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Sub-daily precipitation extremes can have a huge impact on society as they cause hazards such as flooding, erosion and landslides. For example, the July floods in Germany, Belgium and nearby countries, were one of the costliest events in Europe, with insured losses up to USD 13 billion. Climate change is expected to intensify precipitation extremes as atmospheric water content increases by 6-7% per degree of warming, underscoring the need to predict future hydrological hazards. Regional Climate Models (RCMs) typically run at a spatial resolution of 12-25 km, but they insufficiently describe the small-scale features of observed sub-daily precipitation extremes. The past decade, convection-permitting RCMs were developed which run at high resolution (1-4 km), and explicitly resolve deep convection. Confidence in future projections requires that RCMs adequately simulate the statistical features of observed sub-daily extreme precipitation and also represent the physical processes associated with convective events. We propose a diagnostic framework for simulated 1h-24h rainfall extremes that summarizes the overall RCM performance. This includes the following metrics: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling, and spatiotemporal clustering. A substantial part of the work is devoted to the statistical modelling of the metrics with Extreme Value Theory (EVT). We illustrate the evaluation tool with convection-permitting RCM simulations over Belgium against high-frequency observations and assess the benefit of the convection-permitting RCMs with respect to coarser scales. Finally, we give some guidelines for bias correction of simulated precipitation extremes.

The exploitation of urban-material spectral properties is of increasing importance for a broad range of applications, such as urban climate-change modeling and mitigation or specific/dangerous roof-material detection and inventory. A new spectral library dedicated to the detection of roof material was created to reflect the regional diversity of materials employed in Wallonia, Belgium. The Walloon Roof Material (WaRM) spectral library accounts for 26 roof material spectra in the spectral range 350–2500 nm. Spectra were acquired using an ASD FieldSpec3 Hi-Res spectrometer in laboratory conditions, using a spectral sampling interval of 1 nm. The analysis of the spectra shows that spectral signatures are strongly influenced by the color of the roof materials, at least in the VIS spectral range. The SWIR spectral range is in general more relevant to distinguishing the different types of material. Exceptions are the similar properties and very close spectra of several black materials, meaning that their spectral signatures are not sufficiently different to distinguish them from each other. Although building materials can vary regionally due to different available construction materials, the WaRM spectral library can certainly be used for wider applications; Wallonia has always been strongly connected to the surrounding regions and has always encountered climatic conditions similar to all of Northwest Europe.

The Walloon Region is currently managing a database of more than 2000 ‘redevelopment sites', i.e. urban sites that were previously used for industrial activities and/or housing and that are now abandoned. The administration needs to keep this inventory up-to-date so that the necessary urban planning could be done; however, at the moment, this information is obtained via time-consuming on field campaigns. Thanks to the launch of the Copernicus programme, free satellite data are now provided at high temporal resolution, and new monitoring approaches can be implemented. Leveraging a well-established changepoint detection method, this paper shows some preliminary results on how time series of Sentinel-1 and Sentinel-2 data could be jointly used to automatically detect changes in urban areas, thus providing the Walloon Region with a tool that can be exploited for a more efficient management of the ‘redevelopment sites'.

Earth observation technologies offer non-intrusive solutions for monitoring complex and risky sites, such as landfills. In particular, unmanned aerial vehicles (UAVs) offer the ability to acquire data at very high spatial resolution, with full control of the temporality required for the desired application. The versatility of UAVs, both in terms of flight characteristics and on-board sensors, makes it possible to generate relevant geodata for a wide range of landfill monitoring activities. This study aims to propose a robust tool and to provide data acquisition guidelines for the land cover mapping of complex sites using UAV multispectral imagery. For this purpose, the transferability of a state-of-the-art object-based image analysis open-source processing chain was assessed and its sensitivity to the segmentation approach, textural and contextual information, spectral and spatial resolution was tested over the landfill site of Hallembaye (Wallonia, Belgium). This study proposes a consistent open-source processing chain for the land cover mapping using UAV data with accuracies of at least 85%. It shows that low-cost red-green-blue standard sensors are sufficient to reach such accuracies and that spatial resolution of up to 10 cm can be adopted with limited impact on the performance of the processing chain. This study also results in the creation of a new operational service for the monitoring of the active landfill sites of Wallonia.

The Ourthe River, in the south-east of Belgium, has a catchment area of 3500 km2 and is one of the main tributaries of the Meuse River. In the Ourthe, most of the flood events (FE) occur during winter and about 50% of them are due to heavy rainfall events combined with an abrupt melting of the snowpack covering the Ardennes massif during winter. This study aims to determine whether trends in extreme hydroclimatic events generating floods can be detected over the last century in Belgium, where a global warming signal can be observed. Hydroclimatic conditions favourable to floods were reconstructed over 1959–2010 using the regional climate model MAR (‘Modele Atmospherique Regional’) forced by the ERA-Interim/ERA-40, the ERA-20C and the NCEP/NCAR-v1 reanalyses. Extreme run-off events, which could potentially generate floods, were detected using run-off caused by precipitation events and snowpack melting from the MAR model. In the validation process, the MAR-driven temperature, precipitation and snow depth were successfully compared to daily weather data over the period 2008–2014 for 20 stations in Belgium. MAR also showed its ability to detect up to 90% of the hydroclimatic conditions which effectively generated observed floods in the Ourthe River over the period 1974–2010. Conditions favourable to floods in the Ourthe River catchment present a negative trend over the period 1959–2010 as a result of a decrease in snow accumulation and a shortening of the snow season. This trend is expected to accelerate in a warmer climate. However, regarding the impact of the extreme precipitation events evolution on conditions favouring floods, the signal is less clear because the trends depend on the reanalysis used to force the MAR model.

In the context of global warming, the projected Greenland sea level rise contribution is mainly controlled by the interactions between the Greenland ice sheet (GrIS) and the atmosphere, in particular through the temperature and surface mass balance – elevation feedback. In order to evaluate the importance of these feedbacks, we used three methods to represent the interactions between the GrIS model GRISLI and the polar regional atmosphere model MAR, under the RCP 8.5 scenario from 2020 to 2150. In the simplest method, there is no coupling, MAR computes varying atmospheric conditions using a constant GrIS geometry (topography and extent) set to observations and GRISLI is forced by these results. The second is a one-way coupling method which represents the interactions by correcting offline the MAR outputs to account for topography changes computed by GRISLI. The third method is a full two-way coupling in which the ice sheet topography and extent seen by the atmospheric model evolve after each ice sheet model time step. Due to the ice sheet elevation feedback, the two-way coupling method amplifies the projected decrease in surface mass balance, the increase in surface temperature and the GrIS surface thinning for the coastal regions, compared to the no coupling method. Compared to both the one-way and the no coupling methods, the two-way coupling allows the changes of fine scale processes to be represented, such as the increase in katabatic winds over the coast. As a consequence, in 2150, the two-way coupling method computes a GrIS melting contribution to sea level rise 9.3 % larger than the no coupling method, and 2.5 % larger than the one-way coupling methods. After 150 years, the GrIS extent seen by MAR in the two-way method is 52 400 km2 lower than with the no coupling method. Furthermore, in 2150, using a fix ice sheet mask, as in the no coupling method, overestimates by 24 % the SLR contribution from SMB compared to the use of the ice sheet mask as simulated in the two-way method. Beyond the century time-scale, a two-way method becomes necessary in order to avoid an underestimation of the projected ice sheet volume, topography and ice extent reduction. The one-way coupling method however seems to be sufficient to represent the interactions for projections until the end of the 21st century. The no coupling method always underestimates the projected ice sheet volume loss significantly due to the lack of feedback between the GrIS and the atmosphere.