A cold-health watch and warning system, applied to the province of Quebec (Canada).

CONTEXT: A number of studies have shown that cold has an important impact on human health. However, almost no studies focused on cold warning systems to prevent those health effects. For Nordic regions, like the province of Quebec in Canada, winter is long and usually very cold with an observed increase in mortality and hospitalizations throughout the season. However, there is no existing system specifically designed to follow in real-time this mortality increase throughout the season and to alert public health authorities prior to cold waves. OBJECTIVE: The aim is to establish a watch and warning system specifically for health impacts of cold, applied to different climatic regions of the province of Quebec. METHODOLOGY: A methodology previously used to establish the health-heat warning system in Quebec is adapted to cold. The approach identifies cold weather indicators and establishes thresholds related to extreme over-mortality or over-hospitalization events in the province of Quebec, Canada. RESULTS AND CONCLUSION: The final health-related thresholds proposed are between (-15 °C, -23 °C) and (-20 °C, -29 °C) according to the climatic region for excesses of mortality, and between (-13 °C, -23 °C) and (-17 °C, -30 °C) for excesses of hospitalization. These results suggest that the system model has a high sensitivity and an acceptable number of false alarms. This could lead to the establishment of a cold-health watch and warning system with valid indicators and thresholds for each climatic region of Quebec. It can be seen as a complementary system to the existing one for heat warnings, in order to help the public health authorities to be well prepared during an extreme cold event

Regional frequency analysis of droughts: portuguese case

Poster apresentado WCRP (World Climate Research programme)- Workshop on Drought Predictability and Prediction in a Changing Climate: Assessing Current Knowledge and Capabilities, User Requirements and Research Priorities, 2-4 Março 2011, Barcelona (Spain).[Poster introduction] A common problem in drought risk analysis relates to the assessment of the rarity of the events, such as long duration droughts or high magnitude droughts. Being a frequent phenomena in the Southern Europe and in others regions of the world drought constitute a primary natural hazard for human activities. For this reason, and for an improved drought risk management, the preparation of drought hazard maps is an important and urgent task. The drought definition based on deviations from normal conditions or from reference stages implies that droughts can occur in any hydroclimatological region and at any time of the year with the same probability. In order to due so, a large number of quantitative drought characteristics must been considered. Two common approaches to select extreme events from a drought index time series were analyzed: the annual maximum series (AMS) and the partial duration series (PDS) approaches

Software description: Regional frequency analysis of climate variables

This document provides the technical description of a software to be developed in the context of the EUROCLIMA project. EUROCLIMA is a cooperation program between the European Union and Latin America with a special focus in knowledge sharing on topics related to socio-environmental problems associated with climate change. The objective of the project is to improve knowledge of Latin American decision-makers and the scientific community on problems and consequences of climate change, particularly in view of integrating these issues into sustainable development strategies. The software described in this document will have as a general objective to process time series of data from ground stations (initially precipitation and temperature) in order to generate products in the form of spatially-explicit maps. The software will also be able to process any other time series of environmental spatial data. The main aspect characterizing this software is the use of statistics called L-moments to estimate the probability distribution function of climate variables. The L-moments are similar to other statistical moments, but with the advantage of being less susceptible to the presence of outliers and performing better with smaller sample sizes.JRC.H.5-Land Resources Managemen

Effects of inertia distribution on regional frequency heterogeneity

Heterogeneous inertia distribution can result in large regional frequency deviations and inter-area oscillations that exceed protection limits configured based on system-wide averaged performance. This paper examines how the spatial distribution of inertia affects frequency heterogeneity. Along with varying inertia distribution, variations in generator turbine-governor control and network topology are studied to make the results more generalisable across wide-ranging operating conditions. An investigation into the effects of different fast frequency response (FFR) schemes on frequency heterogeneity is also presented. The frequency heterogeneity is quantified by calculating cosine similarity between regional frequency trajectories. The key results are obtained using a two-area model and verified using a mixed AC/DC power system. A key finding is that the localness of regional frequency is independent of the inertia of a specific area, nor of the total system inertia. The inertia ratio, described as the ratio of the disturbance area inertia to that of the non-disturbance area, is shown to have a strong correlation with frequency heterogeneity. This correlation is shown to be very robust to changes in generator dynamics and network topology. Providing derivative FFR within the disturbance area always demonstrates benefits regarding frequency heterogeneity inhibition, whereas droop scheme typicallyintroduces deterioration in frequency heterogeneity

Regional frequency analysis of extreme precipitation for Sicily (Italy)

The analysis of extreme precipitation has always been included among most relevant hydrological applications because of the several important activities linked to the availability of tools for the estimation of extreme rainfall quantiles. These activities include the design of hydraulic civil structures and the evaluation and management of hydraulic and hydrological risk. In this study a frequency analysis of annual maxima precipitation measurements has been carried out for the area of Sicily (Italy). A typical hierarchical regional approach has been adopted for the parameter estimation procedure based on the L-moments method. The identification of homogeneous regions within the procedure has been pursued with a data driven procedure constituted by a principal component analysis of an ensemble of selected auxiliary variables, and a K-means cluster analysis algorithm. Auxiliary variables comprise meteo-climatic information and a representation of the average seasonal distribution of intense events. Results have been evaluated by means of a Monte Carlo experiment based on the comparison between at-site and regional fitted frequency distributions. Moreover, results have been compared with previous analyses performed for the same area. The study provides an updated tool for the modelling of extreme precipitation for the area of Sicily (Italy), with different features respect to previous tools both in terms of definition of homogeneous zones and in terms of parameters of the frequency distribution. Meteo-climatic information and the seasonality of extreme events retrieved from the dataset has been proficuously exploited in the analysis

Regional Frequency Analysis of Rainfall, using L-Moment Method, as A Design Rainfall Prediction

Frequency analysis is a method for predicting the probability of future hydrological events, based on historical data. Generally, frequency analysis of rainfall data and discharge data is performed using the moment method, but this method has a large bias, variant, and slope, thus there is a possibility of producing inaccurate hydrological design magnitudes. Meanwhile, the L-moment method is a linear combination of Probability Weighted Moment, with the ability to process data concisely and linearly. This study was therefore conducted to discover the L-moment method’s capacity to obtain a regional probability distribution and design rainfall, used as a basis for calculating hydrological planning, in anticipation of disasters. The study location, Mount Merapi, was selected to enable a more accurate prediction of maximum rainfall with the capacity to cause cold lava in the area, and consequently, reduce the risk of loss for people living within close proximity. According to the results, the L-moment regional ratio results were τ2R = 0.203, τ3R = 0.166, and τ4R = 0.169. The homogeneity and heterogeneity tests show all rainfall stations are uniform or homogeneous, and no data were released from the discordance test results. Also, the growth factor value increases in each return period design rainfall prediction. In this study, the suitable regional probability distribution for the research area is the Generalized Logistic distribution with formulated design rainfall equation. Regional design rainfall is able to predict possible rainfall within the area. The Test model showed the minimum RBias = 0.45%, maximum RBias = 41.583%, minimum RRSME = 0.45%, and maximum RRSME = 71.01%. Meanwhile, the L-moment method’s stability was shown by the model test minimum error = 1.64% and maximum error = 16.60%. The higher error value in the higher return period shows L-moment is able to reduce bias data, however, this has limitations in the higher return period

On the method of probability weighted moments in regional frequency analysis

In regional flood frequency analysis it is of interest to estimate high quantiles of a local river flow distribution by gathering information from similar stations in the neighborhood. E. g., the popular Index Flood (IF) approach is based on an assumption termed regional homogeneity, which states that the quantile curves of those stations only differ by a site-specific factor, the so-called index flood, and it is assumed that the station's distribution is known up to some finite-dimensional parameter. In this context the method of probability weighted moments (or equivalently L-moments) is most popular for parameter estimation. While the observations often can be regarded as independent in time, a challenge arises from the fact that river flows from nearby stations are strongly dependent in space. To the best of our knowledge, none of the approaches from the literature based on the IF-model and on L-moments is able to take spatial dependence adequately into account. Our goal is to fill this gap. We present asymptotic theory that does not ignore inter-site dependence, which, for instance, allows to evaluate estimation uncertainty. As an application of this theory, a test procedure to check for regional homogeneity under index-flood assumptions is given and reviewed in a simulation study

Modelling intersite dependence for regional frequency analysis of extreme marine events

The duration of observation at a site of interest is generally too low to reliably estimate marine extremes. Regional frequency analysis (RFA), by exploiting the similarity between sites, can help to reduce uncertainties inherent to local analyses. Extreme observations in a homogeneous region are especially assumed to follow a common regional distribution, up to a local index. The regional pooling method, by gathering observations from different sites into a regional sample, can be employed to estimate the regional distribution. However, such a procedure may be highly affected by intersite dependence in the regional sample. This paper derives a theoretical model of intersite dependence, dedicated to the regional pooling method in a "peaks over threshold" framework. This model expresses the tendency of sites to display a similar behavior during a storm generating extreme observations, by describing both the storm propagation in the region and the storm intensity. The proposed model allows the assessment of i) the regional effective duration of the regional sample and ii) different regional hazards, e.g., return periods of storms. An application to the estimation of extreme significant wave heights from the numerical sea-state database ANEMOC-2 is provided, where different patterns of regional dependence are highlighted