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

Journal of Hydrology, Год журнала: 2024, Номер 634, С. 131117 - 131117

Опубликована: Март 24, 2024

Global climate change has led to an increase in the frequency and scale of extreme weather events worldwide, there is urgent need develop better-performing hydrological models improve accuracy streamflow simulations facilitate water resource planning management. The Soil Water Assessment Tool (SWAT) a notable physical foundation widely used research. However, it uses simplified vegetation growth model, introducing uncertainty into simulation results. This study focused on improving model based remotely sensed phenological leaf area index (LAI) data. Phenological data were define dormancy, LAI replaced corresponding simulated by original model. approach improved describing dynamics. Then, enhanced SWAT was coupled with bidirectional long short-term memory (BiLSTM) validate processes upstream Hei River. During validation, performance simulating (R2 = 0.835, NSE 0.819) better than that 0.821, 0.805). In terms evapotranspiration, demonstrated even greater advantages. verification period, compared those R2 values for daily-scale increased from 0.196 −0.269 0.777 0.732, respectively. monthly-scale 0.782 0.678 0.906 0.851, Simultaneously, levels two coupling approaches prediction compared, i.e., direct BiLSTM (SWAT-BiLSTM) (enhanced SWAT-BiLSTM). results showed SWAT-BiLSTM always performed during entire especially which could more accurately predict peak changes. deep learning accuracy.

Язык: Английский

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An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy

Journal of Hydrology, Год журнала: 2023, Номер 629, С. 130558 - 130558

Опубликована: Дек. 7, 2023

Язык: Английский

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Runoff responses to landscape pattern changes and their quantitative attributions across different time scales in ecologically fragile basins

CATENA, Год журнала: 2025, Номер 249, С. 108716 - 108716

Опубликована: Янв. 13, 2025

Язык: Английский

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Analysis of surface water area dynamics and driving forces in the Bosten Lake basin based on GEE and SEM for the period 2000 to 2021

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(6), С. 9333 - 9346

Опубликована: Янв. 8, 2024

Язык: Английский

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Modeling agro-hydrological surface-subsurface processes in a semi-arid, intensively irrigated river basin

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 57, С. 102188 - 102188

Опубликована: Янв. 15, 2025

Язык: Английский

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Integrated Modeling and Management of Non-Point Source Pollution in the Bailin River Basin: Best Practices for Reducing Nutrient Loads

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Фев. 10, 2025

The Bailin River, a key tributary of the Yangtze faces significant water quality challenges due to agricultural non-point source (NPS) pollution exacerbated by industrial discharge and urban runoff. This study employs Soil Water Assessment Tool (SWAT) analyze temporal spatial dynamics runoff as well total nitrogen (TN) phosphorus (TP) loads in River basin from 2020 2023. A critical area analysis was performed identify regions disproportionately contributing pollutant loads. Through various simulations, including different Best Management Practices (BMPs) scenarios, explores their effectiveness reducing nutrient findings reveal that losses are significantly concentrated during flood season, with TN TP accounting for 58.61% 58.92% annual totals, respectively. Specific BMP combining optimized fertilization, vegetation buffer strips, grass ditches, demonstrated substantial reduction, best combinations exceeding 58% reductions both TP. emphasizes necessity targeted interventions areas optimize management strategies achieve better outcomes. Continuous monitoring adaptive practices will be crucial addressing ongoing this basin. Ultimately, research contributes deeper understanding NPS mountainous watersheds highlights effective pathways improved ecological health quality.

Язык: Английский

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Improving the accuracy of daily runoff prediction using informer with black kite algorithm, variational mode decomposition, and error correction strategy

Stochastic Environmental Research and Risk Assessment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 28, 2025

Язык: Английский

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Runoff simulation and analysis of water source in the high-altitude and cold region of the Shaliu River Basin

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102294 - 102294

Опубликована: Апрель 19, 2025

Язык: Английский

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Streamflow response to land use/land cover change in the tropical Andes using multiple SWAT model variants

Journal of Hydrology Regional Studies, Год журнала: 2024, Номер 54, С. 101888 - 101888

Опубликована: Июль 19, 2024

Язык: Английский

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Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments

Water, Год журнала: 2024, Номер 16(19), С. 2805 - 2805

Опубликована: Окт. 2, 2024

This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time-series prediction across diverse catchments and compares its results against SWAT predictions. We found strong evidence of RF’s better performance by adding historical flows time-lags meteorological values over using only actual values. On daily scale, RF demonstrated robust (Nash–Sutcliffe efficiency [NSE] > 0.5), whereas generally yielded unsatisfactory (NSE < 0.5) tended to overestimate up 27% (PBIAS). However, provided monthly predictions, particularly in with irregular flow patterns. Although both models faced challenges predicting peak snow-influenced catchments, outperformed an arid catchment. also exhibited notable advantage terms computational efficiency. Overall, is good choice predictions limited data, preferable understanding hydrological processes depth.

Язык: Английский

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