Three-tier operational precipitation and hydrological forecasting of large-scale un-gauged river basins: Developing a basis for strategic and tactical decisions for water management, agricultural planning and disaster mitigation (INVITED PRESENTATION)

The problem is that a user or user community must make deterministic (yes/no) decisions on multiple time scales about environmental systems containing considerable uncertainty. Such decisions range from strategic to tactical. A useful product takes into account forecasts of the probabilities of a state of the environment (e.g., rain rate, river discharge, flood potential and so on) and quantitative or qualitative information from the user about the consequences of occurrence of a particular environmental state. Considered in tandem, optimal hedging and risk analysis can be undertaken. We use Bangladesh as an example of the three-tiered forecast system and its application to real problems. River discharge forecasts are now supplied to Government of Bangladesh authorities in real time throughout the summer period. Forecasts of river discharge on 1-6 month periods (using the ECMWF couple climate model and hydrological models and statistical methods) are provided every month starting in April. Last year, we were able to forecast the July floods quite well some 3 months in advance. Using Bayesian empirical scheme, we make pentad 20-day forecasts every 5 days of regional rainfall and river discharge. We also introduce a new method of increasing the Bayesian content of the 20-day forecast through the incorporation of estimates of the future states into the empirical scheme from operational ensemble 1-10 day forecasts. Finally, we use the ECMWF ensembles of forecasts (51 per day) to issue 1-10 day forecasts of river discharge and precipitation. River discharge forecasts are made with the additional use of a suite of hydrological models. In addition, we provide threshold probability forecasts on the 1-10 day time scale of significant occurrences such as flood danger level. We note the importance of creating a nest of overlapping forecasts that are both easy to understand and are capable of incorporating user information at even the local or regional level. To this end, forecasts are cast within an easily understood “user metric” which combines the probabilistic forecast with information of impact from the user community. Finally, the methodology should match the available technologies in the locations where the forecasts will be used. All schemes discussed here are easily modifiable, pc-compatible and require only the transmission of data from source regions (e.g., operational centers) to applications centers.