Data collection for cooperative water resources modeling in the Lower Rio Grande Basin, Fort Quitman to the Gulf of Mexico.

Water resource scarcity around the world is driving the need for the development of simulation models that can assist in water resources management. Transboundary water resources are receiving special attention because of the potential for conflict over scarce shared water resources. The Rio Grande/Rio Bravo along the U.S./Mexican border is an example of a scarce, transboundary water resource over which conflict has already begun. The data collection and modeling effort described in this report aims at developing methods for international collaboration, data collection, data integration and modeling for simulating geographically large and diverse international watersheds, with a special focus on the Rio Grande/Rio Bravo. This report describes the basin, and the data collected. This data collection effort was spatially aggregated across five reaches consisting of Fort Quitman to Presidio, the Rio Conchos, Presidio to Amistad Dam, Amistad Dam to Falcon Dam, and Falcon Dam to the Gulf of Mexico. This report represents a nine-month effort made in FY04, during which time the model was not completed

Improving operational short-to medium-range (Sr2mr) streamflow forecasts in the upper zambezi basin and its sub-basins using variational ensemble forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short-to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles avail-able for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]