The transition towards decarbonized power systems requires accounting for the impacts of the climate variability and climate change on renewable energy sources. With the growing share of wind and ...solar power in the European power system and their strong weather dependence, balancing the energy demand and supply becomes a great challenge. We characterize energy compound events, defined as periods of simultaneous low renewable production of wind and solar power, and high electricity demand. Using a logistic regression approach, we examine the influence of meteorological and atmospheric drivers on energy compound events. Moreover, we assess the spatial coherence of energy compound events that pose a major challenge within an interconnected power grid, as they can affect multiple countries simultaneously. On average, European countries are exposed to winter energy compound events more than twice per year. The combination of extremely low temperatures and low wind speeds is associated with a higher probability of occurrence of energy compound events. Furthermore, we show that blocked weather regimes have a major influence on energy compound events. In particular, Greenland and European blocking lead to widespread energy compound events that affect multiple countries at the same time. Our results highlight the relevance of weather regimes resulting in synchronous spatial energy compound events, which might pose a greater risk within a potential fully interconnected European grid.
European countries are exposed to energy compound events, during which it might be challenging to achieve a balanced electricity transmission system, due to simultaneous episodes of high electricity demand (left) and low renewable energy production (right). Synchronous energy compound events can affect multiple countries simultaneously (black starts). In particular, the European power system is vulnerable to blocked weather regimes, which have a large influence on the renewable production and electricity demand in Europe.
Abstract
Hydropower plays a significant role in the transition towards a low-carbon power system, being a renewable energy source that can complement solar and wind power, which are highly ...intermittent. However, hydropower is itself dependent on local weather conditions and climate variability. Moreover, extreme climate conditions, such as hot-dry compound events, can have a major impact on hydropower production (HP). Here, we examine the impacts of hot-dry conditions on HP under current and future climate scenarios in Switzerland, a country where hydropower provides the biggest share (60%) of the total electricity production. Overall, our results point out that the impacts of hot-dry conditions on HP are case-specific. We found that hot-dry compound conditions during the warmer months negatively impact HP in power plants with little or no water storage capacity (run-of-river schemes). On the contrary, schemes with large, seasonal accumulation lakes and significant glacier resources will continue to be able to produce high amounts of HP during hot-dry conditions in summer, which is an important result for Alpine hydropower.
This paper proposes a systematic assessment of the performance of an analytical modeling framework for streamflow probability distributions for a set of 25 Swiss catchments. These catchments show a ...wide range of hydroclimatic regimes, including namely snow-influenced streamflows. The model parameters are calculated from a spatially averaged gridded daily precipitation data set and from observed daily discharge time series, both in a forward estimation mode (direct parameter calculation from observed data) and in an inverse estimation mode (maximum likelihood estimation). The performance of the linear and the nonlinear model versions is assessed in terms of reproducing observed flow duration curves and their natural variability. Overall, the nonlinear model version outperforms the linear model for all regimes, but the linear model shows a notable performance increase with catchment elevation. More importantly, the obtained results demonstrate that the analytical model performs well for summer discharge for all analyzed streamflow regimes, ranging from rainfall-driven regimes with summer low flow to snow and glacier regimes with summer high flow. These results suggest that the model's encoding of discharge-generating events based on stochastic soil moisture dynamics is more flexible than previously thought. As shown in this paper, the presence of snowmelt or ice melt is accommodated by a relative increase in the discharge-generating frequency, a key parameter of the model. Explicit quantification of this frequency increase as a function of mean catchment meteorological conditions is left for future research.
Hydropower is a key energy source in almost all world regions. It fuels social and economic development, ensures electricity security, and is a pillar for renewable electricity production. But ...hydropower and its environmental impacts are vulnerable to climate change. This discussion of model‐based climate change impact assessments and underlying modeling assumptions will help decision‐makers and scientists analyzing existing studies and identifying the most urgent open questions. Rooted in hydrological uncertainty analysis, this discussion focuses on the importance of local factors and on modeling uncertainties for a critical view on our ability to project future hydropower production in different world regions. WIREs Water 2015, 2:271–289. doi: 10.1002/wat2.1083
This article is categorized under:
Engineering Water > Planning Water
Science of Water > Water and Environmental Change
In recent decades, research on mountains has become more inter- and transdisciplinary, but a greater effort is needed if such research is to contribute to a societal transformation toward ...sustainability. Mountain research centers are a crucial actor in this endeavor. Yet, the literature has not paid sufficient attention to how these centers should (re-)design inter- and transdisciplinary research. In this study, we explored this question with a self-reflexive approach. We analyzed the first 15 months of the Interdisciplinary Centre for Mountain Research (CIRM) of the University of Lausanne (Switzerland) through qualitative data collected via interviews and observation. We used a simple model of inter- and transdisciplinarity at the organizational level of a research center. Special attention was devoted to the individual and collective ability to exploit the unexpected (serendipity). Our results indicate an interdependency between the coconstruction of research objects and the creation of integrative partnerships. They also shed light on the types of institutional resources and integrative methodologies that enhance inter- and transdisciplinary research, as well as their challenges. Our experience shows that implementing inter- and transdisciplinarity requires deep changes in research evaluation procedures, research funding policies, and researchers themselves. Serendipity is in turn shown to play an important role in inter- and transdisciplinarity due to its potential to change the research process in creative ways. We speculate that serendipity offers unique opportunities to capitalize on hidden resources that can catalyze a radical transformation of mountain researchers, research organizations, and society in the face of unprecedented global change.
This paper proposes a spectral domain likelihood function for the Bayesian estimation of hydrological model parameters from a time series of model residuals. The spectral domain error model is based ...on the power‐density spectrum (PDS) of the stochastic process assumed to describe residual errors. The Bayesian spectral domain likelihood (BSL) is mathematically equivalent to the corresponding Bayesian time domain likelihood (BTL) and yields the same inference when all residual error assumptions are satisfied (and all residual error parameters are inferred). However, the BSL likelihood function does not depend on the residual error distribution in the original time domain, which offers a theoretical advantage in terms of robustness for hydrological parameter inference. The theoretical properties of BSL are demonstrated and compared to BTL and a previously proposed spectral likelihood by Montanari and Toth (2007), using a set of synthetic case studies and a real case study based on the Leaf River catchment in the U.S. The empirical analyses confirm the theoretical properties of BSL when applied to heteroscedastic and autocorrelated error models (where heteroscedasticity is represented using the log‐transformation and autocorrelation is represented using an AR(1) process). Unlike MTL, the use of BSL did not introduce additional parametric uncertainty compared to BTL. Future work will explore the application of BSL to challenging modeling scenarios in arid catchments and “indirect” calibration with nonconcomitant input/output time series.
Key Points
We propose a Bayesian spectral domain likelihood (BSL) function for the estimation of model parameters from residual time series
BSL is based on the power‐density spectrum (PDS) of the stochastic process assumed to describe the model residuals
BSL only depends on the error autocorrelation structure, not on its time domain distribution, a key advantage for parameter inference
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.
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•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.
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.
•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.
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