Increasing river flood preparedness by real-time warning based on wetness state conditions
Introduction
The overall soil moisture state of a catchment, including soil moisture and ground water levels, is an important factor in the initiation of river floods. This is because the soil moisture has an important role in the hydrological cycle, governing the evaporation, runoff, infiltration and percolation processes (Entekhabi et al., 1994). One therefore could consider real time information on soil moisture conditions in the river catchment as an indicator for potential future floods, hence as input for awareness raising and increasing the preparedness of flood crisis management bodies for potential future floods. This requires the soil moisture data to be continuously assessed/quantified, mapped and communicated (Schaedel and Becker, 2002). However, the soil moisture is highly variable in time, space and depth, depending on the evaporation, the vegetation water demand, the groundwater level, rainfall (intensity and quantity) and soil type. To obtain a sufficient spatial soil moisture coverage, in situ measurements are inadequate. Therefore, indirect techniques are commonly used to assess the soil moisture conditions. These techniques can be divided into three categories: remote sensing based, modelling based and indirect observation techniques. The latter two techniques are further discussed and tested in this paper.
The soil moisture indicators (SMIs) obtained by these techniques are evaluated in their performance to produce accurate flood warnings. A combination of the SMI and the antecedent rainfall is used to calculate the probability of exceedance of a predefined discharge threshold, by means of a logit relation. Not only is a combination of SMI and rainfall tested in its forecasting performance, it is also compared with the prediction performance of the total runoff generated by a rainfall runoff model. The different methods are tested in their predicting capabilities by making use of the Brier score (Brier, 1950).
Hydrological models predict runoff making use of rainfall and evapotranspiration data. Most hydrological models, in operational use for flood modelling and forecasting, are conceptual models, which contain reservoirs, representing the soil moisture content. Depending on the relative storage content of these reservoirs and rainfall and evapotranspiration inputs, runoff is generated. The water storage predicted by these models can be considered as a measure of the catchment soil wetness and saturation level. This saturation level is spatially averaged if a lumped model is considered. Lacava et al. (2010) mentioned that modelled soil moisture data can reliably represent the real soil moisture; they used it to validate remotely sensed soil moisture data. Together with rainfall the initial soil moisture state plays a key role in the runoff response of a basin as predicted by a model (Zehe et al., 2005). Also the baseflow can be used as soil wetness and saturation indicator. Although baseflow is the ground water contribution to the streamflow and soil moisture is related to the upper zone in the soil, both are highly correlated (Van Steenbergen and Willems, 2012), which is also confirmed later on in this paper. Also Tramblay et al. (2010) tested modelled soil moisture and baseflow as SMIs and found that both were valid predictors. Therefore both the water content in the storage reservoir of conceptual hydrological models and the baseflow are in this paper considered as SMIs.
Closely related to the hydrological modelling techniques, indirect observations offer a second approach to estimate the relative soil moisture state of a catchment. By means of filter methods (Chapman, 1999, Arnold and Allen, 1999, Eckhardt, 2005), observed river flow series can be separated in different runoff subflows (e.g. overland flow, interflow, baseflow). Combining these subflow separation results with rainfall and evapotranspiration time series data, empirical water balance computations allow calculation of the relative soil moisture state. In this paper the subflow separation technique proposed by Willems (2009), based on the recursive digital filter proposed by Chapman (1991) is applied. For the soil moisture state estimation based on water balance computation different types of methods could be applied, such as the stochastic approach (Rodriguez-Iturbe et al., 1999, Porporato et al., 2004, Verma et al., 2011) or the quasi-terrestrial water balance method (Moiwo et al., 2011). An alternative empirical method is applied in this study. The advantage of these methods is that only a limited number of model assumptions are needed to make estimates on baseflow or soil moisture content.
Section snippets
Study area
The focus of the study is on the Flanders region in the north of Belgium. In total 154 catchments are considered, covering most of Flanders, including some upstream parts in the north of France and the Walloon region. The total area covered by the models in this study is approximately 22,250 km2. The lumped subcatchment areas vary from 1 to 5227 km2 per modelled catchment. The climatic conditions do not vary a lot between the different catchments. The average annual rainfall varies from 750 mm to
NAM model
NAM (Nedbør-Afstrømnings Model) is a lumped conceptual rainfall–runoff model, implemented in the Mike11 software of DHI Water and Environment. NAM generates three components of the catchment runoff (overland flow, interflow, baseflow) by means of reservoir-based routing and storage components. The storage components describe the storage of catchment water at the surface, in the soil (subsurface) and in the groundwater. The model also contains snow storage, but this is not considered in this
Soil moisture indicators
From Fig. 3, Fig. 5 the seasonal variations of the different SMI are clearly visible. To show that next to the seasonal variability also the time variability among the different SMIs is closely related, Fig. 7 shows a time series plot of the different SMIs for the same example Gete catchment. When the absolute values of the modelled and empirical SMIs are compared (L/Lmax with u/umax and BF with BFfil) large differences exist, but this does not pose problem since the relative values are used as
Conclusions
In this research soil moisture has been used for two purposes. Firstly it is shown that soil moisture monitoring by means of hydrological modelling techniques or indirect observation techniques can be used to provide a general overview of the soil moisture state of different hydrological catchments. Secondly, combining the soil moisture indicator with daily rainfall is very efficient in assessing the probability of exceedance of a predefined discharge threshold for the next two days. This
Acknowledgements
The results presented in this paper were obtained by a research project on flood forecasting for Flanders Hydraulics Research, a division of the Flemish governmental authorities in Belgium.
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