Hydrological ensemble forecasting at ungauged basins: using neighbour catchments for model setup and updating

Abstract

Abstract. In flow forecasting, additionally to the need of long time series of historic discharges for model setup and calibration, hydrological models also need real-time discharge data for the updating of the initial conditions at the time of the forecasts. The need of data challenges operational flow forecasting at ungauged or poorly gauged sites. This study evaluates the performance of different choices of parameter sets and discharge updates to run a flow forecasting model at ungauged sites, based on information from neighbour catchments. A cross-validation approach is applied on a set of 211 catchments in France and a 17-month forecasting period is used to calculate skill scores and evaluate the quality of the forecasts. A reference situation, where local information is available, is compared to alternative situations, which include scenarios where no local data is available at all and scenarios where local data started to be collected at the beginning of the forecasting period. To cope with uncertainties from rainfall forecasts, the model is driven by ensemble weather forecasts from the PEARP-Météo-France ensemble prediction system. The results show that neighbour catchments can contribute to provide forecasts of good quality at ungauged sites, especially with the transfer of parameter sets for model simulation. The added value of local data for the operational updating of the hydrological ensemble forecasts is highlighted.

A review of regionalisation for continuous streamflow simulation

“…Many studies have demonstrated usefulness of limited data (e.g. Vogel and Kroll, 1991;Burn and Boorman, 1993;Binley and Beven, 2003;Wagener et al, 2003;McIntyre and Wheater, 2004;Laaha and Blöschl, 2005;Rojas-Serna et al, 2006;Perrin et al, 2007;Seibert and Beven, 2009;Randrianasolo et al, 2011). On one hand, more research is needed to improve techniques in regionalisation and prediction in ungauged catchments, on the other hand, instrumentation and data assimilation technologies need to be advanced, along with other advancement in hydrological science, to reduce the number of completely ungauged catchments, improve understanding in physical processes of a catchment, and minimise predictive uncertainties.…”

“…Merz and Blöschl (2004) transfer the average value of model parameters from the immediate upstream and downstream neighbouring catchments in Austria and found this approach outperforms kriging or regression method. Randrianasolo et al (2011) use model parameters transfered from neighbouring catchments for ensemble forecast at ungauged catchments in France and show it can provide reasonably good forecasts at the target catchments. They find performance increases as the number of neighbours increases (from 1 to 20 neighbours as donors) and the best single donor is on average about 18 km away from the target.…”

Comparative assessment of predictions in ungauged basins – Part 1: Runoff-hydrograph studies

“…Proximity is usually defined on the basis of distances between the catchment outlets or catchment centroids (Randrianasolo et al, 2011;Zvolenský et al, 2008;Li et al, 2009). It is also possible to use the geostatistical distances, which account for the nestedness of the catchments (e.g.…”

Challenges of Operational River Forecasting

“…The absence of observations makes it more difficult for river forecasters to configure and calibrate models and leads to less real-time situational awareness and a diminished ability to verify forecast accuracy (Stokstad 1999). The scientific community recently completed a decade-long initiative on prediction in ungauged basins, and although initiatives such as these contributed much new understanding and many innovative techniques (Hrachowitz et al 2013), real-time forecasting received less attention and remains a major challenge (Randrianasolo et al 2011).…”