A Framework to Quantify Drought Recovery Time Accounting for the Lagged Effect

ABSTRACT Recovery time of ecosystems from drought is an important index to assess drought impacts and ecosystem resilience. The lagged effect reflects the drought resistance capacity of ecosystems and indicates the initial characteristic of drought recovery. However, prior studies quantifying drought recovery time overlooked the lagged effect. This study proposes a new method to quantify drought recovery time, which, for the first time, takes the lagged effect of drought on ecosystems into account. It categorises the progress from the onset of drought to the end of drought recovery into two distinct phases: the lag phase and the dynamic recovery phase, which are then integrated with drought duration to ascertain the post‐drought recovery time (RT p ). Integrating the lagged effect into the quantification of RT p facilitates the establishment of a coherent relationship between drought indices and recovery criteria, which typically rely on distinct parameters. Yangtze River Basin (YRB), the largest basin in China, is used as the case to validate our method. The results in YRB indicate that incorporating the lagged effect improves the accuracy of RT p estimation and alleviates the discrepancies in its spatial–temporal distribution as assessed by three different drought recovery criteria. In contrast to prior research, this novel approach can also identify cases which achieve recovery before the end of drought events, a phenomenon predominantly observed in regions with low drought resistance. Our proposed method helps to reconcile the contradictory conclusions on quantifying global RT p . As such, it appears to be a novel method that contributes to a more thorough comprehension of the drought recovery process.

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Publication Details

Journal
Hydrological Processes
Published
2025-07-01
DOI
https://doi.org/10.1002/hyp.70194
Citations
1
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
1.11

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article

A Framework to Quantify Drought Recovery Time Accounting for the Lagged Effect

Zhiyong Liu, Qing Wei, Ye Tian, Qian Zhu et al.
1 citations
Hydrological Processes
Hydrology and Drought Analysis
1.11
article

A Framework to Quantify Drought Recovery Time Accounting for the Lagged Effect

Zhiyong Liu, Qing Wei, Ye Tian, Qian Zhu, Xichao Gao, Tiantian Yang
article en
1 citations

Abstract

ABSTRACT Recovery time of ecosystems from drought is an important index to assess drought impacts and ecosystem resilience. The lagged effect reflects the drought resistance capacity of ecosystems and indicates the initial characteristic of drought recovery. However, prior studies quantifying drought recovery time overlooked the lagged effect. This study proposes a new method to quantify drought recovery time, which, for the first time, takes the lagged effect of drought on ecosystems into account. It categorises the progress from the onset of drought to the end of drought recovery into two distinct phases: the lag phase and the dynamic recovery phase, which are then integrated with drought duration to ascertain the post‐drought recovery time (RT p ). Integrating the lagged effect into the quantification of RT p facilitates the establishment of a coherent relationship between drought indices and recovery criteria, which typically rely on distinct parameters. Yangtze River Basin (YRB), the largest basin in China, is used as the case to validate our method. The results in YRB indicate that incorporating the lagged effect improves the accuracy of RT p estimation and alleviates the discrepancies in its spatial–temporal distribution as assessed by three different drought recovery criteria. In contrast to prior research, this novel approach can also identify cases which achieve recovery before the end of drought events, a phenomenon predominantly observed in regions with low drought resistance. Our proposed method helps to reconcile the contradictory conclusions on quantifying global RT p . As such, it appears to be a novel method that contributes to a more thorough comprehension of the drought recovery process.

Hydrological ProcessesVol. 39(7)
Sun Yat-sen University (CN), Nanjing University of Information Science and Technology (CN), China Institute of Water Resources and Hydropower Research (CN), Southeast University (CN), University of Oklahoma (US)
National Natural Science Foundation of China
Openalex Percentile: Top 22%
Hydrology and Drought Analysis
1.11
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