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

Analysis of rainfall abundance and drought occurrence and probability of flood and drought occurrence in Yellow River Basin based on Copula function family DOI Creative Commons
Yuping Han, Jinhang Li,

Mengdie Zhao

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102242 - 102242

Published: Feb. 17, 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