A Comparative Assessment of the Impact of Climate Change and Energy Policies on Alpine Hydropower

Anghileri, Daniela; Botter, Martina; Castelletti, Andrea; Weigt, Hannes; Burlando, Paolo

doi:10.1029/2017wr022289

Cited by 37 publications

(21 citation statements)

References 73 publications

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“…Illustrative examples of the resulting differences in the distributions of the scenarios obtained are shown in Figure 6. Diverse : Figure 4a depicts the situation where four diverse futures are first identified (analogous to Representative Concentration Pathways [RCPs]) with many samples taken around each of these four points (analogous to the use of multiple global and regional climate models to create multiple downscaled realizations of each of the RCPs) of which there are many examples in the water resources literature (Anghileri et al, 2018; Giuliani & Castelletti, 2016; Giuliani, Castelletti, et al, 2016; Haasnoot et al, 2012, 2013; Herman & Giuliani, 2018; Huskova et al, 2016; McPhail et al, 2018). Targeted : Figure 4b depicts a targeted approach to identifying samples that cover “interesting” regions of the system model space, for the situation where the model performance responds monotonically to each input (i.e., an increase in one variable always results in increased or decreased performance).…”

Section: Case Studymentioning

confidence: 99%

“…(Maier et al, 2016). A number of water resources studies have generated explorative scenarios by considering the impact of plausible changes in atmospheric carbon concentrations (Anghileri et al, 2018; Beh et al, 2014, 2015a, 2015b; Giuliani & Castelletti, 2016; Giuliani et al, 2016; Haasnoot et al, 2012, 2013; Herman & Giuliani, 2018; Huskova et al, 2016; McPhail et al, 2018), as well as plausible changes in regional socioeconomic conditions (Haasnoot et al, 2013; Wada et al, 2019). In contrast, normative scenarios consider conditions that represent interesting outcomes, as is the case with scenario discovery (e.g., Bryant & Lempert, 2010; Groves & Lempert, 2007; Hadka et al, 2015; Kasprzyk et al, 2013; Kwakkel, 2017; Kwakkel, Walker, et al, 2016; Matrosov et al, 2013; Trindade et al, 2017); conditions that result in one decision alternative being preferable to another, as is the case with MORE (Ravalico et al, 2010), POMORE (Ravalico et al, 2009) and decision scaling (e.g., Brown et al, 2012); or conditions under which certain decision alternatives no longer perform adequately, as is the case with adaptive tipping point approaches (e.g., Haasnoot et al, 2013; Kwadijk et al, 2010; Kwakkel et al, 2015; Kwakkel, Haasnoot, et al, 2016; Vervoort et al, 2014; Walker, Haasnoot, et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Impact of Scenario Selection on Robustness

McPhail

Maier

Westra

et al. 2020

Water Resources Research

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Multiple plausible future scenarios are being used increasingly in preference to a single deterministic or probabilistic prediction of the future in the long-term planning of water resources systems. These scenarios enable the determination of the robustness of a system-the consideration of performance across a range of plausible futures-and allow an assessment of which possible future system configurations result in a greater level of robustness. There are many approaches to selecting scenarios, and previous studies have observed that the choice of scenarios might affect the estimated robustness of the system. However, these observations have been anecdotal and qualitative. This paper develops a systematic, quantitative methodology for exploring the influence of scenario selection on the robustness and the ranking of decision alternatives. The methodology is illustrated on the Lake Problem. The quantitative results obtained confirm the qualitative observations of previous works, showing that the selection of scenarios is important, as it has a large influence on the robustness value calculated for each decision alternative. However, we show that it has a relatively small influence on how those decision alternatives are ranked. This implies that despite the difference in robustness values, similar decision outcomes will be reached in this case study, regardless of the basis on which the scenarios are obtained. It is also revealed that the impact of the scenarios on the robustness values is due to complex interactions with the system model and robustness metrics.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Scenario Selection on Robustness

McPhail

Maier

Westra

et al. 2020

Water Resources Research

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“…TOPKAPI-ETH, the other frequently used complex distributed model has also been used in several climate change impact applications (Fatichi et al, 2014(Fatichi et al, , 2015bFinger et al, 2012;Anghileri et al, 2018).…”

Section: Climate Change Impact Analysismentioning

confidence: 99%

Why do we have so many different hydrological models? A review based on the case of Switzerland

Horton¹,

Schaefli²,

Kauzlaric³

2022

Preprint

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Hydrology plays a central role in applied as well as fundamental environmental sciences, but it is well known to suffer from an overwhelming diversity of models, in particular to simulate streamflow. Based on Switzerland's example, we discuss here in detail how such diversity did arise even at the scale of such a small country. The case study's relevance stems from the fact that Switzerland shows a relatively high density of academic and research institutes active in the field of hydrology, which led to an evolution of hydrological models that stands exemplarily for the diversification that arose at a larger scale. Our analysis summarizes the main driving forces behind this evolution, discusses drawbacks and advantages of model diversity and depicts possible future evolutions. Although convenience seems to be the main driver so far, we see potential change in the future with the advent of facilitated collaboration through open sourcing and code sharing platforms. We anticipate that this review, in particular, helps researchers from other fields to understand better why hydrologists have so many different models.

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“…The associated lake, the Lac de Dix (Figure 1), has a water storage capacity of 400 billion m 3 (Lane et al, 2014). Water is supplied not only from the~45 km 2 large catchment of the lake itself, but also from the neighbouring valleys through a network of 100 km of transfer tunnels and pumping stations (Margot et al, 1992;Lane et al, 2014;Bakker et al, 2018;Anghileri et al, 2018aAnghileri et al, , 2018b. The water intakes are equipped with sediment traps for both fine-and coarse-grained material, from which sediment is flushed down the river at frequencies up to several times per day (Lane et al, 2017;Bakker et al, 2018).…”

Section: Water and Sediment Abstractionmentioning

confidence: 99%

Disentangling human impact from natural controls of sediment dynamics in an Alpine catchment

Stütenbecker

Costa

Bakker

et al. 2019

Earth Surf Processes Landf

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Human activities have increasingly strong impacts on the sediment dynamics of watersheds, directly, for example through water abstraction and sediment extraction, but also indirectly through climate change. This study aims at disentangling these impacts on natural sediment fluxes for the Borgne River, located in the Alps of southwest Switzerland, using two approaches: First, an assessment of contemporary sediment sources and their relative contribution to the sediment delivered to the catchment outlet is undertaken by geochemical fingerprinting and a mixing model. Second, a spatially distributed conceptual model of suspended sediment production and transfer is used to quantify the contribution of different portions of the catchment to the total sediment yield. The model describes the influence of hydroclimatic variables (rainfall, snowmelt, and ice melt), water diversions and reservoir trapping on the sediment yield accounting for the erodibility of the different land covers present in the catchment. The analysis of different scenarios based on this conceptual model aids the interpretation of the fingerprinting results and the identification of the most important factors controlling sediment fluxes. Although the conceptual model overestimates the contribution of the downstream source area and underestimates the contribution of the upstream source area, the results allow us to qualitatively assess the impacts of different drivers influencing the sediment yield at the catchment scale. The results suggest: (1) high sediment yield from the uppermost part of the catchment due to sediment delivery by glacial ice melt; (2) delayed sediment transfer from areas impacted by water abstraction; and (3) reduced sediment contribution from areas upstream of a major hydropower reservoir that intercepts and traps sediment. Although process (1) and processes (2) and (3) serve to counter one another, our study emphasizes that the relative impacts of Anthropocene climate change and human impacts on sediment delivery may be disentangled through multi‐proxy approaches. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.

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A Comparative Assessment of the Impact of Climate Change and Energy Policies on Alpine Hydropower

Cited by 37 publications

References 73 publications

Impact of Scenario Selection on Robustness

Impact of Scenario Selection on Robustness

Why do we have so many different hydrological models? A review based on the case of Switzerland

Disentangling human impact from natural controls of sediment dynamics in an Alpine catchment

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