Reliability of meteorological drought indices for predicting soil moisture droughts

Abstract

Abstract. Meteorological drought indices based on precipitation and/or evaporation are commonly used to detect the presence, severity and duration of soil moisture droughts. However, it is debatable whether droughts can be adequately characterised using only precipitation and/evaporation, or whether more physical based methods using soil water deficits and pressures is necessary. To address this question, the performances of two commonly used meteorological drought indices, the Standard Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI), are evaluated against soil moisture 10 droughts identified using a physically based soil water model. Our analysis is based on three sites in Eastern Australia, each representing specific soil-climate conditions. Drought duration and severity were estimated using SPI and RDI and soil water pressure data were simulated with Hydrus-1D. The performance of the two drought indices was measured in terms of their correlation with simulated monthly minimum soil water pressures, and their ability to estimate the frequency with which the simulated pressure drops below threshold values. 15There was a significant correlation between the two drought indices (SPI and RDI) and the monthly minimum soil water pressure. Failure rate (FR) and false alarm rate (FAR) of drought indices detect soil moisture drought reasonably well (FR and FAR is <50%) for both drought indices (SPI and RDI) and soil depths (5cm and 30 cm) (except Melbourne). Overall SPI performs better (except shallow soils in Bourke) than RDI. However an uncertainty of the FR and FAR in the soil water retention curve is always higher than the uncertainties of drought indices. The complexity and the uncertainty in the model 20 encourage to use the simple drought indices, however the model provide physically relevant soil water pressure values which are species specific for plants.

Searching for the optimal drought index and timescale combination to detect drought: a case study from the lower Jinsha River basin, China

“…For the matching between forecasts and observations, two monthly series of events were created (one for the events detected according to the ODE values and one for the historical events), where for each month either a "drought" or a "no drought" condition is assigned. Different scores for contrasting this type of dichotomous forecasts (occurrence vs. no occurrence) exist: the Peirce skill score, PSS (Hanssen and Kuipers, 1965;Murphy and Daan, 1985;Peirce, 1884); the Heidke skill score, HSS (Heidke, 1926); the Gilbert skill score, GSS (Schaefer, 1990); or the odds ratio skill score, ORSS (Stephenson, 2000). As recommended by Candogan Yossef et al (2012), the PSS is used in this study.…”

“…To fill the lack of specific drought-related information, most studies assess the performance of drought indices against results from hydrological soil water models (Halwatura et al, 2016;Hao and AghaKouchak, 2013;Trambauer et al, 2014;Vasiliades et al, 2011;Wanders et al, 2010). However, the performance of these types of studies depends on the accuracy of the models.…”

Comparative evaluation of precipitation-temperature based drought indices (DIs): A case study of Moroccan Lower Sebou basin

“…This set of approaches is generally always accompanied by several indicators and indices essential to assess and monitor the different types of drought and the aspect of the hydrological cycle. There are more than 150 applied drought indices (DIs) and indicators worldwide (Halwatura et al, 2016) of which about 40 are commonly used as measures for monitoring spatial and temporal variability in drought-prone regions (WMO & GWP, 2016). In the absence of a universal definition for drought, no single index can meet the objectives of this study under different global climates.…”

Authors reply to Referee #2

“…For the matching between forecasts and observations, two monthly series of events were created (one for the events detected according to the ODE values and one for the historical events), where for each month either a "drought" or a "no drought" condition is assigned. Different scores for contrasting this type of dichotomous forecasts (occurrence vs. no occurrence) exist: the Peirce skill score, PSS (Hanssen and Kuipers, 1965;Murphy and Daan, 1985;Peirce, 1884); the Heidke skill score, HSS (Heidke, 1926); the Gilbert skill score, GSS (Schaefer, 1990); or the odds ratio skill score, ORSS (Stephenson, 2000). As recommended by Candogan Yossef et al (2012), the PSS is used in this study.…”