and (3) adjusting SWAT+ parameters for the SLR-influenced areas to simulate the effects of saltwater intrusion on processes such as plant yield and denitrification. We demonstrate this approach in the Tar-Pamlico River basin, a coastal watershed in eastern North Carolina, USA. We calibrated the model for monthly nitrate load at Washington, NC, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.61. Our findings show that SLR substantially alters nitrate delivery to the estuary, with increased nitrate loads observed in all seasons. Higher load increases were noted in winter and spring due to elevated flows, while higher percentage increases occurred in summer and fall, attributed to reduced plant uptake and disrupted nitrogen cycle transformations. Overall, we observed an increase in mean annual nitrate loads from 155,000 kg NO3-N under baseline conditions to 157,000 kg NO3-N under SLR scenarios, confirmed by a statistically significant paired t-test (p = 2.16 × 10-10). This pioneering framework sets the stage for more sophisticated and accurate modeling of SLR impacts in diverse hydrological scenarios, offering a vital tool for hydrological modelers.

A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time-consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data-driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short-Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5-year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub-basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT-LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling. PRACTITIONER POINTS: We aimed to enhance process-based models for watershed water-quality modeling. The Soil and Water Assessment Tool-Long Short-Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP. It exhibited superior predictive performance when applied to other sub-basins. The hybrid model will overcome the constraints of conventional modeling. It will also enable more effective and efficient modeling.

Response of hydrology and nutrient losses to different extreme rainfall conditions in a coastal watershed influenced by orchards.

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989-2018) and projected future periods (2041-2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO2, this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO3-N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses.

Evaluation of fish habitat suitability based on stream hydrodynamics and water quality using SWAT and HEC-RAS linked simulation.

The objective of this study was to evaluate fish habitat suitability by simulating hydrodynamic and water quality factors using SWAT and HEC-RAS linked simulation considering time-series analysis. A 2.9 km reach of the Bokha stream was selected for the habitat evaluation of Zacco platypus, with hydrodynamic and water quality simulations performed using the SWAT and HEC-RAS linked approach. Based on simulated 10-year data, the aquatic habitat was assessed using the weighted usable area (WUA), and minimum ecological streamflow was proposed from continuous above threshold (CAT) analysis. High water temperature was identified as the most influential habitat indicator, with its impact being particularly pronounced in shallow streamflow areas during hot summer seasons. The time-series analysis identified a 28% threshold of WUA/WUAmax, equivalent to a streamflow of 0.48 m3/s, as the minimum ecological streamflow necessary to mitigate the impact of rising water temperatures. The proposed habitat modeling method, linking watershed-stream models, could serve as a useful tool for ecological stream management.

A deep learning-based model for estimating pollution fluxes from rivers into the sea and its optimization.

Pollution fluxes from rivers into the sea are currently the main source of pollutants in nearshore areas. Based on the source-sink process of the basin-estuary-coastal waters system, the pollution fluxes into the sea and their spatiotemporal heterogeneity were estimated. A deep learning-based model was established to simplify the estimation of pollution fluxes into the sea, with socio-economic drivers and meteorological data as input variables. A method for estimating the contribution rate of pollution fluxes from different spatial gradient was proposed. In this study, we found that (1) the pollution fluxes into the sea of total nitrogen (TN) and total phosphorus (TP) from the Bohai Sea Rim Basin (BSRB) in 1980, 1990, 2000, 2010, and 2020 were 25.38 × 104, 26.12 × 104, 27.27 × 104, 29.82 × 104, 25.31 × 104 and 1.32 × 104, 2.14 × 104, 2.09 × 104, 1.87 × 104, 1.68 × 104 tons, respectively. (2) The proportion of rural life and livestock to the TN was the highest, accounting for 39.18 % and 21.19 %, respectively. The proportion of livestock to the TP was the highest, accounting for 39.20 %, followed by rural life, accounting for 24.72 %. The results indicated that the pollution fluxes in the BSRB were related to human economic activities and relevant environmental protection measures. (3) The deep learning-based model established to estimate runoff pollution fluxes into the sea had the accuracy of over 90 %. (4) As for contribution rate, in terms of the elevation, the range of 0-100 m had the highest proportion, accounting for 39.65 %. The range of 50-100 km from the coastline had the highest proportion, accounting for 18.11 %. In terms of the district, coastal area has the highest proportion, accounting for 38.00 %. This study revealed the changing trends and driving mechanisms of pollution fluxes into the sea over the past 40 years and established a simplified deep learning-based model for estimating pollution fluxes into the sea. Then, we identified regions with high pollution contribution rate. The results can provide scientific references for the adaptive management of the nearshore areas based on the ecosystem.

Spatiotemporal evolution of runoff and sediment and their dominant driving factors in the Lower Jinsha River basin.

The Jinsha River Basin (JRB) contributes a significant amount of sediment to the Yangtze River; however, an imbalance exists between runoff and sediment. The underlying mechanisms and primary factors driving this imbalance remain unclear. In this study, the Shapley Additive Explanation (SHAP) and Geographical Detector Model (GDM) were employed to quantify the importance of the driving factors for water yield (WYLD) and sediment yield (SYLD) using the Soil and Water Assessment Tool (SWAT) model in the JRB. The results indicated that the SWAT model performed well in simulating runoff and sediment, with R2 > 0.61 and NSE > 0.5. Based on the simulated data, SYLD exhibited strong spatiotemporal linkages with WYLD. Temporally, both sediment and runoff showed decreasing trends, with the sediment decrease being more pronounced. Spatially, WYLD and SYLD displayed similar distribution patterns, with low values in the southwest and high values in the northeast. By quantifying the driving factors, we found that climatic factors, including precipitation and potential evapotranspiration, were the main influencing factors for WYLD and SYLD across the entire region, though their contributions to the two variables differed. For WYLD, climatic factors accounted for 70 % of the total influencing factors, whereas their contribution to SYLD was 50 %. Furthermore, soil type and land-use type played significant roles in the SYLD, with importance values of 16 % and 12 %, respectively. Under the influence of surface conditions, the proportion of SYLD in the JRB to the total SYLD in the Yangtze River Basin was greater than that of WYLD. The findings of this study provide scientific evidence and technical support for local environmental impact assessments and the formulation of soil and water conservation plans.

Improving hydrological modeling to close the gap between elevated CO2 concentration and crop response: Implications for water resources.

Rising atmospheric carbon dioxide concentrations ([CO2]) affect crop growth and the associated hydrological cycle through physiological forcing, which is mainly regulated by reducing stomatal conductance (gs) and increasing leaf area index (LAI). However, reduced gs and increased LAI can affect crop water consumption, and the overall effects need to be quantified under elevated [CO2]. Here we develop a SWAT-gs-LAI model by incorporating a nonlinear gs-CO2 equation and a missing LAI-CO2 relationship to investigate the responses of water consumption of grain maize, maize yield, and losses of water and soil to elevated [CO2] in the Upper Mississippi River Basin (UMRB; 492,000 km2). Results exhibited enhanced maize yield with decreased water consumption for increases in [CO2] from 495 ppm to 825 ppm during the historical period (1985-2014). Elevated [CO2] promoted surface runoff but suppressed sediment loss as the predominant impact of LAI-CO2 leading to enhanced surface cover. A comprehensive analysis of future climate change showed increased maize water consumption in comparison to the historical period, driven by the more pronounced effects of overall climate change rather than solely elevated [CO2]. Generally, future climate change promoted maize yield in most regions of the UMRB for three Shared Socioeconomic Pathway (SSP) scenarios. Surface runoff was shown to increase generally in the future with sediment loss increasing by an average of 0.39, 0.42, and 0.66 ton ha-1 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. This was due to negative climatic change effects largely surpassing the positive effect of elevated [CO2], particularly in zones near the middle and lower stream. Our results underscore the crucial role of employing a physically-based model to represent crop physiological processes under elevated [CO2] conditions, improving the reliability of predictions related to crop growth and the hydrological cycle.

Calibration using R-programming and parallel processing at the HUC12 subbasin scale in the Mid-Atlantic region: Development of national SWAT hydrologic calibration.

The first phase of a national scale Soil and Water Assessment Tool (SWAT) model calibration effort at the HUC12 (Hydrologic Unit Code 12) watershed scale was demonstrated over the Mid-Atlantic Region (R02), consisting of 3036 HUC12 subbasins. An R-programming based tool was developed for streamflow calibration including parallel processing for SWAT-CUP (SWAT- Calibration and Uncertainty Programs) to streamline the computational burden of calibration. Successful calibration of streamflow for 415 gages (KGE ≥0.5, Kling-Gupta efficiency; PBIAS ≤15%, Percent Bias) out of 553 selected monitoring gages was achieved in this study, yielding calibration parameter values for 2106 HUC12 subbasins. Additionally, 67 more gages were calibrated with relaxed PBIAS criteria of 25%, yielding calibration parameter values for an additional 150 HUC12 subbasins. This first phase of calibration across R02 increases the reliability, uniformity, and replicability of SWAT-related hydrological studies. Moreover, the study presents a comprehensive approach for efficiently optimizing large-scale multi-site calibration.

Hydrological modelling using SWAT for the assessment of streamflow dynamics in the Ganga River basin.

Growing concerns over water availability arise from the problems of population growth, rapid industrialization, and human interferences, necessitating accurate streamflow estimation at the river basin scale. It is extremely challenging to access stream flow data of a transboundary river at a spatio-temporal scale due to data unavailability caused by water conflicts for assessing the water availability.Primarily, this estimation is done using rainfall-runoff models. The present study addresses this challenge by applying the soil and water assessment tool (SWAT) for hydrological modelling, utilizing high-resolution geospatial inputs. Hydrological modelling using remote sensing and GIS (Geographic Information System) through this model is initiated to assess the water availability in the Ganga River basin at different locations. The outputs are calibrated and validated using the observed station data from Global Runoff Data Centre (GRDC). To check the performance of the model, Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and RSR efficacy measures are initiated in ten stations using the observed and simulated stream flow data. The R2 values of eight stations range from 0.82 to 0.93, reflecting the efficacy of the model in rainfall-runoff modelling. Moreover, the results obtained from this hydrological modelling can serve as valuable resources for water resource planners and geographers for future reference.

Hydrological model parameter regionalization: Runoff estimation using machine learning techniques in the Tha Chin River Basin, Thailand.

Understanding hydrological processes necessitates the use of modeling techniques due to the intricate interactions among environmental factors. Estimating model parameters remains a significant challenge in runoff modeling for ungauged catchments. This research evaluates the Soil and Water Assessment Tool's capacity to simulate hydrological behaviors in the Tha Chin River Basin with an emphasis on runoff predictions from the regionalization of hydrological parameters of the gauged basin, Mae Khlong River Basin. Historical data of Mae Khlong River Basin from 1993 to 2017 were utilized for calibration, followed by validation using data from 2018 to 2022. •Calibration results showed the SWAT model's reasonable accuracy, with R² = 0.85, and the validation with R² of 0.64, indicating a satisfactory match between observed and simulated runoff.•Utilizing Machine Learning (ML) techniques for parameter regionalization revealed nuanced differences in model performance. The Random Forest (RF) model exhibited an R² of 0.60 and the Artificial Neural Networks (ANN) model slightly improved upon RF, showing an R² of 0.61 while the Support Vector Machine (SVM) model demonstrated the highest overall performance, with an R² of 0.63.•This study highlights the effectiveness of the SWAT and ML techniques in predicting runoff for ungauged catchments, emphasizing their potential to enhance hydrological modeling accuracy. Future research should focus on integrating these methodologies in various basins and improving data collection for better model performance.

Water availability and extreme events under climate change scenarios in an experimental watershed of the Brazilian Atlantic Forest.

Climate change has diversified negative implications on environmental sustainability and water availability. Assessing the impacts of climate change is crucial to enhance resilience and future preparedness particularly at a watershed scale. Therefore, the goal of this study is to evaluate the impact of climate change on the water balance components and extreme events in Piabanha watershed in the Brazilian Atlantic Forest. In this study, extreme climate change scenarios were developed using a wide array of global climate models acquired from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Reports (AR6). Two extreme climate change scenarios, DryHot and WetCool, were integrated into the Soil and Water Assessment Tools (SWAT) hydrological model to evaluate their impacts on the hydrological dynamics in the watershed. The baseline SWAT model was first developed and evaluated using different model performance evaluation metrics such as coefficient of determination (R2), Nash-Sutcliffe (NSC), and Kling-Gupta efficiency coefficient (KGE). The model results illustrated an excellent model performance with metric values reaching 0.89 and 0.64 for monthly and daily time steps respectively in the calibration (2008 to 2017) and validation (2018 to 2023) periods. The findings of future climate change impacts assessment underscored an increase in temperature and shifts in precipitation patterns. In terms of streamflow, high-flow events may experience a 47.3 % increase, while low-flows could see an 76.6 % reduction. In the DryHot scenario, annual precipitation declines from 1657 to 1420 mm, with evapotranspiration reaching 54 % of precipitation, marking a 9 % rise compared to the baseline. Such changes could induce water stress in plants and lead to modifications on structural attributes of the ecosystem recognized as the Atlantic rainforest. This study established boundaries concerning the effects of climate change and highlighted the need for proactive adaptation strategies and mitigation measures to minimize the potential adverse impacts in the study watershed.

Hydro-climatic extremes shift the hydrologic sensitivity regime in a cold basin.

Escalating climate extreme events disrupt hydrological processes by affecting both water availability and sediment dynamics. However, the interconnection between hydrological variability and climatic extremes remains underexplored, particularly in cold regions under a changing climate. Here, we develop a yield-based dichotomy framework to examine the impact of shifted climatic extreme patterns on hydrological regimes in the Ishikari River Basin (IRB), Hokkaido, Japan, which is a crucial area for local agriculture and urban development. Utilizing a modified Soil and Water Assessment Tool (SWAT) integrated with downscaled CMIP6-GCM climate projections under Shared Socioeconomic Pathways (SSPs) scenarios, we identified significant annual variability in water and sediment yields associated with extreme climate events. Hot-dry conditions correlate with lower water and sediment yields, whereas increased cold extremes may result in higher sediment yields across the IRB. Our findings also indicate that hotter and drier patterns interact with hydrological processes, potentially establishing new hydrologic regimes and shifting climatic extremes-induced thresholds for yield classification within the IRB. Notably, under SSP585, both water availability and sediment transport are projected to intensify, increasing flood risks and exacerbating sedimentation challenges. Our study highlights the urgent need for adaptive water management strategies to address these anticipated changes in hydrological regimes in response to global climate change.