A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions

Aloui, Sarra; Mazzoni, Annamaria; Elomri, Adel; Aouissi, Jalel; Boufekane, Abdelmadjid; Zghibi, Adel

doi:10.1016/j.jenvman.2022.116799

Cited by 77 publications

(20 citation statements)

References 185 publications

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“…The application of the eco-hydrological models in Mediterranean basins with intermittent streams can be particularly difficult 39 , 44 , 45 . Working with limited data or with low concentrations of pollutants, which are common characteristics of small river basins in the Mediterranean Region, complicates the implementation and calibration of models 73 . For this reason, it is very important to accurately build a methodological approach as well as an expert judgment for selecting information, data, and methods to better describe the processes.…”

Section: Discussionmentioning

confidence: 99%

Modeling pesticides and ecotoxicological risk assessment in an intermittent river using SWAT

Centanni,

Ricci,

De Girolamo

et al. 2024

Sci Rep

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The present work aimed to predict the fate of two pesticides, copper (Cu) and glyphosate in a Mediterranean basin with an intermittent river and to assess the ecotoxicological risk related to their presence in water bodies coupling field measurements of streamflow and pesticide concentrations, and an eco-hydrological model. The Soil and Water Assessment Tool (SWAT) model was calibrated and, subsequently used to assess predicted environmental concentrations of pesticides in surface waters. The ecotoxicological risk related to the presence of Cu and glyphosate in surface water was assessed at the reach scale by using the Toxicity to Exposure Ratio approach (TER). Measurements of glyphosate concentrations (< 0.5 μg l−1) exceeded the maximum European threshold of environmental quality standards for pesticides (EQS) of 0.1 μg l−1. High concentrations of glyphosate were predicted in the wet season and in September, when glyphosate is mostly used in vineyards and olive grove productions. Acute risk (TER < 100) associated with the presence of glyphosate was detected for several reaches. High concentrations of Cu (< 6.5 μg l−1), mainly used as a fungicide in vineyards, were predicted in several river reaches. The results of the ecotoxicological risk assessment revealed that November and January were the critical months during which most of the river reaches showed a chronic risk associated with the presence of Cu.

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Section: Discussionmentioning

confidence: 99%

Modeling pesticides and ecotoxicological risk assessment in an intermittent river using SWAT

Centanni,

Ricci,

De Girolamo

et al. 2024

Sci Rep

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“…It is capable of generating weather data using weather generators in areas with no observational data. The model has been widely applied in many countries and regions 39 .…”

Section: Methodsmentioning

confidence: 99%

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Zhang,

Qi,

Liu

et al. 2023

Sci Rep

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Against the backdrop of accelerated global climate change and urbanization, the frequency and severity of flood disasters have been increasing. In recent years, influenced by climate change, the Hai-River Basin (HRB) has experienced multiple large-scale flood disasters. During the widespread extraordinary flood event from July 28th to August 1st, 2023, eight rivers witnessed their largest floods on record. These events caused significant damage and impact on economic and social development. The development of hydrological models with better performance can help researchers understand the impacts of climate change, provide risk information on different disaster events within watersheds, support decision-makers in formulating adaptive measures, urban planning, and improve flood defense mechanisms to address the ever-changing climate environment. This study examines the potential for enhancing streamflow simulation accuracy in the HRB located in Northeast China by combining the physically-based hydrological model with the data-driven model. Three hybrid models, SWAT-D-BiLSTM, SWAT-C-BiLSTM and SWAT-C-BiLSTM with SinoLC-1, were constructed in this study, in which SWAT was used as a transfer function to simulate the base flow and quick flow generation process based on weather data and spatial features, and BiLSTM was used to directly predict the streamflow according to the base flow and quick flow. In the SWAT-C-BiLSTM model, SWAT parameters with P values less than 0.4 in each hydrological station-controlled watershed were calibrated, while the SWAT-D-BiLSTM model did not undergo calibration. Additionally, this study utilizes both 30 m resolution land use and land cover (LULC) map and the first 1 m resolution LULC map SinoLC-1 as input data for the models to explore the impact on streamflow simulation performance. Among five models, the NSE of SWAT-C-BiLSTM with SinoLC-1 reached 0.93 and the R2 reached 0.95 during the calibration period, and both of them stayed at 0.92 even in the validation period, while the NSE and R2 of the other four models were all below 0.90 in the validation period. The potential impact of climate change on streamflow in the HRB was evaluated by using predicted data from five global climate models from CMIP6 as input for the best-performing SWAT-C-BiLSTM with SinoLC-1. The results indicate that climate change exacerbates the uneven distribution of streamflow in the HRB, particularly during the concentrated heavy rainfall months of July and August. It is projected that the monthly streamflow in these two months will increase by 34% and 49% respectively in the middle of this century. Furthermore, it is expected that the annual streamflow will increase by 5.6% to 9.1% during the mid-century and by 6.7% to 9.3% by the end of the century. Both average streamflow and peak streamflow are likely to significantly increase, raising concerns about more frequent urban flooding in the capital economic region within the HRB.

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“…Existing water quality prediction models can be divided into two types: mechanistic and non-mechanistic models [7,8] . Mechanistic models, such as the Streeter Phelps (S-P) model [9][10][11][12] , QUAL2E model [13][14][15] , Water Quality Analysis Simulation Program (WASP) [15][16][17] , Soil and Water Assessment Tool (SWAT) [18][19][20] , etc., are based on physical and chemical principles and require a large amount of basic and long-term monitoring data. However, such data are often unavailable or incomplete in practice, which limits the applicability and accuracy of mechanistic models.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

pang,

Luo,

et al. 2023

Preprint

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The prediction of water quality in urban rivers plays a crucial role in supporting water environment management. This study collected and organized real-time water quality monitoring data from four water quality monitoring stations in the Fenjiang River Basin of Foshan City, spanning from 2016 to 2021. To reduce noise interference in historical monitoring data, we applied a wavelet packet denoising (WPD) technique. Subsequently, we developed a single-factor water quality prediction model based on Long Short-Term Memory (LSTM), focusing on two key factors of water quality degradation: chemical oxygen demand (COD) and ammonia nitrogen (NH3-N). The results of this study demonstrate that the integration of WPD with LSTM, referred to as WPD-LSTM, outperformed conventional LSTM models in terms of predictive accuracy. Notably, the WPD-LSTM model exhibited superior performance in predicting the impact of COD and NH3-N on water quality in the Fenjiang River, surpassing the traditional LSTM model over a prediction period of 12 hours and 3 days. In the 12-hour prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 55% to 67%, with an average decrease of 61%; the RMSE values of COD predictions in the four monitoring sections decreased by 18% to 51%, with an average decrease of 29%. In the 3-day prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 40% to 83%, with an average decrease of 65%; the RMSE values of COD predictions in the four monitoring sections decreased by 50% to 69%, with an average decrease of 60%. By employing the WPD-LSTM method, this study contributes to improving the precision of water quality prediction, thereby providing valuable insights for effective water environment management in urban river systems.

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A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions

Cited by 77 publications

References 185 publications

Modeling pesticides and ecotoxicological risk assessment in an intermittent river using SWAT

Modeling pesticides and ecotoxicological risk assessment in an intermittent river using SWAT

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

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