• Energy Research

AbstractIn this study, an event‐based approach for developing precipitation indices of extreme hydrological events was proposed to analyse characteristics of rainstorm events over long‐term periods. Statistical properties of hourly rainfall data were summarized from the retrieved rainstorm events between historical (1777–1907) and modern (1961–2010) periods at Seoul rain gauge station, Korea. The trends and variations of rainstorm events reflecting rainfall depth/duration/intensity thresholds and annual maximum rainstorm events with the largest rainfall depths in these two periods were analysed and compared. The study results indicated that, at a 5% significance level, there were differences in the means and variances of the number of rainstorm events and the average rainfall depth/intensity per storm. For the historical period, the average rainfall depth, duration and intensity per storm with total rainfall durations had a temporal linear trend with slopes of 0.011 mm year−1, 0.027 hr year−1, and −0.009 mm hr−1 year−1, respectively; whereas, a general decreasing trend was detected during the modern period with slopes of −0.218 mm year−1, −0.076 hr year−1, and − 0.010 mm hr−1 year−1, respectively. In terms of annual maximum rainfall depth, no significant temporal trend was found in the historical period but an increasing trend (with a slope of 0.710 mm year−1) was detected for the modern period. This study was the first attempt to investigate the variability of rainfall characteristics in Seoul, Korea with high‐resolution historical data dating back to almost 250 years ago, using a storm‐event‐based concept which defined precipitation indices with a holistic consideration of the number of events and their rainfall depth, duration, and intensity.

Hydropower represents an interesting technology: affordable, renewable, and flexible. However, it must cope with climate changes and new energy policies that jeopardize its future. A smooth transition to sustainability requires decision makers to assess the future perspectives of hydropower: about its future revenue and related uncertainty. This investigation requires a multidisciplinary approach as both streamflow and energy mix will evolve. We simulated future streamflow based on eight climate scenarios using a semi-distributed hydrological model for our case study, the Tremorgio hydropower plant located in southern Switzerland. Next, using a hydropower management model we generated income according to these streamflows and twenty-eight electricity price scenarios. Our results indicate that climate change will modify the seasonality of inflows and volumes exploitable for hydropower generation. However, adaptive strategies in the management of reservoirs could minimize revenue losses/maximize revenue gains. In addition, most market scenarios project an increase in revenues, except in the case of high wind and solar energy penetration. Markets do not provide the right incentive, since the deployment of intermittent energy would benefit from more flexible hydropower.

Climate change has repercussions on the management of water resources. Particularly, changes in precipitation and temperature impact hydropower generation and revenue by affecting seasonal electricity prices and streamflow. This issue exemplifies the impact of climate change on the water-energy-nexus, which has raised serious concern. This paper investigates the impact of climate change on hydropower with a multidisciplinary approach. A holistic perspective should be favored as the issue is complex, consequently, we chose to investigate a specific case study in Italy. It allows grasping the details, which matters in mountainous area. We integrated a hydrological model, hydropower management model, nine climate scenarios, and five electricity scenarios for a specific storage hydropower plant. Independently from the scenarios, the results show a glacier volume shrinkage upward of 40% by 2031 and minimum of 50% by 2046. The reservoir mitigates losses of revenue that reach 8% in the worst case, however, are lower compared with run-of-the-river configuration. Changes in price seasonality amplitude also determine modifications in revenues, while temporal shifts appear to be ineffective. For run-of-the-river, any variation in hydrological cycle immediately translates into revenue. Comparing the results of all future scenarios with the base scenario, it can be concluded that an increase in temperature will slightly improve the performances of hydropower.

Rangelands provide the main forage resource for livestock in many parts of the world, but maintaining long-term productivity and providing sufficient income for the rancher remains a challenge. One key issue is to maintain the rangeland in conditions where the rancher has the greatest possibility to adapt his/her management choices to a highly fluctuating and uncertain environment. In this study, we address management robustness and adaptability, which increase the resilience of a rangeland. After reviewing how the concept of resilience evolved in parallel to modelling views on rangelands, we present a dynamic model of rangelands to which we applied the mathematical framework of viability theory to quantify the management adaptability of the system in a stochastic environment. This quantification is based on an index that combines the robustness of the system to rainfall variability and the ability of the rancher to adjust his/her management through time. We evaluated the adaptability for four possible scenarios combining two rainfall regimes (high or low) with two herding strategies (grazers only or mixed herd). Results show that pure grazing is viable only for high-rainfall regimes, and that the use of mixed-feeder herds increases the adaptability of the management. The management is the most adaptive with mixed herds and in rangelands composed of an intermediate density of trees and grasses. In such situations, grass provides high quantities of biomass and woody plants ensure robustness to droughts. Beyond the implications for management, our results illustrate the relevance of viability theory for addressing the issue of robustness and adaptability in non-equilibrium environments.

Assessing the carrying capacity is of primary importance in arid rangelands. This becomes even more important during droughts, when rangelands exhibit non-equilibrium dynamics, and the dynamics of livestock conditions and forage resource are decoupled. Carrying capacity is usually conceived as an equilibrium concept, that is, the consumer density that can co-exist in long-term equilibrium with the resource. As one of the first, here we address the concept of carrying capacity in systems, where there is no feedback between consumer and resource in a limited period of time. To this end, we developed an individual-based model describing the basic characteristics of a rangeland during a drought. The model represents a rangeland composed by a single water point and forage distributed all around, with livestock units moving from water to forage and vice versa, for eating and drinking. For each livestock unit we implemented an energy balance and we accounted for the gut-filling effect (i.e. only a limited amount of forage can be ingested per unit time). Our results showed that there is a temporal threshold above which livestock begin to experience energy deficit and burn fat reserves. We demonstrated that such a temporal threshold increases with the number of animals and decreases with the rangeland conditions (amount of forage). The temporal threshold corresponded to the time livestock take to consume all the forage within a certain distance from water, so that the livestock can return to water for drinking without spending more energy than they gain within a day. In this study, we highlight the importance of a time threshold in the assessment of carrying capacity in non-equilibrium conditions. Considering this time threshold could explain contrasting observations about the influence of livestock number on livestock conditions. In case of private rangelands, the herd size should be chosen so that the spatial threshold equals (or exceeds) the length of the drought.

<p>Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) <em>preconditioned</em>, (b) <em>multivariate</em>, (c) t<em>emporally compounding</em>, and (d) <em>spatially compounding</em> events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors.</p>