Heuristic data-inspired scheme to characterize meteorological and groundwater droughts in a semi-arid karstic region under a warming climate

Autor: Hakan Başağaoğlu, Chetan Sharma, Debaditya Chakraborty, Icen Yoosefdoost, F. Paul Bertetti
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Journal of Hydrology: Regional Studies, Vol 48, Iss , Pp 101481- (2023)
Druh dokumentu: article
ISSN: 2214-5818
DOI: 10.1016/j.ejrh.2023.101481
Popis: Study regionThe Edwards Aquifer Region is located in south-central Texas United States.Study focusThe paper focuses on the development and implementation of a data-inspired heuristic drought identification scheme to (i) quantify the intensity, duration, and frequency of precipitation deficit- and high temperature-driven meteorological droughts (PMet- and TMet-droughts), and (ii) link their propagation to groundwater droughts (GW-droughts) using baseline hydroclimatic measures and prevailing drought conditions derived from historical climate data and regional mitigation strategies.New hydrological insights for the regionBased on the intensity, duration, and timing of PMet- and TMet-droughts in the semi-arid karstic region, we identified three distinct GW-droughts, including persistence-driven, preconditions-driven, and intensity-driven droughts. The analysis revealed that successive heavy precipitation events are needed to end GW-droughts. The scheme also identified TMet-droughts with the longest dry spells, TMet- and PMet-droughts with the highest intensity, and GW-drought with the second-highest intensity on record all occurred over the past 15 years. These findings provide evidence for a warming climate, intensified meteorological droughts, and increasing stress on the aquifer. Among the artificial intelligence models used, Extremely Randomized Trees (ERT) regressor predicted time series of intensity & duration of GW-droughts from hydroclimatic features with high accuracy. The ERT classifier revealed that the duration of PMet droughts and the intensity of TMet droughts are the topmost decisive features in predicting GW-drought intensity in the region.
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