Even though some differences might exist within these two terminologies (pluvial floods are often used in the urban context), both will be used as synonyms here. Another commonly used term is pluvial flood, which is defined as the result of an extreme precipitation event that creates a flood independent of an overflowing water body. In this work, the phrase surface water floods (SWFs ) will be referred to when considering the various observed impacts of intense overland flow of rainwater on the land surface, as opposed to fluvial floods (FFs) which are confined to the proximity of streams. Triggered by more or less intense rainfall, such floods can appear anywhere on a given territory depending on land use, soil type and topography. In France and Switzerland, surface water floods have been estimated accountable for respectively 50% and 45% of all flood damage claims in the past years. They have been observed all around Europe, both in urban and rural areas, and not only around the Mediterranean border where severe storms happen on a more frequent basis (see for some examples). Comparably to river overflowing, inland flood events occurring outside the vicinity of active waterways have had devastating effects worldwide in the past decades. With more extreme precipitations expected in the 21st century due to climate change, increased attention has to be paid to the understanding and modeling of floods as of now. This work overall confirms the relevance of IRIP methodology while suggesting improvements to its core framework to implement better prevention strategies against SWF-related hazards. Multivariate logistic regression is also used to determine the relative weights of upstream and local topography, uphill production areas and rainfall intensity for explaining SWF occurrence. Land use and soil hydraulic conductivity are identified as the most relevant indicators for IRIP to define production areas responsible for downslope deteriorations. A negative relationship between the mean IRIP accumulation scores and the intensity of rainfall is found among damaged plots, confirming that SWFs preferably occur over potentially riskier areas where rainfall is lower. Proportions of damaged plots become even larger when considering areas which experienced heavier precipitations. The results of this study show that the greater the IRIP susceptibility scores, the more SWFs are detected by the remote sensing-based detection algorithm. Six watersheds in the Aude and Alpes-Maritimes departments in the South of France are investigated over more than 2000 km 2 of rural areas during two flash-flood events. Here, the IRIP geomatics mapping model, or “Indicator of Intense Pluvial Runoff”, is faced with rainfall radar measurements and damage maps derived from satellite imagery and supervised classification algorithms. However, in order for these methods to be applicable for prevention purposes, they need to be comprehensively evaluated using proxy data of runoff-related impacts following a given event. Geomatics approaches have also been developed to map susceptibility towards intense surface runoff without explicit hydrological modeling or event-based rainfall forcing.
Using physics-based distributed hydrological models, surface runoff can be simulated from precipitation inputs to investigate regions prone to soil erosion, mudflows or landslides. Along with fluvial floods (FFs), surface water floods (SWFs) caused by extreme overland flow are one of the main flood hazards occurring after heavy rainfall.
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