Abstract:A comparison of the remote-sensing SSEBop method for estimating ETa and the Hamon temperature method for estimating potential ET (ETp) also was conducted, using regressions of all available county averages of ETa for 2005
“…The second ET product was the Simplified Surface Energy Balance (SSEBop). This monthly actual ET dataset is available across CONUS, and was obtained from the USGS Geo Data Portal [29,30]. This approach is based on using remotely sensed thermal retrievals, combined with assimilated meteorological observations (PRISM).…”
Over the last decade, autocalibration routines have become commonplace in watershed modeling. This approach is most often used to simulate a streamflow at a basin’s outlet. In alpine settings, spring/early summer snowmelt is by far the dominant signal in this system. Therefore, there is great potential for a modeled watershed to underperform during other times of the year. This tendency has been noted in many prior studies. In this work, the Soil and Water Assessment Tool (SWAT) model was auto-calibrated with the SUFI-2 routine. A mountainous watershed from Idaho was examined (Upper North Fork). In this study, this basin was calibrated using three estimates of evapotranspiration (ET): Moderate Resolution Imagining Spectrometer (MODIS), Simplified Surface Energy Balance, and Global Land Evaporation: the Amsterdam Model. The MODIS product in particular, had the greatest utility in helping to constrain SWAT parameters that have a high sensitivity to ET. Streamflow simulations that utilize these ET parameter values have improved recessional and summertime streamflow performances during calibration (2007 to 2011) and validation (2012 to 2014) periods. Streamflow performance was monitored with standard objective metrics (Bias and Nash Sutcliffe coefficients) that quantified overall, recessional, and summertime peak flows. This approach yielded dramatic enhancements for all three observations. These results demonstrate the utility of this approach for improving watershed modeling fidelity outside the main snowmelt season.
“…The second ET product was the Simplified Surface Energy Balance (SSEBop). This monthly actual ET dataset is available across CONUS, and was obtained from the USGS Geo Data Portal [29,30]. This approach is based on using remotely sensed thermal retrievals, combined with assimilated meteorological observations (PRISM).…”
Over the last decade, autocalibration routines have become commonplace in watershed modeling. This approach is most often used to simulate a streamflow at a basin’s outlet. In alpine settings, spring/early summer snowmelt is by far the dominant signal in this system. Therefore, there is great potential for a modeled watershed to underperform during other times of the year. This tendency has been noted in many prior studies. In this work, the Soil and Water Assessment Tool (SWAT) model was auto-calibrated with the SUFI-2 routine. A mountainous watershed from Idaho was examined (Upper North Fork). In this study, this basin was calibrated using three estimates of evapotranspiration (ET): Moderate Resolution Imagining Spectrometer (MODIS), Simplified Surface Energy Balance, and Global Land Evaporation: the Amsterdam Model. The MODIS product in particular, had the greatest utility in helping to constrain SWAT parameters that have a high sensitivity to ET. Streamflow simulations that utilize these ET parameter values have improved recessional and summertime streamflow performances during calibration (2007 to 2011) and validation (2012 to 2014) periods. Streamflow performance was monitored with standard objective metrics (Bias and Nash Sutcliffe coefficients) that quantified overall, recessional, and summertime peak flows. This approach yielded dramatic enhancements for all three observations. These results demonstrate the utility of this approach for improving watershed modeling fidelity outside the main snowmelt season.
“…Then, a multiyear mean annual raster image is derived, and finally, area-weighted mean annual evapotranspiration, ESSEBopfalse(CPfalse), is calculated. The SSEBop data provide reliable evapotranspiration estimates over wide-areas and work well for both cropland [9] and urban areas [10]. Fig.…”
Section: Step 4: Estimate Evapotranspiration From Satellite-derived Datamentioning
“…The single-day LST data can overcome poor eight-day LST images where temperature values can be used to calculate the ETf for the pixel of interest. For now, the more appropriate solution for this problem is the acquisition of better temperature data, which may be available from other satellite based sensors with thermal bands, such as ASTER and Landsat [49].…”
Section: Limitations and Suggested Improvementsmentioning
Evapotranspiration is a reliable indicator of wetland health. Wetlands are an important and valuable ecosystem on the South Florida landscape. Accurate wetland Actual Evapotranspiration (AET) data can be used to evaluate the performance of South Florida's Everglades restoration programs. However, reliable AET measurements rely on scattered point measurements restricting applications over a larger area. The objective of this study was to validate the ability of the Simplified Surface Energy Balance (SSEB) approach and the Simple Method (also called the Abtew Method) to provide large area AET estimates for wetland recovery efforts. The study used Moderate Resolution Imaging Spectroradiometer (MODIS) sensor spectral data and South Florida Water Management District (SFWMD) solar radiation data to derive weekly AET values for South Florida. The SSEB-Simple Method approach provided acceptable results with good agreement with observed values during the critical dry season period, when cloud cover was low (rave (n = 59) = 0.700, pave < 0.0005), but requires further refinement to be viable for yearly estimates because of poor performance during wet season months, mainly because of cloud contamination. The approach can be useful for short-term wetland recovery assessment projects that occur during the dry season and/or long term projects that compare site AET rates from dry season to dry season.
OPEN ACCESSRemote Sens. 2015, 7 3614
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
