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A copula-based parametric composite drought index for drought monitoring and applicability in arid Central Asia.

Authors :
Suo, Nanji
Xu, Changchun
Cao, Linlin
Song, Lingling
Lei, Xiaoni
Source :
CATENA. Feb2024, Vol. 235, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• NTDI (nonlinear trivariate drought index) performs better than single type drought indices for drought monitoring. • NTDI can effectively monitor the drought onset and duration. • NTDI shows good potential in the agricultural drought assessment. Due to the complexity of meteorological and hydrological conditions in a changing environment, previous drought indices for monitoring a specific drought type do not reflect the overall regional situation of water scarcity. Therefore, in order to obtain accurate and reliable drought monitoring, a more integrated drought index should be developed to identify drought events comprehensively. In this paper, a non-linear trivariate drought index (NTDI) was constructed based on the joint probability distribution of parametric copulas, combining precipitation (P), potential evapotranspiration (PET), and root zone soil moisture (SM) variables. Subsequently, it was respectively compared with four drought indices, SPEI, SSMI, SC-PDSI and TVDI, and cross-validated with actual recorded drought events and annual crop yield to evaluate its applicability in arid Central Asia (ACA). The results indicated that: (1) Frank copula (1-,3-month scale) and Gumbel copula (6-,12-month scale) were considered to be the best-fitted copula functions for constructing joint probability distributions in the ACA. (2) The NTDI integrated the P-PET and SM drought signals to sensitively capture drought onset and duration, reflecting the combined characteristics of meteorological and agricultural drought. (3) The drought information expressed by NTDI was generally consistent with recorded drought events, and the monitoring results are accurate. (4)The NTDI performed better in agricultural drought monitoring than other drought indices. This study provides a reliable multivariate composite indicator which is significant for drought monitoring, prevention and risk assessment in ACA. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03418162
Volume :
235
Database :
Academic Search Index
Journal :
CATENA
Publication Type :
Academic Journal
Accession number :
173943112
Full Text :
https://doi.org/10.1016/j.catena.2023.107624