Global Evaluation of Optimal Probability Distribution Functions for RDI Assessments

ABSTRACT Drought is caused by an imbalance between precipitation and evapotranspiration. A prolonged lack of precipitation and/or excess evapotranspiration results in insufficient replenishment of runoff and groundwater. Choosing an appropriate drought index is crucial for managing water resources e...

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Published in:Hydrological processes Vol. 39; no. 1
Main Authors: Asadi Zarch, Mohammad Amin, Motraghi, Fatemeh
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2025
Wiley Subscription Services, Inc
Subjects:
Assessments
climate
Climate change
Climatic indexes
CRU TS 4.03 database
Distribution functions
Drought
Drought characteristics
Drought index
droughts
Evapotranspiration
GCMs
Groundwater
Groundwater runoff
Hydrologic processes
Performance evaluation
Potential evapotranspiration
Precipitation
probability
Probability distribution
Probability distribution functions
probability distributions
RDI
Regional development
runoff
Water management
Water resources
Water resources management
ISSN:0885-6087, 1099-1085
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Summary:ABSTRACT Drought is caused by an imbalance between precipitation and evapotranspiration. A prolonged lack of precipitation and/or excess evapotranspiration results in insufficient replenishment of runoff and groundwater. Choosing an appropriate drought index is crucial for managing water resources effectively. The Reconnaissance Drought Index (RDI) which considers both precipitation and potential evapotranspiration is recommended for identifying droughts in a changing climate. Standardising the index involves using a probability distribution, and choosing the correct distribution is important for accurate assessments of drought characteristics. Furthermore, identifying the optimal distributions for RDI assessments ensures reliable evaluations of subsequent hydrological processes. Based on a regional study, the index developers suggest using gamma or log‐normal probability distributions to compute the index using real observations. Furthermore, there is a lack of research on suitable distributions for RDI calculation using GCMs projections (simulated data) in drought projection studies. This global study aims to address these gaps in research by evaluating the performance of probability distributions in calculating RDI. The study consists of two phases: The first phase involves identifying the appropriate distribution for historical observed data, whilst the second phase does the same for future projections from GCMs. To achieve this, 17 probability distributions are applied. The 0.5° × 0.5° gridded CRU data from 1950 to 2018 and projections of 18 GCMs from 2006 to 2080 are utilised. The analysis identified the log logistic, inverse Gaussian and gamma distributions as the best fits for the historical period. For future projections, the gamma, inverse Gaussian and Nakagami distributions are recommended. Finally, the findings revealed for both periods, Fitting to the Best Distribution of any Grid (FBDG) performs the best for large‐scale drought studies using gridded data. In the historical period, the log logistic, inverse Gaussian and gamma distributions are the best, respectively. For the future period, the projections indicate the gamma, inverse Gaussian and Nakagami distributions present the best fit, respectively. For both periods, Fitting to the Best Distribution of any Grid (FBDG) performs the best for large‐scale drought studies using gridded data.
Bibliography:The authors received no specific funding for this work.
Funding
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ISSN:0885-6087
1099-1085
DOI:10.1002/hyp.70037