TY - GEN
T1 - Probabilistic approach to modeling under changing scenarios
AU - Prada, Andres F.
AU - Chu, Maria L.
AU - Guzman, Jorge A.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - APEX model
KW - Fort-Cobb watershed
KW - Global uncertainty and sensitivity analysis
KW - Model development methodology
UR - http://www.scopus.com/inward/record.url?scp=85009100555&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009100555&partnerID=8YFLogxK
U2 - 10.13031/aim.20162459770
DO - 10.13031/aim.20162459770
M3 - Conference contribution
AN - SCOPUS:85009100555
T3 - 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016
BT - 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016
PB - American Society of Agricultural and Biological Engineers
T2 - 2016 ASABE Annual International Meeting
Y2 - 17 July 2016 through 20 July 2016
ER -