Assessing the vulnerability of water resources in the context of climate changes in a small forested watershed using SWAT: A review

A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP

Streamflow is a crucial variable for assessing the available water resources for both human and environmental use. Accurate streamflow prediction plays a significant role in water resource management and assessing the impacts of climate change. This study explores the potential of coupling conceptual hydrological models based on physical processes with machine learning algorithms to enhance the performance of streamflow simulations. Four coupled models, namely SWAT-Transformer, SWAT-LSTM, SWAT-GRU, and SWAT-BiLSTM, were constructed in this research. SWAT served as a transfer function to convert four meteorological features, including precipitation, temperature, relative humidity, and wind speed, into six hydrological features: soil water content, lateral flow, percolation, groundwater discharge, surface runoff, and evapotranspiration. Machine learning algorithms were employed to capture the underlying relationships between these ten feature variables and the target variable (streamflow) to predict daily streamflow in the Sandu-River Basin (SRB). Among the four coupled models and the calibrated SWAT model, SWAT-BiLSTM exhibited the best streamflow simulation performance. During the calibration period (training period), it achieved R2 and NSE values of 0.92 and 0.91, respectively, and maintained them at 0.90 during the validation period (testing period). Additionally, the performance of all four coupled models surpassed that of the calibrated SWAT model. Compared to the tendency of the SWAT model to underestimate streamflow, the absolute values of PBIAS for all coupled models are below 10%, which indicates that there is no significant systematic bias evident. SHapley Additive exPlanations (SHAP) were used to analyze the impact of different feature variables on streamflow prediction. The results indicated that precipitation contributed the most to streamflow prediction, with a global importance of 29.7%. Hydrological feature variable output by the SWAT model played a dominant role in the Bi-LSTM's prediction process. Coupling conceptual hydrological models with machine learning algorithms can significantly enhance the predictive performance of streamflow. The application of SHAP improves the interpretability of the coupled models and enhances researchers' confidence in the prediction results.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45-59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83-87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89-91) and fallow land (CN = 88-93). The period 1985-1995 experienced an increased ET scenario (911-1050 mm), while the recent period (2005-2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Modeling the effects of land use/land cover changes on river runoff using SWAT models: A case study of the Danjiang River source area, China

Land use/land cover (LULC) is a crucial factor that directly influences the hydrology and water resources of a watershed. In order to assess the impacts of LULC changes on river runoff in the Danjiang River source area, we analyzed the characteristics of LULC data for three time periods (2000, 2010, and 2020). The LULC changes during these periods were quantified, and three Soil and Water Assessment Tool (SWAT) models were established and combined with eight LULC scenarios to quantitatively analyze the effects of LULC changes on river runoff. The results revealed a decrease in the cropland area and an increase in the forest, grassland, and urban land areas from 2000 to 2020. Grassland, forest, and cropland collectively accounted for over 94% of the total area, and conversions among these land types were frequent. The SWAT models constructed based on the LULC data demonstrated good calibration and validation results. Based on the LULC data in three periods, the area of each LULC type changed slightly, so the simulation results were not significantly different. In the subsequent LULC scenarios, we found that the expansion of cropland, grassland, and urban areas was associated with increased river runoff, while an increase in forest area led to a decrease in river runoff. Among the various LULC types, urban land exerted the greatest influence on changes in river runoff. This study establishes three SWAT models and combines multiple LULC scenarios, which is novel and innovative. It can provide scientific basis for the rational allocation of water resources and the optimization of LULC structure in the Danjiang River source area.

Load quantification and effect evaluation of urban non-point source pollution in the Licun River based on SWAT model

With the gradual control of point source pollution, the impact of urban nonpoint source pollution on river water quality is becoming more prominent. Regarding the current problem that nonpoint source pollution loads in urban basins are difficult to quantify and the impact on water quality is difficult to analyze, the Licun River basin in Qingdao was selected as the research object. Through the field survey and surface accumulation sampling analysis of the basin, the evaluation model of urban nonpoint source pollution was constructed by revising the land type data of the basin and the urban database of the SWAT model. The results showed that concentration of nitrate in precipitation was most sensitive to the simulation of nitrogen loading; organic P in baseflow was most sensitive to the simulation of phosphorus loading. The Nash-Sutcliffe efficiency coefficient (E_NS) and the coefficients of determination (R²) of the SWAT model for runoff, total phosphorus (TP), and total nitrogen (TN) in the simulation validation period meet the model requirements,indicating a good model fit. In addition, the spatial and temporal distribution characteristics of urban nonpoint source pollution of TN and TP in 2021 were analyzed. In July, rainfall-runoff from the Licun River basin was the most polluted.

A catchment scale assessment of water balance components: a case study of Chittar catchment in South India

The detailed analyses of the water balance components (WBCs) of the catchment help assess the available water resources, especially in the arid climate regions for their sustainable management and development. This paper mainly used the Soil and Water Assessment Tool (SWAT) model to analyze the variation in the WBCs considering the change in the Land Use and Land Cover (LULC) and meteorological variables. For this purpose, the model used the inputs of LULC and meteorological variables between 2001 and 2020 at 5 years and daily time intervals, respectively, from the Chittar river catchment. The developed models were calibrated using SWAT-CUP split-up procedure (pre-calibration and post-calibration). The model was found to be good in calibration and validation, yielding the coefficient of determination (R²) of 0.94 and 0.81, respectively. Furthermore, WBCs of the catchment were estimated for the near future (2021-2030) at the monthly and annual scales. For this endeavor, LULC was forecasted for the years 2021 and 2026 using Cellular Automata (CA)-Artificial Neural Network (ANN), and for the same period, meteorological variables were also forecasted using the smoothing moving average method from the historical data.

Coupling with high-resolution remote sensing data to evaluate urban non-point source pollution in Tongzhou, China

Urban non-point source (NPS) pollution has gradually become one of the important factors affecting the urban water environment. The quantitative evaluation of urban NPS pollution is the priority to identify key control area of urban NPS pollution. Current model applied in China is mainly focused on small-scale area, large-scale spatial continuous simulation is lacking. In this study A spatial continuous evaluation method coupled with high-resolution remote sensing data has been established and the method was applied to Tongzhou, China. With the spatial distribution of land-use type and built-up area which were been obtained by remote sensing technology, the accumulative and wash-off load of urban NPS nitrogen and phosphorus were estimated for the prominent problems of nitrogen and phosphorus nutrient pollution in the rivers in the study area. The main sources of urban NPS Nitrogen and phosphorus pollution are roof and road rainfall runoff respectively. Compared to other urban NPS pollution models, the method developed in this study can quickly realize spatial visualization assessment of urban NPS pollution and provide a means to estimate urban NPS loads in entire city or urban agglomeration, it is applicable for common urban NPS pollutants and also has advantages in areas without data.

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.

Application of Soil and Water Assessment Tool (SWAT) to evaluate the fates of nitrogenous fertilizer in subtropical mountainous watershed tea farms

Extensive nutrient loss is one of the most challenging issues faced by agricultural production regions worldwide. However, diffuse pollution in the subtropical mountainous watersheds is rarely simulated. A watershed model with regional parameter values is essential for watershed management. In this study, SWAT, one of the most popular models was applied to simulate daily discharge (years of 2008-2014), NO₃-N flux (2012-2014), and tea yield (2012-2014) in the Ping-Lin watershed (PLW) of Taiwan, as well as to test the effectiveness of a modified fertilization strategy. The results demonstrated that SWAT was capable of simulating daily discharge variation, daily riverine NO₃-N flux, and tea yield in the PLW. NO₃-N yield of the tea farm (47 kg/ha/yr) was 9 times higher than that of the forest (5.1 kg/ha/yr). A significant proportion (~ 50%) of the input nitrogen (including dry/wet deposition and fertilizer) infiltrated into the soil, resulting in a poor fertilizer uptake efficiency of the tea tree. It was demonstrated that the modified fertilization strategy (apply fertilizer in small rainfall event, i.e., daily rainfall < 20 mm/day, and not in a single day) could increase the nitrogen uptake and harvest yield of the tea tree by 14% and 4%, respectively, with a 10% reduction in nitrogen input. Furthermore, this strategy significantly reduced the nitrogen yields from surface flow (75%), lateral flow (36%), percolation (50%), and groundwater (48%). A popular model with verified parameter values could help in developing a win-win strategy for both farmers and regulators, thus realizing the goals of sustainable agricultural practices.

Response of Variation of Water and Sediment to Landscape Pattern in the Dapoling Watershed

The relationship between water-sediment processes and landscape pattern changes has currently become a research hotspot in low-carbon water and land resource optimization research. The SWAT-VRR model is a distributed hydrological model which better shows the effect of land use landscape change on hydrological processes in the watershed. In this paper, the hydrological models of the Dapoling watershed were built, the runoff and sediment yield from 2006 to 2011 were simulated, and the relationship between landscape patterns and water-sediment yield was analyzed. The results show that the SWAT-VRR model is more accurate and reasonable in describing runoff and sediment yield than the SWAT model. The sub-basins whose soil erosion is relatively light are mostly concentrated in the middle reaches with a slope mainly between 0–5°. The NP, PD, ED, SPIIT, SHEI, and SHDI of the watershed increased slightly, and the COHESION, AI, CONTAG, and LPI showed a certain decrease. The landscape pattern is further fragmented, with the degree of landscape heterogeneity increasing and the connection reducing. The runoff, sediment yield and surface runoff are all extremely significantly negatively correlated with forest, which implies that for more complicated patch shapes of forest which have longer boundaries connecting with the patches of other landscape types, the water and sediment processes are regulated more effectively. Therefore, it can be more productive to carry out research on the optimization of water and soil resources under the constraint of carbon emission based on the SWAT-VRR model.

Hydrological Modeling of Karst Watershed Containing Subterranean River Using a Modified SWAT Model: A Case Study of the Daotian River Basin, Southwest China

Karst watershed refers to the total range of surface and underground recharge areas of rivers (including subterranean rivers and surface rivers) in karst areas. Karst water resources, as the primary source of domestic water supply in southwest China, are vital for the social and economic development of these regions. It is greatly significant to establish a high-precision hydrological model of karst watershed for guiding water resources management in karst areas. Choosing the Daotian river basin in the Wumeng Mountains of Southwest China as the study area, this paper proposed a method for simplifying karst subterranean rivers into surface rivers by modifying the digital elevation model (DEM) based on a field survey and tracer test. This method aims to solve the inconsistency between the topographical drainage divides and actual catchment boundaries in karst areas. The Soil and Water Assessment Tool (SWAT) model was modified by replacing the single-reservoir model in the groundwater module with a three-reservoir model to depict the constraints of multiple media on groundwater discharge in the karst system. The results show that the catchment areas beyond topographic watershed were effectively identified after simplifying subterranean rivers to surface rivers based on the modified DEM data, which ensured the accuracy of the basic model. For the calibration and two validation periods, the Nash–Sutcliffe efficiencies (NSE) of the modified SWAT model were 0.87, 0.83, and 0.85, respectively, and R2 were 0.88, 0.84, and 0.86, respectively. The NSE of the modified SWAT model was 0.09 higher than that of the original SWAT model in simulating baseflow, which effectively improved the simulation accuracy of daily runoff. In addition, the modified SWAT model had a lower uncertainty within the same parameter ranges than the original one. Therefore, the modified SWAT model is more applicable to karst watersheds.

Appraising climate change impacts on future water resources and agricultural productivity in agro-urban river basins

Climate change can have an adverse effect on agricultural productivity and water availability in semi-arid regions, as changes in surface water availability lead to groundwater depletion and resultant losses in crop yield. These inter-relationships necessitate an integrated management approach for surface water, groundwater, and crop yield as a holistic system. This study quantifies the future availability of surface water and groundwater and associated crop production in a large semi-arid agro-urban river basin in which agricultural irrigation is a leader consumer of water. The region of study is the South Platte River Basin (72,000 km²), Colorado, USA. The coupled SWAT-MODFLOW modeling code is used as the hydrologic simulator and forced with five different CMIP5 climate models downscaled by Multivariate Adaptive Constructed Analogs (MACA), each for two climate scenarios, RCP4.5, and RCP8.5, for 1980-2100. The hydrologic model accounts for surface runoff, soil lateral flow, groundwater flow, groundwater-surface water interactions, irrigation from surface water and groundwater, and crop yield on a per-field basis. In all climate models and emission scenarios, an increase of 3 to 5 °C in annual average temperature is projected. Whereas, variation in the projected precipitation depends on topography and distances from mountains. Based on the results of this study, the worst-case climate model in the basin is IPSL-CM5A-MR-8.5. Under this climate scenario, for a 1 °C increase in temperature and the 1.3% reduction in annual precipitation, the basin will experience an 8.5% decrease in stream discharge, 2-5% decline in groundwater storage, and 11% reduction in crop yield. These results indicate the significant effect of climate change on water and food resources of a large river basin, pointing to the need for immediate implementation of conservation practices.

Evaluating the Potential of GloFAS-ERA5 River Discharge Reanalysis Data for Calibrating the SWAT Model in the Grande San Miguel River Basin (El Salvador)

Hydrological modelling requires accurate climate data with high spatial-temporal resolution, which is often unavailable in certain parts of the world—such as Central America. Numerous studies have previously demonstrated that in hydrological modelling, global weather reanalysis data provides a viable alternative to observed data. However, calibrating and validating models requires the use of observed discharge data, which is also frequently unavailable. Recent, global-scale applications have been developed based on weather data from reanalysis; these applications allow streamflows with satisfactory resolution to be obtained. An example is the Global Flood Awareness System (GloFAS), which uses the fifth generation of reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ERA5) as input. It provides discharge data from 1979 to the present with a resolution of 0.1°. This study assesses the potential of GloFAS for calibrating hydrological models in ungauged basins. For this purpose, the quality of data from ERA5 and from the Climate Hazards Group InfraRed Precipitation and Temperature with Station as well as the Climate Forecast System Reanalysis (CFSR) was analysed. The focus was on flow simulation using the Soil and Water Assessment Tool (SWAT) model. The models were calibrated using GloFAS discharge data. Our results indicate that all the reanalysis datasets displayed an acceptable fit with the observed precipitation and temperature data. The correlation coefficient (CC) between the reanalysis data and the observed data indicates a strong relationship at the monthly level all of the analysed stations (CC > 0.80). The Kling–Gupta Efficiency (KGE) also showed the acceptable performance of the calibrated SWAT models (KGE > 0.74). We concluded that GloFAS data has substantial potential for calibrating hydrological models that estimate the monthly streamflow in ungauged watersheds. This approach can aid water resource management.

Effects of the virgin forests to the dissolved organic matters in small forested watershed: a case study of the Tangwang River in Northeast China

Making clear of the background values of different watersheds is an important mission for water resource management and assessment. The background values of the virgin forested watershed with less human activities sometimes have high COD values. In order to investigate this issue, this study take the Tangwang River watershed as a study case, analyzing the monitoring data of COD, BOD, and DO from the year of 2014 to 2018, and taking the water samples in the wet, normal, and dry season of 2019 to make clear the relationships between COD and DOM, the composition, and source of DOM. The non-degradation and terrigenous source DOM contributed to the most of COD values through the fitting analysis. The DOM in the Tangwang River mostly consisted of the humic-like acid and fulvic-like acid. Along with the river, the concentration of DOM had a little decrease, but the composition of DOM did not change obviously, so that the DOM coming from the virgin forest did not degrade in the river, and the river water only had the dilution effect with the tributaries fed in spatially. The organic matters in the virgin forest were easily washed away, so that the DOM concentration had an increase in the wet season temporally. The background COD values of the small virgin forested watershed were usually high as for the leaching effects of the organic matters from the forest, which was worthy noted when conducting the management project of this kind of watershed, in order to adjust measures to local conditions.

SWAT Model Adaptability to a Small Mountainous Forested Watershed in Central Romania

This study aims to build and test the adaptability and reliability of the Soil and Water Assessment Tool hydrological model in a small mountain forested watershed. This ungauged watershed covers 184 km2 and supplies 90% of blue water for the Brașov metropolitan area, the second largest metropolitan area of Romania. After building a custom database at the forest management compartment level, the SWAT model was run. Further, using the SWAT-CUP software under the SUFI2 algorithm, we identified the most sensitive parameters required in the calibration and validation stage. Moreover, the sensitivity analysis revealed that the surface runoff is mainly influenced by soil, groundwater and vegetation condition parameters. The calibration was carried out for 2001–2010, while the 1996–1999 period was used for model validation. Both procedures have indicated satisfactory performance and a lower uncertainty of model results in replicating river discharge compared with observed discharge. This research demonstrates that the SWAT model can be applied in small ungauged watersheds after an appropriate parameterisation of its databases. Furthermore, this tool is appropriate to support decision-makers in conceiving sustainable watershed management. It also guides prioritising the most suitable measures to increase the river basin resilience and ensure the water demand under climate change.

Quantifying the Contribution of Agricultural and Urban Non-Point Source Pollutant Loads in Watershed with Urban Agglomeration

Urban agglomeration is a new characteristic of the Chinese urbanization process, and most of the urban agglomeration is located in the same watershed. Thus, urban non-point source (NPS) pollution, especially the characteristic pollutants in urban areas, aggravates NPS pollution at the watershed scale. Many agricultural studies have been performed at the watershed scale; however, few studies have provided a study framework for estimating the urban NPS pollution in an urban catchment. In this study, an integrated approach for estimating agricultural and urban NPS pollution in an urban agglomeration watershed was proposed by coupling the Soil and Water Assessment Tool (SWAT), the event mean concentration (EMC) method and the Storm Water Management Model (SWMM). The Hun-Taizi River watershed, which contains a typical urban agglomeration and is located in northeastern China, was chosen as the study case. The results indicated that the per unit areas of total nitrogen (TN) and total phosphorus (TP) in the built-up area simulated by the EMC method were 11.9% and 23 times higher than the values simulated by the SWAT. The SWAT greatly underestimated the nutrient yield in the built-up area. This integrated method could provide guidance for water environment management plans considering agricultural and urban NPS pollution in an urban catchment.

Assessment of the effects of human activity and natural condition on the outflow of Syr Darya River: A stepwise-cluster factorial analysis method

Water scarcity causes a series of eco-environmental problems, such as land salinization, biodiversity reduction and food crisis, which seriously restricts the sustainable development of the Aral Sea basin. In this study, a stepwise-cluster factorial analysis (SCFA) approach is proposed for assessing the effects of natural condition and human activity on the outflow of Syr Darya River (abbreviated as OSR) that has significant effects on the eco-environmental restoration of the Aral Sea. SCFA coupled stepwise cluster analysis and factorial analysis cannot only reflect the variability of outflow, but also identify the driving factors quantitatively. The results disclose that, in 1960-1991, the dominant factors (affecting the OSR) are upstream inflow (25.77%) > agricultural water use of Uzbekistan (7.21%) > industrial water use of Uzbekistan (4.53%) > agricultural water use of Kazakhstan (3.81%) > Precipitation (3.66%); interactions between upstream inflow and agricultural water use of Uzbekistan, Kazakhstan and Tajikistan and interactions between reservoir and evapotranspiration have the significant effects on the OSR. Results also indicate that, in 1992-2015, the dominant factors that affect the OSR are agricultural water use of Uzbekistan (23.31%) > agricultural water use of Kazakhstan (22.15%) > industrial water use of Uzbekistan (8.31%) > domestic water use of Kazakhstan (4.68%) > agricultural water use of Tajikistan (4.54%) > domestic water use of Uzbekistan (4.41%); the interactions between industrial water use and agricultural water use of Uzbekistan, Kazakhstan and Tajikistan and the interactions between reservoir and upstream inflow have the pivotal effects on OSR. In the future, when the agricultural water use of the basin decrease as 4% and the industry water use of Uzbekistan decrease as 2%, the OSR may recover to the middle level of 1970s. The results help identify the major factors affecting the outflow of Syr Darya River as well as seek an effective approach to restore the eco-environment of Aral Sea basin.