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The study investigates the role of snow for the climate change impacts in hydrological regimes across the Lena river basin in Yakutia, Eastern Siberia using a hydrological model constrained by in-situ and satellite-based snow and river discharge observations. The river runoff observations in large and medium sized rivers show an increase over recent decades that can be associated with increasing air temperature and precipitation, as well as changes in snow, glaciers, and permafrost. We assessed the relation between changes in snow and streamflow using the satellite-based ESA CCI snow data and the hydrological model HYPE. The streamflow trend analysis showed a general pattern of increasing monthly mean and minimum stream flows from October to April, but more frequent in larger river basins, and especially if the last 20 years are included in the trend analysis. This can be explained by the increasing autumn precipitation, but the absence of change in annual maximum flow and streamflow in June also suggests relation to changes in the snow. The snow data shows a pattern of decreasing maximum snow water equivalent in the western part of the study area, and a corresponding decreasing trend of number of days with snow cover. These results are in line with the trends in observed streamflow; a short snow cover period (and increasing amount of autumn and winter rainfall, not shown here) as well as a lower maximum snow water equivalent could contribute both to the increasing winter runoff, and the absence of increasing streamflow in early summer. This work was conducted as part of the HYPE-ERAS project funded by FORMAS (project DNR: 2019-02332), RFBR (project No. 20-55-71005), and JST (Grant No. JPMJBF2003) through the Belmont Forum Collaborative Research Action: Resilience in the Rapidly Changing Arctic.

Water and disaster risk management require accurate information about hydrometeorological extremes. Estimation of rare events by extreme value analysis is hampered by short observational records. Probabilistic seasonal forecasts allow assessing the uncertainty in the estimation of extremes. From meteorological seasonal reforecasts and therewith driven hydrological simulations, we create hundred- to thousand-year-long surrogate timeseries across Europe. We identify independent samples based on the assessment of the forecast skill, and extract precipitation and streamflow extremes to explore the impact of sample size on return period estimations. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is reduced at 100-year samples, and stabilizes at very low uncertainty around 500 years. We discuss the benefits and limitations of this method, and how it can be applied to study climate change and multiple extremes.

HydroGFD3 (Hydrological Global Forcing Data) is a data set of bias-adjusted reanalysis data for daily precipitation and minimum, mean, and maximum temperature. It is mainly intended for large-scale hydrological modelling but is also suitable for other impact modelling. The data set has an almost global land area coverage, excluding the Antarctic continent and small islands, at a horizontal resolution of 0.25∘, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period, currently 1979 until 5 d ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number of wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly such that no additional input data about the number of wet days are necessary. The daily mean temperature is adjusted toward the monthly mean of the observations and applied to 1 h time steps of the ERA5 reanalysis. Daily mean, minimum, and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access the data set at https://doi.org/10.5281/zenodo.3871707 (Berg et al., 2020).

Robust information of hydrometeorological extremes is important for effective risk management, mitigation and adaptation measures by public authorities, civil and engineers dealing for example with water management. Typically, return values of certain variables, such as extreme precipitation and river discharge, are of particular interest and are modelled statistically using Extreme Value Theory (EVT). However, the estimation of these rare events based on extreme value analysis are affected by short observational data records leading to large uncertainties.In order to overcome this limitation, we propose to use the latest seasonal meteorological prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) and seasonal hydrological forecasts generated with the pan-European E-HYPE model of the original period 1993-2015 and to extend the dataset to longer synthetic time series by pooling single forecast months to surrogate years. To ensure an independent dataset, the seasonal forecast skill is assessed in advance and months (and lead months) with positive skill are excluded. In this study, we simplify the method and work with samples of 6- and 4-month forecasts (instead of the full 7-month forecasts) depending on the statistical independency of the variables. It enables the record to be extended from the original 23 years to 3450 and 2300 surrogate years for the 6- and 4-month forecasts respectively.Furthermore, we investigate the robustness of estimated 50- and 100-year return values for extreme precipitation and river discharge using 1-year block maxima that are fitted to the Generalized Extreme Value distribution. Surrogate sets of pooled years are randomly constructed using the Monte-Carlo approach and different sample sizes are chosen. This analysis reveals a considerable reduction in the uncertainty of all return period estimations for both variables for selected locations across Europe using a sample size of 500 years. This highlights the potential in using the ensembles of meteorological and hydrological seasonal forecasts to obtain timeseries of sufficient length and minimize the uncertainty in the extreme value analysis.

The Operational Water Service of C3S (developed by the Swedish Meteorological and Hydrological Institute (SMHI)) aims to help a broad range of water managers with water allocation, flood management, ecological status and industrial water use, to adapt their strategies in order to adapt to climate variability and change. The aim is to speed up the workflow in climate-change adaptation by using seasonal hydrological forecasts and climate-impact indicators. This is done by offering an interactive web application with refined data, guidance and practical showcases to water managers across Europe. Policy makers will find a comprehensive overview for Europe with key messages and consultants can use the service for developing climate impact assessments and adaptation strategies.The development of the current operational climate service for water management is based on the experience from two previous proof-of-concepts and will also be aligned with the hydrological model system of the Copernicus Emergency Management Service (CEMS). The service is uses data from the Climate Data Store and the operational hydrological seasonal forecasting system runs entirely in the European Centre for Medium range Weather Forecasts (ECMWF) technical environment, although developed by SMHI.The operational Water Service of C3S will be launched during the spring of 2021, and a series of activities and user interactions will be organised to ensure that the applications developed for the service fulfil the users’ needs. Here, we present the development process of the operational service and key outcomes from co-design interactions and resulting applications. The key issues identified by the user community were: i) clear visualisation and graphical representation of skill in seasonal forecasts and confidence in climate projections, ii) need of detailed documentation and process transparency in hydrological models and production of data, iii) user guidance and tutorials are needed for better understanding of the applications, and iv) workflows and scripts for indicator production in new applications for developers of information systems.

HydroGFD (Hydrological Global Forcing Data) is a data set of bias adjusted reanalysis data for daily precipitation, and minimum, mean, and maximum temperature. It is mainly intended for large scale hydrological modeling, but is also suitable for other impact modeling. The data set has an almost global land area coverage, excluding the Antarctic continent, at a horizontal resolution of 0.25°, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period; currently 1979 until five days ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology, and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number or wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly, such that no additional input data about the number of wet days is necessary. The daily mean temperature is adjusted toward the monthly mean of the observations, and applied to 1 h timesteps of the ERA5 reanalysis. Daily mean, minimum and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access to the data set at https://doi.org/10.5281/zenodo.3871707.

Hydrological forecasting benefits substantially from good initial conditions, which translate information into the forecast. It is therefore important to perform frequent updates of the initial state of the model before the forecast, which demands good meteorological forcing data. For a continental or global hydrological model, it is difficult to find observational data sets which fulfill the requirements of (i) long time series for calibration and spin up, (ii) consistent quality, (iii) at least daily time steps, and (iv) at least data for temperature and precipitation. HydroGFD3 is a new data set that fulfills all the criteria and provides real-time updated data.HydroGFD3 builds upon the ERA5 reanalysis data set, and performs a bias correction for each new produced month. In contrast to earlier versions (Berg et al., 2018), HydroGFD3 is based on a multi-source climatological background, upon which individual days are produced by adding anomalies from different freely available monthly global observational data sets. These are then disaggregated based on the ERA5 reanalysis. For production redundancy and local tailoring, HydroGFD3 is produced in several tiers, each using different observational data sets originating from GPCC and CPC. Further, intermediate daily updates of the reanalysis through the source ERA5T allow the data set to be updated to within a few days of real-time.To reach actual real-time, one tier is based on a bias correction method calibrated on the period 1980-2009, which is applied on ERA5T, and further prolonged to current day using the ECMWF deterministic forecasts. The assumption for this to work is that the forecasts have a similar bias as the reanalysis model, which is currently the case. The method also allows bias correction of the forecasts themselves; solving the issue of “drift” in the forecasts as the hydrological model adjusts to the (biased) climatological state of the forcing data.Berg, Peter, Chantal Donnelly, and David Gustafsson. "Near-real-time adjusted reanalysis forcing data for hydrology." Hydrology and Earth System Sciences 22.2 (2018): 989-1000.

Comprehensive flood risk modeling is crucial for understanding, assessing, and mitigating flood risk. Modeling extreme events is a well-established practice in the atmospheric and hydrological sciences and in the insurance industry. Several specialized models are used to research extreme events including atmospheric circulation models, hydrological models, hydrodynamic models, and damage and loss models. Although these model types are well established, and coupling two to three of these models has been successful, no assessment of a full and comprehensive model chain from the atmospheric to local scale flood loss models has been conducted. The present study introduces a model chain setup incorporating a GCM/RCM to model atmospheric processes, a hydrological model to estimate the catchment's runoff reaction to precipitation inputs, a hydrodynamic model to identify flood-affected areas, and a damage and loss model to estimate flood losses. Such coupling requires building interfaces between the individual models that are coherent in terms of spatial and temporal resolution and therefore calls for several pre- and post-processing steps for the individual models as well as for a computationally efficient strategy to identify and model extreme events. The results show that a coupled model chain allows for good representation of runoff for both long-term runoff characteristics and extreme events, provided a bias correction on precipitation input is applied. While the presented approach for deriving loss estimations for particular extreme events leads to reasonable results, two issues have been identified that need to be considered in further applications: (i) the identification of extreme events in long-term GCM simulations for downscaling and (ii) the representativeness of the vulnerability functions for local conditions.

Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17  %–44  % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

Regional climate modelling is used to better capture the hydrological cycle, which is fundamental for climate impact investigations. However, the output of these models is affected by biases that hamper its direct use in impact modelling. Here, we present and evaluate the performance of two high-resolution (2 km) climate simulations of precipitation in the Alpine region and develop a new bias correction technique for precipitation suitable for complex topography. The latter is based on quantile mapping, which is applied separately across a number of non-overlapping regions defined through cluster analysis. This technique allows removing prominent biases while it aims at minimising disturbances to the physical consistency of the simulation. The simulations span the period 1979–2005 and are carried out with the Weather Research and Forecasting model (WRF), driven by the reanalysis ERA-Interim (hereafter WRF-ERA), and the Community Earth System Model (hereafter WRF-CESM). The simulated precipitation is in both cases validated against observations. In a first step, Switzerland is classified into regions of similar temporal variability of precipitation. Similar spatial patterns emerge in all datasets, with a clear Northwest-Southeast separation following the main orographic features of this region. The daily evolution and the annual cycle of precipitation in WRF-ERA closely reproduces the observations. This is in contrast to WRF-CESM, which shows a different seasonality with peak precipitation in Winter and not in Summer as in the observations or in WRF-ERA. The application of the new bias correction technique minimises systematic biases in the WRF-CESM simulation, and substantially improves the seasonality, while the temporal and physical consistency among simulated variables is preserved.

Radiocarbon dioxide (14CO2, reported in Δ14CO2) can be used to determine the fossil fuel CO2 addition to the atmosphere, since fossil fuel CO2 no longer contains any 14C. After the release of CO2 at the source, atmospheric transport causes dilution of strong local signals into the background and detectable gradients of Δ14CO2 only remain in areas with high fossil fuel emissions. This fossil fuel signal can moreover be partially masked by the enriching effect that anthropogenic emissions of 14CO2 from the nuclear industry have on the atmospheric Δ14CO2 signature. In this paper, we investigate the regional gradients in 14CO2 over the European continent and quantify the effect of the emissions from nuclear industry. We simulate the emissions and transport of fossil fuel CO2 and nuclear 14CO2 for Western Europe using the Weather Research and Forecast model (WRF-Chem) for a period covering 6 summer months in 2008. We evaluate the expected CO2 gradients and the resulting Δ14CO2 in simulated integrated air samples over this period, as well as in simulated plant samples. We find that the average gradients of fossil fuel CO2 in the lower 1200 m of the atmosphere are close to 15 ppm at a 12 km × 12 km horizontal resolution. The nuclear influence on Δ14CO2 signatures varies considerably over the domain and for large areas in France and the UK it can range from 20 to more than 500% of the influence of fossil fuel emissions. Our simulations suggest that the resulting gradients in Δ14CO2 are well captured in plant samples, but due to their time-varying uptake of CO2, their signature can be different with over 3‰ from the atmospheric samples in some regions. We conclude that the framework presented will be well-suited for the interpretation of actual air and plant 14CO2 samples.

[1] The 14C/C abundance in CO2(Δ14CO2) promises to provide useful constraints on regional fossil fuel emissions and atmospheric transport through the large gradients introduced by anthropogenic activity. The currently sparse atmospheric Δ14CO2 monitoring network can potentially be augmented by using plant biomass as an integrated sample of the atmospheric Δ14CO2. But the interpretation of such an integrated sample requires knowledge about the day‒to‒day CO2 uptake of the sampled plants. We investigate here the required detail in daily plant growth variations needed to accurately interpret regional fossil fuel emissions from annual plant samples. We use a crop growth model driven by daily meteorology to reproduce daily fixation of Δ14CO2 in maize and wheat plants in the Netherlands in 2008. When comparing the integrated Δ14CO2 simulated with this detailed model to the values obtained when using simpler proxies for daily plant growth (such as radiation and temperature), we find differences that can exceed the reported measurement precision of Δ14CO2(∼2‰). Furthermore, we show that even in the absence of any spatial differences in fossil fuel emissions, differences in regional weather can induce plant growth variations that result in spatial gradients of up to 3.5‰ in plant samples. These gradients are even larger when interpreting separate plant organs (leaves, stems, roots, or fruits), as they each develop during different time periods. Not accounting for these growth‒induced differences in Δ14CO2 in plant samples would introduce a substantial bias (1.5–2 ppm) when estimating the fraction of atmospheric CO2 variations resulting from nearby fossil fuel emissions.

Radiocarbon dioxide (14CO2, reported in Δ14CO2) can be used to determine the fossil fuel CO2 addition to the atmosphere, since fossil fuel CO2 no longer contains any 14C. After release of CO2 at the source, atmospheric transport causes dilution of strong local signals into the background and detectable gradients of Δ14CO2 only remain in areas with high fossil fuel emissions. This fossil fuel signal can moreover be partially masked by the enriching effect that anthropogenic emissions of 14CO2 from the nuclear industry have on the atmospheric Δ14CO2 signature. In this paper, we investigate the regional gradients in 14CO2 over the European continent and quantify the effect of the emissions from nuclear industry. We simulate the emissions of fossil fuel CO2 and nuclear 14CO2 for Western Europe for a period covering 6 months in 2008 and their transport using the Weather Research and Forecast model (WRF-Chem). We evaluate the expected CO2 gradients and the resulting Δ14CO2 in simulated integrated air samples over this period, as well as in simulated plant samples. We find that the average gradients of fossil fuel CO2 in the lower 1200 m of the atmosphere are close to 15 ppm at a 12 km × 12 km horizontal resolution. The nuclear influence on Δ14CO2 signatures varies considerably over the domain and for large areas in France and UK it can range from 20% to more than 500% of the influence of fossil fuel emissions. Our simulations suggest that the resulting gradients in Δ14CO2 are well captured in plant samples, but due to their time-varying uptake of CO2 their signature can be different with over 3‰ from the atmospheric samples in some regions. We conclude that the framework presented will be well-suited for the interpretation of actual air and plant 14CO2 samples.