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Abstract High elevation or high latitude hydropower production (HP) strongly relies on water resources that are influenced by glacier melt and are thus highly sensitive to climate warming. Despite of the wide-spread glacier retreat since the development of HP infrastructure in the 20th century, little quantitative information is available about the role of glacier mass loss for HP. In this paper, we provide the first regional quantification for the share of Alpine hydropower production that directly relies on the waters released by glacier mass loss, i.e. on the depletion of long-term ice storage that cannot be replenished by precipitation in the coming decades. Based on the case of Switzerland (which produces over 50% of its electricity from hydropower), we show that since 1980, 3.0%–4.0% (1.0–1.4 TWh yr−1) of the country-scale hydropower production was directly provided by the net glacier mass loss and that this share is likely to reduce substantially by 2040–2060. For the period 2070–2090, a production reduction of about 1.0 TWh yr−1 is anticipated. The highlighted strong regional differences, both in terms of HP share from glacier mass loss and in terms of timing of production decline, emphasize the need for similar analyses in other Alpine or high latitude regions. Graphical abstract Image 1 Highlights • First quantification of Alpine hydropower production share from glacier mass loss. • Since 1980, 1.0 to 1.4 TWh yr-1 of Swiss hydropower comes from glacier mass loss. • Expected country-scale production reduction by 2070–2090 of 1.0 TWh yr-1. • Notable regional differences despite of continuous retreat of all Swiss glaciers. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new extension of the classical degree-day snowmelt model applicable to hourly simulations for regions with limited data and adaptable to a broad range of spatially-explicit hydrological models. The snowmelt schemes have been tested with a point measurement dataset at the Cotton Creek Experimental Watershed (CCEW) in British Columbia, Canada and with a detailed dataset available from the Dranse de Ferret catchment, an extensively monitored catchment in the Swiss Alps. The snowmelt model performance is quantified with the use of a spatially-explicit model of the hydrologic response. Comparative analyses are presented with the widely-known, grid-based method proposed by Hock which combines a local, temperature-index approach with potential radiation. The results suggest that a simple diurnal cycle of the degree-day melt parameter based on minimum and maximum temperatures is competitive with the Hock approach for sub-daily melt simulations. Advantages of the new extension of the classical degree-day method over other temperature-index methods include its use of physically-based, diurnal variations and its ability to be adapted to data-constrained hydrological models which are lumped in some nature. [Copyright &y& Elsevier]

Meeting carbon-reduction targets will require thorough consideration of climate variability and climate change due to the increasing share of climate-sensitive renewable energy sources (RES). One of the main concerns arises from situations of low renewable production and high demand, which can hinder the power system. We analysed energy droughts, defined as periods of low energy production (wind plus solar generation) or high residual load (demand minus production), in terms of two main properties: duration and severity. We estimated the joint return periods associated with energy droughts of residual load and power production. We showed that moderate winter energy droughts of both low renewable production and high residual load occur every half a year, while summer events occur every 3.6 and 2.4 years (on average). As expected, the occurrence of energy droughts tends to decrease with the degree of the severity of the energy drought, and moderate and extreme energy droughts showed longer return periods for most countries. In general, we found a large variability across Europe in summer, with some countries (e.g. Italy) being more sensitive to energy droughts. Our results highlight the relevance of sharing RES during prolonged periods of low production and high demand. • European countries are frequently exposed to moderate winter energy droughts. • Copulas model the dependence between the duration and the severity of energy droughts. • Energy droughts are more frequent and longer lasting in winter than in summer. • Joint return periods increase with the degree of the severity of the energy droughts. [ABSTRACT FROM AUTHOR]

• Evaluation of 12 gridded actual evaporation datasets for hydrological modelling. • Comparison of four distinct model calibration strategies. • Process-diagnostic of model outputs with multiple independent variables. • Calibration only on spatial patterns of evaporation improves the model responses. • Model calibration strategy determines the utility of the evaporation data. Twelve actual evaporation datasets are evaluated for their ability to improve the performance of the fully distributed mesoscale Hydrologic Model (mHM). The datasets consist of satellite-based diagnostic models (MOD16A2, SSEBop, ALEXI, CMRSET, SEBS), satellite-based prognostic models (GLEAM v3.2a, GLEAM v3.3a, GLEAM v3.2b, GLEAM v3.3b), and reanalysis (ERA5, MERRA-2, JRA-55). Four distinct multivariate calibration strategies (basin-average, pixel-wise, spatial bias-accounting and spatial bias-insensitive) using actual evaporation and streamflow are implemented, resulting in 48 scenarios whose results are compared with a benchmark model calibrated solely with streamflow data. A process-diagnostic approach is adopted to evaluate the model responses with in-situ data of streamflow and independent remotely sensed data of soil moisture from ESA-CCI and terrestrial water storage from GRACE. The method is implemented in the Volta River basin, which is a data scarce region in West Africa, for the period from 2003 to 2012. Results show that the evaporation datasets have a good potential for improving model calibration, but this is dependent on the calibration strategy. All the multivariate calibration strategies outperform the streamflow-only calibration. The highest improvement in the overall model performance is obtained with the spatial bias-accounting strategy (+29%), followed by the spatial bias-insensitive strategy (+26%) and the pixel-wise strategy (+24%), while the basin-average strategy (+20%) gives the lowest improvement. On average, using evaporation data in addition to streamflow for model calibration decreases the model performance for streamflow (-7%), which is counterbalance by the increase in the performance of the terrestrial water storage (+11%), temporal dynamics of soil moisture (+6%) and spatial patterns of soil moisture (+89%). In general, the top three best performing evaporation datasets are MERRA-2, GLEAM v3.3a and SSEBop, while the bottom three datasets are MOD16A2, SEBS and ERA5. However, performances of the evaporation products diverge according to model responses and across climatic zones. These findings open up avenues for improving process representation of hydrological models and advancing the spatiotemporal prediction of floods and droughts under climate and land use changes. [ABSTRACT FROM AUTHOR]