Soil and Water Assessment Tool-Based Prediction of Runoff Under Scenarios of Land Use/Land Cover and Climate Change Across Indian Agro-Climatic Zones: Implications for Sustainable Development Goals

Water, Год журнала: 2025, Номер 17(3), С. 458 - 458

Опубликована: Фев. 6, 2025

Assessing runoff under changing land use/land cover (LULC) and climatic conditions is crucial for achieving effective sustainable water resource management on a global scale. In this study, the focus was predictions across three diverse Indian watersheds—Wunna, Bharathapuzha, Mahanadi—spanning distinct agro-climatic zones to capture varying hydrological complexities. The soil assessment (SWAT) tool used simulate future influenced by LULC climate change explore related sustainability implications, including challenges proposing countermeasures through action plan (SAP). methodology integrated high-resolution satellite imagery, cellular automata (CA)–Markov model projecting changes, downscaled data representative concentration pathways (RCPs) 4.5 8.5, representing moderate extreme scenarios, respectively. SWAT calibration validation demonstrated reliable predictive accuracy, with coefficient of determination values (R2) > 0.50 confirming reliability in simulating processes. results indicated significant increases surface due urbanization, reaching >1000 mm, 600 400 mm southern southeastern Wunna, northwestern Mahanadi, respectively, especially 2040 RCP 8.5. These findings indicate that quality, agricultural productivity, urban infrastructure may be threatened. proposed SAP includes nature-based solutions, like wetland restoration, climate-resilient strategies mitigate adverse effects partially achieve development goals (SDGs) clean action. This research provides robust framework watershed similar regions worldwide.

Язык: Английский

---

DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion

Journal of Hydrology, Год журнала: 2024, Номер 643, С. 131996 - 131996

Опубликована: Сен. 16, 2024

Язык: Английский

---

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting

Journal of Environmental Management, Год журнала: 2024, Номер 364, С. 121466 - 121466

Опубликована: Июнь 12, 2024

Язык: Английский

---

Improved random vector functional link network with an enhanced remora optimization algorithm for predicting monthly streamflow

Journal of Hydrology, Год журнала: 2024, Номер 650, С. 132496 - 132496

Опубликована: Дек. 16, 2024

Язык: Английский

---

Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning

Journal of Hydrology, Год журнала: 2024, Номер 649, С. 132440 - 132440

Опубликована: Дек. 3, 2024

Язык: Английский

---

Modelling of basin-scale nutrient loading variations under the synergistic influences of climate change and socioeconomic development

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132673 - 132673

Опубликована: Янв. 1, 2025

Язык: Английский

---

Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106350 - 106350

Опубликована: Янв. 1, 2025

Язык: Английский

---

A machine learning model integrating spatiotemporal attention and residual learning for predicting periodic air pollutant concentrations

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106438 - 106438

Опубликована: Март 1, 2025

Язык: Английский

---

Leveraging multi-source data and teleconnection indices for enhanced runoff prediction using coupled deep learning models

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 27, 2025

Accurate medium- to long-term runoff forecasting is crucial for flood control, drought resilience, water resources development, and ecological improvement. Traditional statistical methods struggle utilize multifaceted variable information, leading lower prediction accuracy. This study introduces two innovative coupled models-SRA-SVR SRA-MLPR-to enhance by leveraging the strengths of deep learning approaches. Stepwise Regression Analysis (SRA) was employed effectively handle high-dimensional data multicollinearity, ensuring that only most influential predictive variables were retained. Support Vector (SVR) Multi-Layer Perceptron (MLPR) chosen due their strong adaptability in capturing nonlinear relationships extracting latent hydrological patterns. The integration these significantly improves accuracy model stability. By integrating 80 atmospheric circulation indices as teleconnection variables, models tackle critical challenges such data, dynamics. Yalong River Basin, characterized complex processes diverse climatic influences, serves case validation. results show that: (1) Compared baseline single models, SRA-MLPR reduced RMSE (from 798.47 594.45) 26% MAPE 34.79 22.90%) 34%, while achieving an NSE 0.67 0.76) improvement 13%, particularly excelling extreme scenarios. (2) inclusion not enriched feature set but also improved stability, with demonstrating enhanced capability relationships. (3) A one-month lag identified optimal predictor basin-scale runoff, providing actionable insights into temporal (4) To interpretability, SHAP (SHapley Additive exPlanations) analysis quantify contribution predictions, revealing dominant climate drivers interactions. indicate Northern Hemisphere Polar Vortex East Asian Trough exert significant control over dynamics, influence modulated large-scale oscillations North Atlantic Oscillation (NAO) Pacific Decadal (PDO). (5) models' scalability validated through modular design, allowing seamless adaptation contexts. Applications include forecasting, optimized reservoir operations, adaptive resource planning. Furthermore, demonstrates potential generalizable tools basins varying geographic conditions. highlights robust across indices, proposed stability offering valuable prevention, mitigation, management. These methodological advancements bridge gap between approaches, a scalable framework accurate interpretable hydrological, climatological, environmental predictions. Given escalating brought about change, findings this make contributions sustainable management, decision-making support, disaster preparedness at global level.

Язык: Английский

---

Variational mode decomposition coupled LSTM with encoder-decoder framework: an efficient method for daily streamflow forecasting

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 13, 2024

Язык: Английский

---