Soil moisture and streamflow deficit anomaly index: an approach to quantify drought hazards by combining deficit and anomaly

Abstract

Abstract. Drought is understood as both a lack of water (i.e., a deficit compared to demand) and a temporal anomaly in one or more components of the hydrological cycle. Most drought indices, however, only consider the anomaly aspect, i.e., how unusual the condition is. In this paper, we present two drought hazard indices that reflect both the deficit and anomaly aspects. The soil moisture deficit anomaly index, SMDAI, is based on the drought severity index, DSI (Cammalleri et al., 2016), but is computed in a more straightforward way that does not require the definition of a mapping function. We propose a new indicator of drought hazard for water supply from rivers, the streamflow deficit anomaly index, QDAI, which takes into account the surface water demand of humans and freshwater biota. Both indices are computed and analyzed at the global scale, with a spatial resolution of roughly 50 km, for the period 1981–2010, using monthly time series of variables computed by the global water resources and the model WaterGAP 2.2d. We found that the SMDAI and QDAI values are broadly similar to values of purely anomaly-based indices. However, the deficit anomaly indices provide more differentiated spatial and temporal patterns that help to distinguish the degree and nature of the actual drought hazard to vegetation health or the water supply. QDAI can be made relevant for stakeholders with different perceptions about the importance of ecosystem protection, by adapting the approach for computing the amount of water that is required to remain in the river for the well-being of the river ecosystem. Both deficit anomaly indices are well suited for inclusion in local or global drought risk studies.

Analyzing the informative value of alternative hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale

“…This concept is not new and several definitions were already summarized in Wilhite and Glantz (1985), e.g., drought is a "period during which streamflows are inadequate to supply established uses under a given water management system" (Linsley et al, 1975 in Wilhite andGlantz, 1985: 115). Nevertheless, only a few studies exist where the combination of anomaly and deficit was translated into drought hazard indicators for soil moisture (Palmer, 1965;Cammalleri et al, 2016b;Popat and Döll, 2021) and streamflow (Popat and Döll, 2021). In the present study, the water deficit aspect of drought is represented by the indicators CDQI1-WUs and CDQI1-WUs-EFR where surface water demand is taken as the threshold (Table 1).…”

“…Drought hazard is generally defined as a climate-induced anomaly, i.e., a period of below-normal water availability (McKee et al, 1993;van Lanen, 2006;van Loon, 2015). This concept can be broadened by assuming that a drought only occurs if the anomaly coincides with a water deficit for people or ecosystems (Cammalleri et al, 2016b;Popat and Döll, 2021). This concept is not new and several definitions were already summarized in Wilhite and Glantz (1985), e.g., drought is a "period during which streamflows are inadequate to supply established uses under a given water management system" (Linsley et al, 1975 in Wilhite andGlantz, 1985: 115).…”

“…CDQI1-WUs-EFR is the only indicator in this study that takes into account ecosystem health, an aspect that should be included in a global-scale DEWS. Alternatively, the cumulative anomaly-deficit indicator QDAI (Popat and Döll, 2021), considering EFR based on a similar approach, can inform decision-makers and water users about the drought hazard for water supply.…”

“…Drought occurs when there is a prolonged time period with less water than normal in different components of the hydrological cycle (van Loon et al, 2016) but the term drought also has the connotation that during the drought period there is less water than required (Popat and Döll, 2021). No universal definition of "drought" exists (Lloyd-Hughes, 2014).…”

“…From time series of standardized or non-standardized severity, further drought characteristics can be derived such as the probability of a drought event of a certain severity (e.g. Cammalleri et al, 2016a;Popat and Döll, 2021).…”