Risk of Return Levels for Spatial Extreme Events

Abstract. The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series (PDS) over the standardly used annual maxima series (AMS) in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing PDS) being superior to the block maxima approach (employing AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was visible from neither the square-root criterion nor standardly used graphical diagnosis (mean residual life plot) but rather from a direct comparison of AMS and PDS in combined quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best-suited approach. This will make the analyses more robust, not only in cases where threshold selection and dependency introduces biases to the PDS approach but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend the use of conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

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Local-scale uncertainty of seasonal mean and extreme values of in-situ snow depth and snow fall measurements

10.5194/tc-2021-125 ◽

2021 ◽

Author(s):

Moritz Buchmann ◽

Michael Begert ◽

Stefan Brönnimann ◽

Christoph Marty

Keyword(s):

Extreme Events ◽

Snow Depth ◽

Extreme Values ◽

Local Scale ◽

Swiss Alps ◽

Scale Uncertainty ◽

Return Levels ◽

Maximum Snow Depth ◽

Climate Indicators ◽

The Impact

Abstract. Measurements of snow depth and snowfall on the daily scale can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are mostly located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall as well as snow onset and disappearance dates, which are calculated for various seasons (December to February (DFJ), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential uncertainty. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA which show median uncertainties of less than 5 % for all indicators. Local-scale uncertainty ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. Highest (lowest) percentage of station pairs with uncertainty less than 15 % is observed for days with snow on the ground with 90 % (average snow depth, 30 %). Median differences of snow disappearance dates are rather small (three days) and similar to the ones found for snow onset dates (two days). An analysis of potential sunshine duration could not explain the higher uncertainties in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3-day new snow sum, which are often used for prevention measures. The found return levels are within each other’s 95 % confidence intervals for all (but two) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and extremal indices. Knowledge about such uncertainties in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

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Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

10.5194/nhess-2016-373 ◽

2016 ◽

Author(s):

Matthias Schlögl ◽

Gregor Laaha

Keyword(s):

Extreme Events ◽

Extreme Weather ◽

Extreme Value ◽

Added Value ◽

Extreme Value Statistics ◽

Extreme Weather Events ◽

Mean Residual Life ◽

Data Set ◽

Partial Duration Series ◽

Return Levels

Abstract. The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series over the standardly used annual maxima series in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing partial duration series, PDS) being superior to the block maxima approach (employing annual maxima series, AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behaviour was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was neither visible from the square-root criterion, nor from standardly used graphical diagnosis (mean residual life plot), but from a direct comparison of AMS and PDS in synoptic quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best suited approach. This will make the analyses more robust, in cases where threshold selection and dependency introduces biases to the PDS approach, but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management, but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

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Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements

The Cryosphere ◽

10.5194/tc-15-4625-2021 ◽

2021 ◽

Vol 15 (10) ◽

pp. 4625-4636

Author(s):

Moritz Buchmann ◽

Michael Begert ◽

Stefan Brönnimann ◽

Christoph Marty

Keyword(s):

Extreme Events ◽

Snow Depth ◽

Extreme Values ◽

Local Scale ◽

Swiss Alps ◽

Detection Methods ◽

Return Levels ◽

Maximum Snow Depth ◽

Climate Indicators ◽

The Impact

Abstract. Daily measurements of snow depth and snowfall can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are usually located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall, and snow onset and disappearance dates, which are calculated for various seasons (December to February (DJF), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential variability. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA, which show median variations of less than 5 % for all indicators. Local-scale variability ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. The highest percentage (90 %) of station pairs with variability of less than 15 % is observed for days with snow on the ground. The lowest percentage (30 %) of station pairs with variability of less than 15 % is observed for average snow depth. Median differences of snow disappearance dates are rather small (3 d) and similar to the ones found for snow onset dates (2 d). An analysis of potential sunshine duration could not explain the higher variabilities in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3 d new snow sum, which are often used for avalanche prevention measures. The found return levels are within each other's 95 % confidence intervals for all (but three) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of variabilities of commonly used snow indicators and extremal indices. Knowledge about such variabilities in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

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The Value of High-Resolution Met Office Regional Climate Models in the Simulation of Multihourly Precipitation Extremes

Journal of Climate ◽

10.1175/jcli-d-13-00723.1 ◽

2014 ◽

Vol 27 (16) ◽

pp. 6155-6174 ◽

Cited By ~ 93

Author(s):

Steven C. Chan ◽

Elizabeth J. Kendon ◽

Hayley J. Fowler ◽

Stephen Blenkinsop ◽

Nigel M. Roberts ◽

...

Keyword(s):

High Resolution ◽

Time Scales ◽

Return Period ◽

Extreme Events ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Value Theory ◽

Precipitation Extremes ◽

Return Levels

Abstract Extreme value theory is used as a diagnostic for two high-resolution (12-km parameterized convection and 1.5-km explicit convection) Met Office regional climate model (RCM) simulations. On subdaily time scales, the 12-km simulation has weaker June–August (JJA) short-return-period return levels than the 1.5-km RCM, yet the 12-km RCM has overly large high return levels. Comparisons with observations indicate that the 1.5-km RCM is more successful than the 12-km RCM in representing (multi)hourly JJA very extreme events. As accumulation periods increase toward daily time scales, the erroneous 12-km precipitation extremes become more comparable with the observations and the 1.5-km RCM. The 12-km RCM fails to capture the observed low sensitivity of the growth rate to accumulation period changes, which is successfully captured by the 1.5-km RCM. Both simulations have comparable December–February (DJF) extremes, but the DJF extremes are generally weaker than in JJA at daily or shorter time scales. Case studies indicate that “gridpoint storms” are one of the causes of unrealistic very extreme events in the 12-km RCM. Caution is needed in interpreting the realism of 12-km RCM JJA extremes, including short-return-period events, which have return values closer to observations. There is clear evidence that the 1.5-km RCM has a higher degree of realism than the 12-km RCM in the simulation of JJA extremes.

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