1 WaterWare description Data management, Objects Monitoring, time series Hydro-meteorological data, forecastsHydro-meteorological data, forecasts Rainfall-runoff:

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Presentation transcript:

1 WaterWare description Data management, Objects Monitoring, time series Hydro-meteorological data, forecastsHydro-meteorological data, forecasts Rainfall-runoff: RRM, floods Irrigation water demand Water budget modelling Water quality: STREAM, SPILL Multi-criteria optimization, DSS User support, system maintenance Data management, Objects Monitoring, time series Hydro-meteorological data, forecastsHydro-meteorological data, forecasts Rainfall-runoff: RRM, floods Irrigation water demand Water budget modelling Water quality: STREAM, SPILL Multi-criteria optimization, DSS User support, system maintenance

2 Forecasting: scheduled model runs Automatic/scheduled forecast runs with cascading models: Hydro-meteorology (MM5, WRF, GFS) Rainfall-runoff (upstream basins, floods ?) Water demand (irrigation, other major users Dynamic water budget, allocation, reservoir operations Water quality (local, basin wide) Operational control (optimization)

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5 Temperature forecast 120 hours (up to a week) NCEP/NOAA GFS data Dynamic Downscaling: hourly, 1-3 km 120 hours (up to a week) NCEP/NOAA GFS data Dynamic Downscaling: hourly, 1-3 km

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8 Precipitation forecast 120 hours (up to a week) NCEP/NOAA GFS data Dynamic Downscaling: hourly, 1-3 km 120 hours (up to a week) NCEP/NOAA GFS data Dynamic Downscaling: hourly, 1-3 km

9 Wind(vector) forecast 120 hours (up to a week) NCEP/NOAA GFS data Dynamic Downscaling: hourly, 1-3 km 120 hours (up to a week) NCEP/NOAA GFS data Dynamic Downscaling: hourly, 1-3 km

10 Weather forecast: reliability ? Comparing 5 day forecast results with “historical” re- analysis data that integrate observations.

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13 Stochastic forecasts: ensembles

14 Stochastic forecasts: ensembles Probability of exceeding some target levels anywhere in the domain “in the future”

15 Knowledge and uncertainty...