Establishment of Low Water Runoff Forecast Model in Yichun River Basin by Multiple Linear Regression Method

Xie, Shiyao; Zhang, Kaiwen; Li, Yang; Chang-lei, Dai

doi:10.1088/1755-1315/474/7/072067

Cited by 1 publication

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“…However, the prediction results of multiple linear regression model are still able to reflect the overall trend of runoff series [68,69]. Meanwhile, because the multiple linear regression model is characterized by rapid calculation, simple operation, and intuitive results, multiple linear regression is still applied in the research of runoff prediction [70][71][72].…”

Section: Multiple Regression Modelmentioning

confidence: 99%

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

Wan

2023

Atmosphere

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

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Section: Multiple Regression Modelmentioning

confidence: 99%

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

Wan

2023

Atmosphere

View full text Add to dashboard Cite

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Establishment of Low Water Runoff Forecast Model in Yichun River Basin by Multiple Linear Regression Method

Cited by 1 publication

References 0 publications

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

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