Towards an ensemble-based short-term flood forecasting using an event-based flood model- incorporating catchment-average estimates of soil moisture

Abstract

Extreme floods pose a threat to life and property and accurate and reliable flood forecasts are required to mitigate the consequences of these events. In this paper, we propose a novel Monte-Carlo based framework that utilizes an ensemble-based short-term flood forecasting model. An event-based rainfall-runoff model is selected due to its simplicity and wide use by industry practitioners. However, a challenge with event models is that they are unable to account for the initial condition of the catchment wetness at the commencement of a particular rainfall event. To address this issue, we used independent catchment-average estimates of soil moisture to estimate catchment losses at the commencement and over the duration of the event. The adopted framework enables uncertainty in catchment losses and rainfall depth and patterns to be quantified and incorporated in forecasts. The accuracy and reliability of stochastic components of this framework are evaluated using ensemble verification measures. Later, these measures are used to evaluate four scenarios based on different combinations of deterministic and stochastic conditions. In the stochastic scenarios, the uncertainty of the flood forecasts is reliably quantified (88% reliability on average). The results show that operationally available estimates of catchment wetness can be used to account for uncertainty from event-based loss parameters, and that the uncertainty from average forecast rainfall depth is considerably more important than the uncertainty from forecast losses and the spatio-temporal patterns of the rainfall. This study has shown that event-based models have the potential to be applied within a probabilistic framework to operationally generate probabilistic flood forecasts.