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Probabilistic approach to modeling under changing scenarios

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2016 ASABE Annual International Meeting  162459770.(doi:10.13031/aim.20162459770)
Authors:   Andres Felipe Prada Sepulveda, Maria Librada Chu, Jorge Alberto Guzman Jaimes
Keywords:   APEX model, Global uncertainty and sensitivity analysis, Model development methodology, Fort-Cobb watershed

Abstract. The complexity of the hydrologic system challenges the development of models under changing scenarios (e.g. climate, land use, or management). Finding the most accurate model parameters becomes a time-consuming task. Practitioners select the best model either by trial and error or by optimization algorithms to determine the set of parameters with the highest metric for a given scenario. However, these parameters are expected to change when the scenario changes especially under future projections. Several parameter combinations that acceptably represent the system, i.e. equifinality, may exist but were not included during the calibration process. This fact questions the current model parametrization strategies and leads to the development of a new methodology to overcome this difficulty. In this study, a probabilistic approach using global uncertainty and sensitivity analysis was used to develop a hydrologic model. The Agricultural Policy/Environmental eXtender (APEX) model was developed for the FortCobb watershed in Oklahoma. Probabilistic inputs (e.g. parameters, rainfall, land management) were used to derive the spectrum of responses of the model. Acceptable simulations were then used to establishing the most probable values of the desired model outputs (e.g., WYLD, N load, and Crop Yield) at a given time in the study area. This methodology also evaluates the uncertainty of model parameterization and calibration by considering the multiple functional hypothesis of system behavior. This process facilitates the comprehension of the watershed response and the simulation of different land management practices scenarios.

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