Flash flood modeling applications and the concept of regionalization

1.4.3.1. Flash flood modeling applications

Many scientists have also researched this kind of phenomena in the previous time, and they also pointed out that there are many problems to solve. PiÑOl et al. used TOP MODEL to simulate the hydrological event in Spanish catchments and they realized that the spatial soil depth heterogeneity, as well as the characteristics and the localized nature of downslope flows of water in the soil, are the most difficult to be described in the model (Piñol et al., 1997). They also pointed out that models with very large numbers of parameters would not be easy to calibrate. Many applications using TOP MODEL have been made in the Mediterranean area and lead to promising data and results although there is a need for improvement for wetting up period or extreme events such as storm (Blöschl et al., 2008;

Durand et al., 1992; Saulnier and Le Lay, 2009).

Another study has claimed that flash flood forecasting could not be characterized via only the deterministic and mechanistic approach, due to the complicated processes involved in its generation and propagation (Montz and Gruntfest, 2002). Many other applications of real-time flash flood forecasting have been proposed to describe the flood process. Indeed, a more recent research has introduced a semi-distributed model with empirical SCS concept on a hill slope and a Muskingum scheme (reservoir-type model) in the river into presenting flash

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flood, in which the key parameters are derived from topographic conventional data, land cover maps and field surveys (Foody et al., 2004).

Another application is proposed using a spatially distributed model base in the physical process of the water cycle and flood genesis (Moussa and Chahinian, 2009). When the surface runoff is dominated in the hydrology process, this model is well-adapted; however, in the results, it is shown that the performance of the model decreases without the intense flood events. Thus, there is a question for the model to be well-represented for hydrological processes during both drought and flood periods.

Because of numerous and complex processes involved in a flash flood, the notion of the model framework including different concepts for each process of generation and propagation in flash flood was suggested to use. Each model can be a simplified hydrological physically-based model taking into account the spatial variability of different processes combines with the runoff process over hill-slopes derived from the kinematic wave approximation. The built model called MARINE stands for Model of Anticipation of Runoff and INondations for Extreme events (Estupina-Borrell et al., 2006; Roux et al., 2011).

1.4.3.2. The concept of regionalization

Although rainfall-runoff models are crucial tools for prediction and flood forecast, they must still be improved in order to gain for temporal and spatial extrapolation.

Regionalization aims to transpose models from gauged catchments to ungauged one, and many regionalization studies have been performed up to now (McIntyre et al., 2005; Merz and Blöschl, 2004; Oudin et al., 2008; Young, 2006). Several methodologies can be used for regionalization:

Regionalization based on regression: it allows for defining a posteriori relationship between catchment attributes and model parameters at gauged catchments. After determination of these relationships, one can define parameters in ungauged catchments based on its physio-climatic characteristics.

Regionalization based on spatial proximity: this approach considers that the same parameter values can be associated with geographic neighbors with an assumption that physio-climatic characteristics of the explored region are homogeneous.Regionalization based on physical similarity: This type of regionalization approach represents the

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combination of the previous two. It is based on a hydrologic similarity between an ungauged and gauged site where parameter transfer is not geographically based but rather in terms of similar catchment descriptors behavior.

However, previous studies revealed a poor efficiency of the models when transposed to ungauged catchments (Aubert et al., 2014; Bastola et al., 2008; Lee et al., 2005; Norbiato et al., 2009; Viviroli et al., 2009). The failure of regionalization may be mainly attributable to the equifinality, which makes that several sets of parameters can be accepted for modeling rainfall-runoff relationship; and makes difficult the spatial comparison and the interpretation of those parameters. Another difficulty in regionalization is due to the lack of appropriate descriptors of the catchment properties (mainly the soil properties) when using methods such as multivariable relationships (Oudin et al., 2010). In addition, scaling problems can interact with the interpretation of the parameters: scaling means that the parameters are not independent of the size of the catchment, for several possible reasons:

heterogeneity of the surface (and possibly subsurface) features, change in the dominant processes, inadequacy of the equations at different scales, mismatch between the local field data and the aggregated parameters (Blöschl, 2001; Gentine et al., 2012; Vinogradov et al., 2011). Thus, further researches are needed to improve the performance of the regionalization.

1.5. Conclusion

The hydrological processes, flood processes, an overview of the model, Mediterranean floods and the applications of several models on Mediterranean floods have been described in this chapter, which provided the theoretical background for our study. In the next chapter, we will detail the methodology which is applied for Mediterranean flood forecasting. It is the combination of two widely-used models SCS production model and LR routing model.

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