Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States

Abstract

Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000-2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R 2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.Keywords: evapotranspiration; water resources; remote sensing; water balance; evaporation; transpiration Background and RationaleEvapotranspiration (ET) is the quantity of water that includes net water evaporation and plant transpiration. It plays a significant role in the hydrologic cycle, returning about 70% of precipitation across the conterminous United States (CONUS) to the atmosphere [1,2]. Because it is generally the second largest component of the water budget, following precipitation, researchers and water resource managers require accurate and reliable ET estimates to understand water availability and distribution for both short and long-term water resources management.The use of remote sensing-based methods of estimating ET has increased in recent years [3], and has proven useful in various applications such as agricultural water use monitoring and estimation [4], and hydrologic simulation [5]. These methods are particularly advantageous in their ability to produce estimates on timescales as short as weeks. The remote sensing approaches generally use surface energy balance arguments to partition the net incoming solar radiation energy into sensible heat flux, ground heat flux, and latent heat flux (energy equivalent of ET mass flux) [6][7][8][9][10]. Different algorithms applied to the same input remote sensing data sets can make predictions of the latent heat

Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity

“…Temporal variation was incorporated into our models using watershed averages of four dynamic predictors available at monthly time steps for the period of interest. The four dynamic predictors were monthly average precipitation, average temperature, maximum temperature (from PRISM model) and MODIS-derived evapotranspiration. , Following the same procedures used to create the StreamCat data set (), we calculated watershed averages for each NHD+ segment in the contiguous United States for each month during the period of interest (2000–2015). We then extracted the temporally and spatially specific observations of each of the four dynamic predictors (extracted precipitation, mean temperature, maximum temperature, and mean ET) that matched the time (month and year) and location (NHD+ segment) of each SC observation.…”

“…Temporal variation was incorporated into our models using watershed averages of four dynamic predictors available at monthly time steps for the period of interest. The four dynamic predictors were monthly average precipitation, average temperature, maximum temperature (from PRISM model 29 ) and MODIS-derived evapotranspiration 30,39 . Following the same procedures used to create the StreamCat data set (https://github.com/USEPA/StreamCat), we calculated watershed averages for each NHD+ segment in the contiguous United States for each month during the period of interest (2000-2015).…”

Urbanization Impacts on Evapotranspiration Across Various Spatio‐Temporal Scales

“…The SSEBop remotely sensed ET relies on energy balance calculations and is not directly constrained by water availability. Therefore, errors in the energy balance may lead to estimates of ET whose total magnitude can exceed the available water (Reitz et al., 2017). This typically occurs in arid conditions, where our findings are globally consistent with urbanization increasing ET.…”

Urban evaporative consumptive use for water‐scarce cities in the United States and Mexico

“…Ground measurements of ET from instruments including lysimeters, evaporation pans, eddy covariance towers, and flux towers can give accurate point measurements of ET that are useful for validating and providing insights on controlling factors, as well as calibrating ET estimation methods. Although useful for ET studies, ground measurements are often difficult to obtain for large areas, do not capture spatial variability, and can have uncertainties caused by calibration errors and measurement biases over long time periods (Allen et al, 2011; Reitz et al, 2017). Ground‐measurement devices are often expensive to install and maintain, making widespread use inefficient and ground measurements impractical to use for city‐scale ECU measurements.…”

“…Although multiple methods have been used to successfully estimate ET for agricultural areas, the heterogeneity of urban land use and cover makes it particularly difficult to estimate urban ECU using traditional ET measurement methods (Anderson & Vivoni, 2016;Grimmond & Oke, 1999;Litvak et al, 2017a;Qiu, Tan, Wang, Yu, & Yan, 2017). Given the uncertainty inherent in ET estimation methods, particularly when applying these methods to urban landscapes, using a combination of methods to estimate urban ECU can provide a more accurate overall representation of urban ECU and the identification of separate components of urban ECU (Allen, Pereira, Howell, & Jensen, 2011;Kim & Kaluarachchi, 2018;Reitz, Senay, & Sanford, 2017).…”