Optimizing Spatial Discretization According to Input Data in the Soil and Water Assessment Tool: A Case Study in a Coastal Mediterranean Watershed

Water, Journal Year: 2025, Volume and Issue: 17(2), P. 239 - 239

Published: Jan. 16, 2025

Spatial discretization in hydrological models has a strong impact on computation times. This study investigates its effect the performance of Soil and Water Assessment Tool (SWAT) applied to French Mediterranean watershed. It quantifies how spatial (the number sub-basins response units (HRUs)) affects SWAT model’s simulating daily streamflow whether this depends choice soil land use input datasets. Sixty-eight model configurations were created using various datasets 17 setups, evaluated from 2001 2021 with Kling–Gupta efficiency (KGE) metric. The key findings include (1) while does not performance, increasing HRUs significantly degrades it (KGE loss 0.13 0.26) regardless or (2) is found be more sensitive variations than datasets, but observed decline attributed calibration process increased heterogeneity types rather dataset resolution. (3) Minimizing may improve both accuracy simulations computational model.

Language: Английский

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Prediction of Vegetation Indices Series Based on SWAT-ML: A Case Study in the Jinsha River Basin

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 958 - 958

Published: March 8, 2025

Vegetation dynamics significantly influence watershed ecohydrological processes. Physically based hydrological models often have general plant development descriptions but lack vegetation data for simulations. Solar-induced chlorophyll fluorescence (SIF) and the Normalized Difference Index (NDVI) are widely used in monitoring research. Accurately predicting long-term SIF NDVI can support of anomalies trends. This study proposed a SWAT-ML framework, combining Soil Water Assessment Tool (SWAT) machine learning (ML), Jinsha River Basin (JRB). The lag effects that responds to using hydrometeorological elements were considered while SWAT-ML. Based on SWAT-ML, series from 1982 2014 reconstructed. Finally, spatial temporal characteristics JRB analyzed. results showed following: (1) framework simulate processes with satisfactory (NS > 0.68, R2 0.79 SWAT; NS 0.77, MSE < 0.004 ML); (2) index’s mean value increases (the Z value, significance indicator Mann–Kendall method, is 1.29 0.11 NDVI), whereas maximum decreases (Z = −0.20 −0.42 NDVI); (3) greenness −2.93 −0.97 value) middle reaches. However, intensity vegetation’s physiological activity value= 3.24 2.68 value). Moreover, increase lower reaches 3.24, 2.68, 1.84 SIFmax, SIFave, NDVImax, NDVIave, respectively). In reaches, connection between factors stronger than NDVI. research developed new provide reference complex simulation.

Language: Английский

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Integrating artificial intelligence and machine learning in hydrological modeling for sustainable resource management

International Journal of River Basin Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: March 27, 2025

Language: Английский

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The spatial-temporal changes in water balance components under future climate change in the Gorganroud Watershed, Iran

Water Cycle, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

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SWAT and CMIP6-driven hydro-climate modeling of future flood risks and vegetation dynamics in the White Oak Bayou Watershed, United States

Earth Systems and Environment, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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Assessment of flood risk based on CMIP6 for the Northern foothills of Qinling mountain

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(4)

Published: April 1, 2025

Language: Английский

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