HR: 15:45h
AN: H53F-08    [Abstracts]
TI: Short to Medium-Range Hydrometeorological Forecasts in the Rio Grijalva Basin, Mexico
AU: * Uribe, E M
EM: edgar@hwr.arizona.edu
AF: Department of Hydrology and Water Resources, University of Arizona, United States
AU: Shuttleworth, W J
EM: shuttle@hwr.arizona.edu
AF: Department of Hydrology and Water Resources, University of Arizona, United States
AU: Gupta, H V
EM: hoshin_g@hwr.arizona.edu
AF: Department of Hydrology and Water Resources, University of Arizona, United States
AU: Mullen, S L
EM: mullen@jet.atmo.arizona.edu
AF: Department of Hydrology and Water Resources, University of Arizona, United States
AU: Mullen, S L
EM: mullen@jet.atmo.arizona.edu
AF: Department of Atmospheric Sciences, University of Arizona, United States
AU: Zeng, X
EM: xubin@gogo.atmo.arizona.edu
AF: Department of Atmospheric Sciences, University of Arizona, United States
AB: This paper describes research in support of a project to enable the interpretation of modeled meteorological fields in terms of streamflow in the Rio Grijalva basin, located in southern Mexico. The Rio Grijalva basin is the most important basin in terms of hydropower production, and one of the basins most affected by floods in Mexico. So establishing a short to medium-range hydrometeorological forecasting system is recommended. A physical, distributed, hydrological model (MMS-PRMS) is established through the following steps: 1) basin parameterization, 2) parameter optimization, and 3) implementation of modeled meteorological fields into the resulting hydrological model. Most datasets for topographic, soil and vegetation parameter derivation for the MMS- PRMS are only available in the United States so an alternative derivation methodology from global, publicly available, surrogate datasets is proposed. Parameter optimization is performed through the Shuffled Complex Evolution method with the use of a local hydrometeorological network. The documentation of these initial steps is considered relevant for other hydrological modelers in Mexico and other countries where hydrological models, parameterization datasets, and optimization tools are limited. The short-term predictive capabilities of the resulting model are tested using modeled rainfall and temperature from the North American Regional Reanalysis (NARR). A relevant bias in NARR-rainfall is identified. Methodologies for a probabilistic bias-correction and uncertainty estimation in the meteorological fields are proposed. The bias-identification and correction are perhaps the most important results. Thus suggesting NARR fields should be should follow a similar process previous to their analysis.
DE: 1816 Estimation and forecasting
DE: 1840 Hydrometeorology
DE: 1847 Modeling
DE: 1879 Watershed
DE: 3355 Regional modeling
SC: Hydrology [H]
MN: 2007 Joint Assembly