Abstract
The objectives of the FloodScale project were to progress in the knowledge, understanding and simulation of hydrological processes leading to flash floods in the Mediterranean area. These flash floods and the rainfall events that trigger them lead, each year, to fatalities and a large amount of damage in this region, but processes leading to their generation remained poorly known and hierarchized. The FloodScale project contributed significantly to the 'hydrology' part of the international HyMeX (Hydrological Cycle in the Mediterranean Experiment) program. Several aspects were tackled in the project: observation, understanding and simulation of flash floods, with in the background, two fundamental questions in hydrology: the change of scale problem and the prediction in ungauged catchments. These two questions are particularly relevant for flash floods that often occur in small ungauged catchments but that may interest a large territory, requiring understanding and modelling at small scale and over a large area. For the observation of flash floods and data collection allowing tackling the two scientific questions mentioned above, a multi-scale observation strategy was set up in two meso-scales catchments of the Cévennes region: the Gard and Ardèche catchments. Three scales were considered in the sampling strategy: 1/ the hillslope scale for documenting hydrological processes; 2/ the small catchment scale (1-100 km2) where the spatial variability of landscapes and hydrological processes was monitored and described and 3/ the meso-scale catchment scales (100-2000 km2) relevant for management and warning. These observations combined continuous measurements during four years (rainfall, water level in streams, discharge, soil moisture) and opportunistic measurements during the four autumns of the project: gauging of flooding rivers, sampling of soil moisture and geochemistry sampling of rainfall, streams and soil water. The observation strategy was found relevant and allowed documenting both the 'normal' catchment behavior and a selection of flash floods across scales. The project also allowed evaluating several new measurements techniques such as the saturometer for estimating soil infiltration capacity but also the interest of non-contact measurements for 1/ estimating soil depth or monitoring water fluxes thanks to electrical resistivity, 2/ estimating flooding discharges (use of portable surface velocity radars, fixed cameras and analysis of citizens videos). Methods for merging radar rainfall and pluviometers data were also proposed and improved and proved relevant to describe properly rainfall time and space variability. We should also mention methodological progress for quantifying rainfall and discharge uncertainty. Analysis of the collected data showed a large variability of soil hydraulic properties on the hillslope scale, whereas those properties were quite stable across hillslopes. At the scale of the whole Cévennes-Vivarais region, geology and land use were found discriminant for explaining soil hydraulic properties spatial variability. A high degree of soil depth variability is also a characteristic of the soils in the region and it is necessary to take it into account into models. Hillslope experiments, but also the analysis of opportunistic or continuous geochemistry sampling show the large contribution of quick lateral sub-surface flow in granite and schist geology with a natural vegetation, but also their role in sedimentary and agricultural areas. Soil water storage inferred from existing data bases was also found to be greatly underestimated because they do not account for water storage in the deep weathered and fractured bedrock layers. The soil-bedrock interface was found to be pervious, with high infiltration capacities. Analysis of soil moisture data combined with the hydrological response show threshold effect at the transition between dry and wet conditions with a low hydrological response below this humidity threshold and a large variability in the response when soil water content is above this threshold. The simulation and modelling of flash floods was performed using a hypothesis testing framework, where various functioning hypotheses are assessed by comparing modeled and observed values. The project results proved the validity and interest of this iterative approach. At the small catchment scales, various models, based on different hypotheses about dominant processes were used. The results show that one process is generally not sufficient to reproduce the observed response and that sub-surface fluxes must be included in the modeling as well as the spatial variability of soil water storage. More specifically, a distributed modelling approach, designed from field observations was shown to be relevant and robust to the change of scale. At the regional scale, top-down and bottom-up approaches were set up and assessed. The studies confirm the need to account for water storage in deeper weathered horizons for a correct simulation of soil water storage capacity. They also show that geology must be also taken into account to correctly simulate the spatial variability of the hydrological response. Both approaches allow a satisfactory simulation of this variability, in particular during flash floods. Those models provide simulations in ungauged catchments. During the project a large effort was dedicated to data collection, validation and documentation for inclusion in several data bases. The collected data set document various aspects of flash floods at various scales and is quite unique. All the collected data were only partially analyzed and exploited during the project, with a focus on sites analyses to verify the consistency of the data and have a first insight into active processes. The most promising perspective of the work is certainly a global analysis of the data sets by pooling all the data from the various sites together to obtain a picture of hydrological responses on the scale of the whole region. The effort of documenting rainfall and discharge uncertainty on rainfall and discharge also offers rich perspectives to better take uncertainty into account in models, in their evaluation and calibration.