A Probabilistic Analysis of Drought Areal Extent Using SPEI-Based Severity-Area-Frequency Curves and Reanalysis Data

Abstract

Assessing and monitoring the spatial extent of drought is of key importance to forecasting the future evolution of drought conditions and taking timely preventive and mitigation measures. A commonly used approach in regional drought analysis involves spatially interpolating meteorological variables (e.g., rainfall depth during specific time intervals, deviation from long-term average rainfall) or drought indices (e.g., Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index) computed at specific locations. While plotting a drought descriptor against the corresponding percentage of affected areas helps visualize the historical extent of a drought, this approach falls short of providing a probabilistic characterization of the severity of spatial drought conditions. That can be overcome by identifying drought Severity-Area-Frequency (SAF) curves over a region, which establishes a link between drought features with a chosen probability of recurrence (or return period) and the corresponding proportion of the area experiencing those drought conditions. While inferential analyses can be used to estimate these curves, analytical approaches offer a better understanding of the main statistical features that drive the spatial evolution of droughts. In this research, a technique is introduced to mathematically describe the Severity-Area-Frequency (SAF) curves, aiming to probabilistically understand the correlation between drought severity, measured through the SPEI index, and the proportion of the affected region. This approach enables the determination of the area’s extent where SPEI values fall below a specific threshold, thus calculating the likelihood of observing SAF curves that exceed the observed one. The methodology is tested using data from the ERA5-Land reanalysis project, specifically studying the drought occurrences on Sicily Island, Italy, from 1950 to the present. Overall, findings highlight the improvements of incorporating the spatial interdependence of the assessed drought severity variable, offering a significant enhancement compared to the traditional approach for SAF curve derivation. Moreover, they validate the suitability of reanalysis data for regional drought analysis.

Synchronized Structure and Teleconnection Patterns of Meteorological Drought Events over the Yangtze River Basin, China

“…These objective and quantitative indices are effective tools for assessing the status and characteristics of meteorological drought, enabling the quantification of temporal characteristics such as the frequency, intensity, duration, and severity of MDEs [28][29][30]. In addition to temporal variability, many existing studies have characterized the spatial behaviors of MDEs using severity-area-duration (SAD) and severity-area-frequency (SAF) curves, offering valuable insights into the spatial evolution of MDEs in terms of centroid, displacement, and direction [31][32][33][34]. For instance, Palazzolo et al [34] introduced a probabilistic framework to describe the SPEI-based SAF curves, aiming to assess the drought severity as a function of the SPEI at the regional scale and link it to the spatial extent of the affected area.…”

“…In addition to temporal variability, many existing studies have characterized the spatial behaviors of MDEs using severity-area-duration (SAD) and severity-area-frequency (SAF) curves, offering valuable insights into the spatial evolution of MDEs in terms of centroid, displacement, and direction [31][32][33][34]. For instance, Palazzolo et al [34] introduced a probabilistic framework to describe the SPEI-based SAF curves, aiming to assess the drought severity as a function of the SPEI at the regional scale and link it to the spatial extent of the affected area. Nonetheless, the spatiotemporal topological structure of MDEs over large-scale areas (i.e., the association relationships of synchronous MDE occurrences) and the involved spatial extents of synchronous MDEs still remain unclear.…”