An assessment of best practices of extreme weather insurance and directions for a more resilient society

Extreme weather resilience has been defined as being based on three pillars: resistance (the ability to lower impacts), recovery (the ability to bounce back), and adaptive capacity (the ability to learn and improve). These resilience pillars are important both before and after the occurrence of extreme weather events. Extreme weather insurance can influence these pillars of resilience depending on how particular insurance mechanisms are structured. We explore how the lessons learnt from the current best insurance practices can improve resilience to extreme weather events. We employ an extensive inventory of private property and agricultural crop insurance mechanisms to conduct a multi-criteria analysis of insurance market outcomes. We draw conclusions regarding the patterns in the best practice from six European countries to increase resilience. We suggest that requirements to buy a bundle extreme weather event insurance with general insurance packages are strengthened and supported with structures to financing losses through public-private partnerships. Moreover, support for low income households through income vouchers could be provided. Similarly, for the agricultural sector we propose moving towards comprehensive crop yield insurance linked to general agricultural subsidies. In both cases a nationally representative body can coordinate the various stakeholders into acting in concert

A European aerosol phenomenology - 7 : High-time resolution chemical characteristics of submicron particulate matter across Europe

Similarities and differences in the submicron atmospheric aerosol chemical composition are analyzed from a unique set of measurements performed at 21 sites across Europe for at least one year. These sites are located between 35 and 62 degrees N and 10 degrees W - 26 degrees E, and represent various types of settings (remote, coastal, rural, industrial, urban). Measurements were all carried out on-line with a 30-min time resolution using mass spectroscopy based instruments known as Aerosol Chemical Speciation Monitors (ACSM) and Aerosol Mass Spectrometers (AMS) and following common measurement guidelines. Data regarding organics, sulfate, nitrate and ammonium concentrations, as well as the sum of them called non-refractory submicron aerosol mass concentration ([NR-PM1]) are discussed. NR-PM1 concentrations generally increase from remote to urban sites. They are mostly larger in the mid-latitude band than in southern and northern Europe. On average, organics account for the major part (36-64%) of NR-PM1 followed by sulfate (12-44%) and nitrate (6-35%). The annual mean chemical composition of NR-PM1 at rural (or regional background) sites and urban background sites are very similar. Considering rural and regional background sites only, nitrate contribution is higher and sulfate contribution is lower in midlatitude Europe compared to northern and southern Europe. Large seasonal variations in concentrations (mu g/m(3)) of one or more components of NR-PM1 can be observed at all sites, as well as in the chemical composition of NR-PM1 (%) at most sites. Significant diel cycles in the contribution to [NR-PM1] of organics, sulfate, and nitrate can be observed at a majority of sites both in winter and summer. Early morning minima in organics in concomitance with maxima in nitrate are common features at regional and urban background sites. Daily variations are much smaller at a number of coastal and rural sites. Looking at NR-PM1 chemical composition as a function of NR-PM1 mass concentration reveals that although organics account for the major fraction of NR-PM1 at all concentration levels at most sites, nitrate contribution generally increases with NR-PM1 mass concentration and predominates when NR-PM1 mass concentrations exceed 40 mu g/m(3) at half of the sites.Peer reviewe

A particle method strategy to estimate subsidence induced by a high-dimensional disc-strain model for reservoir compaction

This work is part of the "Subsidence" DeepNL project which aims to identify subsurface drivers of subsidence above the Groningen (the Netherlands) gas field and to forecast future subsidence. The hydrocarbon extraction in Groningen induces a pressure reduction in the gas reservoir which triggers compaction and land subsidence. This deep-subsurface process is modeled by a disc-shaped reservoir model, which is a superposition of individual nuclei of strain based on the Geertsma's approach. We estimate the surface deformation and the strength of the disc strain using a particle method. We apply the method to one single nucleus of strain at 3 km depth and extend to a disc-shape geometry. Synthetic experiments with a single nucleus of strain and with discs of varying sizes, 2.2 km to 13.3 km diameter, at 3 km depth are performed to assess the performance of the method for an increasing degree of complexity. Sequential Importance Resampling prevents the sample degeneracy when the number of nuclei increases. Adding a jitter noise in the resampling step avoids an impoverishment of the ensemble values. The results indicate that the method estimates the surface deformation and the strength for a large number of sources and for a relatively small effective ensemble size. In further investigations, localization can provide an additional means to deal with increasing dimensions and a relatively small ensemble size

An assessment of best practices of extreme weather insurance and directions for a more resilient society

Extreme weather resilience has been defined as being based on three pillars: resistance (the ability to lower impacts), recovery (the ability to bounce back), and adaptive capacity (the ability to learn and improve). These resilience pillars are important both before and after the occurrence of extreme weather events. Extreme weather insurance can influence these pillars of resilience depending on how particular insurance mechanisms are structured. We explore how the lessons learnt from the current best insurance practices can improve resilience to extreme weather events. We employ an extensive inventory of private property and agricultural crop insurance mechanisms to conduct a multi-criteria analysis of insurance market outcomes. We draw conclusions regarding the patterns in the best practice from six European countries to increase resilience. We suggest that requirements to buy a bundle extreme weather event insurance with general insurance packages are strengthened and supported with structures to financing losses through public-private partnerships. Moreover, support for low income households through income vouchers could be provided. Similarly, for the agricultural sector we propose moving towards comprehensive crop yield insurance linked to general agricultural subsidies. In both cases a nationally representative body can coordinate the various stakeholders into acting in concert

From one- to two-phase sampling to reduce costs of remote sensing-based estimation of land-cover and land-use proportions and their changes

The estimation of the proportion of land-cover and land-use classes is considered by exploiting remote sensing-based imagery. A pure-panel survey based on point sampling is adopted. An initial sample of points is selected by means of tessellation stratified sampling (TSS). The sample points are classified based on the imagery available for the years of interest to estimate land-cover or land-use proportions and their changes. To reduce the sampling effort, the initial selection of points is viewed as the first phase of sampling and a subsample of these points is selected in a second phase by means of one-per-stratum stratified sampling (OPSS). Land-use estimation at any subsequent year is then based on the classifications performed on the points of the second-phase sample. Two-phase estimators of proportions and of their changes are suggested, and their theoretical properties are derived. Presumably conservative estimators of their variances are proposed. A check of the precision lost involved when changing from one- to two-phase sampling is determined from the assessment of land use in Italy as a case study and from an artificial population generated to resemble the current land-use situation in Italy