Short To Medium Range Hydrometeorological Forecasting In The Rio Grijalva Basin, Mexico

Abstract

The Rio Grijalva basin is the most important basin in Mexico in terms of hydropower production and damages related to extreme rainfall events. This study investigates establishing a short- to medium-range hydrometeorological forecasting system for this basin which comprises a hydrological model and a regional Numerical Weather Prediction Model (NWPM). A physical, distributed, hydrological model (MMS-PRMS) is implemented through the following steps: (1) basin parameterization; (2) interfacing to observed meteorological fields, and (3) parameter optimization. The datasets normally used to parameterize the MMS-PRMS are only available in the US so an alternative methodology for deriving parameters from globally available public datasets was devised. Modeled streamflow calculated by model with the initial parameters was in good agreement with observed streamflow, and optimization yielded even better agreement. The predictive capabilities of the hydrological model was then tested by implementing modeled rainfall and temperature from the North American Regional Reanalysis (NARR), these data being used as a surrogate for those that would be available from a regional NWPM. A significant bias in NARR-rainfall was identified and a novel probabilistic correction procedure devised. This procedure was then extended to provide estimates of uncertainty in the modeled streamflow. Within the calculated uncertainty, the modeled streamflow calculated with these corrected NARR data is in good agreement with modeled streamflow calculated using local meteorological data.