Ilmastonmuutoksen ääri-ilmiöihin liittyvän riskienhallinnan kustannus–hyötyanalyysi osana julkista päätöksentekoa. IRTORISKI-hankkeen loppuraportti.

IRTORISKI-hankkeessa tutkittiin, miten kustannus–hyötyanalyysin käyttöä ilmastonmuutoksen sopeutumissuunnittelussa voitaisiin helpottaa niin, että sitä pystyttäisiin hyödyntämään kustannustehokkaasti sekä ilmastonmuutokseen liittyvien vaarojen priorisoinnissa että ennaltaehkäisevien toimenpiteiden vertailussa. Tutkimuksessa käytettiin esimerkkitapauksina jokitulvaa ja rankkasateiden aiheuttamaa tulvaa kaupunkiolosuhteissa. Tapahtumapuuanalyysia laajennettiin siten, että siitä käyvät ilmi sekä suorat vahingot että lopulliset makrotaloudelliset vaikutukset. Arviot suorista taloudellisista vahingoista perustuivat aikaisempiin tutkimuksiin, kun taas makrotaloudellisia vaikutuksia simuloitiin yleisen tasapainon mallin avulla. Tapaustutkimusten valinnasta, tapahtumapuun käytöstä, sen laajennusosasta sekä lasketuista makrotaloudellisista vaikutuksista keskusteltiin sidosryhmien edustajien kanssa kolmessa asiantuntijaistunnossa.

Constraining large earthquakes along the Andaman trench using deepwater turbidites: prospects and challenges

The 2004 Sumatra-Andaman earthquake was unprecedented in terms of its magnitude (M-w 9.2), rupture length along the plate boundary (1300 km) and size of the resultant tsunami. Since 2004, efforts are being made to improve the understanding of the seismic hazard in the Sumatra-Andaman subduction zone in terms of recurrence patterns of major earthquakes and tsunamis. It is reasonable to assume that previous earthquake events in the Myanmar Andaman segment must be preserved in the geological record in the form of seismo-turbidite sequences. Here we present the prospects of conducting deep ocean palaeoseismicity investigations in order to refine the quantification of the recurrence pattern of large subduction-zone earthquakes along the Andaman-Myanmar arc. Our participation in the Sagar Kanya cruise SK-273 (in June 2010) was to test the efficacy of such a survey. The primary mission of the cruise, along a short length (300 km) of the Sumatra Andaman subduction front was to collect bathymetric data of the ocean floor trenchward of the Andaman Islands. The agenda of our piggyback survey was to fix potential coring sites that might preserve seismo-turbidite deposits. In this article we present the possibilities and challenges of such an exercise and our first-hand experience of such a preliminary survey. This account will help future researchers with similar scientific objectives who would want to survey the deep ocean archives of this region for evidence of extreme events like major earthquakes.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Nonlinear preconditioning for efficient and accurate interface capturing in simulation of multicomponent compressible flows

Single fluid schemes that rely on an interface function for phase identification in multicomponent compressible flows are widely used to study hydrodynamic flow phenomena in several diverse applications. Simulations based on standard numerical implementation of these schemes suffer from an artificial increase in the width of the interface function owing to the numerical dissipation introduced by an upwind discretization of the governing equations. In addition, monotonicity requirements which ensure that the sharp interface function remains bounded at all times necessitate use of low-order accurate discretization strategies. This results in a significant reduction in accuracy along with a loss of intricate flow features. In this paper we develop a nonlinear transformation based interface capturing method which achieves superior accuracy without compromising the simplicity, computational efficiency and robustness of the original flow solver. A nonlinear map from the signed distance function to the sigmoid type interface function is used to effectively couple a standard single fluid shock and interface capturing scheme with a high-order accurate constrained level set reinitialization method in a way that allows for oscillation-free transport of the sharp material interface. Imposition of a maximum principle, which ensures that the multidimensional preconditioned interface capturing method does not produce new maxima or minima even in the extreme events of interface merger or breakup, allows for an explicit determination of the interface thickness in terms of the grid spacing. A narrow band method is formulated in order to localize computations pertinent to the preconditioned interface capturing method. Numerical tests in one dimension reveal a significant improvement in accuracy and convergence; in stark contrast to the conventional scheme, the proposed method retains its accuracy and convergence characteristics in a shifted reference frame. Results from the test cases in two dimensions show that the nonlinear transformation based interface capturing method outperforms both the conventional method and an interface capturing method without nonlinear transformation in resolving intricate flow features such as sheet jetting in the shock-induced cavity collapse. The ability of the proposed method in accounting for the gravitational and surface tension forces besides compressibility is demonstrated through a model fully three-dimensional problem concerning droplet splash and formation of a crownlike feature. (C) 2014 Elsevier Inc. All rights reserved.

Flux Transport Dynamos: From Kinematics to Dynamics

Over the past several decades, Flux-Transport Dynamo (FTD) models have emerged as a popular paradigm for explaining the cyclic nature of solar magnetic activity. Their defining characteristic is the key role played by the mean meridional circulation in transporting magnetic flux and thereby regulating the cycle period. Most FTD models also incorporate the so-called Babcock-Leighton (BL) mechanism in which the mean poloidal field is produced by the emergence and subsequent dispersal of bipolar active regions. This feature is well grounded in solar observations and provides a means for assimilating observed surface flows and fields into the models in order to forecast future solar activity, to identify model biases, and to clarify the underlying physical processes. Furthermore, interpreting historical sunspot records within the context of FTD models can potentially provide insight into why cycle features such as amplitude and duration vary and what causes extreme events such as Grand Minima. Though they are generally robust in a modeling sense and make good contact with observed cycle features, FTD models rely on input physics that is only partially constrained by observation and that neglects the subtleties of convective transport, convective field generation, and nonlinear feedbacks. Here we review the formulation and application of FTD models and assess our current understanding of the input physics based largely on complementary 3D MHD simulations of solar convection, dynamo action, and flux emergence.

Model and parameter uncertainty in IDF relationships under climate change

Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty. (C) 2015 Elsevier Ltd. All rights reserved.

Analytical approach to quantile estimation in regional frequency analysis based on fuzzy framework

Regional frequency analysis is widely used for estimating quantiles of hydrological extreme events at sparsely gauged/ungauged target sites in river basins. It involves identification of a region (group of watersheds) resembling watershed of the target site, and use of information pooled from the region to estimate quantile for the target site. In the analysis, watershed of the target site is assumed to completely resemble watersheds in the identified region in terms of mechanism underlying generation of extreme event. In reality, it is rare to find watersheds that completely resemble each other. Fuzzy clustering approach can account for partial resemblance of watersheds and yield region(s) for the target site. Formation of regions and quantile estimation requires discerning information from fuzzy-membership matrix obtained based on the approach. Practitioners often defuzzify the matrix to form disjoint clusters (regions) and use them as the basis for quantile estimation. The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds. The lost information cannot be utilized in quantile estimation, owing to which the estimates could have significant error. To avert the loss of information, a threshold strategy (TS) was considered in some prior studies. In this study, it is analytically shown that the strategy results in under-prediction of quantiles. To address this, a mathematical approach is proposed in this study and its effectiveness in estimating flood quantiles relative to DFA and TS is demonstrated through Monte-Carlo simulation experiments and case study on Mid-Atlantic water resources region, USA. (C) 2015 Elsevier B.V. All rights reserved.

Decreasing intensity of monsoon low-frequency intraseasonal variability over India

Understanding the changing nature of the intraseasonal oscillatory (ISO) modes of Indian summer monsoon manifested by active and break phase, and their association with extreme rainfall events are necessary for probabilistic estimation of flood-related risks in a warming climate. Here, using ground-based observed rainfall, we define an index to measure the strength of monsoon ISOs and show that the relative strength of the northward-propagating low-frequency ISO (20-60 days) modes have had a significant decreasing trend during the past six decades, possibly attributed to the weakening of large-scale circulation in the region during monsoon season. This reduction is compensated by a gain in synoptic-scale (3-9 days) variability. The decrease in low-frequency ISO variability is associated with a significant decreasing trend in the percentage of extreme events during the active phase of the monsoon. However, this decrease is balanced by significant increasing trends in the percentage of extreme events in the break and transition phases. We also find a significant rise in the occurrence of extremes during early and late monsoon months, mainly over eastern coastal regions. Our study highlights the redistribution of rainfall intensity among periodic (low-frequency) and non-periodic (extreme) modes in a changing climate scenario.

Evaluation of the Index-Flood Approach Related Regional Frequency Analysis Procedures

Index-flood related regional frequency analysis (RFA) procedures are in use by hydrologists to estimate design quantiles of hydrological extreme events at data sparse/ungauged locations in river basins. There is a dearth of attempts to establish which among those procedures is better for RFA in the L-moment framework. This paper evaluates the performance of the conventional index flood (CIF), the logarithmic index flood (LIF), and two variants of the population index flood (PIF) procedures in estimating flood quantiles for ungauged locations by Monte Carlo simulation experiments and a case study on watersheds in Indiana in the U.S. To evaluate the PIF procedure, L-moment formulations are developed for implementing the procedure in situations where the regional frequency distribution (RFD) is the generalized logistic (GLO), generalized Pareto (GPA), generalized normal (GNO) or Pearson type III (PE3), as those formulations are unavailable. Results indicate that one of the variants of the PIF procedure, which utilizes the regional information on the first two L-moments is more effective than the CIF and LIF procedures. The improvement in quantile estimation using the variant of PIF procedure as compared with the CIF procedure is significant when the RFD is a generalized extreme value, GLO, GNO, or PE3, and marginal when it is GPA. (C) 2015 American Society of Civil Engineers.

Integrating climate change impacts to improve understanding of coastal climate change: heavy rains, strong winds, and high seas in coastal Hawaii, Alaska and the Pacific Northwest

Coastal storms, and the strong winds, heavy rains, and high seas that accompany them pose a serious threat to the lives and livelihoods of the peoples of the Pacific basin, from the tropics to the high latitudes. To reduce their vulnerability to the economic, social, and environmental risks associated with these phenomena (and correspondingly enhance their resiliency), decision-makers in coastal communities require timely access to accurate information that affords them an opportunity to plan and respond accordingly. This includes information about the potential for coastal flooding, inundation and erosion at time scales ranging from hours to years, as well as the longterm climatological context of this information. The Pacific Storms Climatology Project (PSCP) was formed in 2006 with the intent of improving scientific understanding of patterns and trends of storm frequency and intensity - “storminess”- and related impacts of these extreme events. The project is currently developing a suite of integrated information products that can be used by emergency managers, mitigation planners, government agencies and decision-makers in key sectors, including: water and natural resource management, agriculture and fisheries, transportation and communication, and recreation and tourism. The PSCP is exploring how the climate-related processes that govern extreme storm events are expressed within and between three primary thematic areas: heavy rains, strong winds, and high seas. To address these thematic areas, PSCP has focused on developing analyses of historical climate records collected throughout the Pacific region, and the integration of these climatological analyses with near-real time observations to put recent weather and climate events into a longer-term perspective.(PDF contains 4 pages)

Flood risk management: What are the main drivers of prevention?

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Climate change and its risk reduction by mangrove ecosystem of Bangladesh

Climate change is amongst the most dreaded problems of the new millennium. Bangladesh is a coastal country bounded by Bay of Bengal on its southern part and here natural disasters are an ongoing part of human life. This paper discusses about the possible impact of climate change through tropical cyclones, storm surges, coastal erosion and sea level rise in the coastal community of Bangladesh and how they cope with these extreme events by the help of mangrove ecosystem. Both qualitative and quantitative discussions are made by collected data from different research work those are conducted in Bangladesh. Mangrove ecosystem provides both goods and services for coastal community, helps to improve livelihood options and protect them from natural disaster by providing variety of environmental support

Análisis de tendencias de temperaturas extremas (mínimas y máximas diarias) y eventos extremos (olas de frío y de calor) en la Cornisa Cantábrica (1924-2014).

[ES]En este trabajo se han analizado las tendencias de las temperaturas extremas y los eventos extremos en la Cornisa Cantábrica a partir de los registros diarios de catorce observatorios de la base de datos NCDC-GSOD (1973-2014) y cuatro observatorios centenarios también situados en esta región de ECA&D (1928-2014). Para ello, se han calculado los índices climáticos desarrollados por el ETCCDMI y se ha cuantificado la significación de las tendencias mediante el test de Mann-Kendall. Se ha observado un ascenso de las temperaturas extremas y eventos extremos en la mayoría de las estaciones, siendo el aumento más acusado a lo largo de los últimos cuarenta años

The potential consequences of climate variability and change on coastal areas and marine resources: report of the Coastal Areas and Marine Resources Sector Team, U.S. National Assessment of the Potential Consequences of Climate Variability and Change, U.S. Global Change Research Program

Coastal and marine ecosystems support diverse and important fisheries throughout the nation’s waters, hold vast storehouses of biological diversity, and provide unparalleled recreational opportunities. Some 53% of the total U.S. population live on the 17% of land in the coastal zone, and these areas become more crowded every year. Demands on coastal and marine resources are rapidly increasing, and as coastal areas become more developed, the vulnerability of human settlements to hurricanes, storm surges, and flooding events also increases. Coastal and marine environments are intrinsically linked to climate in many ways. The ocean is an important distributor of the planet’s heat, and this distribution could be strongly influenced by changes in global climate over the 21st century. Sea-level rise is projected to accelerate during the 21st century, with dramatic impacts in low-lying regions where subsidence and erosion problems already exist. Many other impacts of climate change on the oceans are difficult to project, such as the effects on ocean temperatures and precipitation patterns, although the potential consequences of various changes can be assessed to a degree. In other instances, research is demonstrating that global changes may already be significantly impacting marine ecosystems, such as the impact of increasing nitrogen on coastal waters and the direct effect of increasing carbon dioxide on coral reefs. Coastal erosion is already a widespread problem in much of the country and has significant impacts on undeveloped shorelines as well as on coastal development and infrastructure. Along the Pacific Coast, cycles of beach and cliff erosion have been linked to El Niño events that elevate average sea levels over the short term and alter storm tracks that affect erosion and wave damage along the coastline. These impacts will be exacerbated by long-term sea-level rise. Atlantic and Gulf coastlines are especially vulnerable to long-term sea-level rise as well as any increase in the frequency of storm surges or hurricanes. Most erosion events here are the result of storms and extreme events, and the slope of these areas is so gentle that a small rise in sea level produces a large inland shift of the shoreline. When buildings, roads and seawalls block this natural migration, the beaches and shorelines erode, threatening property and infrastructure as well as coastal ecosystems.

Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand

The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.