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Human activities both aggravate and alleviate streamflow drought. Here we show that aggravation is dominant in contrasting cases around the world analysed with a consistent methodology. Our 28 cases included different combinations of human-water interactions. We found that water abstraction aggravated all drought characteristics, with increases of 20%–305% in total time in drought found across the case studies, and increases in total deficit of up to almost 3000%. Water transfers reduced drought time and deficit by up to 97%. In cases with both abstraction and water transfers into the catchment or augmenting streamflow from groundwater, the water inputs could not compensate for the aggravation of droughts due to abstraction and only shift the effects in space or time. Reservoir releases for downstream water use alleviated droughts in the dry season, but also led to deficits in the wet season by changing flow seasonality. This led to minor changes in average drought duration (−26 to +38%) and moderate changes in average drought deficit (−86 to +369%). Land use showed a smaller impact on streamflow drought, also with both increases and decreases observed (−48 to +98%). Sewage return flows and pipe leakage possibly counteracted the effects of increased imperviousness in urban areas; however, untangling the effects of land use change on streamflow drought is challenging. This synthesis of diverse global cases highlights the complexity of the human influence on streamflow drought and the added value of empirical comparative studies. Results indicate both intended and unintended consequences of water management and infrastructure on downstream society and ecosystems.

This paper proposes a methodology for estimating the transient probability distribution of yearly hydrological variables conditional to an ensemble of projections built from multiple general circulation models (GCMs), multiple statistical downscaling methods (SDMs), and multiple hydrological models (HMs). The methodology is based on the quasi-ergodic analysis of variance (QE-ANOVA) framework that allows quantifying the contributions of the different sources of total uncertainty, by critically taking account of large-scale internal variability stemming from the transient evolution of multiple GCM runs, and of small-scale internal variability derived from multiple realizations of stochastic SDMs. This framework thus allows deriving a hierarchy of climate and hydrological uncertainties, which depends on the time horizon considered. It was initially developed for long-term climate averages and is here extended jointly to (1) yearly anomalies and (2) low-flow variables. It is applied to better understand possible transient futures of both winter and summer low flows for two snow-influenced catchments in the southern French Alps. The analysis takes advantage of a very large data set of transient hydrological projections that combines in a comprehensive way 11 runs from four different GCMs, three SDMs with 10 stochastic realizations each, as well as six diverse HMs. The change signal is a decrease in yearly low flows of around −20  % in 2065, except for the more elevated catchment in winter where low flows barely decrease. This signal is largely masked by both large- and small-scale internal variability, even in 2065. The time of emergence of the change signal is however detected for low-flow averages over 30-year time slices starting as early as 2020. The most striking result is that a large part of the total uncertainty – and a higher one than that due to the GCMs – stems from the difference in HM responses. An analysis of the origin of this substantial divergence in HM responses for both catchments and in both seasons suggests that both evapotranspiration and snowpack components of HMs should be carefully checked for their robustness in a changed climate in order to provide reliable outputs for informing water resource adaptation strategies.

The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. The probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.

Spatial verification methods able to handle high-resolution ensemble forecasts and analysis ensembles are increasingly required because of the increasing development of such ensembles. An ensemble extension of the structure–amplitude–location (SAL) spatial verification method is proposed here. The ensemble SAL (eSAL) allows for verifying ensemble forecasts against a deterministic or ensemble analysis. The eSAL components are equal to those of SAL in the deterministic case, thus allowing the comparison of deterministic and ensemble forecasts. The Mesoscale Verification Intercomparison over Complex Terrain (MesoVICT) project provides a dataset containing deterministic and ensemble precipitation forecasts as well as a deterministic and ensemble analysis for case studies in summer 2007 over the greater Alpine region. These datasets allow for testing of the sensitivity of SAL and eSAL to analysis uncertainty and their suitability for the verification of ensemble forecasts. Their sensitivity with respect to the main parameter of this feature-based method—the threshold for defining precipitation features—is furthermore tested for both the deterministic and ensemble forecasts. Our results stress the importance of using meaningful thresholds in order to limit any unstable behavior of the threshold-dependent SAL components. The eSAL components are typically close to the median of the distribution of deterministic SAL components calculated for all combinations of ensemble members of the forecast and the analysis, with considerably less computational time. The eSAL ensemble extension of SAL can be considered as a relevant summary measure that leads to more easily interpretable SAL diagrams.

Quantifying model uncertainty and internal variability components in climate projections has been paid a great attention in the recent years. For multiple synthetic ensembles of climate projections, we compare the precision of uncertainty component estimates obtained respectively with the two Analysis of Variance (ANOVA) approaches mostly used in recent works: the popular Single Time approach (STANOVA), based on the data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the available simulation period. We show that the precision of all uncertainty estimates is higher when more members are used, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more precise than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3–5 times smaller than STANOVA ones. Except for STANOVA when less than three members is available, the precision is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the precision is low for QEANOVA to very low for STANOVA. In the most unfavorable configurations (small number of members, large internal variability), large over- or underestimation of uncertainty components is thus very likely. In a number of cases, the uncertainty analysis should thus be preferentially carried out with a time series approach or with a local-time series approach, applied to all predictions available in the temporal neighborhood of the target prediction lead time.

Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers. Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in three European catchments (Spain, Finland, France) characterised by different climate, geology, and anthropogenic use. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in mesoscale river networks (between 120 and 350 km2). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a random forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, citizen science applications) are used to build the predictive model. This study aims to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativity) on the performance of the predictive model. Results show that the hybrid modelling approach developed in this study allows the spatio-temporal patterns of drying to be accurately predicted in the three catchments, with a sensitivity criterion above 0.9 for the prediction of dry events in the Finnish and French case studies and 0.65 in the Spanish case study. This study shows the value of combining different data sources of observed flow condition to reduce the uncertainty in predicting flow intermittence.

Stream temperature appears to be increasing globally, but its rate remains poorly constrained due to a paucity of long-term data and difficulty in parsing effects of hydroclimate and landscape variability. Here, we address these issues using the physically based thermal model T-NET (Temperature-NETwork) coupled with the EROS semi-distributed hydrological model to reconstruct past daily stream temperature and streamflow at the scale of the entire Loire River basin in France (105 km2 with 52 278 reaches). Stream temperature increased for almost all reaches in all seasons (mean =+0.38 ∘C decade−1) over the 1963–2019 period. Increases were greatest in spring and summer, with a median increase of + 0.38 ∘C (range =+0.11 to +0.76 ∘C) and +0.44 ∘C (+0.08 to +1.02 ∘C) per decade, respectively. Rates of stream temperature increases were greater than for air temperature across seasons for the majority of reaches. Spring and summer increases were typically greatest in the southern part of the Loire basin (up to +1 ∘C decade−1) and in the largest rivers (Strahler order ≥5). Importantly, air temperature and streamflow could exert a joint influence on stream temperature trends, where the greatest stream temperature increases were accompanied by similar trends in air temperature (up to +0.71 ∘C decade−1) and the greatest decreases in streamflow (up to −16  % decade−1). Indeed, for the majority of reaches, positive stream temperature anomalies exhibited synchrony with positive anomalies in air temperature and negative anomalies in streamflow, highlighting the dual control exerted by these hydroclimatic drivers. Moreover, spring and summer stream temperature, air temperature, and streamflow time series exhibited common change points occurring in the late 1980s, suggesting a temporal coherence between changes in the hydroclimatic drivers and a rapid stream temperature response. Critically, riparian vegetation shading mitigated stream temperature increases by up to 0.16 ∘C decade−1 in smaller streams (i.e. < 30 km from the source). Our results provide strong support for basin-wide increases in stream temperature due to joint effects of rising air temperature and reduced streamflow. We suggest that some of these climate change-induced effects can be mitigated through the restoration and maintenance of riparian forests.

Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and to derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. The current paper thus proposes (1) to apply the data assimilation scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly timescale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8 km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Combining the daily and yearly assimilation schemes, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran reference surface reanalysis over France available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is freely available at https://doi.org/10.5281/zenodo.4005573 (precipitation, Devers et al., 2020b) and https://doi.org/10.5281/zenodo.4006472 (temperature, Devers et al., 2020c).

Climate and land use changes, as well as human water use and flow alteration, are causing worldwide shifts in river flow dynamics. During the last decades, low flows, flow intermittence, and drying have increased in many regions of the world, including Europe. This trend is projected to continue and amplify in the future, resulting in more frequent and intense hydrological droughts. However, due to a lack of data and studies on temporary rivers in the past, little is known about the processes governing the development of flow intermittence and drying, their timing and frequency, or their long-term evolution under climate change. Moreover, understanding the impact of climate change on the drying up of rivers is crucial to assess the impact of climate change on aquatic ecosystems, including the biodiversity and functional integrity of freshwater systems. This study is one of the first to present future projections of drying in intermittent river networks and to analyse future changes in the drying patterns at a high spatial and temporal resolution. Flow intermittence projections were produced using a hybrid hydrological model forced with climate projection data from 1985 until 2100 under three climate scenarios in six European drying river networks. The studied watershed areas are situated in different biogeographic regions, located in Spain, France, Croatia, Hungary, Czechia, and Finland, and their areas range from 150 to 350 km2. Additionally, flow intermittence indicators were developed and calculated to assess (1) changes in the characteristics of the drying spells at the reach scale and (2) changes in the spatial extent of drying in the river network at various time intervals. The results for all three climate scenarios show that drying patterns are projected to increase and expand in time and space, despite differences in the amplitude of changes. Temporally, in addition to the average frequency of drying events, the duration increases over the year. Seasonal changes are expected to result in an earlier onset and longer persistence of drying throughout the year. Summer drying maxima are likely to shift to earlier in the spring, with extended drying periods or additional maxima occurring in autumn and extending into the winter season in some regions. A trend analysis of extreme events shows that the extreme dry spells observed in recent years could become regular by the end of the century. Additionally, we observe transitions from perennial to intermittent reaches in the future.