The European Alps are well positioned to contribute significantly to the energy transition. In addition to sites with above-average potential for wind and solar power, the “water towers” of Europe ...provide flexible, low-carbon power generation as well as energy storage. In the future, hydropower systems are expected to become more than mere electricity generators, serving a key role as flexible complements to intermittent power generators and as providers of large-scale seasonal and daily energy storage. Energy transition on national and European scales can be facilitated by expanding the capacity of pumped storage hydropower (PSHP) plants. Yet the extension of hydropower production, in particular PSHP, remains controversial, primarily due to environmental concerns. Focusing on 2 Alpine countries, Austria and Switzerland, this paper provides a system view of hydropower production and energy storage in the Alps. It discusses advantages and drawbacks of various assessment tools and identifies gaps and needs for the integrated assessment of PSHP plants. It concludes that instruments that evaluate the impacts and sustainability of PSHP projects need to be developed, elaborated, and applied in a participatory manner, in order to promote public dialogue, increase social acceptance, and, ideally, encourage energy consumers to become advocates of a sustainable energy future.
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NMLJ, NUK, SAZU, UL, UM, UPUK
The combination of drought and heat affects forest ecosystems by deteriorating the health of trees, which can lead to large‐scale die‐offs with consequences on biodiversity, the carbon cycle, and ...wood production. It is thus crucial to understand how drought events affect tree health and which factors determine forest susceptibility and resilience. We analyze the response of Central European forests to the 2018 summer drought with 10 × 10 m satellite observations. By associating time‐series statistics of the Normalized Difference Vegetation Index (NDVI) with visually classified observations of early wilting, we show that the drought led to early leaf‐shedding across 21,500 ± 2,800 km2, in particular in central and eastern Germany and in the Czech Republic. High temperatures and low precipitation, especially in August, mostly explained these large‐scale patterns, with small‐ to medium‐sized trees, steep slopes, and shallow soils being important regional risk factors. Early wilting revealed a lasting impact on forest productivity, with affected trees showing reduced greenness in the following spring. Our approach reliably detects early wilting at the resolution of large individual crowns and links it to key environmental drivers. It provides a sound basis to monitor and forecast early‐wilting responses that may follow the droughts of the coming decades.
We analyze the response of Central European forests to the 2018 summer drought with 10 × 10 m satellite observations. By associating time‐series statistics of the Normalized Difference Vegetation Index with aerial observations of early wilting, we show that the drought led to early leaf‐shedding across 21,500 ± 2,800 km2, in particular in Germany and the Czech Republic. Temperature and precipitation mostly explained these large‐scale patterns, with small to medium‐sized trees, steep slopes, and shallow soils being regional risk factors. Our approach provides a sound basis to monitor and forecast early wilting that may follow the droughts of the coming decades.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Hydrological forecasts can benefit from model climatologies compiled from long, consistent, retrospective forecasts (reforecasts). Typical areas of application include the estimation of return ...periods for extreme events, the detection of model deficiencies, such as systematic errors or the calibration of current forecasts using the reforecasts, and suitable observations. One difficulty when creating long reforecasts is the availability of good states for the model initialization, as a sufficiently dense network of meteorological observations is rarely available in most catchments for a long period. With this study, the creation of forecast and reforecasts in such catchments is motivated by considering two aspects: first, a comparison where it is shown that hydrological forecasts benefit most from high‐quality initial conditions on the basis of local observations, however, less accurate initial conditions using ERA‐interim reanalysis are also sufficient to provide skillful hydrological forecasts useful for many users. Second, we demonstrate that hydrological reforecasts compiled without long‐term meteorological observations have an additional value, e.g., they allow for more skillful runoff forecasts. Utilizing those reforecasts can compensate for forecast errors induced by less accurate initializations. For this study, hourly hydrological reforecasts, based on the PREcipitation‐Runoff‐EVApotranspiration HRU Model (PREVAH) were used, with an 18 year long, five‐member global ensemble reforecast data set from the European Centre for Medium Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) as forcing for lead times up to 10 days. Three mesoscale catchments of different characteristics in the Swiss Alps were analyzed.
Key Points
Skillful hydrological ensemble forecast are possible in unobserved catchments
Ensemble reforecast can be used to correct forecast errors in unobserved catchments
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BFBNIB, CEKLJ, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
•We compare three hydrological models of different complexity•Novel comparison with respect to soil moisture observations•Moreover comparison of runoff performance, and of performance during dry and ...wet extremes•Increasing complexity not necessarily leading to better model performance•Simple conceptual model is a hard benchmark, especially for soil moisture
In recent decades considerable progress has been made in climate model development. Following the massive increase in computational power, models became more sophisticated. At the same time also simple conceptual models have advanced. In this study we validate and compare three hydrological models of different complexity to investigate whether their performance varies accordingly. For this purpose we use runoff and also soil moisture measurements, which allow a truly independent validation, from several sites across Switzerland. The models are calibrated in similar ways with the same runoff data. Our results show that the more complex models HBV and PREVAH outperform the simple water balance model (SWBM) in case of runoff but not for soil moisture. Furthermore the most sophisticated PREVAH model shows an added value compared to the HBV model only in case of soil moisture. Focusing on extreme events we find generally improved performance of the SWBM during drought conditions and degraded agreement with observations during wet extremes. For the more complex models we find the opposite behavior, probably because they were primarily developed for prediction of runoff extremes. As expected given their complexity, HBV and PREVAH have more problems with over-fitting. All models show a tendency towards better performance in lower altitudes as opposed to (pre-) alpine sites. The results vary considerably across the investigated sites. In contrast, the different metrics we consider to estimate the agreement between models and observations lead to similar conclusions, indicating that the performance of the considered models is similar at different time scales as well as for anomalies and long-term means. We conclude that added complexity does not necessarily lead to improved performance of hydrological models, and that performance can vary greatly depending on the considered hydrological variable (e.g. runoff vs. soil moisture) or hydrological conditions (floods vs. droughts).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the ...modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter.Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test for the extent to which expert knowledge can improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the inverse distance weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down set-up relying on parameter and process constraints and an experimentalists' set-up based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed.The simulation results showed that (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up set-up performed better than the top-down one when simulating short-duration events, but similarly to the top-down set-up when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up set-up can help identify uncertainty sources, but is prone to overconfidence problems, whereas the top-down set-up seems to accommodate uncertainties in the input data best. Overall, modellers' and experimentalists' concept of model realism differ. This means that the level of detail a model should have to accurately reproduce the DRPs expected must be agreed in advance.
Reliable predictions of the energy consumption and production is important information for the management and integration of renewable energy sources. Several different Machine Learning (ML) ...methodologies have been tested for predicting the energy consumption/production based on the information of hydro-meteorological data. The methods analysed include Multivariate Adaptive Regression Splines (MARS) and various Quantile Regression (QR) models like Quantile Random Forest (QRF) and Gradient Boosting Machines (GBM). Additionally, a Nonhomogeneous Gaussian Regression (NGR) approach has been tested for combining and calibrating monthly ML based forecasts driven by ensemble weather forecasts. The novelty and main focus of this study is the comparison of the capability of ML methods for producing reliable predictive uncertainties and the application of monthly weather forecasts. Different skill scores have been used to verify the predictions and their uncertainties and first results for combining the ML methods applying the NGR approach and coupling the predictions with monthly ensemble weather forecasts are shown for the southern Switzerland (Canton of Ticino). These results highlight the possibilities of improvements using ML methods and the importance of optimally combining different ML methods for achieving more accurate estimates of future energy consumptions and productions with sharper prediction uncertainty estimates (i.e., narrower prediction intervals).
In Alpine regions, future changes in glacier and snow cover are expected to change runoff regimes towards higher winter but lower summer discharge. The low summer discharge will coincide with the ...highest water demand for irrigation, and local and regional water shortages are expected to become more likely. One possible measure to adapt to these changes can be the extension of current uses of artificial reservoirs and natural lakes to the provision of water for the alleviation of water shortage. This study assesses the potential of reservoirs and natural lakes for the alleviation of water shortages in a nationwide analysis in Switzerland. To do so, we estimated water supply and demand under current and future conditions both under normal and extreme runoff regimes for 307 catchments. Water demand was assessed for various categories including drinking water, industrial use, artificial snow production, agriculture, ecological flow requirements, and hydropower production. The aggregated supply and demand estimates were used to derive water surplus/shortage estimates. These were then compared to the storage capacity of reservoirs and natural lakes within a catchment to determine the potential for alleviating summer water scarcity. Our results show that water shortage is expected mainly in the lowland region north of the Alps, and less in the Alps. In this lowland region, the potential of natural lakes for alleviating water scarcity is high. This potential is lower in the Alps where it is expected to increase or decrease under future conditions depending on the region of interest. Catchments with a high storage capacity can potentially contribute to the alleviation of water shortage downstream. We conclude that a spatial mismatch between water scarcity and storage availability exists since water stored in reservoirs on the southern side of the Alps is often not available for the use on the northern side.
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•Estimation of water supply, demand, and surplus/shortage for Switzerland•Assessment of the potential of reservoirs and lakes for alleviating summer shortages•Future alleviation potential only slightly higher than today under normal conditions•Increase or decrease of alleviation potential under extreme conditions•Catchment-scale storage capacity often not sufficient to cover water shortages
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The 2018 drought event had severe ecological, economic, and social impacts. How extreme was it in Switzerland? We addressed this question by looking at different types of drought, including ...meteorological, hydrological, agricultural, and groundwater drought, and at the two characteristics deficit and deficit duration. The analysis consisted of three main steps: (1) event identification using a threshold-level approach, (2) drought frequency analysis, and (3) comparison of the 2018 event to the severe 2003 and 2015 events. In Step 2 the variables precipitation, discharge, soil moisture, and low-flow storage were first considered separately in a univariate frequency analysis; pairs of variables were then investigated jointly in a bivariate frequency analysis using a copula model for expressing the dependence between the two variables under consideration. Our results show that the 2018 event was especially severe in north-eastern Switzerland in terms of soil moisture, with return periods locally exceeding 100 years. Slightly longer return periods were estimated when discharge and soil moisture deficits were considered together. The return period estimates depended on the region, variable, and return period considered. A single answer to the question of how extreme the 2018 drought event was in Switzerland is therefore not possible – rather, it depends on the processes one is interested in.
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