A global drought monitoring system and dataset based on ERA5 reanalysis: A focus on crop‐growing regions

Abstract

Drought monitoring systems are real‐time information systems focused on drought severity data. They are useful for determining the drought onset and development and defining the spatial extent of drought at any time. Effective drought monitoring requires databases with high spatial and temporal resolution and large spatial and temporal coverage. Recent reanalysis datasets meet these requirements and offer an excellent alternative to observational data. In addition, reanalysis data allow better quantification of some variables that affect drought severity and are more seldom observed. This study presents a global drought dataset and a monitoring system based on the Standardized Precipitation Evapotranspiration Index (SPEI) and ERA5 reanalysis data. Computation of the atmospheric evaporative demand for the SPEI follows the FAO‐56 Penman‐Monteith equation. The system is updated weekly, providing near real‐time information at a 0.5° spatial resolution and global coverage. It also contains a historical dataset with the values of the SPEI at different time scales since January 1979. The drought monitoring system includes the assessment of drought severity for dominant crop‐growing areas. A comparison between SPEI computed from the ERA5 and CRU datasets shows generally good spatial and temporal agreement, albeit with some important differences originating mainly from the different spatial patterns of SPEI anomalies, as well as from employing long‐term climate trends for different regions worldwide. The results show that the ERA5 dataset offers robust results and supports its use for drought monitoring. The new system and dataset are publicly available at the link https://global‐drought‐crops.csic.es/.

Multimodel simulations revealed strong spatial heterogeneity in temperature‐, photoperiod‐ and moisture‐driven modes of autumn phenology in Northern Hemisphere grasslands (25° N–55° N)

“…The results showed that the annual average temperature across the three stations from WFDEI was just 0.46°C higher than the ground‐observed temperature, while the annual precipitation from WFDEI was slightly greater by approximately 22 mm than that from the ground observations; therefore, we think the accuracy of temperature and precipitation from WFDEI is acceptable for this study overall. Meanwhile, for driving some models with continuous water conditions as input, a weekly standardized precipitation evapotranspiration index (SPEI) product at 0.5° spatial resolution calculated from ERA5 reanalysis data was linearly interpolated into 1‐day intervals and used to quantify daily moisture conditions (https://global-drought-crops.csic.es/#map_name=all_spei_0.5) (Vicente‐Serrano et al, 2022).…”

Climate data for Odesa, Ukraine in 2021–2050 based on EURO‐CORDEX simulations

“…Considering not only precipitation increase/decrease but also significant temperature increase due to global warming, Vicente‐Serrano, Beguería, and López‐Moreno (2010) concluded that there is an advantage in the use of drought index that includes both the precipitation and temperature and described the Standardized Precipitation Evapotranspiration Index (SPEI). In recent years, this index has been widely used and, moreover, there are global‐scale interactive datasets and monitoring systems for this index (Vicente‐Serrano et al, 2023; Vicente‐Serrano, Beguería, López‐Moreno, Angulo, & El Kenawy, 2010). In this study, we calculate the SPEI at 1‐, 3‐, 6‐ and 12‐month basis using monthly mean temperature and precipitation sum for the period 1991–2050.…”

Modeling climate migration: dead ends and new avenues

“…The strong focus on temperature and precipitation as predictors of climate mobility in existing models is not an unavoidable necessity. Global observational datasets of floods (Tellman et al, 2021), droughts (Vicente-Serrano et al, 2022), storms (Geiger et al, 2018), wildfires (Artés et al, 2019), and crop yields (Kim et al, 2021;FAOSTAT, 2022) have become available at high quality. In addition, model-based historical reconstructions of these and other variables are available from model intercomparison initiatives such as ISIMIP (Warszawski et al, 2014).…”