Document Type

Presentation

Publication Date

8-1-2014

Abstract

Hydrological forecasting is an important instrument for more effective water management, such as warning and protection against water-related hazards, real-time operation of water infrastructure, improved water allocation, and environmental monitoring. Recent advances within radar rainfall estimation and nowcasting, ensemble-based numerical weather prediction (NWP), in-situ and satellite monitoring, and hydrological data assimilation are opening up new opportunities in real-time hydrological forecasting. NWP ensemble products can be used as input to hydrological forecast models to produce probabilistic forecasts and estimation of forecast uncertainty of the hydrological variables of interest. In this regard, key scientific challenges are understanding, quantification, and propagation of the different uncertainty sources in the forecast modelling chain and updating of the hydrological forecast model using data assimilation. To fully utilise the complementary nature of various types of in-situ and remote sensing measurements of hydrological variables, multi-variate data assimilation within integrated hydrological modelling systems is required. This involves development and implementation of robust and computationally efficient data assimilation algorithms that are feasible for real-time applications. In this paper a general framework for hydrological ensemble forecasting and data assimilation is presented. A seamless forecasting system is developed that supports water management at different temporal scales, ranging from short- and medium-range (few hours to some days ahead), which is relevant for e.g. flood forecasting and warning, to long-range or seasonal forecasting (several months ahead) for optimising water allocation and reservoir operation. Case studies are presented that highlight the potential of the framework developed for forecasting and data assimilation with the MIKE SHE distributed and integrated hydrological modelling system.

Comments

Session R48, Data Processing: Data to Computations

 
 

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