Spatial discretization in hydrological models has a strong impact on computation times. This study investigates its effect on the performance of the Soil and Water Assessment Tool (SWAT) applied to a French Mediterranean watershed. It quantifies how spatial discretization (the number of sub-basins and hydrological response units (HRUs)) affects the SWAT model’s performance in simulating daily streamflow and whether this effect depends on the choice of soil and land use input datasets. Sixty-eight SWAT model configurations were created using various soil and land use datasets and 17 discretization setups, evaluated from 2001 to 2021 with the Kling–Gupta efficiency (KGE) metric. The key findings include (1) while the number of sub-basins does not impact model performance, increasing HRUs significantly degrades it (KGE loss of 0.13 to 0.26) regardless of the number of sub-basins or input datasets. (2) SWAT is found to be more sensitive to variations in soil datasets than in land use datasets, but the observed performance decline with more HRUs is attributed to the calibration process and the increased heterogeneity in soil types rather than input dataset spatial resolution. (3) Minimizing the number of HRUs may improve both the accuracy of streamflow simulations and the computational efficiency of the SWAT model.

Soil erosion is a major environmental concern in tropical mountain ecosystems where steep terrain, intense rainfall and dynamic land use changes contribute to the accelerated degradation of natural resources. This study assesses the spatiotemporal patterns of potential soil erosion in the Tapesco River watershed, a peri-urban territory located in Costa Rica's Central Volcanic Mountain Range, for the years 1986, 1998, 2011 and 2019. The Revised Universal Soil Loss Equation (RUSLE) was implemented within a Geographic Information System (GIS) framework, incorporating five critical factors: rainfall erosivity (R), soil erodibility (K), topographic slope length and steepness (LS), land cover (C) and conservation practices (P). By interpreting these factors as proxies of hydrological processes-such as rainfall energy, runoff generation, infiltration capacity and hillslope hydrological connectivity-the analysis provides insight into how water-driven erosion mechanisms evolve under land use change. The results reveal significant changes in erosion rates strongly associated with land use transitions and climatic variability over the study period. Forested and pasture lands consistently exhibited lower erosion rates, whereas areas under annual crops and steep slopes were subject to markedly greater soil loss. A substantial increase in erosion was observed between 1986 and 1998 followed by a partial recovery by 2019, corresponding with a decline in agricultural land use and the expansion of forest and pasture areas. Furthermore, an erosion risk exposure map identified that 28.9% of the watershed-mainly in the eastern and upper watershed-remains highly vulnerable to erosion. These findings underscore the value of spatially explicit erosion modelling as a critical tool for informing sustainable land management and targeted soil conservation efforts in fragile tropical mountain landscapes. These findings demonstrate how shifts in hydrological processes-particularly runoff concentration, rainfall-runoff response and surface-vegetation interactions-mediate erosion dynamics over time. Overall, the study highlights how soil erosion modelling can improve understanding of hydrological functioning in tropical peri-urban landscapes and provide actionable information for integrated soil-water management.

Floods are one of the most dangerous and disastrous natural hazards that cause economic damages and human loss. In particular, flash floods related to heavy precipitation represent a major hazard on the French Eastern Mediterranean coast where growing population and tourism increase the exposure and vulnerability of coastal cities to extreme events. A few well recorded severe events have demonstrated the vulnerability of this area to river floods during the last 15 years. Consequently, various modelling approaches has been recently proposed to support flood prevention and mitigation in urban region. However, mapping floods events with limited computational costs remains challenging. In this study, four open-source numerical flood mapping tools are compared regarding their ability at simulating past flood events over five catchments in the southeastern region of France. The flood mapping models range from the simple Height Above Nearest Drainage approach (MHYST) to more complex methods that solve the full shallow water equations (LISFLOOD-FP DG2). Models of intermediate complexity, such as a 1D shallow water solver (HEC-RAS) and a 2D cellular automata (CAflood), are also included. Model evaluations are performed based on water depth accuracy estimation against high water marks data. The flood mapping tools are also compared in terms of flood extent using critical success indices. This study outlines how the more complex models provide the more accurate and realistic flood simulations, however with high computationally demanding which requires the deployment of substantial computer resources before their use in operational flood systems.

Assessing the benefits of increasing the discretization level of semi-distributed hydrological models is of great importance for hydrological applications. The impact of spatial discretization on model performance is investigated with the use of the Soil and Water Assessment Tool (SWAT) model when applied on a Mediterranean watershed (Argens, France). This study aims to explore how the spatial discretization (number of sub-basins and of hydrological response units (HRUs)) affects the model’s performance at simulating daily streamflows, and if the choice of soil and land use input datasets modifies model accuracy. Low and moderate resolution soil (5 km and 250 m) and land use (400 m and 100 m) maps are considered. Four SWAT input sets are created, each corresponding to a different combination of land use and soil datasets. Each input set is used to build 17 configurations with an increasing number of sub-basins (4, 12, and 18) and HRUs (from 4 to 320). The 68 models (4 input sets x 17 configurations) are evaluated on the 2001-2021 period using the Kling-Gupta efficiency (KGE) metric. Results indicate no influence of the number of sub-basins on SWAT performance. However, increasing the number of HRUs leads to a significant performance decrease (from 0.13 to 0.26 of KGE loss), regardless of the number of sub-basins and input datasets. The SWAT model is found to be more sensitive to soil dataset than to land use dataset. Despite significant differences in hydrological soil groups between the two soil maps, no clear impact on the derived hydrological properties is observed, such as the curve number. The observed decline in SWAT performance with an increasing number of HRUs is attributed to the calibration process rather than the soil and land use input datasets. This study suggests that, when the calibration of the semi-distributed SWAT model is not performed at the finer spatial HRU level, an increase in the spatial discretization does not lead to an improvement of the overall model accuracy. Therefore, minimizing the number of HRUs during the watershed subdivision is recommended for getting optimal simulations of streamflow while dealing with the computational efficiency of SWAT.

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The percentage of wildfires that are ignited by an undetermined origin is substantial in Europe and Mediterranean France. Forest fire experts have recognized the significance of fires with an unknown ignition source since documentation and research of fire causes are important for creating appropriate fire policies and prevention strategies. The use of machine learning in wildfire science has increased considerably and is driven by the increasing availability of large and high-quality datasets. However, the absence of comprehensive fire-cause data hinders the utility of existing fire databases. This study trains and applies a machine-learning based model to classify the cause of fire ignition based on several environmental and anthropogenic features in Southern France using an eXplainable Artificial Intelligence framework. The results demonstrate that the source of unknown caused wildfires can be predicted at various levels of accuracy/natural fires have the highest accuracy (F1-score 0.87) compared to human-caused fires such as accidental (F1-score 0.74) and arson (F1-score 0.64). Factors related to spatiotemporal properties as well as topographic characteristics are considered the most important features in determining the classification of unknown caused fires for the specific area.

Forest fires burn an average of about 440 000 ha each year in southern Europe. These fires cause numerous casualties and deaths and destroy houses and other infrastructure. In order to elaborate on suitable firefighting strategies, complex interactions between human and environmental factors must be taken into account. In this study, we investigated the spatiotemporal evolution in the burned area over a 50-year period (1970–2019) and its interactions with topography (slope aspect and inclination) and vegetation type in southeastern France by exploiting the geographic information system (GIS) databases. Data were analyzed for two 25-year periods (1970–1994 and 1995–2019), since a new fire suppression policy was put into place after 1994, which focused on rapid extinction of fires in their early phase. In the last 25 years, the burned area decreased sharply, and the geographic distribution of fires also changed, especially in regions where large fires occur (Var administrative division). Elsewhere, even though forest fires remain frequent, the total extent of the burned area decreased substantially. Fire hotspots appear closer to built-up areas in the west, are randomly distributed in the east, and they almost completely disappear in the central region of the study area where there is a history of large fires. Slope orientation presents an increasingly important role in the second period; south-facing slopes are preferred the most by fire, and north-facing slopes are preferentially avoided. Even though the slope inclination is less affected by the new firefighting strategy, low slope inclinations are even more avoided after 1994. The greatest proportion of the burned area is strongly associated with the location of sclerophyllous vegetation clusters which exhibit highly fire prone and expand in area over time. Natural grasslands are also preferred by fire, while broadleaved, coniferous, and mixed forest are increasingly avoided by fire.

Knowledge on fire ignition causes and their spatiotemporal patterns can greatly enhance the efficiency of fire management and fire strategies. In France, the majority of forest fire research is based on a 2x2 km gridded database that provides amongst other information, the cause of fire ignition. According to the same database however, approximately 75% of all fires between 1973 and 2020 were recorded without a cause of ignition. Therefore, information on fire causes for a very large part of the fires that were recorded in the last 50 years is not taken into consideration and can potentially provide significant evidence on patterns of different fire ignition causes. In the current study, we used for the first time a point geodatabase in order to predict the cause of fire ignition by applying several machine learning methodologies and modelling multiple environmental and anthropogenic drivers. As arson fires are of particular interest in SE France since they are the most frequent and cause the largest volume of burned area, we plan to analyse their spatiotemporal evolution over the last decades.

Flood risk is a significant challenge for sustainable spatial planning, particularly concerning climate change and urbanization. Phrasing suitable land planning strategies requires assessing future flood risk and predicting the impact of urban sprawl. This study aims to develop an innovative approach combining land use change and hydraulic models to explore future urban flood risk, aiming to reduce it under different vulnerability and exposure scenarios. SPOT-3 and Sentinel-2 images were processed and classified to create land cover maps for 1995 and 2019, and these were used to predict the 2040 land cover using the Land Change Modeler Module of Terrset. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. We have compared flood risk in 1995, 2019, and 2040. Although flood risk increases with urbanization, population density, and the number of hospitals in the flood plain, especially in the coastal region, the area exposed to high and very high risks decreases due to a reduction in poverty rate. This study can provide a theoretical framework supporting climate change related to risk assessment in other metropolitan regions. Methodologically, it underlines the importance of using satellite imagery and the continuity of data in the planning-related decision-making process.

Les rivieres en tresses sont des hydrosystemes davantage etudies depuis ces dernieres annees1,2. L'utilisation d'indicateurs permet de dresser un diagnostic sur ces milieux, notamment leur dynamique hydrosedimentaire. Dans un contexte actuel de restauration, de nombreuses operations sont entreprises sur ces milieux, afin d'atteindre le bon etat ecologique. La comprehension des trajectoires a long terme de ces hydrosystemes est donc necessaire afin d'evaluer leur etat de sante ainsi que les actions de restauration. L'objectif de cette etude est de realiser un diagnostic sur la dynamique hydrosedimentaire au sein de ces rivieres en tresses et un retour d'experience suite aux operations de restauration. Cette etude se base sur l'analyse de quatre rivieres restaurees durant ces 10 dernieres annees et situees en region PACA et en Italie. L'etude porte egalement sur la Roya (Italie), un systeme en tresses anthropise et non restaure qui a permis une validation des indicateurs dans un contexte different. La demarche methodologique consiste en une analyse de l'evolution spatio-temporelle de differents parametres physiques, comme l'evolution de la bande active (W*,3) ou sa rugosite (BRI*,3). Les donnees utilisees sont des orthophotos (1950-2015), des donnees MNT (LiDAR), et des donnees photogrammetriques ont egalement ete testees. L'etude morphodynamique nous a permis d'approfondir la comprehension des dynamiques hydrosedimentaires de ces facies en tresses suite aux operations de restauration. Les premiers resultats suggerent que l'hydrosysteme parvient a retourner vers un patron en tresses, avec une bande active plus large (W* stable) et un patron en tresses plus marque (BRI* faible) suite aux operations de restauration.

Urban growth transforms vegetated lands into impervious surfaces, thereby increasing runoff and peak discharge. In this study, the relationships between building growth, developed area, and impervious area were examined to determine the impact of peri-urbanization on peak discharge. This was modeled for four dates spanning 50 years (1964-2014) in a Mediterranean catchment located in S-E France. Imperviousness was estimated from building footprints for 1964, 1978, 1999, and 2014 and buffer zones around buildings. The Terrset Runoff module and HEC-HMS were used to model the impacts of urbanization on peak discharge. The number of buildings in the 235 km(2) catchment increased by 207.3%, the developed area increased by 149.7%, and catchment imperviousness increased by only 75.5%. Impervious area increased much more slowly than building growth would suggest, so the increase in peak discharge was smaller than expected (4-8% in the 50-year interval).

As in many other European countries, urbanisation and urban sprawl along the French Mediterranean coast are a major concern. Understanding this phenomenon requires both multi-level and multi-disciplinary approaches. In this perspective, this article presents a framework for the observation and analysis of urban sprawl in the French Mediterranean coastal zone. Developed in the context of a scientific coastal observatory with four contrasting study sites, the framework was designed to structure the observation and analysis of urban sprawl dynamics and their driver variables. Although urban expansion is currently slowing in coastal zones, local exceptions can be found and accounted for by historical urban planning and environmental protection measures, local residential tax policies and contradictory perceptions of coastal zones by residents. Our multi-disciplinary initiative is capable of integrating different temporal and spatial scales and has proven relevant in analysing urban sprawl in coastal areas. It shows the need to study coastal areas at finer scales to identify specific dynamics in their local contexts, since these represent the scale at which administrative decisions are made.

The Mediterranean basin has undergone widespread land cover change. Urbanization of coastal areas, land abandonment of steeper slopes, and agricultural intensification in alluvial plains are recurrent themes. The objective of this study was to examine how vineyard land cover changes have affected agricultural soil erosion in a 50 year period (1950-2011). The study area covers a 235 km(2) catchment located near the Gulf of St Tropez. Aerial photographs were used to map land cover in 1950, 1982, 2003 and 2011, and the RUSLE soil erosion model was run to estimate soil erosion. Between 1950 and 2011, vineyard went from about 2,426 ha to 1,561 ha. Mean soil erosion increased as vineyard slopes became steeper (11.8 T ha(-1), 13.2 T ha(-1), 14.4 T ha(-1) and 13.5 T ha(-1) for 1950,1982, 2003 and 2011). Total erosion decreased after 1982: 28,621 T y(-1) in 1950, 29,030 T y(-1) in 1982, 22,848 T y(-1) in 2003, and 21,074 T y(-1) in 2011 Total soil loss in 2011 is about 75% of values in 1950-1982, so impacts on water pollution and channel dredging have evolved positively over time.

Wildfires burn > 450,000 ha of forest every year in Euro-Mediterranean countries. Many fires originate in the Wildland Urban Interface (WUI) where housing density and weather conditions affect fire occurrence. Housing density is determined by long term land use policies while weather conditions evolve quickly. The first objective was to quantify the impacts of land use policy onWUI characteristics and fire risk in SE France during 1990-2012. The second objective was to quantify how Fire Weather Index (FWI) is related to fire occurrence. WUI was mapped from 1990, 1999, and 2012 building layers and crossed with a NDVI derived vegetation layer. In all, 12 WUI categories were derived: 4 building density classes and 3 vegetation layers. The I87 FWI was based on daily temperature, wind speed, relative humidity and soil water content. Despite a 30% increase in the number of new buildings, WUI area increased by only 5% as new housing filled in open space in existing WUI area. This trend can be linked to national level urban planning legislation and forest fire protection laws. Major driver variables determining housing location were aspect, slope, and distance to city centers. Fire frequency and burned area were nonlinearly related to FWI: 73% of the 99 fires occurred during weeks with FWI values >= 90 even though these accounted for only 44% of all weeks. Burned area was even more sensitive to FWI since 97% of total burned area occurred during weeks with mean FWI values >= 90. All days with burned areas > 100 ha had FWI values > 150. The study demonstrated that WUI legislation can be an efficient tool to limit WUI fire risk. FWI results suggest the predicted increase in extreme summer heat events with global warming could increase burned area as firefighting resources are stretched beyond capacity. (C) 2017 Elsevier B.V. All rights reserved.