Abstract
Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (Rs) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET.
Highlights
Evapotranspiration (ET) is of paramount importance for terrestrial water balance as it represents the second largest component after precipitation, and it links climate, hydrology, and ecosystem processes that couple water and energy budgets [1]
Spatial resolution/scale is one of the challenges related to ET estimation
Spatial resolution/scale is one of the challenges related to ET estimation, in heterogeneous natural environments such as the San Rafael River corridor, which has in heterogeneous natural environments such as the San Rafael River corridor, which has a wide range of vegetation types and other ground features
Summary
Evapotranspiration (ET) is of paramount importance for terrestrial water balance as it represents the second largest component after precipitation, and it links climate, hydrology, and ecosystem processes that couple water and energy budgets [1]. Direct measurements of ET using ground instrumentation such as eddy covariance (EC) or lysimeters only work appropriately for homogenous surfaces and are limited to small sampling areas [3] At large scales, such as watersheds or biomes, these methods are difficult to employ due to the complexity of hydrometeorological processes [4]. To understand the spatial heterogeneity of the landscape for accurate estimation of surface energy fluxes, latent heat flux (LE) or evapotranspiration (ET), advanced techniques are needed To address these needs, the scientific community has developed land surface models, mathematical representations of land–atmosphere exchange, to quantify surface energy and water balance, which drive climatic and earth system processes [6]. These models are helpful tools that can provide vital information to track ecosystem responses to dynamic changes in climate and environmental components [7,8]
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