Three-tier operational probabilistic 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 <p> HR: <span class="hr"> 13:30h<br></span> AN: <span class="an"> H43E-01 <a href="../absbook/sm05_H43E.html">[Abstracts]</a><br></span> TI: <span class="ti"> Three-tier operational probabilistic precipitation and hydrological forecasting of large-scale un-gauged river basins: Developing a basis for strategic and tactical decisions for water management, agricultural planning AU: <span class="au"> * webster, p <br></span> EM: <span class="em"> pjw@eas.gatech.edu<br></span> AF: <span class="af"> School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 ferst drive, atlanta, ga 30332 United States <br></span> AU: <span class="au"> Hopson, T <br></span> EM: <span class="em"> hopson@enso.colorado.edu<br></span> AF: <span class="af"> Program in Atmospheric and Ocean Sciences (PAOS), University of Colorado, Program in Atmospheric and Oceanic Science Duane Physics Building, Room D335 311 UCB, Boulder, CO 80309 United States <br></span> AU: <span class="au"> Carlos Hoyos, C <br></span> EM: <span class="em"> choyos@eas.gatech.edu<br></span> AF: <span class="af"> School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 ferst drive, atlanta, ga 30332 United States <br></span> AU: <span class="au"> Chang, H <br></span> EM: <span class="em"> hrc@eas.gatech.edu<br></span> AF: <span class="af"> School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 ferst drive, atlanta, ga 30332 United States <br></span> AU: <span class="au"> Subbiah, A <br></span> EM: <span class="em"> subbiah@adpc.net<br></span> AF: <span class="af"> Asian Disaster Preparedness Centre, (ADPC), P.O. Box 4, Klong Luang, Pathumthani 12120, Bangkok, Thailand <br></span> AU: <span class="au"> Palmer, T <br></span> EM: <span class="em"> palmer@ecmwf.int<br></span> AF: <span class="af"> European Centre for Medium Range Weather Forecasts (ECMWF),, Shinfield Park , Readin, RG2 9AX United Kingdom <br></span> AB: <span class="ab"> 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. Because of lack of Ganges and Brahmaputra river flow information from India upriver of the borders of the Bangaldesh, we have had to treat the Ganges and Brahmaputra river catchment areas as essentially ungauged. Through the development of hydrological techniques in conjunction with ensemble forecasts from ECMWF, river discharge forecasts are now supplied to Government of Bangladesh authorities in real time throughout the summer period. In addition, as a biproduct of these efforts, we also produce regional precipitation forecasts for Bangladesh and a number of regions within India. 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. Finally, we use the ECMWF ensembles of forecasts (51 predictions per day) to issue 1-10 day probabilistic forecasts of river discharge and precipitation. River discharge is 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. Finally, these forecasts are cast within an easily understood "user metric" which combines the probabilistic forecast with information of impact from the user community. <br></span> DE: <span class="de"> 1821 Floods<br></span> DE: <span class="de"> 1833 Hydroclimatology<br></span> SC: <span class="sc"> Hydrology [H]<br></span> MN: <span class="mn"> 2005 Joint Assembly<br></span></p></body></html>