The CR of Evaporation: A Calibration‐Free Diagnostic and Benchmarking Tool for Large‐Scale Terrestrial Evapotranspiration Modeling

Abstract

Monthly evapotranspiration (ET) rates for 1979–2015 were estimated by the latest, calibration‐free version of the complementary relationship (CR) of evaporation over the conterminous United States. The results were compared to similar estimates of three land surface models (Noah, VIC, Mosaic), two reanalysis products (National Centers of Environmental Protection Reanalysis II, ERA‐Interim), two remote‐sensing‐based (Global Land Evaporation Amsterdam Model, Penman‐Monteith‐Leuning) algorithms, and the spatially upscaled eddy‐covariance ET measurements of FLUXNET‐MTE. Model validations were performed via simplified water‐balance derived ET rates employing Parameter‐Elevation Regressions on Independent Slopes Model precipitation, United States Geological Survey two‐ and six‐digit Hydrologic Unit Code (HUC2 and HUC6) discharge, and terrestrial water storage anomalies from Gravity Recovery and Climate Experiment, the latter for 2003–2015. The CR outperforms all other multiyear mean annual HUC2‐averaged ET estimates with root‐mean‐square error = 51 mm/year, R = 0.98, relative bias of −1%, and Nash‐Sutcliffe efficiency = 0.94, respectively. Inclusion of the Gravity Recovery and Climate Experiment data into the annual water balances for the shorter 2003–2015 period does not have much effect on model performance. Similarly, the CR outperforms all other models for the linear trend of the annual ET rates over the HUC2 basins. Over the significantly smaller HUC6 basins where the water‐balance validation is more uncertain, the CR still outperforms all other models except FLUXNET‐MTE, which has the advantage of possible local ET measurements, a benefit that clearly diminishes at the HUC2 scale. As the employed CR is calibration‐free and requires only very few meteorological inputs, yet it yields superior ET performance at the regional scale, it may serve as a diagnostic and benchmarking tool for more complex and data intensive models of terrestrial evapotranspiration rates.

A review of the complementary principle of evaporation: from the original linear relationship to generalized nonlinear functions

“…However, E max MT in Eq. (16) may became invalid under conditions with relatively strong available energy yet weak winds (Ma and Zhang, 2017) and was replaced with E max Pen (Crago and Qualls, 2018) in the latest version. After rescaling, Crago (Table 2), C2016 and S2017 demonstrate improvements in their evaporation estimation performance (Crago and Qualls, 2018;Crago et al, 2016;Szilagyi et al, 2017).…”

“…The generalized complementary principle has received much attention for its promising applications in estimating evaporation upon its proposal (Ai et al, 2017;Brutsaert et al, 2017Brutsaert et al, , 2020Han and Tian, 2018a;Liu et al, 2016;Szilagyi et al, 2016;Zhang et al, 2017). However, the boundary conditions and proper mathematical forms of the generalized complementary functions are still under study (Crago et al, 2016;Han and Tian, 2018a;Ma and Zhang, 2017;Szilagyi et al, 2017). In this review, we summarize the 56-year development of the complementary principle with a specific focus on its evolution from a symmetric linear CR to generalized nonlinear functions.…”

Complementary principle of evaporation: From original linear relationship to generalized nonlinear functions

“…In practice,  was also determined from the observed E values in wet condition when E is close to Pen E and/or PT E (Kahler and Brutsaert, 2006;Ma et al, 2015a;Wang et al, 2019). A novel method by using observed air temperature and humidity data under wet environment 375 was proposed by Szilagyi et al (2017) when measured E is lacking, and was successfully used for large-scale CR model applications (Ma and Szilagyi, 2019;Ma et al, 2019).…”

Enhancing the Noah‐MP Ecosystem Response to Droughts With an Explicit Representation of Plant Water Storage Supplied by Dynamic Root Water Uptake

“…The USGS runoff of each hydrological unit was generated by combining the historical flow data collected at stream gauges, drainage basins of the stream gauges, and boundaries of the hydrological units (Brakebill et al, 2011). This data set contains monthly runoff from 1901 to 2016 at various basin scales from 18 first‐level two‐digit basins (HUC2; Figure 2) to eight‐digit basins (HUC8) and has been regarded as a close surrogate of natural runoff for hydroclimate studies (Ashfaq et al, 2013; Ma & Szilagyi, 2019; Oubeidillah et al, 2014; Velpuri et al, 2013). The HUC2 basins are further divided into 334 third‐level six‐digit basins (HUC6) (Seaber et al, 1987).…”