Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use, and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lake and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 137 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh ∕ dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with a R2 > 0.8, an average R2 of 0.91 and a standard deviation of 0.05. For these lakes and for lakes with a nearly constant lake area (coefficient of variation < 0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage and (3) a predominant lack of data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the US, Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97 with a standard deviation of 0.041, and a normalized RMSE of 7.42 %. These results show a high potential for measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will not only provide a historical lake and reservoir volume variation record, but will also help to improve our understanding of the behaviour of lakes and reservoirs and their representation in (large-scale) hydrological models.

Abstract. This paper presents a coupled observation and modelling strategy aiming at improving the understanding of processes triggering flash floods. This strategy is illustrated for the Mediterranean area using two French catchments (Gard and Ardèche) larger than 2000 km 2 . The approach is based on the monitoring of nested spatial scales: (1) the hillslope scale, where processes influencing the runoff generation and its concentration can be tackled; (2) the small to medium catchment scale (1-100 km 2 ), where the impact of the network structure and of the spatial variability of rainfall, landscape and initial soil moisture can be quantified; (3) the larger scale (100-1000 km 2 ), where the river routing and flooding processes become important. These observations are part of the HyMeX (HYdrological cycle in the Mediterranean EXperiment) enhanced observation period (EOP), which will last 4 years (2012)(2013)(2014)(2015). In terms of hydrological modelling, the objective is to set up regional-scale models, while addressing small and generally ungauged catchments, which represent the scale of interest for flood risk assessment. Topdown and bottom-up approaches are combined and the models are used as "hypothesis testing" tools by coupling model development with data analyses in order to incrementally evaluate the validity of model hypotheses. The paper first presents the rationale behind the experimental set-up and the instrumentation itself. Second, we discuss the associated modelling strategy. Results illustrate the potential of the approach in advancing our understanding of flash flood processes on various scales.

Flash floods represent one of the most destructive natural hazards in the Mediterranean region. These floods result from very intense and spatially heterogeneous rainfall events. Distributed hydrological models are valuable tools to study these phenomena and increase our knowledge on the main processes governing the generation and propagation of floods over large spatial scales. They are generally built using a bottom-up approach that generalizes small-physics representations of processes. However, top-down or data-driven approach is increasingly shown to provide also valuable knowledge. A simplified semi-distributed continuous hydrological model, named SIMPLEFLOOD, was developed, based on the simple dynamical system approach (SDSA) proposed by Kirchner (WRR, 2009, 45, W02429), and applied to the Ardèche catchment in France (2388 km 2). This data-driven method assumes that discharge at the outlet of a given catchment can be expressed as a function only of catchment storage. It leads to a 3-parameter nonlinear model according to rainfall and runoff observations. This model was distributed over sub-catchments and coupled with a kinematic wave based flow propagation module. The parameters were estimated by discharge