Determination of Soft Partitioning Thresholds for Reservoir Drought Warning Levels Under Socio-Hydrological Drought

Yewei Liu, Xiaohua Xu (), Rencai Lin, Weifeng Yang, Peisheng Yang, Siying Li and Hongxin Wang
Additional contact information
Yewei Liu: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Xiaohua Xu: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Rencai Lin: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Weifeng Yang: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Peisheng Yang: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Siying Li: Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Hongxin Wang: The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China

Agriculture, 2025, vol. 15, issue 13, 1-22

Abstract: The failure of traditional drought indices to capture the dynamic supply–demand imbalance in socio-hydrological systems hinders proactive water management and necessitates novel assessment frameworks. The reservoir drought warning water level, serving as a dynamic threshold indicating supply–demand imbalance, provides a critical basis for drought early warning. From a socio-hydrological drought perspective, this study develops a framework for determining staged and graded soft partition thresholds for reservoir drought warning water levels, encompassing three key stages: water stress analysis, phase classification, and threshold determination. First, water demands for the ecological, agricultural, and domestic sectors were quantified based on hydrological analysis and official operational rules. Second, an optimized KPCA-Fisher model delineated the intra-annual supply–demand dynamics into distinct periods. Thirdly, the soft partition thresholds were formulated by coupling these multi-sectoral demands with water deficit rates using a triangular membership function. Applied to the Xianan Reservoir, the framework yielded distinct drought warning thresholds for the identified main flood, critical demand, and dry seasons. Validation against historical droughts (2019 and 2022) confirmed that these soft thresholds more accurately tracked the drought evolution process compared to traditional hard partitions. Furthermore, a sensitivity analysis identified the ecological water demand methodology as a key factor influencing the thresholds, particularly during the critical demand period. The proposed framework for determining staged and graded reservoir drought warning water levels better reflects the complexity of socio-hydrological systems and provides a scientific basis for refined reservoir drought early warnings and management under changing environments.

Keywords: socio-hydrological drought; drought warning water level; soft partition threshold; drought identification; Xianan Reservoir (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/13/1408/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/13/1408/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:13:p:1408-:d:1690952

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().