Abstract
Abstract. Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote-sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman–Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that integrates the Moderate Resolution Imaging Spectroradiometer (MODIS) derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth–Wallace (SW) framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at 13 core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of −4 %. These error statistics showed relatively better accuracies than a widely used but previous version of the SEB-based Surface Energy Balance System (SEBS) model, which utilized a simple NDVI-based parameterization of surface roughness (zOM), and the PM-based MOD16 ET. SEBS was found to overestimate (PBIAS = 28 %) and MOD16 was found to underestimate ET (PBIAS = −26 %). The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapor pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories, as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient, points to the potential for this simplified analytical model for near-real-time ET mapping from regional to continental scales.
Highlights
Evapotranspiration (ET) is highly variable in space and time and plays a fundamental role in hydrology and land– atmosphere interactions
Combining results from 13 core AmeriFlux sites, it is apparent that STIC1.2 captured 66 % of the observed variability (R2 = 0.66) in 8-day cumulative ET (Table 3) with an overall root mean square error (RMSE), mean absolute error (MAE), and percent bias (PBIAS) of 7.5 mm, 5.4 mm, and −3 %, respectively
This paper establishes the first ever regional-scale implementation of a simplified thermal remote-sensing-based model, Surface Temperature Initiated Closure (STIC1.2) for spatially explicit ET mapping, which is independent of any empirical parameterization of aerodynamic/surface conductances and aerodynamic temperature
Summary
Evapotranspiration (ET) is highly variable in space and time and plays a fundamental role in hydrology and land– atmosphere interactions. Despite many advancements in mapping spatially distributed ET, some fundamental challenges remain in existing SEB algorithms including (a) the inequality between TR and the aerodynamic temperature (T0), which is essentially responsible for the exchanges of H and λE (Chávez et al, 2010; Boulet et al, 2012); (b) a non-unique relationship between T0 and TR due to differences between the roughness lengths (i.e., effective source/sink heights) for momentum (z0M) and heat (z0H) within vegetation canopy and substrate complex (Troufleau et al, 1997; Paul et al, 2014; van Dijk et al, 2015b); (c) the unavailability of a universally agreed model to estimate T0 (Colaizzi et al, 2004); and (d) the lack of a physically based or analytical gA model To overcome these challenges, we implement the current version of a recently developed analytical ET model, the Surface Temperature Initiated Closure (STIC, version 1.2; Mallick et al, 2014, 2015, 2016), using the Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop spatially distributed ET maps
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
