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

2017 ◽  
Vol 17 (4) ◽  
pp. 515-531 ◽  
Author(s):  
Matthias Schlögl ◽  
Gregor Laaha
Keyword(s):  
Extreme Events ◽  
Information Gain ◽  
Extreme Weather ◽  
Extreme Value ◽  
Added Value ◽  
Extreme Value Statistics ◽  
Extreme Weather Events ◽  
Mean Residual Life ◽  
Data Set ◽  
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 (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.

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

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.

scholarly journals Impacts of compound extreme weather events on ozone in the present and future

2018 ◽  
Author(s):  
Junxi Zhang ◽  
Yang Gao ◽  
Kun Luo ◽  
L. Ruby Leung ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Extreme Events ◽  
Heat Waves ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Low Wind Speed ◽  
Weather Events ◽  
The Us ◽  
The Future ◽  
Compound Events ◽  
Ozone Episodes

Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) was used to study the effect of extreme weather events on ozone in US for historical (2001–2010) and future (2046–2055) periods under RCP8.5 scenario. During extreme weather events, including heat waves, atmospheric stagnation, and their compound events, ozone concentration is much higher compared to non-extreme events period. A striking enhancement of effect during compound events is revealed when heat wave and stagnation occur simultaneously and both high temperature and low wind speed promote the production of high ozone concentrations. In regions with high emissions, compound extreme events can shift the high-end tails of the probability density functions (PDFs) of ozone to even higher values to generate extreme ozone episodes. In regions with low emissions, extreme events can still increase high ozone frequency but the high-end tails of the PDFs are constrained by the low emissions. Despite large anthropogenic emission reduction projected for the future, compound events increase ozone more than the single events by 10 % to 13 %, comparable to the present, and high ozone episodes are not eliminated. Using the CMIP5 multi-model ensemble, the frequency of compound events is found to increase more dominantly compared to the increased frequency of single events in the future over the US, Europe, and China. High ozone episodes will likely continue in the future due to increases in both frequency and intensity of extreme events, despite reductions in anthropogenic emissions of its precursors. However, the latter could reduce or eliminate extreme ozone episodes, so improving projections of compound events and their impacts on extreme ozone may better constrain future projections of extreme ozone episodes that have detrimental effects on human health.

scholarly journals Extreme events in total ozone over Arosa – Part 1: Application of extreme value theory

2010 ◽  
Vol 10 (20) ◽  
pp. 10021-10031 ◽  
Author(s):  
H. E. Rieder ◽  
J. Staehelin ◽  
J. A. Maeder ◽  
T. Peter ◽  
M. Ribatet ◽  
...  
Keyword(s):  
Time Series ◽  
Frequency Distribution ◽  
Extreme Events ◽  
Extreme Value Theory ◽  
Total Ozone ◽  
Volcanic Eruptions ◽  
Value Theory ◽  
Extreme Value ◽  
Data Set ◽  
Ozone Data

Abstract. In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not adequately address the structure of the extremes. We show that statistical extreme value methods are appropriate to identify ozone extremes and to describe the tails of the Arosa (Switzerland) total ozone time series. In order to accommodate the seasonal cycle in total ozone, a daily moving threshold was determined and used, with tools from extreme value theory, to analyse the frequency of days with extreme low (termed ELOs) and high (termed EHOs) total ozone at Arosa. The analysis shows that the Generalized Pareto Distribution (GPD) provides an appropriate model for the frequency distribution of total ozone above or below a mathematically well-defined threshold, thus providing a statistical description of ELOs and EHOs. The results show an increase in ELOs and a decrease in EHOs during the last decades. The fitted model represents the tails of the total ozone data set with high accuracy over the entire range (including absolute monthly minima and maxima), and enables a precise computation of the frequency distribution of ozone mini-holes (using constant thresholds). Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and major volcanic eruptions, can be identified in the observed frequency of extreme events throughout the time series. Overall the new approach to analysis of extremes provides more information on time series properties and variability than previous approaches that use only monthly averages and/or mini-holes and mini-highs.

Sea State Statistics and Extreme Waves Observed by Satellite

2008 ◽  
Author(s):  
S. Lehner ◽  
J. Schulz-Stellenfleth ◽  
Thomas Ko¨nig ◽  
X. Li
Keyword(s):  
Extreme Events ◽  
Wave Spectrum ◽  
Ocean Waves ◽  
Wave Mode ◽  
Extreme Value Statistics ◽  
Altimeter Data ◽  
Sea State ◽  
Data Set ◽  
Wave Properties ◽  
D Wave

For the design of ships as well as for the investigation of ship accidents it is important to have knowledge about both the two dimensional spectral wave properties as well as extreme value statistics of ocean waves. Although numerical wave models have reached a high level of accuracy, they still have weaknesses with respect to the details of the 2-D wave spectrum. Furthermore standard models like WAM provide estimates of the 2-D wave spectrum, i.e., second order sea state statistics and therefore lack information on individual wave properties and the occurrence of extreme events. In this study the potential of global Synthetic Aperture Radar (SAR) wave mode data acquired by the European satellites ERS-2 and ENVISAT to investigate ship accidents is discussed and compared to altimeter data and ECMWF model results. These data are acquired independent of light and weather conditions on a global scale. A historic data set of ERS-2 wave mode data acquired between 1998 and 2000 is co-located with accidents which occurred during that time. ENVISAT ASAR wave mode data acquired since 2002 are considered, too. Different ocean wave parameters like significant wave height and wave periods are derived from the SAR data. The potential role of the respective wave conditions for some recent accident is discussed in detail. This includes in particular the analysis of cross sea conditions, groupiness and extreme events.

scholarly journals Assessing the impacts of extreme weather events on potable water quality: the value to managers of a highly participatory, integrated modelling approach

2018 ◽  
Vol 2 (1) ◽  
pp. 9-24
Author(s):  
Edoardo Bertone ◽  
Oz Sahin ◽  
Russell Richards ◽  
Anne Roiko
Keyword(s):  
Water Quality ◽  
Decision Support ◽  
Extreme Events ◽  
Drinking Water Treatment ◽  
Extreme Weather ◽  
Integrated Modelling ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Sd Model

Abstract A decision support tool was created to estimate the treatment efficiency of an Australian drinking water treatment system based on different combinations of extreme weather events and long-term changes. To deal with uncertainties, missing data, and nonlinear behaviours, a Bayesian network (BN) was coupled with a system dynamics (SD) model. The preliminary conceptual structures of these models were developed through stakeholders' consultation. The BN model could rank extreme events, and combinations of them, based on the severity of their impact on health-related water quality. The SD model, in turn, was used to run a long-term estimation of extreme events' impacts by including temporal factors such as increased water demand and customer feedback. The integration of the two models was performed through a combined Monte Carlo–fuzzy logic approach which allowed to take the BN's outputs as inputs for the SD model. The final product is a participatory, multidisciplinary decision support system allowing for robust, sustainable long-term water resources management under uncertain conditions for a specific location.

Anthropogenic land cover change impact on climate extremes during the 21st century

2020 ◽  
Author(s):  
Benjamin Quesada ◽  
Souleymane Sy
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Extreme Events ◽  
Regional Scale ◽  
Extreme Weather ◽  
Northeastern Brazil ◽  
Eastern Africa ◽  
Extreme Weather Events ◽  
Rainfall Extremes ◽  
Global Mean

<p>Beyond global mean temperatures, anthropogenic land cover change (LCC) can have significant impacts at regional and seasonal scales but also on extreme weather events to which human, natural and economical systems are highly vulnerable. However, the effects of LCC on extreme events remain either largely unexplored at global and regional scale and/or without consensus. Here, using several Earth System Models under two different LCC scenarios (the RCP8.5 and RCP2.6 Representative Concentration Pathways) and analyzing 20 extreme weather indices, we find future LCC substantially modulates projected weather extremes particularly at regional level.</p><p>On average by the end of the 21<sup>st</sup> century, under RCP8.5 and RCP2.6 scenarios, future LCC robustly lessens global projections of high rainfall extremes. Accounting for LCC diminishes regional projections of heavy precipitation days or consecutive wet days by more than 50% in southern Africa or northeastern Brazil but intensifies projected dry days in eastern Africa by 30%. LCC do not substantially affect projections of global and regional temperature extremes projections (<5%), but it can impact global rainfall extremes 2.5 times more than global mean rainfall projections.</p><p>Under RCP2.6 scenario, global LCC impacts are similar but of lesser magnitude while at regional scale in Amazon or Asia, LCC enhances drought projections. We investigate the underlying biophysical drivers behind those projected changes.</p><p>We stress here that multi-coupled modelling frameworks incorporating all aspects of land use-land cover change and more model-data benchmarking are needed for reliable projections of extreme events.</p><div> <div> </div> </div>

scholarly journals Extreme weather events and high Colombian food prices: A non-stationary extreme value approach

2021 ◽  
Author(s):  
Luis Fernando Melo-Velandia ◽  
Camilo Andrés Orozco-Vanegas ◽  
Daniel Parra-Amado
Keyword(s):  
Weather Conditions ◽  
Extreme Weather ◽  
Extreme Value ◽  
Food Prices ◽  
Extreme Weather Events ◽  
Processed Foods ◽  
Fuel Prices ◽  
Weather Events ◽  
High Precipitation ◽  
Extreme Value Model

Given the importance of climate change and the increase of its severity under extreme weather events, we analyze the main drivers of high food prices in Colombia between 1985 and 2020 focusing on extreme weather shocks like a strong El Ni˜no.We estimate a non-stationary extreme value model for Colombian food prices. Our findings suggest that perishable foods are more exposed to extreme weather conditions in comparison to processed foods. In fact, an extremely low precipitation level explains only high prices in perishable foods. The risk of high perishable food prices is significantly larger for low rainfall levels (dry seasons) compared to high precipitation levels (rainy seasons). This risk gradually results in higher perishable food prices. It is non linear and is also significantly larger than the risk related to changes in the US dollar-Colombian peso exchange rate and fuel prices. Those covariates also explain high prices for both perishable and processed foods. Finally, we find that the events associated with the strongest El Ni˜no in 1988 and 2016 are expected to reoccur once every 50 years.

scholarly journals Extreme Weather Events and Its Impacts on Rice Production in Coastal Odisha Region of India

2021 ◽  
Author(s):  
S Vijayakumar ◽  
A.K. Nayak ◽  
N. Manikandan ◽  
Suchismita Pattanaik ◽  
Rahul Tripathi ◽  
...  
Keyword(s):  
Grain Yield ◽  
Extreme Events ◽  
Heavy Precipitation ◽  
Extreme Weather ◽  
Significant Trend ◽  
Extreme Weather Events ◽  
Positive Trend ◽  
Rice Grain ◽  
Weather Events ◽  
Precipitation And Temperature

Abstract The study investigates trend in extreme daily precipitation and temperature over coastal Odisha, India. 18 weather indices (8 related to temperature and 10 related to rainfall) were calculated using RClimDex software package for the period 1980–2010 . Trend analysis was carried out using linear regression and non-parametric Mann-Kendall test to find out the statistical significance of various indices. Results indicated, a strong and significant trend in temperature indices while the weak and non-significant trend in precipitation indices. The positive trend in Tmax mean, Tmin mean, TN90p (warm nights), TX90p (warm days), diurnal temperature range (DTR), warm spell duration indicator (WSDI), consecutive dry days (CDD) indicates increasing the frequency of warming events in coastal Odisha. Similarly, positive trend in highest maximum 1-day precipitation (RX1), highest maximum 2 consecutive day precipitation (RX2), highest maximum 3 consecutive day precipitation (RX3), highest maximum 5 consecutive day precipitation (RX5), number of heavy precipitation days (≥64.5mm), number of very heavy precipitation days (≥124.5mm) and negative trend in the number of rainy days (R2.5mm), consecutive wet days (CWD) indicate changes toward the more intense and poor distribution of precipitation in coastal Odisha. The combined effect of precipitation and temperature extreme events showed negative effects on rice grain yield. With the increasing number of extreme events there was sharp decline in rice grain yield was observed in the same year in all the coastal districts. This study emphasizes the need for new technology/management practice to minimize the impacts of extreme weather events on rice yield.

scholarly journals Awareness of climate change's impacts and motivation to adapt are not enough to drive action: A look of Puerto Rican farmers after Hurricane Maria

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244512
Author(s):  
Luis Alexis Rodríguez-Cruz ◽  
Meredith T. Niles
Keyword(s):  
Climate Change ◽  
Puerto Rican ◽  
Adaptive Capacity ◽  
Extreme Events ◽  
Structural Equation ◽  
Climate Adaptation ◽  
Psychological Distance ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Events

Understanding how perceptions around motivation, capacity, and climate change’s impacts relate to the adoption of adaptation practices in light of experiences with extreme weather events is important in assessing farmers’ adaptive capacity. However, very little of this work has occurred in islands, which may have different vulnerabilities and capacities for adaptation. Data of surveyed farmers throughout Puerto Rico after Hurricane Maria (n = 405, 87% response rate) were used in a structural equation model to explore the extent to which their adoption of agricultural practices and management strategies was driven by perceptions of motivation, vulnerability, and capacity as a function of their psychological distance of climate change. Our results show that half of farmers did not adopt any practice or strategy, even though the majority perceived themselves capable and motivated to adapt to climate change, and understood their farms to be vulnerable to future extreme events. Furthermore, adoption was neither linked to these adaptation perceptions, nor to their psychological distance of climate change, which we found to be both near and far. Puerto Rican farmers’ showed a broad awareness of climate change’s impacts both locally and globally in different dimensions (temporal, spatial, and social), and climate distance was not linked to reported damages from Hurricane Maria or to previous extreme weather events. These results suggest that we may be reaching a tipping point for extreme events as a driver for climate belief and action, especially in places where there is a high level of climate change awareness and continued experience of compounded impacts. Further, high perceived capacity and motivation are not linked to actual adaptation behaviors, suggesting that broadening adaptation analyses beyond individual perceptions and capacities as drivers of climate adaptation may give us a better understanding of the determinants to strengthen farmers’ adaptive capacity.

Extreme Weather and Climate

2017 ◽  
Author(s):  
Friederike Otto
Keyword(s):  
Extreme Events ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Event ◽  
Weather Forecasts ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Weather Events ◽  
Global Mean

Natural disasters and extreme weather events have been of great societal importance throughout history and often brought everyday life to a catastrophic halt, in a way sometimes comparable to wars and epidemics, only without the lead time. Extreme weather events with large impacts serve as an anchor point of the collective memory of the population in the affected area. Every northern German of the right age remembers the storm surge of 1962 and where they were at the time and has friends or family effected by the event. The “dust bowl” of the 1930s with extensive droughts and heat waves shaped the life of a generation in the United States, and the Sahel droughts in the 1960s and 1970s led to famine and dislocation of population on a massive scale the region arguably never quite recovered from. Hurricane Hyian in 2013 is said to have directly influenced the outcome of the annual Conference of the Parties (COP) United Nation Framework Convention for Climate Change Negotiations in Warsaw, leading to the inclusion of a mechanism to deal with loss and damage from climate-related disasters. Though earthquakes are still fairly unpredictable on short timescales, this is not the case for weather events. Weather forecasts today are so good that we normally know the time and location of the landfall of a hurricane within a 100-mile radius days in advance. Improvements in the prediction of slow-onset events such as droughts (which depend on the rainfall over a large region and whole season) are less striking but have still improved dramatically in the late 20th and early 21st centuries. One of the major reasons for the large increase in the accuracy of weather forecasts is the exponential increase in computing power, which allows scientists to predict and study extreme weather events using complex computer models, simulating possible weather events under certain conditions to understand the statistics of and physical mechanisms behind extreme events. Extreme events are by definition rare and thus impossible to understand from historical records of weather observation alone. Despite the progress on our understanding of and ability to predict extreme weather events, substantial uncertainties remain. Two aspects are of particular importance. Firstly, we know that the climate is changing, having observed almost a one-degree increase in global mean temperature. However, global mean temperature doesn’t kill anyone, extreme weather events do. Their frequency and intensity is changing and will continue to change, but the extent of these changes depends on a host of both global and local factors. Secondly, whether or not a rare weather event leads to extreme impacts depends largely on the vulnerability and exposure of the affected societies. If these are high, even a perfectly forecasted weather event leads to disaster.

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