Abstract
Abstract. Evapotranspiration (ET) links the hydrological, energy and carbon cycles on the land surface. Quantifying ET and its spatio-temporal changes is also key to understanding climate extremes such as droughts, heatwaves and flooding. Regional ET estimates require reliable observation-based gridded ET datasets, and while many have been developed using physically based, empirically based and hybrid techniques, their efficacy, and particularly the efficacy of their uncertainty estimates, is difficult to verify. In this work, we extend the methodology used in Hobeichi et al. (2018) to derive two new versions of the Derived Optimal Linear Combination Evapotranspiration (DOLCE) product, with observationally constrained spatio-temporally varying uncertainty estimates, higher spatial resolution, more constituent products and extended temporal coverage (1980–2018). After demonstrating the efficacy of these uncertainty estimates with out-of-sample testing, we derive novel ET climatology clusters for the land surface, based on the magnitude and variability of ET at each location on land. The new clusters include three wet and three dry regimes and provide an approximation of Köppen–Geiger climate classes. The verified uncertainty estimates and extended time period then allow us to examine the robustness of historical trends spatially and in each of these six ET climatology clusters. We find that despite robust decreasing ET trends in some regions these do not correlate with behavioural ET clusters. Each cluster, and the majority of the Earth's surface, shows clear robust increases in ET over the recent historical period. The new datasets DOLCE V2.1 and DOLCE V3 can be used for benchmarking global ET estimates and for examining ET trends respectively.
Highlights
Understanding the spatio-temporal variability of evapotranspiration (ET) is a critical part of understanding the processes that lead to high impact weather phenomena, such as droughts (Han et al, 2018; Quesada-Montano et al, 2019; Sheffield et al, 2012; Teuling et al, 2013), heatwaves (Teuling, 2018; Ukkola et al, 2018) and flooding (Dawdy et al, 1972; Sharma et al, 2018)
We find that each Köppen climate (KC) class overlaps with only one ET regime with only two exceptions (Table 4): (i) land characterised by a “Dry Steppe Hot arid” climate belongs to the “Mild low ET with medium variability regime”, but in two regions, the Indian Deccan plateau and Argentinean Gran Chaco low forests, where the climate is BSh, the ET regime is “Mild high ET with medium variability”; (ii) Regions with a “Mild temperate Fully humid Hot summer” climate overlap with the “Mild high ET with medium variability” regime in coastal regions and to the “Very high ET with low variability” regime in inland regions
The new datasets are the result of several key improvements over their predecessor, incorporating more parent products in Derived Optimal Linear Combination Evapotranspiration (DOLCE) V2.1, more in situ data, testing a range of alternative implementations of its weighting and bias-correction approach, increased spatial resolution, and covering a longer time period
Summary
Understanding the spatio-temporal variability of evapotranspiration (ET) is a critical part of understanding the processes that lead to high impact weather phenomena, such as droughts (Han et al, 2018; Quesada-Montano et al, 2019; Sheffield et al, 2012; Teuling et al, 2013), heatwaves (Teuling, 2018; Ukkola et al, 2018) and flooding (Dawdy et al, 1972; Sharma et al, 2018). As detailed below when describing the datasets we use here, “physically based” approaches use equations that represent different physical, chemical, and biological processes and incorporate satellitebased atmospheric forcing and parameterisation of land surface characteristics, while “empirical” approaches integrate ground-based measurements of ET together with satellite data and ground-based measurements of vegetation characteristics and land surface parameters.
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