View in EDS

A framework for identifying discriminative model, key factors, and precipitation blocking threshold on triggering drought propagation in the Xijiang River Basin (XRB).

Saved in:
Bibliographic Details
Title: A framework for identifying discriminative model, key factors, and precipitation blocking threshold on triggering drought propagation in the Xijiang River Basin (XRB).
Authors: Zhang, Shinan1,2 (AUTHOR), Lin, Qingxia1,2 (AUTHOR) lqxhhu@foxmail.com, Chang, Wenjuan1,2 (AUTHOR), Wu, Zhiyong3 (AUTHOR), Peng, Tao1,2 (AUTHOR), Guo, Jiali1,2 (AUTHOR), Wang, Xinzhi1,2 (AUTHOR)
Source: Climate Dynamics. Mar2026, Vol. 64 Issue 3, p1-23. 23p.
Subjects: Droughts, Machine learning, Prediction models, Watersheds
Abstract: Exploring the triggers of drought propagation is essential for understanding drought dynamics. Current research primarily provides probability thresholds for drought propagation based on Copula and Bayesian approaches. However, water resource managers are more interested in determining whether droughts can actually be triggered, rather than solely receiving probabilistic reminders. In this study, we propose a framework for identifying the discriminative model, key factors, and precipitation blocking thresholds that trigger meteorological-to-agricultural drought in the Xijiang River Basin (XRB). The results highlight the influence of non-effective precipitation days (NEPD), meteorological drought duration, the meteorological drought area and its spatial complexity (A_GAM) on triggering propagation. Daily precipitation exceeding 3 mm begins to mitigate drought propagation. Through analyzing 45 actual drought events using 36 models comprising 4 factor combinations and 9 machine learning methods, we found that the GANs-enhanced K-nearest neighbors (KNN) algorithm is the optimal discriminative model. Sensitivity analysis based on the model reveals that a reduction in NEPD (daily precipitation ≤ 3 mm) can decrease the propagation ratio by 11.1%. From 2025 to 2099, the propagation ratios under SSP1-2.6, 2–4.5, 3–7.0, and 5–8.5 are all projected to exceed 73.0%. If measures are taken to reduce NEPD, propagation could be inhibited by 6.0% to 17.6%. The proposed framework enables more direct determination of whether large-scale meteorological drought will trigger agricultural drought and quantifies the precipitation mitigation effect. These findings can provide scientific support for agricultural drought early warning systems. [ABSTRACT FROM AUTHOR]
Copyright of Climate Dynamics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Exploring the triggers of drought propagation is essential for understanding drought dynamics. Current research primarily provides probability thresholds for drought propagation based on Copula and Bayesian approaches. However, water resource managers are more interested in determining whether droughts can actually be triggered, rather than solely receiving probabilistic reminders. In this study, we propose a framework for identifying the discriminative model, key factors, and precipitation blocking thresholds that trigger meteorological-to-agricultural drought in the Xijiang River Basin (XRB). The results highlight the influence of non-effective precipitation days (NEPD), meteorological drought duration, the meteorological drought area and its spatial complexity (A_GAM) on triggering propagation. Daily precipitation exceeding 3 mm begins to mitigate drought propagation. Through analyzing 45 actual drought events using 36 models comprising 4 factor combinations and 9 machine learning methods, we found that the GANs-enhanced K-nearest neighbors (KNN) algorithm is the optimal discriminative model. Sensitivity analysis based on the model reveals that a reduction in NEPD (daily precipitation ≤ 3 mm) can decrease the propagation ratio by 11.1%. From 2025 to 2099, the propagation ratios under SSP1-2.6, 2–4.5, 3–7.0, and 5–8.5 are all projected to exceed 73.0%. If measures are taken to reduce NEPD, propagation could be inhibited by 6.0% to 17.6%. The proposed framework enables more direct determination of whether large-scale meteorological drought will trigger agricultural drought and quantifies the precipitation mitigation effect. These findings can provide scientific support for agricultural drought early warning systems. [ABSTRACT FROM AUTHOR]
ISSN:09307575
DOI:10.1007/s00382-026-08078-3