Abstract
Abstract. Operational probabilistic forecasts of river discharge are essential for effective water resources management. Many studies have addressed this topic using different approaches ranging from purely statistical black-box approaches to physically based and distributed modeling schemes employing data assimilation techniques. However, few studies have attempted to develop operational probabilistic forecasting approaches for large and poorly gauged river basins. The objective of this study is to develop open-source software tools to support hydrologic forecasting and integrated water resources management in Africa. We present an operational probabilistic forecasting approach which uses public-domain climate forcing data and a hydrologic–hydrodynamic model which is entirely based on open-source software. Data assimilation techniques are used to inform the forecasts with the latest available observations. Forecasts are produced in real time for lead times of 0–7 days. The operational probabilistic forecasts are evaluated using a selection of performance statistics and indicators and the performance is compared to persistence and climatology benchmarks. The forecasting system delivers useful forecasts for the Kavango River, which are reliable and sharp. Results indicate that the value of the forecasts is greatest for intermediate lead times between 4 and 7 days.
Highlights
Operational probabilistic hydrological modeling and river discharge forecasting is an active research topic in water resources engineering and applied hydrology (Pagano et al., 2014)
System is continuously updated and modified, performance of precipitation forecasts should be regularly checked during operational application of the hydrologic forecasting system
We have presented an operational probabilistic river discharge forecasting system for poorly gauged basins which relies exclusively on public-domain, open-source software and data
Summary
Operational probabilistic hydrological modeling and river discharge forecasting is an active research topic in water resources engineering and applied hydrology (Pagano et al., 2014). A state of the art river discharge forecasting system consists of a weather forecast or an ensemble of weather forecasts (Cloke and Pappenberger, 2009), a hydrologic–hydrodynamic modeling system and a data assimilation approach to inform the forecasts with all available in situ and remote sensing observations. Many studies have shown that operational hydrological models can benefit from the assimilation of in situ or satellite remote sensing observations. Different techniques and approaches have been presented (Liu et al, 2012) They differ both in terms of the type of data that are assimilated to the models, the assimilation algorithms used and in terms of the assimilation strategy, i.e., which model components, states and/or parameters are updated. The particle filter (Moradkhani et al, 2005) can be used, which does not require the assumption of Gaus-
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