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Aquatic ecosystem recovery from anthropogenic degradation can be hampered by internal feedbacks that stabilize undesirable states. The challenges of managing and predicting alternative states in lakes are well known, but state shifts in rivers and their attendant effects on ecosystem function remain understudied despite strong recent evidence that such shifts can and do occur. Using three decades of measurements of key state variables such as turbidity, nutrient concentrations, Corbicula fluminea clam densities, and chlorophyll a, including hourly dissolved oxygen, we investigated a sudden shift from phytoplankton to macrophyte dominance in the middle Loire River (France), and its associated effects on the rivers metabolic regime. We show, instead, that despite large and synchronous shifts across all state variables, changes in gross primary production and ecosystem respiration were modest (25% and 14% declines, respectively) and that these shifts lagged the ecosystem state changes by a decade or more. The shift to a macrophyte-dominated state reduced the sensitivity of primary production to abiotic drivers, altered element cycling efficiency, flipped the net carbon balance from positive to negative, and, crucially, weakened the temporal coupling between production and respiration. This weakened coupling, detected using Granger causality, increased the temporal autocorrelation of net ecosystem production, yielding a robust early warning indicator of both state- and metabolic-shifts that may provide valuable guidance for river restoration.

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.

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.

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human-natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.

Low-flow simulation and forecasting remains a difficult issue for hydrological modellers, and intercomparisons can be extremely instructive for assessing existing lowflow prediction models and for developing more efficient operational tools. This research presents the results of a collaborative experiment conducted to compare low-flow simulation and forecasting models on 21 unregulated catchments in France. Five hydrological models (four lumped storagetype models - Gardenia, GR6J, Mordor and Presages - and one distributed physically oriented model - SIM) were applied within a common evaluation framework and assessed using a common set of criteria. Two simple benchmarks describing the average streamflow variability were used to set minimum levels of acceptability for model performance in simulation and forecasting modes. Results showed that, in simulation as well as in forecasting modes, all hydrological models performed almost systematically better than the benchmarks. Although no single model outperformed all the others for all catchments and criteria, a few models appeared to be more satisfactory than the others on average. In simulation mode, all attempts to relate model efficiency to catchment or streamflow characteristics remained inconclusive. In forecasting mode, we defined maximum useful forecasting lead times beyond which the model does not bring useful information compared to the benchmark. This maximum useful lead time logically varies between catchments, but also depends on the model used. Simple multi-model approaches that combine the outputs of the five hydrological models were tested to improve simulation and forecasting efficiency. We found that the multi-model approach was more robust and could provide better performance than individual models on average

Aquatic ecosystem recovery from anthropogenic degradation can be hampered by internal feedbacks that stabilize undesirable states. The challenges of managing and predicting alternative states in lakes are well known, but state shifts in rivers and their attendant effects on ecosystem function remain understudied despite strong recent evidence that such shifts can and do occur. Using three decades of measurements of key state variables such as turbidity, nutrient concentrations, Corbicula fluminea clam densities, and chlorophyll a, including hourly dissolved oxygen, we investigated a sudden shift from phytoplankton to macrophyte dominance in the middle Loire River (France), and its associated effects on the rivers metabolic regime. We show, instead, that despite large and synchronous shifts across all state variables, changes in gross primary production and ecosystem respiration were modest (25% and 14% declines, respectively) and that these shifts lagged the ecosystem state changes by a decade or more. The shift to a macrophyte-dominated state reduced the sensitivity of primary production to abiotic drivers, altered element cycling efficiency, flipped the net carbon balance from positive to negative, and, crucially, weakened the temporal coupling between production and respiration. This weakened coupling, detected using Granger causality, increased the temporal autocorrelation of net ecosystem production, yielding a robust early warning indicator of both state-and metabolic-shifts that may provide valuable guidance for river restoration.

The aim of this paper is to investigate the use of statistical correction techniques in hydrological ensemble prediction. Ensemble weather forecasts (precipitation and temperature) are used as forcing variables to a hydrologic forecasting model for the production of ensemble streamflow forecasts. The impact of different bias correction strategies on the quality of the forecasts is examined. The performance of the system is evaluated when statistical processing is applied: to precipitation and temperature forecasts only (pre-processing from the hydrological model point of view), to flow forecasts (post-processing) and to both. The pre-processing technique combines precipitation ensemble predictions with an analog forecasting approach, while the post-processing is based on past errors of the hydrological model when simulating streamflows. Forecasts from 11 catchments in France are evaluated. Results illustrate the importance of taking into account hydrological uncertainties to improve the quality of operational streamflow forecasts.