We estimate E0 using the FAO-56 model of Penman-Monteith reference evapotranspiration, driven by radiative and meteorological forcings from NASA’s MERRA-2 reanalysis dataset. The E0 output is then spatially downscaled to match sub-grid variability provided by the International Water Management Institute (IWMI) monthly climatologic potential evaporation grids. The resulting E0 reanalysis spans the globe at a 0125-degree spatial resolution, and is daily from January 1, 1980, to within a month of the present. The E0 reanalysis verifies well against station-derived observations across Africa.
As drought is driven by, and/or reflected in, variations in E0, the question arises, “what drives E0 variability across the globe?” We answer this by decomposing the temporal variability of E0 into all of its physical drivers through a mean-value, second-moment uncertainty analysis in which both the sensitivity of E0 to its drivers and their observed variabilities are accounted for. Amongst other things, this rigorous variability analysis informs us as to which drivers are the most important to represent well in estimating E0.
We use the new E0 reanalysis in estimating the Evaporative Demand Drought Index (EDDI), which has been shown to provide early warning of agricultural and hydrologic drought across the continental US. Uniquely, EDDI leverages relationships of a fully physical E0 and actual evapotranspiration, measuring E0’s physical response to surface drying due to land/atmosphere interactions, without need for precipitation or surface moisture data. EDDI provides a perspective of drought from the atmospheric demand side previously missing from the “convergence of evidence” approach to drought monitoring and provides a valuable planning window for land and water resource managers and agricultural producers.
The new E0 reanalysis permits explicit attribution of changes in the drought signal in E0 to its individual drivers. We outline the methodology and results of this uncertainty analysis and attribution study across the space-time domains of established droughts, demonstrating the drivers of E0 variability and the evaporative drivers of crop stress and food insecurity, concentrating on Famine Early Warning Systems Network (FEWS NET) food-insecure countries during established drought periods.
Here, we present these developments so far, including the development of a new global E0 reanalysis and its verification, and the rigorous variability analysis across the globe. Further, we demonstrate the power of E0 in (i) driving EDDI for famine early warning, and (ii) attributing the drivers of drought from the evaporative perspective.