The multi-national catchment of the Upper Danube covers an area of more than 100,000 km2 and is of great ecological and economic value. Its hydrological states (e.g., runoff conditions, snow cover states or groundwater levels) affect fresh-water supply, agriculture, hydropower, transport and many other sectors. The timely knowledge of the current status is therefore of importance to decision makers from administration or practice but also the interested public. Therefore, a web-based, near real-time hydrological information system was conceptualized and developed for the Upper Danube upstream of Vienna (Upper Danube HIS), utilizing ERA5 reanalysis data (ERA5) and hydrological simulations provided by the semi-distributed hydrological model COSERO. The ERA5 reanalysis data led to comparatively high simulation performance for a total of 65 subbasins with a median NSE and KGE of 0.69 and 0.81 in the parameter calibration and 0.63 and 0.75 in the validation period. The Upper Danube HIS was implemented within the R programming environment as a web application based on the Shiny framework. This enables an intuitive, interactive access to the system. It offers various capabilities for a hydrometeorological analysis of the 65 subbasins of the Upper Danube basin, inter alia, a method for the identification of hydrometeorological droughts. This proof of concept and system underlines how valuable information can be obtained from freely accessible data and by the means of open source software and is made available to the hydrological community, water managers and the public.
<p>In the last years, the demand of reliable seasonal streamflow forecasts has increased with the aim of incorporating them into decision support systems for e.g. river navigation, power plant operation &#160;or drought risk management. Recently, the concept of &#8220;climate services&#8221; has gained stronger attention in Europe, thereby incorporating useful information derived from climate predictions and projections that support adaptation, mitigation and disaster risk management. In the frame of one of these climate services currently in development, Clim2Power project, a seasonal forecast system for discharge in the Upper Danube upstream Vienna has been established.</p><p>Seasonal forecasts are generated using a dynamical approach running a hydrological model (COSERO) with forecasted climate input provided by DWD (Germany's National Meterological Service). The climate forecasts are based on a large ensemble of predictions, available up to 6 months. After the application of a statistical downscaling method, the climate forecasts have a spatial resolution of 6km. The predictability is related to two main contributions: meteorological forcings (i.e. temperature and precipitation predictability) and initial basin states at the time the forecast is issued.</p><p>The Upper Danube basin with a catchment area of approx. 100.000 km<sup>2</sup> is characterized by complex topography dominated by the Alps, elevations range from about 150 m to slightly under 4000 m. Therefore, the skill of the seasonal forecast is highly influenced by the resolution of the meteorological data, and likewise by the hydrological processes that take place, especially, regarding melting processes. Downscaled hindcasts over the last 20 years, generated with the identical setup as the seasonal forecasts, are used in this contribution to assess the skill of the seasonal forecasts. In addition, some post-processing corrections, based on historical observations, are used to adjust the bias of the forecasts. Nevertheless, remaining non-systematic error patterns do not allow complete bias correction. Apart from the biases, also the correlation patterns show a limited skill. We conclude that the seasonal discharge forecasting is still not sufficient to incorporate the results into water resources decision support systems within the studied Alpine basins.</p>
<p>The skill of seasonal hydro-meteorological forecasts with a lead time of up to six months is currently limited, since they frequently exhibit random but also systematic errors. Bias correction algorithms can be applied and provide an effective approach in removing historical biases relative to observations. Systematic errors in hydrology model outputs can be consequence of different sources: i) errors in meteorological data used as input data, ii) errors in the hydrological model response to climate forcings, iii) unknown/unobservable internal states and iv) errors in the model parameterizations, also due to unresolved subgrid scale variability.</p><p>Normally, bias correction techniques are used to correct meteorological, e.g. precipitation data, provided by climate models. Only few studies are available applying these techniques to hydrological model outputs. Standard bias correction techniques used in literature can be classified into scaling-, and distributional-based methods. The former consists of using multiplicative or additive scaling factors to correct the modeled simulations, while the later methods are quantile mapping techniques that fit the distribution of the simulation to fit to the observations. In this study, the impact of different bias correction techniques on the seasonal discharge forecasts skill is assessed.</p><p>As a case study, a seasonal discharge forecasting system developed for the Danube basin upstream of Vienna, is used. The studied basin covers an area of around 100 000 km<sup>2</sup> and is subdivided in 65 subbasins, 55 of them gauged with a long historical record of observed discharge. The forecast system uses the calibrated hydrological model, COSERO, which is fed with an ensemble of seasonal temperature and precipitation forecasts. The output of the model provides an ensemble of seasonal discharge forecasts for each of the (gauged) subbasins. Seasonal meteorological forecasts for the past (hindcast), together with historical discharge observations, allow to assess the quality of the seasonal discharge forecasting system, also including the effects of different bias correction methods. The corrections applied to the discharge simulations allow to eliminate potential systematic errors between the modeled and observed values.</p><p>Our findings generally suggest that the quality of the seasonal forecasts improve when applying bias correction. Compared to simpler methods, which use additive or multiplicative scaling factors, quantile mapping techniques tend to be more appropriate in removing errors in the ensemble seasonal forecasts.</p>
<p>In the last two decades, different climate downscaling initiatives provided climate scenarios for Europe. The most recent initiative, CORDEX, provides Regional Climate Model (RCM) data for Europe with a spatial resolution of 12.5 km, while the previous initiative, ENSEMBLES, had a spatial resolution of 25 km. They are based on different emission scenarios, Representative Concentration Pathways (RCPs) and Special Report on Emission Scenarios (SRES) respectively.</p><p>A study carried out by Stanzel et al. (2018) explored the hydrological impact and discharge projections for the Danube basin upstream of Vienna when using either CORDEX and ENSEMBLES data. This basin covers an area of 101.810<sup></sup>km<sup>2</sup> with a mean annual discharge of 1923 m<sup>3</sup>/s at the basin outlet. The basin is dominated by the Alps, large gradients and is characterized by high annual precipitations sums which provides valuable water resources available along the basin. Hydropower therefore plays an important role and accounts for more than half of the installed power generating capacity for this area. The estimation of hydropower generation under climate change is an important task for planning the future electricity supply, also considering the on-going EU efforts and the &#8220;Green Deal&#8221; initiative.</p><p>Taking as input the results from Stanzel et al. (2018), we use transfer functions derived from historical discharge and hydropower generation data, to estimate potential changes for the future. The impact of climate change projections of ENSEMBLE and CORDEX in respect to hydropower generation for each basin within the study area is determined. In addition, an assessment of the impact on basins dominated by runoff river plants versus basins dominated by storage plants is considered.</p><p>The good correlation between discharge and hydropower generation found in the historical data suggests that discharge projection characteristics directly affect the future expected hydropower generation. Large uncertainties exist and stem from the ensembles of climate runs, but also from the potential operation modes of the (storage) hydropower plants in the future.</p><p>&#160;</p><p>&#160;</p><p>References:</p><p>Stanzel, P., Kling, H., 2018. From ENSEMBLES to CORDEX: Evolving climate change projections for Upper Danube River flow. J. Hydrol. 563, 987&#8211;999. https://doi.org/10.1016/j.jhydrol.2018.06.057</p><p>&#160;</p>
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