Suchergebnisse

Cover -- Copyright -- Contents -- Preface -- About the author -- 1. Water sources and quality -- 1.1 Availability of Fresh Water -- 1.1.1 Surface water -- 1.1.2 Groundwater -- 1.1.3 Siting of wells and boreholes -- 1.1.4 Determination of yield -- 1.2 Rainwater -- 1.3 Wastewater and Recycling -- 1.4 Seawater or Saline Water -- 1.5 Water Pollution -- 1.5.1 Protection of public health -- 1.5.2 Pollution risk assessment and management -- 1.6 International Standards and Criteria -- 1.6.1 Microbiological determinants -- 1.6.2 Chemical and physical determinants -- 1.6.3 Organic pollution -- 1.6.4 Stability of water supplies -- 1.7 Water Treatment -- 1.8 References -- 2. Urban water supply -- 2.1 Water Use -- 2.1.1 Volumes required -- 2.1.2 Planning basis -- 2.1.3 Peak factors -- 2.1.4 Pressure requirements -- 2.2 Reservoir Storage Requirements -- 2.2.1 Elevated storage and pumps -- 2.2.2 Balancing volume -- 2.2.3 Other storage reservoirs -- 2.3 Pipe Fluid Mechanics -- 2.3.1 The fundamental equations of fluid flow -- 2.4 Flow Head Loss Relationships -- 2.4.1 Empirical flow formulae -- 2.4.2 Rational flow formulae -- 2.5 Water Hammer and Flow Control -- 2.5.1 Valves and other fittings -- 2.6 Pipeline Optimisation -- 2.7 Optimum Reservoir Sizes -- 2.8 Pump Characteristic Curves -- 2.9 References -- 3. Water demand management and loss control -- 3.1 Controlling Water Use -- 3.2 Economic Theory of Supply and Demand -- 3.2.1 Effect of metering -- 3.2.2 Management by use of tariffs -- 3.3 Timing -- 3.3.1 Long-term (planning and design) -- 3.3.2 Operational time-frame -- 3.3.3 Crisis management -- 3.3.4 Notes on management by use of tariffs -- 3.4 The Cost of Water -- 3.4.1 Future trends -- 3.5 Value of Water -- 3.6 Loss Control -- 3.7 Water Harvesting -- 3.8 References -- 4. Sewerage -- 4.1 Flow in Circular Drains -- 4.1.1 Manning equation.

Abstract. Natural hazards, such as European windstorms, have widespread
effects that result in insured losses at multiple locations throughout a
continent. Multivariate extreme-value statistical models for such
environmental phenomena must therefore accommodate very high dimensional
spatial data, as well as correctly representing dependence in the extremes to
ensure accurate estimation of these losses. Ideally one would employ a
flexible model, able to characterise all forms of extremal dependence.
However, such models are restricted to a few dozen dimensions, hence an a
priori diagnostic approach must be used to identify the dominant form of
extremal dependence. Here, we present various approaches for exploring the dominant extremal
dependence class in very high dimensional spatial hazard fields: tail
dependency measures, copula fits, and conceptual loss distributions. These
approaches are illustrated by application to a data set of high-dimensional
historical European windstorm footprints (6103 spatial maps of 3-day maximum
gust speeds at 14 872 locations). We find there is little evidence of
asymptotic extremal dependency in windstorm footprints. Furthermore,
empirical extremal properties and conceptual losses are shown to be well
reproduced using Gaussian copulas but not by extremally dependent models such
as Gumbel copulas. It is conjectured that the lack of asymptotic dependence
is a generic property of turbulent flows. These results open up the
possibility of using geostatistical Gaussian process models for fast
simulation of windstorm hazard fields.

Abstract
For climate services to be relevant and informative for users, scientific data definitions need to match users' perceptions or beliefs. This study proposes and tests novel yet simple methods to compare beliefs of timing of recurrent climatic events with empirical evidence from multiple historical time series. The methods are tested by applying them to the onset date of the monsoon in Bangladesh, where several scientific monsoon definitions can be applied, yielding different results for monsoon onset dates. It is a challenge to know which monsoon definition compares best with people's beliefs. Time series from eight different scientific monsoon definitions in six regions are compared with respondent beliefs from a previously completed survey concerning the monsoon onset.
Beliefs about the timing of the monsoon onset are represented probabilistically for each respondent by constructing a probability mass function (PMF) from elicited responses about the earliest, normal, and latest dates for the event. A three-parameter circular modified triangular distribution (CMTD) is used to allow for the possibility (albeit small) of the onset at any time of the year. These distributions are then compared to the historical time series using two approaches: likelihood scores, and the mean and standard deviation of time series of dates simulated from each belief distribution.
The methods proposed give the basis for further iterative discussion with decision-makers in the development of eventual climate services. This study uses Jessore, Bangladesh, as an example and finds that a rainfall definition, applying a 10 mm day−1 threshold to NCEP–NCAR reanalysis (Reanalyis-1) data, best matches the survey respondents' beliefs about monsoon onset.

AbstractThere is a pressing need for simple and reliable risk transfer mechanisms that can pay out quickly after natural disasters without delays caused by loss estimation, and the need for long historical claims records. One such approach, known as parametric insurance, pays out when a key hazard variable exceeds a predetermined threshold. However, this approach to catastrophe risk, based on making deterministic binary predictions of loss occurrence, is susceptible to basis risk (mismatch between payouts and realized losses).A more defensible approach is to issue probabilistic predictions of loss occurrence, which then allows uncertainty to be properly quantified, communicated, and evaluated. This study proposes a generic probabilistic framework for parametric trigger modeling based on logistic regression, and idealized modeling of potential damage given knowledge of a hazard variable. We also propose various novel methods for evaluating the quality and utility of such predictions as well as more traditional trigger indices.The methodology is demonstrated by application to flood‐related disasters in Jamaica from 1998 to 2016 using gridded precipitation data as the hazard variable. A hydrologically motivated transformation is proposed for calculating potential damage from daily rainfall data. Despite the simplicity of the approach, the model has substantial skill at predicting the probability of occurrence of loss days as demonstrated by traditional goodness‐of‐fit measures (i.e., pseudo‐R2 of 0.55) as well as probabilistic verification diagnostics such as receiver operating characteristics. Using conceptual models of decisionmaker expenses, we also demonstrate that the system can provide considerable utility to involved parties, e.g., insured parties, insurers, and risk managers.

Abstract. A decline in damaging European windstorms has led to a reduction in insured losses in the 21st century. This decline is explored by identifying a damaging windstorm characteristic and investigating how and why this characteristic has changed in recent years. This novel exploration is based on 6103 high-resolution model-generated historical footprints (1979–2014), representing the whole European domain. The footprint of a windstorm is defined as the maximum wind gust speed to occur at a set of spatial locations over the duration of the storm. The area of the footprint exceeding 20 ms−1 over land, A20, is shown to be a good predictor of windstorm damage. This damaging characteristic has decreased in the 21st century, due to a statistically significant decrease in the relative frequency of windstorms exceeding 20 ms−1 in north-western Europe, although an increase is observed in southern Europe. This is explained by a decrease in the quantiles of the footprint wind gust speed distribution above approximately 18 ms−1 at locations in this region. In addition, an increased variability in the number of windstorm events is observed in the 21st century. Much of the change in A20 is explained by the North Atlantic Oscillation (NAO). The correlation between winter total A20 and winter-averaged mean sea-level pressure resembles the NAO pattern, shifted eastwards over Europe, and a strong positive relationship (correlation of 0.715) exists between winter total A20 and winter-averaged NAO. The shifted correlation pattern, however, suggests that other modes of variability may also play a role in the variation in windstorm losses.

Since the end of the Cold War, the United States has intervened militarily in a number of civil conflicts. This book examines four US-sponsored interventions (Panama, Somalia, Haiti and Bosnia) seeking to provide an insight into the conditions under which intervention and the nation-building which follows are likely to succeed

Abstract. Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼ 60 years of comprehensive reanalysis data that are dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to the choice of the historical period used. This study proposes a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints. A good description of extreme wind speeds is obtained by assuming that gust speed peaks over threshold are distributed exponentially, i.e. a generalised Pareto distribution having a zero shape parameter. The threshold and tail scale parameter are estimated at each location and used to calculate estimates of the 10- and 200-year return levels. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels and modulating the threshold, with a less detectable influence on rarer extremes and the tail scale parameter. The length of historical data required to have the lowest error in estimating return levels is quantified using both observed and simulated time series of the historical NAO. For reducing errors in estimating 200-year return levels of an independent 10-year period, a data catalogue of at least 20 years is required. For lower return levels the NAO has a stronger influence on estimated return levels, and so there is more variability in estimates. Using theoretical estimates of future NAO states, return levels are largely outside the historical uncertainty, indicating significant increases in risk potential from windstorms in the next 100 years. Our method presents a framework for assessing high-return-period events across a range of hazards without the additional complexities of a full catastrophe model.