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A Probabilistic Drought Recovery Assessment Model Using Remote Sensing and Ground-Based Observations

Creative Commons 'BY' version 4.0 license

During 2012-2016, California experienced its most extreme drought in recent history due to a combination of record high temperatures and exceptionally low precipitation. Estimates for when a drought is expected to end are fundamental for risk mitigation and water management. A key question is: What is the likelihood of drought recovery given the current drought condition and expected precipitation in the coming wet-season?

A crucial component of drought recovery assessments is the estimation of terrestrial water storage (TWS). Most previous studies have primarily focused on surface-water hydrology (precipitation and/or runoff) for estimating changes in TWS, neglecting the contribution of groundwater to the recovery time of the system. Here I propose a probabilistic model for drought recovery assessments that integrates both ground-based observations and NASA’s Gravity Recovery and Climate Experiment (GRACE) data, which comprises all terrestrial water sources. I estimated the probability that meteorological inputs, precipitation minus evapotranspiration and runoff, over three different climate scenarios would balance the September 2015 TWS deficit. My results indicate that the probability of recovery for an average wet-season is between 33-57%, for an El Niño year it is between 44-54%, and for a year with a 6-month standard precipitation index (SPI) greater than 1.3 (very wet) it is between 70-96%. I conclude that the predicted El Niño conditions for the 2015-16 wet season did not guarantee drought recovery any more than an average year, unless the event was comparable in volume to that of the 1982-83 and 1997-98 El Niño years (i.e., SPI > 1.3).

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