Initial condition 1. Soil moisture

Soil moisture has great effects on hydrological and meteorological processes; in particular, it controls the process of partitioning rainfall into runoff and infiltration. The magnitude of the flood event is strongly influenced by the initial soil moisture conditions of the catchment (Tramblay et al., 2010). Thus, it is necessary to accurately estimate soil moisture for a range of hydrological applications, including floods and drought forecasting and assessment. The content of water in the first active meters of soil plays a central role in the regulation of the hydraulic and energy transfers between the soil, the surface and the atmosphere (Vischel et al., 2008). Thus, it is very important to have a realistic representation of the spatial variability of near-surface soil moisture to represent the hydrological fluxes in the subsurface at various scales (Zehe and Blöschl, 2004) and to link between hydrological and atmospheric processes (Montaldo and Albertson, 2003; Ronda et al., 2002).

However, it is challenging to obtain an accurate estimation of soil moisture due to its high spatial and temporal variability (Western and Blöschl, 1999). In small scale, among other factors, this variability mainly depends on topography, soil type, precipitation and vegetation (Western et al., 2002). Besides, the spatial variability of soil moisture also strongly controls the runoff. In particular, the dominant flow path varies under different soil moisture conditions, which in turn affects the runoff peaks and response time (Patil et al., 2014; Penna et al., 2011).

Information about soil moisture with increasing temporal and spatial resolutions (Wagner et al., 2007) are currently available from different sources, including the in situ measurements (for example the International Soil Moisture Network ISMN (Dorigo et al., 2011)), satellite sensors information (for example the Advanced Microwave Scanning Radiometer for Earth observation AMSRE (Owe et al., 2008), the Advanced SCATterometer ASCAT (Bartalis et al., 2007), the Soil Moisture and Ocean Salinity Mission SMOS (Kerr et al., 2010)), and from land surface models (for example, the ERA-Interim/Land data set (Balsamo et al., 2015)).

Soil moisture can also be used as a proxy to assess the wetness state of the catchment to predict flood hydrographs. Many studies have been investigating the assimilation of soil

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moisture observations into rainfall-runoff (RR) modeling (Brocca et al., 2008; Javelle et al., 2016; Massari et al., 2014; Tramblay et al., 2012). Besides, Yu et al. used the output of the daily SWAT continuous model embedded in an event-based sub-daily SWAT model (Yu et al., 2018). Field monitoring of soil moisture was also used (Tramblay et al., 2010) and satellite soil moisture data were suggested (Tramblay et al., 2012). At this point, the relationship between the initial condition of the model and the external predictors remains however little known and needs further exploration.

Among the others, the SIM model is one of the French models which can produce soil moisture daily data (Habets et al., 2008; Quintana Seguí et al., 2009).

SIM model

The SIM model is the combination of three independent models: SAFRAN which provides atmospheric forcing analysis, IBSA which computes the surface water and energy budgets, and MODCOU which computes the evolution of the aquifer and the river flow. The SIM model system was firstly tested for France catchment in 1999. It was extended for all over France since 2002 and has been used operationally at Meteo-France to monitor the near real-time water resources since 2003.

For the ISBA model which give the output of the soil moisture, most of the parameter is defined by the soil and vegetation classification except the subgrid runoff parameter and the subgrid drainage parameter. The soil classification derived from ECOCLIMAP database (Champeaux et al., 2005). The vegetation classification based on CLC 1990 database, associated with a climate map. It is claimed that quite correct for the forested areas, vineyards, and urban area but not distinguish the various crops that are aggregated into a single class (Habets et al., 2008).

The information of soil which the origin comes from FAO-UNESCO soil map of the word gave the three qualitative properties of soil: soil color (light, medium, dark), soil texture (fine, medium, coarse) and drainage (free, medium, impeded). Soil depth is related to the root zone of the plant, which derived from the type of vegetation.

The SIM model supplied output indexes once a day at 6 UT, over an 8x8 km² grid mesh of France, for three layers (Boone et al., 1999): the surface layer (1cm deep), the root layer and the deep layer (depths depending on the type of vegetation).

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1.2.2.2.2. Base flow

Another initial condition index we can consider is base flow. Base flow is generated by the amount of precipitation that infiltrates through the subsurface and discharges to the streams. Various basin characteristics, including catchment geology, climate, soils, topography and land cover, can affect the amount of base flow that discharges to the streams.

High rates of infiltration, recharges and groundwater storage can increase base flow, while high rates of evapotranspiration and runoff can reduce base flow (Brutsaert, 2005). The subsurface storage and drainage network structures are strongly dependent on the geological characteristics (Price et al., 2011), while the rate of infiltration, hydraulic conductivity, and groundwater recharge depend on soil characteristics (Pirastru and Niedda, 2013). Besides, the topographic characteristics can also influence base flow by affecting the movement of water across the surface and subsurface, which in turn can influence the infiltration, flow process and rates of water transmission (McGuire et al., 2005). Moreover, the vegetation can also affect the base flow by changing the rate of interception, evapotranspiration, infiltration, and recharge of subsurface storage (Nie et al., 2011). Last but not least, temperature, precipitation, and other climatic factors can have impacts on base flow as they can change the rate of evaporation, infiltration, and recharge. These climatic factors can also cause snowmelt runoff, which can also alter the base flow (Tague and Grant, 2009).

Information about baseflow can be obtained by inferring from field measurements of different characteristics, including temperature, tracer concentrations and flow by seepage meters which are installed in the stream beds (Becker et al., 2004). However, these techniques are often challenging to be applied over an entire catchment. Baseflow is thus often estimated using different baseflow separation methods. The estimation of baseflow can be based on either the linear storage-discharge relationship between aquifer and stream (Barnes, 1939; Hall, 1968) or nonlinear storage-discharge relationship (Wittenberg, 2003;

Wittenberg and Sivapalan, 1999), depending on the characteristics of the catchments.

Besides, baseflow can also be estimated using hydrological reasoning without physically-based mathematical framework, including four main categories of methods: (i) graphical separation (Sloto and Crouse, 1996), (ii) conceptual models (Eckhardt, 2005; Huyck et al.,

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2005) (iii) recession analysis (Tallaksen, 1995) and (iv) recursive digital filters (Arnold and Allen, 2007; Nathan and McMahon, 1990).