Impacts of extreme weather events on transport infrastructure in Norway

Frauenfelder, Regula; Solheim, Anders; Isaksen, Ketil; Romstad, Bård; Dyrrdal, Anita Verpe; Ekseth, Kristine; Harbitz, Alf; Harbitz, C. B.; Haugen, Jan Erik; Hygen, Hans Olav; Haakenstad, Hilde; Jaedicke, Christian; Jönsson, Arne; Klæboe, Ronny; Ludvigsen, Johanna; Meyer, Nele M.; Rauken, Trude; Skaland, Reidun Gangstø; Sverdrup-Thygeson, Kjetil; Aaheim, Asbjørn; Bjordal, Heidi; Fevang, Per-Anton

doi:10.5194/nhess-2017-437

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…We would expect some of the counts to fall outside the confidence interval even if the predicted and observed data have an identical distribution, and the sum of the number of years out of a total of N years with the observed counts outside the predicted interval (n 05 ðtÞÁn 95 ðtÞ) is also expected to follow a binomial distribution for perfect predictions. Figure 9 shows a comparison between predicted number of precipitation events exceeding 10 mm/day [a threshold used by road authorities in Norway (Frauenfelder et al, 2013)] and the counts of observed events. The downscaled results reproduced the realistic multiyear variability with a correlation between the predicted annual median count n m (t) and observed count n o (t) of 0.6.…”

Section: Resultsmentioning

confidence: 99%

On downscaling probabilities for heavy 24-hour precipitation events at seasonal-to-decadal scales

Benestad

Mezghani

2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T We examine a method for predicting variations in the probabilities and occurrence of intense local 24-hour precipitation events over seasonal, annual, and 5-yr intervals. The wet-day mean m can be used to describe the exponential distribution for the wet-day amounts, and provides a basis for forecasting the likelihood of seeing an event above a given threshold. A regression-based downscaling model is calibrated on annual m and wet-day frequency f w , respectively, and then tested on seasonal to multi-annual scales. The probabilities for heavy precipitation statistics are taken as the product between a fitted cumulative exponential distribution for the wet-day amounts m and f w . The annual number of heavy precipitation events is estimated from the annual probability, where a 90% confidence interval on the number is computed using the 5 and 95 percentiles of a binomial distribution for the predicted probability. The analysis identifies a strong link between large-scale predictors such as mean sea-level pressure or surface temperature and the wet-day frequency. There was a weaker dependency of the wet-day mean to large-scale predictors, which suggests that local-scale processes have a stronger influence on the 24-hour precipitation amounts. The results also suggest that similar physical processes influence the wet-day mean m on different timescales except for during winter, and that similar physical processes may explain the wet-day frequency on seasonal to annual time scales. The implication is that models calibrated on annual data samples in some cases can give skilful predictions on seasonal time scales.

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Section: Resultsmentioning

confidence: 99%

On downscaling probabilities for heavy 24-hour precipitation events at seasonal-to-decadal scales

Benestad

Mezghani

2015

Tellus A: Dynamic Meteorology and Oceanography

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“…Recent extreme flooding events have blocked roads for extended periods, affected houses, telecommunication, food supply, electricity, and access to hospitals and other medical aids. At least 27% of the public roads in Norway are vulnerable to avalanches and rockslides (Frauenfelder et al 2013). Future scenarios list further increases in flooding as a severe threat (Hanssen-Bauer et al 2017), notably along the coast where the topography is most rugged, rainfall highest, and infrastructure most vulnerable.…”

Section: B the Norway Casementioning

confidence: 99%

Buffering Climate Change with Nature

Hessen

Vandvik

2022

Weather, Climate, and Society

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It is increasingly evident that climate sustainability depends not only on societal actions and responses, but also on ecosystem functioning and responses. The capacity of global ecosystems to provide services such as sequestering carbon and regulating hydrology is being strongly reduced both by climate change itself and by unprecedented rates of ecosystem degradation. These services rely on functional aspects of ecosystems that are causally linked—the same ecosystem components that efficiently sequester and store carbon also regulate hydrology by sequestering and storing water. This means that climate change adaptation and mitigation must involve not only preparing for a future with temperature and precipitation anomalies, but also actively minimizing climate hazards and risks by conserving and managing ecosystems and their fundamental supporting and regulating ecosystem services. We summarize general climate–nature feedback processes relating to carbon and water cycling on a broad global scale before focusing on Norway to exemplify the crucial role of ecosystem regulatory services for both carbon sequestration and hydrological processes and the common neglect of this ecosystem–climate link in policy and landscape management. We argue that a key instrument for both climate change mitigation and adaptation policy is to take advantage of the climate buffering and regulative abilities of a well-functioning natural ecosystem. This will enable shared benefits to nature, climate, and human well-being. To meet the global climate and nature crises, we must capitalize on the importance of nature for buffering climate change effects, combat short-term perspectives and the discounting of future costs, and maintain or even strengthen whole-ecosystem functioning at the landscape level. Significance Statement Natural ecosystems such as forests, wetlands, and heaths are key for the cycling and storage of water and carbon. Preserving these systems is essential for climate mitigation and adaptation and will also secure biodiversity and associated ecosystem services. Systematic failure to recognize the links between nature and human well-being underlies the current trend of accelerating loss of nature and thereby nature’s ability to buffer climate changes and their impacts. Society needs a new perspective on spatial planning that values nature as a sink and store of carbon and a regulator of hydrological processes, as well as for its biodiversity. We need policies that fully encompass the role of nature in preventing climate-induced disasters, along with many other benefits for human well-being.

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“…The data used does not include the correction for undercatch due to the wind, and the relation between the precipitation and elevation is introduced only locally around the station locations. As a result, the predicted precipitation field may potentially underestimate the actual precipitation, especially at higher elevations where the station network is sparser such as at the mountainous region of the southern part of Norway (Frauenfelder et al, 2017;Lussana et al, 2018). Accordingly, we introduced a precipitation correction factor to take this into account so that the long-term water balance is correct.…”

Section: Model Parameters and Calibrationmentioning

confidence: 99%

Predicting hourly flows at ungauged small rural catchments using a parsimonious hydrological model

Tsegaw

Alfredsen

Skaugen

et al. 2019

Journal of Hydrology

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Streamflow data is important for studies of water resources and flood management, but an inherent problem is that many catchments of interest are ungauged. The lack of data is particularly the case for small catchments, where flow data with high temporal resolution is needed. This paper presents an analysis of regionalizing parameters of the Distance Distribution Dynamics (DDD) rainfall-runoff model for predicting hourly flows at small-ungauged rural catchments. The performance of the model with hourly time resolution has been evaluated (calibrated and validated) for 41 small gauged catchments in Norway (areas from 1 km 2 -50 km 2 ). The model parameters needing regionalization have been regionalized using three different methods: multiple regression, physical similarity (single-donor and pooling-group based methods), and a combination of the two methods. Seven independent catchments, which are not used in the evaluation, are used for validation of the regionalization methods. All the three methods (the multiple regression, pooling-group, and combined methods) perform satisfactorily (0.5 ≤ KGE < 0.75). The combined method (which combines multiple regression and pooling-group) performed slightly better than the other methods. Some model parameters, namely those describing recession characteristics, estimated by the regionalization methods, appear to be a better choice than those estimated locally from short period of hydro-meteorological data for some test catchments. The singledonor method did not perform satisfactorily. The satisfactory performance of the combined method shows that regionalization of DDD model parameters is possible by combining multiple regression and physical similarity methods.

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Impacts of extreme weather events on transport infrastructure in Norway

Cited by 4 publications

References 21 publications

On downscaling probabilities for heavy 24-hour precipitation events at seasonal-to-decadal scales

On downscaling probabilities for heavy 24-hour precipitation events at seasonal-to-decadal scales

Buffering Climate Change with Nature

Predicting hourly flows at ungauged small rural catchments using a parsimonious hydrological model

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