@JournalArticle {3178,
	title = {USE OF PRECIPITATION FORECASTS FROM WRF, GFS AND GEFS ATMOSPHERIC MODELS AT RIVER AVE BASIN (PORTUGAL) FOR OPERATIONAL MANAGEMENT OF A DRAINAGE SYSTEM},
	journal = {Revista Eletr{\^o}nica de Gest{\~a}o e Tecnologias Ambientais (GESTA)},
	volume = {8},
	year = {2020},
	month = {2020-09-20 00:00:00},
	pages = {69-82},
	abstract = {
The forecasting and warning systems used in water resources management and drainage systems operation have had significant developments in recent years. These developments resulted from the availability of meteorological information in real time, in particular from measurements by sensors in satellites, measurement through meteorological radar and forecasts of atmospheric models for different time horizons. All environmental forecasting models are uncertain and this uncertainty varies over time and space. This work aims to present the results of the evaluation of the evolution of the error associated with different short-term forecasts. The Delft-FEWS (Flood Early Warning System) platform was used to import and process observation and forecast data available for the river Ave basin, located in northern Portugal. The measured meteorological data were obtained from the National Water Resources Information System (SNIRH), at four new meteorological stations installed in the basin and radar reflectivity data measured by the meteorological\ radar operated by Meteogalicia. The forecasts evaluated correspond to the rainfall simulated by atmospheric models developed by the National Oceanic and Atmospheric Administration (NOAA) and Meteogalicia, namely the Global Forecast System (GFS), Global Ensemble Forecast System (GEFS) and Weather Research and Forecasting (WRF) model operated by Meteogalicia. The uncertainty associated with the predicted rainfall was evaluated considering forecast horizons of one to four days. The best results were obtained for the WRF model during precipitation events that occurred between January 2017 and May 2018 and presented average relative errors that varied between 7\% (one forecast day) and 29\% (four days). The implemented system thus allows, from an operational point of view, to forecast extreme events in advance of two days.
},
	keywords = {Hydroinformatics, Precipitation forecasts, river Ave basin, uncertainty analysis, warning systems},
	doi = {10.9771/gesta.v8i1.36638},
	author = {Pinho, J. L. S. and Silva, A. and Faria, R.}
}