Revisión sistemática del modelo SWAT como herramienta para evaluar el balance hídrico ante el cambio climático en Perú (2019-2024)

The assessment of future discharge impacts on the Gandak River Basin is crucial for understanding potential climate change effects and planning effective water resource management. This study employs the Soil and Water Assessment Tool (SWAT) model integrated with machine learning techniques to evaluate and predict the future discharge patterns in the basin. The Gandak River Basin, a significant tributary of the Ganges, plays a vital role in regional agriculture, hydropower, and ecosystem services, making it imperative to understand the potential changes in its hydrological dynamics. The SWAT model, a comprehensive, semi-distributed hydrological model, simulates the effects of land management practices, climate variability, and water management strategies on water, sediment, and agricultural chemical yields in large complex watersheds. SWAT’s capability to incorporate various climatic inputs, land use, soil properties, and topography enables it to simulate hydrological processes with high accuracy. However, the complexity and non-linearity of hydrological processes often necessitate the incorporation of advanced data-driven techniques to enhance prediction accuracy and robustness. In this study, machine learning algorithms, including Random Forest, Support Vector Machines, and Neural Networks, are integrated with SWAT to improve the model’s predictive performance. These algorithms are trained on historical discharge data, climate variables, and SWAT-simulated outputs to capture the non-linear relationships and complex interactions within the hydrological system. The hybrid model leverages the strengths of both physically-based and data-driven approaches, providing a more comprehensive understanding of the future discharge scenarios under various climate change projections. The research involves hbias-correcting climate projections from General Circulation Models (GCMs) to derive high-resolution climate inputs for the SWAT model. Scenarios based on Shared Socio-Economic Pathways (SSPs) are employed to simulate future climatic conditions. The SWAT model is calibrated and validated using observed discharge data from the Gandak River Basin, ensuring the reliability of the simulations. Subsequently, the machine learning models are trained on the SWAT outputs and historical data, creating an ensemble approach to predict future discharge. Results indicate significant variability in future discharge patterns under different climate scenarios. The integrated SWAT and machine learning model captures the seasonal and inter-annual variability in discharge more accurately than the standalone SWAT model. The findings suggest potential increases in peak discharge events during the monsoon season, with implications for flood risk management. Conversely, reduced discharge during the dry season could impact water availability for agriculture and domestic use, necessitating adaptive water management strategies. The study highlights the importance of combining physically-based hydrological models with machine learning techniques to enhance the prediction of hydrological responses to climate change. The integrated approach provides valuable insights for policymakers and stakeholders in the Gandak River Basin, aiding in the development of sustainable water resource management plans to mitigate the adverse impacts of future climate variability. This research underscores the need for continuous monitoring, adaptive management, and the incorporation of advanced modeling techniques to address the complexities of climate change impacts on river basins.

Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations

The process-based Soil and Water Assessment Tool (SWAT) model and the data-driven radial basis neural network (RBNN) model were evaluated for simulating sediment load for the Nagwa watershed in Jharkhand, India, where soil erosion is a severe problem. The SWAT model calibration and uncertainty analysis were performed with the Sequential Uncertainty Fitting algorithm version 2 and the bootstrap technique was applied on the RBNN model to analyse uncertainty in model output. The percentage of data bracketed by the 95% prediction uncertainty (95PPU) and the r factor were the two measures used to assess the goodness of calibration. Comparison of the results of the two models shows that the value of r factor (r = 0.41) in the RBNN model is less than that of SWAT model (r = 0.79), which means there is a wider prediction interval for the SWAT model results. More values of observed sediment yield were bracketed by the 95PPU in the RBNN model. Thus, the RBNN model estimates the sediment yield values more accurately and with less uncertainty.Editor D. Koutsoyiannis; Associate editor H. AksoyCitation Singh, A., Imtiyaz, M., Isaac, R.K., and Denis, D.M., 2014. Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed, India. Hydrological Sciences Journal, 59 (2), 351–364.

In this paper, the author investigated the effects of climate change on streamflow in Srepok watershed. The climate change scenarios were built by downscaling method (delta change method) based on the outputs of MIROC 3.2 Hires GCM. The SWAT (Soil and Water Assessment Tool) model was used to investigate the impacts on streamflow under climate change scenarios. The calibration and validation results showed that the SWAT model was able to simulate the streamflow well. Their difference in simulating the streamflow under future climate scenarios was also investigated. Results indicated a 1.3-3.9oC increase in annual temperature and a -4.4 to -0.5% decreases in annual precipitation corresponded to a decrease in streamflow of about -7.6 to -2.8%. The large decrease in precipitation and runoff are observed in the dry season.

Two important resources for human existence on the earth are land and water. It demands adequate management to obtain maximum utilization. The use of hydrological models for management of watershed resources is becoming more prevalent in decision-making processes. SWAT (Soil and Water Assessment Tool) is a promising model for simulating runoff, sediment and nutrient transport and erosion under various management scenarios. This work provides an overview of the advancements made in hydrological modeling using SWAT in previous studies. Numerous hydrological phenomena have been evaluated across a variety of study areas with the use of the SWAT model. Many investigations, including those on streamflow and land use planning, have employed the SWAT model with extremely effective implementation, as demonstrated by the reviews examined in this study. Thus, to review the studies conducted by different researcher’s using the SWAT is the primary objective of this paper. This research focuses on the literature on SWAT model applications in India.

Using an improved SWAT model to simulate hydrological responses to land use change: A case study of a catchment in tropical Australia

The Lobo watershed is an agricultural area where the use of fertilizers by farmers is intensive, causing eutrophication problems that deteriorate the quality of drinking water distributed to the population. Since the phenomenon of eutrophication is directly linked to runoff, it is essential to model the flow in order to better control the transfer of nutrients responsible for eutrophication. It is within this framework that this study was conducted. The objective of this study is to assess the ability of the semi-distributed SWAT (Soil and Water Assessment Tool) model to simulate runoff in the Lobo watershed. The methodology adopted was based on the use of the QSWAT graphical interface to manipulate and execute the main functions of the SWAT model from QGIS tools. The hydrological modeling was carried out with the QSWAT interface for SWAT 2012. The results showed good performance for the flow calibration (1982-1984) with the evaluation criteria R2, NSE and PBIAS respectively of 0.64, 0.64 and 3.1. In the validation period (1984-1987), the model also showed good performance in the streamflow simulation for R2 and NSE of 0.84 and 0.76 respectively as values. However, for the PBAIS criterion, the result was less good but still remains satisfactory with a value of 19.6. It emerges from this study that the SWAT model is suitable for simulating water transfer and can therefore be used to study the transfer of pollutants in the fight against eutrophication in the Lobo watershed.

The present study aims to analyse the effect of climate and land cover changes on the discharge of the Upper Beas River basin. The study used climate, soil, land use/land cover, and elevation data in the Soil and Water Assessment Tool (SWAT) model to estimate the discharge that is calibrated and validated using the observed discharge data of the Thalout gauge site. The findings suggest that basin hydrology is more sensitive to climate change than land cover changes. Mean annual discharge shows a rise between 0.31 and 9.65% under climate change scenarios, but it may decline by 9% under land cover change scenarios by the mid-21st century. Under all the climate change and land cover scenarios, seasonal variations in discharge are more prominent than annual changes. Water availability would be more in pre-monsoon season because of warming in the future. These changes would be beneficial in the short run, but may adversely affect the water availability in the long run.

Climate change is currently one of the most critical issues in watershed management, and typical paddy systems should be addressed by watershed modeling approach in paddy-dominant landscapes. This study is designed to evaluate and enhance the watershed modeling approach currently used to characterize the impacts of climate change on hydrologic and water quality responses while considering a paddy environment. APEX-paddy, which is a newly developed and modified APEX (Agricultural Policy/Environmental eXtender) model for paddy ecosystems, was coupled with SWAT (Soil and Water Assessment Tool) model to take advantage of the strengths of the two models. The resulting hybrid model, SWAPX, was calibrated and validated using observed data from 2008 to 2017 for two sites in the study watershed. Compared to SWAT, the accuracy of SWAPX was improved, showing statistically better results in the downstream including more paddy field areas. Ten GCMs were selected, and the characteristics of these GCMs were evaluated to assess the impacts of climate change scenarios. When applying the climate change scenarios to the SWAPX model, the results indicated that the future streamflow would increase due to increased rainfall. The results also showed that total nitrogen (T-N) loads would increase rapidly in the near future, then decrease gradually through the 2090s (2091–2100). T-N load was affected by the characteristics of rainfall patterns (e.g., daily maximum rainfall and rainfall intensity) occurring in various GCMs. This approach will be helpful for decision-makers in adapting to climate change and evaluating Best management practices (BMP) for paddy-dominant watersheds.

Assessment of future climate and vegetation canopy change impacts on hydrological behavior of Chungju dam watershed using SWAT model

A holistic approach for determining the hydrology of the mar menor coastal lagoon by combining hydrological & hydrodynamic models

The Illinois State Water Survey has been developing hydrologic and hydraulic models for watersheds in the Illinois River basin as part of the Illinois Rivers Decision Support System (ELRDSS). The hydrologic model is based on the U.S. Environmental Protection Agency's BASINS 3.1 modeling system. The Soil and Water Assessment Tool (SWAT) and Hydrologic Simulation Program — Fortran (HSPF), which are part of the BASINS system, were used to simulate the hydrology of watersheds in the Illinois River basin. Both SWAT and HSPF are comprehensive watershed models that also have the capability to simulate sediment transport. Based on the topographic, and hydrographic, land use, and soil types data, hydrologic models (SWAT and HSPF) were developed for the Court Creek watershed. The Court Creek watershed was divided into 35 subwatersheds and 346 HRUs. Two outlets specified in the models correspond to the streamflow gaging/sediment monitoring stations in the Court Creek watershed. Both models use the same precipitation and temperature data, and the potential evapotranspiration (PEVT), potential surface evaporation (EVAP), and other climate data. The simulation flow and sediments from the HSPF and SWAT models were compared graphically and statistically. Overall relative errors of simulated flow to the observed flow are –0.2 and 3.8% for HSPF and SWAT, respectively, and the relative errors of sediment load are –15 and –47%, respectively. Based on the correlation and the Nash-Sutcliffe Efficiency coefficients of simulated flows, the HSPF model outperformed the SWAT model for daily and monthly flow. However, the models performed almost equally well on the annual average. As for the suspended sediment load, the HSPF model performed slightly better than the SWAT model.

Estimation of water balance and water yield in the Reedy Fork-Buffalo Creek Watershed in North Carolina using SWAT

Effects of land-use data resolution on hydrologic modelling, a case study in the upper reach of the Heihe River, Northwest China

Predicting of daily Khazir basin flow using SWAT and hybrid SWAT-ANN models