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Remote Sensing-based Estimates of Potential Evapotranspiration for Hydrologic Modeling in the Upper Colorado River Basin Region


Potential Evapotranspiration (PET) is used as a common input to calculate evaporative demand in hydrological, ecological and biological modeling. Dynamic and distributed measurement of PET is important for improved hydrologic predictions at the watershed scale since PET varies with time and space. In this work, an advanced dynamic PET estimation is proposed by integrating geostationary satellite products into a currently existing remote sensing-based PET algorithm and evaluated in the framework of operational hydrologic forecasting modeling. The development work is approached through a series of studies. At first, a previously developed Moderate Resolution Imaging Spectroradiometer (MODIS) based PET (MODIS-PET) product applied over several flux towers and basins in the Upper Colorado River Basin (UCRB) to determine its applicability and predictive ability in comparison to other ground based distributed PET methods. Results from this primary study indicate the MODIS-PET is an improved PET estimation method compared to the other two contemporary distributed PET products that were tested over this geographically complex study region. In addition to elevation and cloud cover, uncertainties are associated with the MODIS-PET algorithm pertaining from three model variables; land surface temperature, air temperature and surface emissivity. The crude hypothetical sinusoidal curve considered in the conversion of instantaneous MODIS-PET to the daily PET estimation can potentially be replaced with satellite data with improved temporal resolution. Hence, integration of Geostationary Operational Environmental Satellites (GOES), a series of geostationary satellites with frequent observations, data in the MODIS-PET algorithm is performed in the second part. The coupling of GOES within the MODIS-PET algorithm shows significant improvement over the previously developed stand-alone MODIS-PET product, especially for cloudy days and high temperature pixels. Finally, evaluation of these two remote sensing products (merged GOES-MODIS and stand-alone MODIS) is undertaken as lumped input in the National Weather Service (NWS) River Forecasting Centers (RFC) operational forecasting models in two mountainous watersheds from the UCRB. The preliminary results show that PETs estimated using the satellite data is a suitable replacement of the static PET in the snow-dominated basins of the UCRB region. The new remote-sensing PET available in near-real-time which we advocate will ultimately provide more reasonable representation of current climatological conditions for streamflow forecasting, drought monitoring and crop water demand.

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