Abstract
Abstract. Identifying and quantifying drought in retrospective is a necessity for better understanding drought conditions and the propagation of drought through the hydrological cycle and eventually for developing forecast systems. Hydrological droughts refer to water deficits in surface and subsurface storage, and since these are difficult to monitor at larger scales, several studies have suggested exploiting total water storage data from the GRACE (Gravity Recovery and Climate Experiment) satellite gravity mission to analyze them. This has led to the development of GRACE-based drought indicators. However, it is unclear how the ubiquitous presence of climate-related or anthropogenic water storage trends found within GRACE analyses masks drought signals. Thus, this study aims to better understand how drought signals propagate through GRACE drought indicators in the presence of linear trends, constant accelerations, and GRACE-specific spatial noise. Synthetic data are constructed and existing indicators are modified to possibly improve drought detection. Our results indicate that while the choice of the indicator should be application-dependent, large differences in robustness can be observed. We found a modified, temporally accumulated version of the Zhao et al. (2017) indicator particularly robust under realistic simulations. We show that linear trends and constant accelerations seen in GRACE data tend to mask drought signals in indicators and that different spatial averaging methods required to suppress the spatially correlated GRACE noise affect the outcome. Finally, we identify and analyze two droughts in South Africa using real GRACE data and the modified indicators.
Highlights
Droughts are recurrent natural hazards that affect the environment and economy with potentially catastrophic consequences
As a result of this procedure, we identified three clusters located in eastern Brazil (EB), southern Africa (SA), and western India (WI), which were affected by droughts in the past (e.g., Parthasarathy et al, 1987; Rouault and Richard, 2003; Coelho et al, 2016)
We find that regional-scale Drought Severity Index (DSI) and drought index (DI) indicators, as well as the outputs derived by the Thomas method for southern Africa computed from averaging total water storage changes (TWSCs) first, are different to the averaging indicators computed at grid scale from TWSC
Summary
Droughts are recurrent natural hazards that affect the environment and economy with potentially catastrophic consequences. With climate change and population growth, the frequency and impact of droughts are projected to increase for many regions of the world (IPCC, 2013). In this study we focus on hydrological drought, a multiscale problem which may last weeks or many years and which may affect local or continental regions. The severe drought between mid-2011 and mid-2012 affected millions of people in the entire eastern Africa region (Somalia, Djibouti, Ethiopia, and Kenya) and led to famine with an estimated 258 000 deaths (Checchhi and Robinson, 2013). From 2012 to 2016, the US state of California experienced a historical drought that adversely affected groundwater levels, forests, crops, and fish populations and led to widespread land subsidence (Mann and Gleick, 2015; Moore et al, 2016). For South Africa, due to a complex rainfall regime, areas and the percentage of land surface affected by drought can vary strongly (Rouault and Richard, 2005)
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