A joint probabilistic index for objective drought identification: the case study of Haiti

Abstract

Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such an extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programmes or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions by combining in a probabilistic framework two consolidated drought indices: the standardized precipitation index (SPI) and the vegetation health index (VHI). The new index, called the probabilistic precipitation vegetation index (PPVI), is scalable, transferable all over the globe and can be updated in near real time. Furthermore, it is a remote-sensing product, since precipitation is retrieved from satellite data and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of the PPVI has been evaluated by means of a receiver operating characteristic curve and compared to that of the SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early-warning systems.

Interactions between precipitation, evapotranspiration and soil-moisture-based indices to characterize drought with high-resolution remote sensing and land-surface model data

“…To track drought status and to analyze the interactions of the land-atmosphere system, modern drought monitoring combines evapotranspiration, soil moisture and even vegetation anomalies in composite drought indices, such as the Objective Blends of Drought Indicators (OBDI) integrated in the U.S. Drought Monitor (USDM; Svoboda et al, 2002) or the Combined Drought Indicator (CDI) of the European Drought Observatory (Sepulcre-Canto et al, 2012). The use of this combined approach to monitoring drought is on an upward trend because even parsimonious composite drought indices like the probabilistic precipitation vegetation index (PPVI) (Monteleone et al, 2020) outperform the capabilities of common indices to characterize drought. One of the major advantages of composite indices is that they facilitate the characterization of drought from multiple perspectives (e.g., meteorological, hydrological or agricultural).…”

Space–time variability in soil moisture droughts in the Himalayan region

“…The soil moisture deficit index (SMDI), developed by Narasimhan and Srinivasan (2005), accounts for variability in soil moisture over a long period. Soil moisture can be derived from hydrological models as an intermediate result, along with other components of the hydrological cycle (for instance, discharge).…”

“…The SMDI was developed with the following three major characteristics: the ability to assess short-term dry conditions, the ability to indicate drought in any season, and the ability to function in any climatic zone. These characteristics of the SMDI are ideal for agricultural drought monitoring (Narasimhan and Srinivasan, 2005). The calculation of the SMDI involves the calculation of the soil water deficit (SD) from soil water/moisture (SW).…”

“…Although 2006 (winter) shows the lowest SPI, only 25 % of the area is under severely dry conditions, as per the SMDI value. Only in some years or in seasons therein do both the SPI and SMDI indicate similar dry conditions, such as in the winter of 2006, the pre-monsoon season of 1984of , 1994of , and 1996of , the monsoon season in 1982of , 1983of , 1994of , 2005of , and 2006of , and the post-monsoon season in 1981of , 1991of , and 1994 5 also indicates one of the lowest levels of precipitation during these periods.…”

“…There are many drought indices, such as the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), evapotranspiration deficit index (ETDI), soil moisture deficit index (SMDI), aggregate drought index (ADI), standardised run-off index (SRI), probabilistic precipitation vegetation index (PPVI), and Palmer drought severity index (PDSI), which indicates the differential nature of droughts that might occur at different time intervals and lag times (Bayissa et al, 2018;Huang et al, 2015;Monteleone et al, 2020;Narasimhan and Srinivasan, 2005). Focusing on soil moisture and variability, SMDI takes into account more variables (such as evapotranspiration, soil properties, and root depth) than SPI and SPEI, which take into account precipitation and precipitation and evapotranspiration, respectively.…”