Abstract
Abstract. Accurately estimating large-scale evapotranspiration (ET) rates is essential to understanding and predicting global change. Evapotranspiration models that are applied at a continental scale typically operate on relatively large spatial grids, with the result that the heterogeneity in land surface properties and processes at smaller spatial scales cannot be explicitly represented. Averaging over this spatial heterogeneity may lead to biased estimates of energy and water fluxes. Here we estimate how averaging over spatial heterogeneity in precipitation (P) and potential evapotranspiration (PET) may affect grid-cell-averaged evapotranspiration rates, as seen from the atmosphere over heterogeneous landscapes across the globe. Our goal is to identify where, under what conditions, and at what scales this “heterogeneity bias” could be most important but not to quantify its absolute magnitude. We use Budyko curves as simple functions that relate ET to precipitation and potential evapotranspiration. Because the relationships driving ET are nonlinear, averaging over subgrid heterogeneity in P and PET will lead to biased estimates of average ET. We examine the global distribution of this bias, its scale dependence, and its sensitivity to variations in P vs. PET. Our analysis shows that this heterogeneity bias is more pronounced in mountainous terrain, in landscapes where spatial variations in P and PET are inversely correlated, and in regions with temperate climates and dry summers. We also show that this heterogeneity bias increases on average, and expands over larger areas, as the grid cell size increases.
Highlights
Estimates of evapotranspiration (ET) fluxes have significant implications for future temperature predictions
Even the highest-resolution continental-scale evapotranspiration models, such as those that are embedded in Earth system models (ESMs), typically cannot explicitly represent the spatial heterogeneity of land surface hydrological properties at scales that are important to atmospheric fluxes
Because evapotranspiration (ET) processes are inherently bounded by water and energy constraints, over the long term, ET is always a nonlinear function of available water and potential evapotranspiration (PET), whether this function is expressed as a Budyko curve or another ET model
Summary
Estimates of evapotranspiration (ET) fluxes have significant implications for future temperature predictions. Rouholahnejad Freund and Kirchner (2017) quantified the impact of subgrid heterogeneity on grid-average ET using a simple Budyko curve (Turc, 1954; Mezentsev, 1955) in which long-term average ET is a nonlinear function of longterm averages of precipitation (P ) and potential evaporation (PET) They showed mathematically that averaging over spatially heterogeneous P and PET results in overestimation of ET within the Budyko framework (Fig. 1). Their analysis implies that large-scale ESMs that overlook land surface heterogeneity will yield biased evapotranspiration estimates due to the inherent nonlinearity in ET processes. Our hypotheses, derived from the Budyko framework as summarized in Eq (4) below, are that (1) strongly heterogeneous landscapes, such as mountainous terrain, will exhibit greater heterogeneity bias; (2) this bias will be larger in climates where P and PET are inversely correlated in space; and (3) heterogeneity bias will decrease as the spatial scales of averaging decrease
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