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Accueil du site → Master → Etats Unis → 2019 → PROCESS BASED MODELING OF SURFACE ENERGY FLUXES, EVAPOTRANSPIRATION, SOIL MOISTURE, AND SOIL TEMPERATURE IN THE US SOUTHERN PLAINS

Celis, Jorge Andres

PROCESS BASED MODELING OF SURFACE ENERGY FLUXES, EVAPOTRANSPIRATION, SOIL MOISTURE, AND SOIL TEMPERATURE IN THE US SOUTHERN PLAINS

Celis, Jorge Andres

Titre : PROCESS BASED MODELING OF SURFACE ENERGY FLUXES, EVAPOTRANSPIRATION, SOIL MOISTURE, AND SOIL TEMPERATURE IN THE US SOUTHERN PLAINS

Auteur : Celis, Jorge Andres

Université de soutenance : University of Oklahoma

Grade : MASTER OF SCIENCE IN GEOGRAPHY (2019)

Résumé partiel
This study aims to evaluate the capability and transferability of a physically-based hydrologic model to understand the trade-offs between precipitation, soil moisture and surface energy fluxes at sites with different vegetation types in the U.S. Southern Plains. One of the benefits of training a process-based model is the capacity to use it as a complement to standard weather stations for predicting energy fluxes, soil temperature and moisture estimations. Modeling of the terrestrial surface soil moisture and temperature, and boundary layer energy fluxes is key for understanding the spatio-temporal variability of hydro-meteorological conditions that drive normal and extreme (i.e. floods and droughts) events. Soil moisture (SM), surface energy fluxes (SEF) and soil temperatures (ST) play an important role in the ground and near surface hydro-energetic dynamics, especially in water exchange processes such as the evapotranspiration (ET). ET is an important variable for understanding the energy, water and biogeochemical budgets. This study uses the Triangulated Irregular Network TIN-based Real Time Integrated Basin Simulator (tRIBS), a continuous physically-based distributed hydrological model, to provide estimations of the surface energy balance (SEB) components in typical environments of the U.S. Southern Plains. Both calibration and validation of the model are performed using available Eddy Covariance Tower (ECT) observations distributed on crops and grasslands in Oklahoma. The model calibration is based on a hybrid strategy that uses a manual procedure followed by an optimization algorithm based on the Shuffled Complex Evolution (SCE) theory. All data used to parametrize the model is free-access

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