Abstract
The feasibility of using spatially distributed information to improve the predictive ability of a spatially distributed land surface water and energy balance model (LSM) was explored at the U.S. Department of Agriculture Agricultural Research Service (USDA‐ARS) Walnut Gulch Experimental Watershed in southeastern Arizona. The inclusion of spatially variable soil and vegetation information produced unrealistic simulations that were inconsistent with observations, which was likely an artifact of both discretely assigning a single set of parameters to a given area and inadequate knowledge of spatially varying parameter values. Because some of the model parameters were not measured or are abstract quantities a multiobjective least squares strategy was used to find catchment averaged parameter values that minimize the prediction error of latent heat flux, soil heat flux, and surface soil moisture. This resulted in a substantial improvement in the model's spatially distributed performance and yielded valuable insights into the interaction and optimal selection of model parameters.
Highlights
Many aspectsof land surfacewater and energy-balancemodel (LSM) calibrationhave beenexploredin other investigations
Distributed expectedto vary spatially.Send, becausethe performanceobservationsof LSM predictions are scarce, and when of the model is to be judged in terms of its ability to available, have relatively high uncertainty[Franks et al, simultaneousplyredictthe soil moisturestatesandthe latent, 1998]
33,425 vegetationparametersperform to the control run, Gupta et al [1999], this situationinvolvesthe problemof indicatingthat spatialvariationin theseparametershas little finding parameterestimateswhich can simultaneously effect on predictions.All of the simulationsusingspatially minimize several noncommensurable criteria
Summary
Many aspectsof LSM calibrationhave beenexploredin other investigations. For example, Sellers et al [1989]A land surfacewater and energy-balancemodel (LSM) employedmanualcalibrationof nineparameterisn the simple [Famigliettiand Wood, 1994] was used to simulatethe biosphere(SiB) modelto improveits comparisonwith field spatiallydistributedbehaviorof the U.S Departmenot f observations. Distributed expectedto vary spatially.Send, becausethe performanceobservationsof LSM predictions are scarce, and when of the model is to be judged in terms of its ability to available, have relatively high uncertainty[Franks et al, simultaneousplyredictthe soil moisturestatesandthe latent, 1998]. 3. Land Surface Model obtainedfrom Dicla'nsonet al. Variable Parameters for eachsoil seriesin the soil survey.
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