Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China DOI Creative Commons
Hanli Wan

Atmosphere, Journal Year: 2023, Volume and Issue: 14(10), P. 1576 - 1576

Published: Oct. 18, 2023

Global climate change will modify precipitation and temperatures’ temporal spatial distribution, trigger more extreme weather events, impact hydrological processes. The Yangtze River basin is one of the world’s largest basins, understanding future changes vital for water resource management supply. Research on predicting in upper (UYRB) introducing machine learning algorithms to analyze factors, including indicators, surface runoff urgently needed. In this study, a statistical downscaling model (SDSM) was used forecast UYRB, Mann–Kendall (MK) or modified (MMK) trend test at 5% level significance applied trends. Spearman rank correlation (SRC) random forest regression (RFR) were employed identify key climatic factors affecting from annual precipitation, temperature, maximum 5-day (R×5Day), number tropical nights (TR), consecutive dry days (CDD), RFR also predict runoff. Based results, we found that, compared selected historical period (1985–2014), mean (temperature) during mid-term (2036–2065) increased by 18.93% (12.77%), 17.78% (14.68%), 20.03% (17.03%), 19.67% (19.29%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5, respectively, long term (2071–2100), 19.44% (12.95%), 22.01% (21.37%), 30.31% (30.32%), 34.48% (37.97%), respectively. warming humidification characteristics northwestern UYRB pronounced. influencing (R×5day), temperature. Because humidification, expected increase relative period. (long term) 12.09% (12.58%), 8.15% (6.84%), 8.86% (8.87%), 5.77% (6.21%) implementation sustainable development pathways low radiative forcing scenario can be effective mitigating change, but same time, it may risk floods UYRB.

Language: Английский

Oasis by the Sea DOI
Daniela A. Ottmann

Cities research series, Journal Year: 2025, Volume and Issue: unknown, P. 163 - 190

Published: Jan. 1, 2025

Language: Английский

Citations

0
Assessment and Prediction of Water Yield in the Chandra Basin, Western Himalaya, India DOI

M. Sobhana,

Vinay Kumar Gaddam, Gnana Siva Sai Venkatesh Mendu

et al.

Published: March 24, 2025

Language: Английский

Citations

0
Enhanced rainfall-runoff modeling with hybrid machine learning and NRCS: bridging AI and hydrology DOI

Nawbahar Faraj Mustafa

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(4)

Published: April 24, 2025

Language: Английский

Citations

0
Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction DOI Open Access
Tao Xie, Lu Chen, Bin Yi

et al.

Water, Journal Year: 2023, Volume and Issue: 16(1), P. 69 - 69

Published: Dec. 24, 2023

Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance runoff prediction. IKNN-MME dynamically adjusts model weights based on similarity historical data, acknowledging influence different training data features localized predictions. By combining enhanced (KNN) algorithm with adaptive weighting, it offers more powerful flexible ensemble. This evaluates performance across four basins United States compares other multi-model ensemble methods benchmark models. The results underscore its outstanding adaptability, offering promising avenue for improving forecasting.

Language: Английский

Citations

7
Climate-Driven Dynamics of Runoff in the Dayekou Basin: A Comprehensive Analysis of Temperature, Precipitation, and Anthropogenic Influences over a 25-Year Period DOI Open Access

Erwen Xu,

Xiaofeng Ren, Isaac Dennis Amoah

et al.

Water, Journal Year: 2024, Volume and Issue: 16(7), P. 919 - 919

Published: March 22, 2024

Understanding runoff dynamics is vital for effective water management in climate-affected areas. This study focuses on the Dayekou basin China’s Qilian Mountains, known their high climate variability. Using 25 years of data (1994–2018) river runoff, precipitation, and temperature, statistical methods were applied to explore annual variations change impacts these parameters. Results reveal a significant variability (132.27 225.03 mm), precipitation (340.19 433.29 average temperature (1.38 2.08 °C) over period. Decadal rising rates 17 mm 0.25 °C with peak occurring 1998–2000, 2008, 2016. The distribution also exhibited unimodal pattern, peaking at 39.68 July. cumulative during low periods constituted only 13.84% total, concentrated second half year, particularly June-October flood season. correlation analysis underscored strong relationship between (correlation coefficient > 0.80), while was weaker < 0.80). 25-year provides valuable insights into variation, elucidating interconnected effects basin, substantial implications sustainable development amid challenges.

Language: Английский

Citations

1
Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China DOI Creative Commons
Hanli Wan

Atmosphere, Journal Year: 2023, Volume and Issue: 14(10), P. 1576 - 1576

Published: Oct. 18, 2023

Global climate change will modify precipitation and temperatures’ temporal spatial distribution, trigger more extreme weather events, impact hydrological processes. The Yangtze River basin is one of the world’s largest basins, understanding future changes vital for water resource management supply. Research on predicting in upper (UYRB) introducing machine learning algorithms to analyze factors, including indicators, surface runoff urgently needed. In this study, a statistical downscaling model (SDSM) was used forecast UYRB, Mann–Kendall (MK) or modified (MMK) trend test at 5% level significance applied trends. Spearman rank correlation (SRC) random forest regression (RFR) were employed identify key climatic factors affecting from annual precipitation, temperature, maximum 5-day (R×5Day), number tropical nights (TR), consecutive dry days (CDD), RFR also predict runoff. Based results, we found that, compared selected historical period (1985–2014), mean (temperature) during mid-term (2036–2065) increased by 18.93% (12.77%), 17.78% (14.68%), 20.03% (17.03%), 19.67% (19.29%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5, respectively, long term (2071–2100), 19.44% (12.95%), 22.01% (21.37%), 30.31% (30.32%), 34.48% (37.97%), respectively. warming humidification characteristics northwestern UYRB pronounced. influencing (R×5day), temperature. Because humidification, expected increase relative period. (long term) 12.09% (12.58%), 8.15% (6.84%), 8.86% (8.87%), 5.77% (6.21%) implementation sustainable development pathways low radiative forcing scenario can be effective mitigating change, but same time, it may risk floods UYRB.

Language: Английский

Citations

1