Downsizing parameter ensembles for simulations of rare floods

For extreme-flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrological model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrological model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme-flood estimation so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analysing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated thanks to a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of rare floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed.

Downsizing parameter ensembles for simulations of extreme floods

For extreme flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrologic model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrologic model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme flood estimation, so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analyzing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated with a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of extreme floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed.

Can assimilation of crowdsourced streamflow observations in hydrological modelling improve flood prediction?

Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate such observations into mathematical water models have also being developed, including data assimilation. Besides, in recent years, the continued technological improvement has stimulated the spread of low-cost sensors that allow for employing crowdsourced and obtain observations of hydrological variables in a more distributed way than the classic static physical sensors allow. However, such measurements have the main disadvantage to have asynchronous arrival frequency and variable accuracy. For this reason, this study aims to demonstrate how the crowdsourced streamflow observations can improve flood prediction if integrated in hydrological models. Two different types of hydrological models, applied to two case studies, are considered. Realistic (albeit synthetic) streamflow observations are used to represent crowdsourced streamflow observations in both case studies. Overall, assimilation of such observations within the hydrological model results in a significant improvement, up to 21 % (flood event 1) and 67 % (flood event 2) of the Nash–Sutcliffe efficiency index, for different lead times. It is found that the accuracy of the observations influences the model results more than the actual (irregular) moments in which the streamflow observations are assimilated into the hydrological models. This study demonstrates how networks of low-cost sensors can complement traditional networks of physical sensors and improve the accuracy of flood forecasting.