Soil And Water Assessment Tool Research Articles

The Effects of Climate Change on Streamflow, Nitrogen Loads, and Crop Yields in the Gordes Dam Basin, Turkey

The Mediterranean region is highly vulnerable to climate change. Longer and more intense heatwaves and droughts are expected. The Gordes Dam in Turkey provides drinking water for Izmir city and irrigation water for a wide range of crops grown in the basin. Using the Soil and Water Assessment Tool (SWAT), this study examined the effects of projected climate change (RCP 4.5 and RCP 8.5) on the simulated streamflow, nitrogen loads, and crop yields in the basin for the period of 2031–2060. A hierarchical approach to define the hydrological response units (HRUs) of SWAT and the Fast Automatic Calibration Tool (FACT) were used to reduce computational time and improve model performance. The simulations showed that the average annual discharge into the reservoir is projected to increase by between 0.7 m3/s and 4 m3/s under RCP 4.5 and RCP 8.5 climate change scenarios. The steep slopes and changes in precipitation in the study area may lead to higher simulated streamflow. In addition, the rising temperatures predicted in the projections could lead to earlier spring snowmelt. This could also lead to increased streamflow. Projected nitrogen loads increased by between 8.8 and 25.1 t/year. The results for agricultural production were more variable. While the yields of poppy, tobacco, winter barley, and winter wheat will increase to some extent because of climate change, the yields of maize, cucumbers, and potatoes are all predicted to be negatively affected. Non-continuous and limited data on water quality and crop yields lead to uncertainties, so that the accuracy of the model is affected by these limitations and inconsistencies. However, the results of this study provide a basis for developing sustainable water and land management practices at the catchment scale in response to climate change. The changes in water quality and quantity and the ecological balance resulting from changes in land use and management patterns for economic benefit could not be fully demonstrated in this study. To explore the most appropriate management strategies for sustainable crop production, the SWAT model developed in this study should be further used in a multi-criteria land use optimization analysis that considers not only crop yields but also water quantity and quality targets.

Evaluation of hydrological responses to climate change in the Gibe Gojeb catchment, southwestern Ethiopia

ABSTRACT The expected influences of climate alteration on hydrological responses in the Gibe Gojeb catchment, southwestern Ethiopia, were evaluated using the Soil and Water Assessment Tool (SWAT) model and regional climate models (RCMs). This study emphasizes the evaluation of the climate change impact on hydrological responses. The RCM data were downloaded from CORDEX-Africa. Power transformation and delta change methods were used for bias correction of precipitation and temperature, respectively. The calibration and validation of the SWAT model showed good agreement, and climate change impact was simulated. Accordingly, precipitation, soil water content, percolation, water yield, and groundwater are projected to decrease by 28.18, 7.5, 9.1, 5.4, and 9.2%, respectively, under RCP4.5 in the future (2021–2050). Besides, surface runoff and potential and actual evapotranspiration are projected to increase by 17.4, 11.32, and 5.06%, respectively. Soil water content, percolation, water yield, and precipitation are projected to decrease by 10.51, 5.63, 2.76, and 21.57%, respectively, under RCP8.5 in the future. Rain-fed crop production and hydropower generation in the catchment would be highly vulnerable to the likely effects of climate change. The attained results help to develop effective adaptation measures to reduce the ongoing impacts of climate change and draft long-term water resource management plans in the Gibe Gojeb catchment.

Impacts of water regulation through a reservoir on drought dynamics and propagation in the Pilica River watershed

Study regionThe Pilica River watershed, located in central Poland, is the largest left tributary of the Vistula River basin. Study focusThe study aims to assess the impact of human activities on drought dynamics by considering the specific role of the Sulejów Reservoir. Identifying factors contributing to drought development is essential for decision-makers to implement appropriate management measures. The temporal overlap method is used to distinguish between climate and human-driven droughts. The changes in drought propagation are assessed using the time shift method at various accumulation time scales. To assess the spatial distribution of drought, we applied the Soil and Water Assessment Tool (SWAT) model to simulate soil moisture and streamflow response at a spatial scale. New hydrological insights for the regionThe findings highlight that about 75% of meteorological droughts lead to soil moisture droughts, and about 70% result in hydrological droughts. The results show the contribution of human activities to soil moisture drought (∼25%) and hydrological drought (∼10%), indicating that not all droughts are driven by meteorological factors alone. The key finding suggests a shorter and longer time scale of around 3 and 12 months for soil moisture drought and hydrological drought propagation analysis, respectively. Moreover, the decrease in the duration and severity of drought during 2005–2019 compared to 1997–2004 suggests that the magnitude of the reservoir impact depends on its purpose and operational strategies.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Insights to the Water Balance of a Boreal Watershed Using a SWAT Model.

Abstract The hydrological characteristics of a watershed play a crucial role in shaping ecosystems within the Boreal zone and have a significant impact on regional environments. Knowing these characteristics, such as the distinctive topography, vegetation, soil composition, and climatic conditions in the Canadian Boreal zone, is essential for implementing sustainable water management. This study focuses on assessing the hydrological dynamics of the Upper Humber River Watershed (UHRW) in western Newfoundland, Canada, using the Soil and Water Assessment Tool (SWAT) model. The UHR watershed includes sub-basins and hydrological response units (HRUs), with diverse land uses, soil types, and slope characteristics. Key parameters influencing streamflow simulation were identified through sensitivity analysis, including the runoff curve number, the effective hydraulic conductivity, the temperature lapse rate, the soil evaporation compensation factor, and the available water capacity of the soil layer. The SWAT model, using data from the Reidville hydrometric station, shows favorable performance metrics, with R2 values of 0.79 and 0.83 during the calibration and evaluation periods, respectively. The model effectively captures seasonal and monthly flow patterns, displaying right-skewed distributions and seasonal variations. The analyzed hydrological parameters, such as precipitation, evaporation, transpiration, surface runoff, and groundwater flow, reveal their significant contributions to the water balance. The flow duration curve analysis indicates the model's capability to estimate peak and low flows, with slight under-prediction during the recession phase. Seasonal analysis further supports the model's performance, with positive Nash-Sutcliffe Efficiency (NSE) values ranging from 0.65 to 0.91. The study concludes that the SWAT model is suitable for simulating the hydrological processes in the studied watershed providing valuable insights for sustainable water resource management and decision-making in the UHRW. The results can be useful for other Boreal zone watersheds.

Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin

We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level.

Predicting Ili River streamflow change and identifying the major drivers with a novel hybrid model

Study regionIli River, the main river in the Lake Balkhash basin. Study focusThis study introduces a novel hybrid model by coupling the Soil and Water Assessment Tool (SWAT) hydrological model with machine learning algorithms. It aims to simulate and forecast the streamflow of the Ili River, clearly delineating the roles of meteorological factors and anthropogenic factors in the process of streamflow change. New hydrological insights for the regionDuring the period 1960–2020, the contribution of climate change to streamflow variation was 104.59%-113.07%, the contribution of land use ranged from −10.75% to −4.59%, and the impact of reservoir construction was −2.27%. The predictive outcomes of the hybrid model indicate that, under the SSP2–4.5 and SSP5–8.5 scenarios, the streamflow of the Ili River is projected to increase by 12.8% and 14.3% respectively in the future period (2021–2100), in comparison to the historical period (1960–2020). The warming and humidification from 2021 to 2100 will lead to changes in the streamflow components of the Ili River, with a decrease in the proportion of groundwater flow and an increase in the proportion of surface flow. The results of this study provide a reference for the rational utilization and scientific management of water resources in the Ili River Basin.

Effects of vegetation restoration on runoff and its components in the mountainous Haihe River Basin

Study regionTwo typical mountainous Haihe River Basins: The Xiaojue Basin and the Yehe River Basin Study focusesThe mountainous Haihe River Basin has suffered from a water crisis duo to large-scale restoration. Quantifying the hydrological impacts of vegetation restoration is necessary for local water management. In this study, we used the Soil and Water Assessment Tool (SWAT) hydrological model, in combination with two vegetation restoration scenarios, to analyze the effects of vegetation restoration on runoff and its runoff components (surface runoff, baseflow, and total runoff) New hydrological insights for the regionOur results indicated a general decrease in surface runoff and an increase in baseflow as a result of vegetation restoration. At the seasonal timescale, total runoff and surface runoff decreases mainly concentrated in the flood season, and slightly increased in the non-flood season, and baseflow showed the opposite trend during the flood-season in the Xiaojue and Yehe River Basin. The magnitude of change in runoff components in the Yehe River Basin was higher than that in the Xiaojue Basin, and the Yehe River Basin has a larger water yield capability. This study advances our knowledge on the way in which vegetation affects runoff and offers recommendations for water resources management

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098–0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Modelling impacts of climate change on snow drought, groundwater drought, and their feedback mechanism in a snow-dominated watershed in western Canada

Snow accumulation and its melt are key hydrological processes in cold watersheds, which can affect groundwater (GW). With climate change projected to alter snow processes in these regions, understanding their impacts on the development of droughts is vital. A deficit in snow precipitation or accelerated snowmelt due to warming can trigger snow drought, potentially leading to GW drought. To investigate this relationship at a watershed scale, we coupled the Soil and Water Assessment Tool (SWAT) and MODFLOW to simulate various surface and groundwater processes under historical (1980–2013) and future (2040–2073) warming scenarios in western Canada. We calibrated, validated, and verified our models using streamflow, GW heads, and snow depth data from multiple hydrometric stations, observation wells, and a grid product. Using simulated data, we analyzed snow and GW drought characteristics, their dominant physical processes, and GW response time across eco-hydro(geo)logical regions like Mountains, Foothills, and Plains. Historical data show Mountains are experiencing more snow droughts while Plains are facing greater GW droughts. However, future scenarios suggest increased snow droughts in all regions and a shift towards more severe GW droughts in Plains. Historical response time spans 4–6 months from Mountains to Plains, with projected reductions in Mountains and Foothills, and a slight increase in Plains in the future. The dominant physical processes controlling GW response across all regions are soil moisture and percolation, with curve number displaying more significance in Mountains, and water yield exerts more control in Foothills and Plains. During cold seasons, SWE and snowmelt had minimal impact on GW response in Plains, while they presented a major role in Mountains. This study lays the basis for further research on snow-groundwater interactions in cold watersheds, aiding water resource management in mid-to-high latitude regions and providing a unified framework for analyzing snow and GW drought relationships.

Assessing the impacts of rice terraces and ponds on the sediment and phosphorus loads in a typical hilly watershed of Three Gorge Reservoir, China

Phosphorus is the primary eutrophication cause of river, lake, reservoir and estuary and it may be one of the principal pollutants for the agricultural watershed with middle and small cities. The sediment and phosphorus loads, related migration paths and contribution of source regions to receiving waters at different sections of a river basin are not clear. The Soil and Water Assessment Tool (SWAT) model was utilized to investigate the phosphorus loads and the relationships among water resources, sediment, and phosphorus loads in one of the important tributaries of the Three Gorge Reservoir (TGR) of China, and to investigate the retention dynamics of phosphorus in the river continuum and the influencing factors. It showed that under the scenario of rice terraces and ponds, the annual sediment loss and total phosphorus load were 125t and 2.5t, being reduced by 40% to 80% compared to the natural slope scenario; the daily peak value of suspended sediment load was reduced by 30% to 75%, and the relationship between the rainfall and runoff was weakened slightly; the retention rate of phosphorus sources reached 67% to 87%. The results confirm that the rice terraces and ponds have altered local hydrological conditions, enhanced a substantial water, sediment, and phosphorus retention capacity that is highly consistent over time and space scales, thereby altering the predominant forms of phosphorus and improving the environmental resilience in soil and nutrition loss for the hilly watershed of the TGR.

Climate change in the Biebrza Basin—Projections and ecohydrological implications

Over the last decades observed climate change in Poland had a significant impact on the condition and functioning of the environment. Thus, it is crucial to analyze further future changes to be able to cope with the potential effects of changing climate conditions. In this study, we aimed to assess the impact of projected climate change on meteorological and hydrological conditions in the Biebrza Basin. We analyzed seasonal and annual changes in air temperature, precipitation, streamflow and flood characteristics using the hydrological Soil and Water Assessment Tool (SWAT) model. We examined projected changes for two future time horizons (2024–2050 and 2074–2100) under the Representative Concentration Pathways (RCP) 4.5 and 8.5 using an ensemble of nine EUROCORDEX model scenarios. Climate change projections indicated an increase in precipitation by up to +17 % (+117 mm) and air temperature by up to 3.8 °C by the end of the 21st century. In the analyzed flow gauges a considerable increase in low and mean flows is projected in the future. High flows are projected to slightly decrease for Sztabin, remain at a similar level for Dębowo and slightly increase for Osowiec and Burzyn. Flood area and volume will slightly increase in future horizons. The greatest increase in flood duration (by up to 16 days) is projected for RCP8.5 by the end of the 21st century. It seems, that the expected hydrological conditions, both in the short and long term, will become more stable and improve the conditions for the development of wetlands.

Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

Exploring the spatial and temporal changes of compound disasters: A case study in Gaoping River, Taiwan

Global climate change has resulted in unusual climatic events of increasing intensity and frequency with severe impacts. An individual disaster is often coupled with another at the same time or in the form of a cascade. Major issues discussed in disaster management range from risks, environmental vulnerability, and resiliency, to the identification of human disaster-inducing land uses and their locations across a region, particularly in watersheds. The accurate identification of these disaster-inducing areas – that is, those locations of land use that may cause or contribute to making downstream impacts worse than they would be in the absence of such land uses – would be of assistance for disaster management agencies in order to mitigate disasters in advance. This study applies spatial autocorrelation statistics to explore the spatial and temporal dynamics associated with compound disasters. The study then utilizes the Soil and Water Assessment Tool (SWAT) to calculate runoff volume and sediment discharge to identify the locations of human disaster-inducing land uses. Our modeling outcomes show that there are various kinds of spatial and temporal clusters among compound disasters, and that certain areas are affected by similar disasters regularly while other locations might be the cause of these regular disasters.

The role of ponds in pesticide dissipation at the catchment scale: The case of the Save agricultural catchment (Southwestern France)

Pesticides are a major source of pollution for ecosystems. In agricultural catchments, ponds serve as buffer areas for pesticide transfers and biogeochemical hotspots for pesticide dissipation. Some studies have highlighted the specific impact of ponds on the dynamics of pesticides, but knowledge of their cumulative effect at the watershed scale is scarce. Hence, using a modelling approach, we assessed the cumulative role of ponds in pesticide transfer in an agricultural basin (Southwest of France, 1110 km2). The Soil and Water Assessment Tool (SWAT) model was used to model the Save basin, including 197 ponds selected with a Multi-Criteria Decision Aiding Model based on their pesticide interception capacities. The daily discharge, the suspended sediment loads and two herbicide loads (i.e. S-metolachlor and aclonifen) in dissolved and particulate phases were accurately simulated from January 2002 to July 2014 at a daily time step. The presence of ponds resulted in a yearly mean reduction at the watershed outlet of respectively 61 % and 42 % of aclonifen and S-metolachlor fluxes compared to the simulations in the absence of ponds. Sediment-related processes were the most efficient for pesticide dissipation, leading to a mean dissipation efficiency by ponds of 51.0 % for aclonifen and 34.4 % for S-metolachlor. This study provides a first quantification of the cumulative role of ponds in pesticide transfer at the catchment scale in an intensive agricultural catchment. Environmental implicationThe article focuses on two pesticides (aclonifen and S-metolachlor) and their transfer and fate at the catchment scale. The cumulative role of ponds in pesticide dissipation was studied for the first time at this scale. The new methodology introduced by the study improves the modelling of pesticide transfers at the catchment scale. The study of the cumulative role of ponds allowed the assessment of their efficiency in pesticide removal and highlighted the different mitigation pathways. This study is environmentally relevant: it led to better and more precise pesticide modelling at catchment scale and confirmed the role of ponds as potential off-field mitigation strategies.

Characterizing streamflow regimes using a distributed model for sustainable resource management in the humid tropics

Estimating streamflow regimes on small tropical islands is challenging due to limited observational data, poor accuracy of remote sensing, and the limitations of coarse-scale climate models, restricting our understanding of hydrological processes and the management of water resources. The inadequacy of data has also affected the development of instream flow standards (i.e., environmental flows, e-flows) needed to protect ecological and cultural values central to island communities. Estimates of water availability for agriculture, hydropower, and drinking water supply are also critical for informed investments in water resource planning. We utilized high-resolution (250 m) gridded daily rainfall data to parameterize the Soil and Water Assessment Tool (SWAT) to characterize streamflow regimes at ungauged locations in a watershed on Kaua‘i Island. The resultant flow statistics were then used to describe the daily, seasonal, and annual availability of surface water to evaluate the implications of potential e-flow scenarios on water available for run-of-river hydropower. Compared to rainfall data derived from a single station within the watershed, the gridded daily rainfall product increased the accuracy of model output from not satisfactory to good. Model techniques using high resolution rainfall data may substitute for long-term hydrological data collection across complex tropical environments and provide data for making sustainable management decisions at ungauged locations.

Farmer adoption-based prompt networking and modeling for targeting optimal agro-conservation practices

Despite the availability of robust agricultural models, the successful on-ground implementation of all Best Management Practices (BMPs) remains elusive due to the lack of consideration for farmers' preferences. This study develops a unique network-based optimization technique coupled with Soil and Water Assessment Tool (SWAT) which delineates combined set of cost-effective BMPs at field-scale based on farmer's conservation identity in Ganges River Basin, India. SWAT was calibrated and validated using 10-year monthly discharge and nitrate data, and the selected BMPs were simulated using sequential uncertainty fitting framework. Farmers belonging to the Shahpur subdistrict were found to have the maximum normalized conservation identity of 90%, whereas the northeastern portion of the watershed contributed a maximum nitrate load of 7.5–9.92 kg/acre to the watershed outlet. An optimized combination of BMPs (riparian buffer-conservation tillage) with an efficiency of 0.28 was obtained through the modified network technique that targeted subbasins of the Morna subdistrict using farmer conservation identities. The results provide valuable guidance for watershed modelers, conservation planners, and researchers in pinpointing regions for targeted farmer training, subsidies, and other initiatives aimed at facilitating prompt field-scale BMP adoption. A flexible feature of the approach is its ability to modify the BMP action plans by replacing even technically feasible, cost-effective combinations based on farmers' preferences.

Application of SWAT Model for Hydrological Simulation of Rapti River Basin

This study aimed at application of SWAT model for hydrological simulations of Rapti River Basin (RRB) water systems. The Rapti River originates from Nepal and then it comes in India. SWAT (Soil and Water Assessment Tool) model was used for hydrological simulation of the RRB surface and sub surface water systems. SWAT is a comprehensive, semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. The RRB was discretised into 4 sub-basins and 630 hydrological response units (HRUs) and calibration and validation was carried out at Bagasoti using monthly flow data of 11 years, respectively. We first calibrated the model in SWAT-CUP which is a decision-making framework that incorporates a semi-automated approach (SUFI2) using manual calibration and incorporating sensitivity and uncertainty analysis. Parameter sensitivity analysis helps focus the calibration and uncertainty analysis and is used to provide statistics for goodness-of-fit. In this study Calibration has been done between simulated and observed discharge data (1974-1985) for 50 simulations with 6 parameters that is Curve number (CN2 = 0.945), Groundwater delay (GW_DELAY = 50), Baseflow alpha factor (ALPHA_BF = 0.58), Manning's "n" value for the main channel (CH_N2 = 0.15), Effective hydraulic conductivity in main channel alluvium (CH_K2 = 10.20) and Available water capacity of the soil layer (SOL_AWC = 0.28). The results were analysed and compared with the observational data. The model performance evaluation showed acceptable ranges of values (i.e., Nash Sutcliff was 0.75 and R2 was 0.71). After model calibration, in order to predict water balance, the model was validated by using the best parameter.

Evaluation of the Best Management Practices for Reducing Phosphorus Load in a Watershed in Terms of Cost and Greenhouse Gas Emissions

Effective management of water quality in watersheds is crucial because it is directly linked to the sustainability of aquatic ecosystems. In conventional watershed management, best management practices (BMPs) have been instrumental in addressing deteriorating water quality issues caused by non-point source pollution. Greenhouse gas (GHG) emissions have emerged as a global concern, necessitating immediate and diverse environmental actions to mitigate their impacts. This study aims to explore BMPs that maximize total phosphorus (TP) load removal efficiencies, while minimizing costs and GHG emissions within watersheds, using the Soil and Water Assessment Tool (SWAT) and non-dominated sorting genetic algorithm III (NSGA-III). The Yeongsan River Watershed between 2012 and 2021 was selected as the study area. Hydrological and BMP data were analyzed. Applying identical BMPs to the watershed showed that the BMPs with high TP removal efficiency may not be effective in terms of cost and GHG emissions. Therefore, the optimal combination of BMPs for the Yeongsan River Watershed was determined using NSGA-III considering TP removal efficiency, cost, and GHG emissions. This study is the first to consider GHG emissions at the watershed level when applying BMPs and is expected to contribute to the development of BMP implementation incorporating GHG emissions.

Forecasting water scarcity with an optimized Soil and Water Assessment Tool (SWAT) model under representative concentration pathway (RCP) scenarios

Abstract Population growth and climate change have increased the demand for water resources, and there is a need to predict water scarcity effectively. The soil and water assessment tool model has been optimized in this work to estimate water scarcity under several representative concentration pathway scenarios. To improve the Soil and Water Assessment Tool model, a new version of the dwarf mongoose optimization technique named Improved Dwarf Mongoose Optimization has been developed. The findings showed that the optimized Soil and Water Assessment Tool model performs better than other optimization models in terms of water scarcity prediction accuracy. The findings indicated that the improved Soil and Water Assessment Tool model can be used to predict water scarcity in other locations and provide valuable insights into the future of water resource management. According to the results, water availability also decreased in the representative concentration pathways, but the trend declined more in the representative concentration pathways 8.5 scenarios. Critical times of water scarcity have been discovered that can guide water management techniques. The results of this study can help policymakers make informed choices about water management and develop adaptive methods to mitigate the effects of water scarcity.