SEBAL (Surface Energy Balance Algorithm for Land) is an image-processing model comprised of twenty-five computational submodels that calculates evapotranspiration (ET) and other energy exchanges at the Earth's surface. SEBAL uses digital image data collected by Landsat or other remote sensing satellites measuring thermal infrared radiation in addition to visible and near-infrared. SEBAL was originally developed in the Netherlands by Bastiaanssen and was modified during the Idaho study for application to mountainous terrain and clear, cold lakes. In an application to the Bear River Basin of southeastern Idaho, USA, ET was computed as a component of the surface energy balance on a pixel-by-pixel basis. ET for periods in between satellite overpasses was computed using ratios of ET from SEBAL to reference ET computed using data from ground-based weather stations. These ratios were essentially customized "crop coefficients" that were determined uniquely for each pixel of an image. This initial application and testing of SEBAL in Idaho indicates substantial promise as an efficient, accurate, and inexpensive procedure to predict the actual evaporation fluxes from irrigated lands throughout a growing season. Predicted ET has been compared with ground measurements of ET by lysimeter with good results, with monthly differences averaging /spl plusmn/16%, but with seasonal differences of only 4% due to reduction in random error. ET maps via SEBAL provide the means to quantify, in terms of both the amount and spatial distribution, the ET on a field by field basis within each state. In particular, the Idaho Department of Water Resources (IDWR) will use results to predict total, net depletion of water from the Bear River system resulting from irrigation diversions.
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