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The recent development of the FYRE climate (French Hydroclimate REanalysis), a high-resolution ensemble daily reanalysis of precipitation and temperature covering the period 1871–2012 and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions HyDRE spanning the 1871–2012 period. Two sources of uncertainties have been taken into account: (1) the climate uncertainty by using forcings from all 25 ensemble members provided by FYRE Climate, and (2) the streamflow measurement error by perturbing observations used during the calibration. The hydrological model error based on the relative discrepancies between observed and simulated streamflow has been further added to derive the HydREM streamflow reconstructions. These two reconstructions are compared to other hydrological reconstruction with different meteorological inputs, hydrological reconstructions from machine learning algorithm and independent/dependent observations. Overall the results show the added value of the HydRE and HydREM reconstructions in terms of quality, uncertainty estimation, and representation of extremes, therefore allowing to better understand the variability of past hydrology over France.

An increase of dissolved organic carbon (DOC) in inland waters has been reported across the northern temperate region but the effects of this on whole lake ecosystems, often combined with other anthropogenic stressors like nutrient inputs and warming, are poorly known. The effects of these changes on different component of the ecosystem were assessed in an experiment using twenty-four large (3000L) outdoor mesocosms simulating shallow lakes. Two different temperature regimes (ambient and ambient +4 °C) combined with three levels of organic matter (OM, added as filtered peaty water), simulating the DOC increase that is predicted to take place over the next 4 to 21 years were used. Neither temperature nor OM had significant effects on net ecosystem production, respiration or gross primary production. Phytoplankton chlorophyll a concentration was not significantly affected by warming, however in summer, autumn and winter it was significantly higher in mesocosms receiving intermediate OM levels (July–Feb DOC concentrations 2–6 mg L−1). Summer cyanobacterial blooms were highest in intermediate, and lowest in the highest OM treatments. OM concentration also influenced total macroinvertebrate abundance which was greater in spring and summer in mesocosms with intermediate and high OM. Fish abundance was not significantly affected by OM concentration, but abundance was greater in ambient (55 fish subsample−1) compared to heated mesocosms (17 fish subsample−1) and maximum abundance occurred two weeks later compared to heated mesocosms. The results suggest that changes in OM may have a greater effect on shallow lakes than temperature and that phytoplankton, especially cyanobacteria, benefit from intermediate OM concentrations, therefore, nuisance algal blooms might increase in relatively clear shallow eutrophic lakes where DOC concentrations increase.

La profondeur historique des observations de debit est generalement limitee aux 50 dernieres annees, periode trop restreinte pour explorer l’evolution a long terme des etiages extremes et des impac...

The recently developed French hYdrometerological Reanalysis (FYRE) covers the period 1871-2012 and provide high-resolution ensemble reconstructions of both climate and hydrology over France. FYRE Climate combines a statistical downscaling of the global Twentieth Century reanalysis (Caillouet et al., 2019) with in-situ station observations through Ensemble Kalman filter (EnKF) data assimilation (Devers et al., 2020). FYRE Climate is composed of 25 members of daily temperature and precipitation fields on a 8~km grid over France. It served as forcings for a conceptual hydrological model over 661 near-natural catchments to build streamflow reconstructions spanning 142 years. These reconstructions have then been combined with historical streamflow observations, again through EnKF data assimilation, to build the FYRE Hydro 25-member daily hydrological reanalysis over the 661 catchments.FYRE Hydro is here validated with various types of documentary evidence (poem, complaint letter, and photograph), focusing on extreme low-flow events and their spatial and temporal fingerprint. They serve as examples of naturally extreme hydrological events that are exacerbated through human interventions, the magnitude of which has yet to be consistently quantified over the course of the Anthropocene. ReferencesCaillouet, L., Vidal, J.-P., Sauquet, E., Graff, B., Soubeyroux, J.-M. (2019) SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France. Earth System Science Data, 11, 241-260. https://doi.org./10.5194/essd-11-241-2019Devers, A., Vidal, J.-P., Lauvernet, C., Graff, B., Vannier, O. (2020) A framework for high-resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions. Quarterly Journal of the Royal Meteorological Society. https://doi.org./10.1002/qj.3663

International audience; It is generally accepted that drought is one of the most costly weather-related natural hazards. In 2015, a long-lasting drought hit Europe, particularly affecting central and eastern Europe. In some regions it was the driest (North Slovakia) and in others (Czech Republic and Poland) it was the second driest summer of the last 50 years (following 2003). Key questions are: (i) how extreme are these events, not only in terms of hydro-meteorological characteristics but also impacts? and (ii) how are these impacts managed?

International audience; This research presents a study of extreme low flow events that occurred from 1871 onwards in a large number of near-natural catchments in France. It aims at assessing and comparing their characteristics to improve our knowledge on historical events and to provide a selection of benchmark events for climate change adaptation purposes. The historical length of streamflow observations is generally limited to the last 50 years and therefore covers too small a sample of extreme low flow events to properly explore the long-term evolution of their characteristics and associated impacts. In order to overcome this limit, this study takes advantage of a 140-year ensemble hydrometeorological dataset over France based on: (1) probabilistic precipitation and temperature downscaling of the global Twentieth Century Reanalysis over France (Caillouet et al., 2016a), and (2) continuous hydrological modelling based on high-resolution meteorological reconstructions as forcings over the whole period. The resulting SCOPE Hydro dataset provides an ensemble of 25 equally plausible daily streamflow time series for a reference network of more than 600 stations in France over the 1871-2012 period. Extreme low-flow events are identified using a combination of a fixed threshold and a daily variable threshold. The procedure is applied to the 25 simulated time series as well as to the observed time series in order to compare observed and reconstructed events over the recent period, and to characterize in a probabilistic way unrecorded historical events. A spatial matching procedure at the scale of France is then developed in order to spatially assemble local extreme events into the same spatio-temporal event. After these steps, an event can be studied at the local or national scale through its spatial extent, duration or severity characteristics in a probabilistic setting (Caillouet et al., 2016b). This work identifies past and little known exceptional extreme events (1878, 1893, 1942-1949) or recent but poorly documented events (1972, 1978, 1985) besides well-known events (1921, 1976, 1989-1990, 2011). The evolution of these events since 1871 shows that an increasing part of the French territory is affected by extreme low-flow events since the 1940s. Finally, a good coherence is found between reconstructed events and narrative sources on historical droughts. For the first time, extreme low-flow events are described in a homogeneous manner over 140 years for a large set of near-natural French catchments, enabling detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology.

International audience; This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the last century built on the NOAA 20th century global extended atmospheric reanalysis (20CR, Compo et al., 2011). It aims at delivering appropriate meteorological forcings for continuous distributed hydrological modelling over the last 140 years. The longer term objective is to improve our knowledge of major historical hydrometeorological events having occurred outside of the last 50-year period, over which comprehensive reconstructions and observations are available. It would constitute a perfect framework for assessing the recent observed events but also future events projected by climate change impact studies. The Sandhy (Stepwise ANalogue Downscaling method for Hydrology) statistical downscaling method (Radanovics et al., 2013), initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between 20CR predictors – temperature, geopotential shape, vertical velocity and relative humidity – and local predictands – precipitation and temperature – relevant for catchment-scale hydrology. Multiple predictor domains for geopotential shape are retained from a local optimisation over France using the Safran near-surface reanalysis (Vidal et al., 2010). Sandhy gives an ensemble of 125 equally plausible gridded precipitation and temperature time series over the whole 1871-2012 period. Previous studies showed that Sandhy precipitation outputs are very slightly biased at the annual time scale. Nevertheless, the seasonal precipitation signal for areas with a high interannual variability is not well simulated. Moreover, winter and summer temperatures are respectively over- and underestimated. Reliable seasonal precipitation and temperature signals are however necessary for hydrological modelling, especially for evapotranspiration and snow accumulation/snowmelt processes. Two different post-processing methods are considered to correct monthly precipitation and temperature time series. The first one applies two new analogy steps, using the sea surface temperature (SST) and the large-scale two-meter temperature. The second method is a calendar selection that keeps the closest analogue dates in the year for each target date. A sensitivity study has been performed to assess the final number of analogues dates to retain for each method. A comparison to Safran over 1958-2010 shows that biases on the interannual cycle of precipitation and temperature are strongly reduced with both methods. Using two supplementary analogy levels moreover leads to a large improvement of correlation in seasonal temperature time series. These two methods have also been validated before 1958 thanks to both raw observations and homogenized time series. The two post-processing methods come with some advantages and drawbacks. The calendar selection allows to slightly better correct for seasonal biases in precipitation and is therefore adapted in a forecasting context. The selection with two supplementary analogy levels would allow for possible season shifts and SST trends and is therefore better suited for climate reconstruction and climate change studies.