A Copula Function–Monte Carlo Method-Based Assessment of the Risk of Agricultural Water Demand in Xinjiang, China DOI Creative Commons
Xianli Wang, Zhigang Zhao,

Feilong Jie

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(11), P. 2000 - 2000

Published: Nov. 7, 2024

Agricultural water resources in Xinjiang, China, face significant supply and demand contradictions. risk is a key factor impacting resource management. This study employs the copula function (CF) Monte Carlo (MC) methods to evaluate agricultural at 66 stations Xinjiang. The evaluation based on marginal distributions of precipitation (PR) reference evapotranspiration (RET). findings classify Xinjiang’s precipitation–evapotranspiration relationship into three types: evapotranspiration, precipitation, transition. Regions south Tianshan Mountains (TMs) primarily exhibit characteristics. Ili River Valley areas north TMs display Other have transitional Both annual RET Xinjiang follow Generalized Extreme Value (GEV) distribution. Frank CF effectively describes coupling between RET, revealing negative correlation. correlation stronger weaker south. varies significantly across regions, with precipitation–RET being crucial influencing factor. index (DI) for decreases as probability (RP) increases. stability DI greatest evapotranspiration-type followed by transition-type, weakest precipitation-type regions. When RP constant, order transition, types. quantifies spatial pattern advantage CF–MC method lies its ability assess this without needing crop planting structures variations. However, it less effective few meteorological or short monitoring periods. Future efforts should focus accurately assessing data-deficient areas. are guiding regulation efficient use

Language: Английский

Impact assessment of urban waterlogging on roads trafficability and emergency sites accessibility under extreme rainfall events based on numerical modeling DOI

Zhang Kehan,

Mei Chao,

Jiahong Liu

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105285 - 105285

Published: Feb. 1, 2025

Language: Английский

Citations

1
The Exacerbating Effect Mechanism of Tidal Jacking on Waterlogging Hazards in Coastal Cities DOI Creative Commons
Yan Liu, Hao Wang, Yi Ding

et al.

Water Resources Research, Journal Year: 2025, Volume and Issue: 61(3)

Published: March 1, 2025

Abstract The tidal jacking effect is a crucial factor exacerbating waterlogging in coastal cities, but its mechanism complex and difficult to quantify. In this study, comprehensive framework established explore how exacerbates waterlogging. includes three components: hydrodynamic simulations of urban combing rainfall tide levels, analysis the drainage system reveal impedes water flow waterlogging, quantification changes flooded buildings assess impact hazards. Taking Liede River Basin Guangzhou, China, as case results show that levels intensify through series cascading processes: outfalls, impeded pipeline drainage, pipe overflow, eventually surface When encounters jacking, number duration jacked outfalls increase, extending full pipes. This leads 9%–43% increase overflow 4%–27% expansion area. exceeds under jacking. Tidal proportion areas with different risk concentrating higher downstream. also causes differential losses among building types. study provides essential insights into level offers evidence for mitigating

Language: Английский

Citations

0
Compound Flood Risk Assessment of Extreme Rainfall and High River Water Level DOI Open Access
Wanchun Li,

Chengbo Wang,

Jiangming Mo

et al.

Water, Journal Year: 2025, Volume and Issue: 17(6), P. 841 - 841

Published: March 14, 2025

Urban flooding is typically caused by multiple factors, with extreme rainfall and rising water levels in receiving bodies both contributing to increased flood risks. This study focuses on assessing urban risks Jinhua City, Zhejiang Province, China, considering the combined effects of high river levels. Using historical data from station (2005–2022), constructed a joint probability distribution via copula function. The findings show that risk significantly higher than each factor separately, indicating ignoring their interaction could greatly underestimate Scenario simulations using Infoworks ICM model demonstrate areas range 0.67% 5.39% under baseline scenario but increase 8.98–12.80% when 50a return period level. High play critical role increasing extent depth flooding, especially low coincides These highlight importance compound disaster-causing factors assessment can serve as reference for drainage control planning management.

Language: Английский

Citations

0
Does urban green infrastructure lead to equity issues for flood vulnerable areas? A case study in an urbanized polder area DOI
Kejing Zhou, Fanhua Kong, Haiwei Yin

et al.

Cities, Journal Year: 2025, Volume and Issue: 162, P. 105941 - 105941

Published: April 7, 2025

Language: Английский

Citations

0
A Copula Function–Monte Carlo Method-Based Assessment of the Risk of Agricultural Water Demand in Xinjiang, China DOI Creative Commons
Xianli Wang, Zhigang Zhao,

Feilong Jie

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(11), P. 2000 - 2000

Published: Nov. 7, 2024

Agricultural water resources in Xinjiang, China, face significant supply and demand contradictions. risk is a key factor impacting resource management. This study employs the copula function (CF) Monte Carlo (MC) methods to evaluate agricultural at 66 stations Xinjiang. The evaluation based on marginal distributions of precipitation (PR) reference evapotranspiration (RET). findings classify Xinjiang’s precipitation–evapotranspiration relationship into three types: evapotranspiration, precipitation, transition. Regions south Tianshan Mountains (TMs) primarily exhibit characteristics. Ili River Valley areas north TMs display Other have transitional Both annual RET Xinjiang follow Generalized Extreme Value (GEV) distribution. Frank CF effectively describes coupling between RET, revealing negative correlation. correlation stronger weaker south. varies significantly across regions, with precipitation–RET being crucial influencing factor. index (DI) for decreases as probability (RP) increases. stability DI greatest evapotranspiration-type followed by transition-type, weakest precipitation-type regions. When RP constant, order transition, types. quantifies spatial pattern advantage CF–MC method lies its ability assess this without needing crop planting structures variations. However, it less effective few meteorological or short monitoring periods. Future efforts should focus accurately assessing data-deficient areas. are guiding regulation efficient use

Language: Английский

Citations

0