ABSTRACT

Bayesian methods have been developed to analyze three main types of uncertainties, namely: the model uncertainty, the parameter uncertainty, and the sampling errors. To illustrate these techniques on a real case study, a model has been developed to quantify the various uncertainties when predicting the global proportion of coliform positive samples (CPS) in a water distribution system where bacterial pollution indicators are weekly monitored by sanitation authorities. The data used to fit and validate the model correspond to water samples gathered in the suburb of Paris. The model uncertainty has been evaluated in the reference class of generalized linear multivariate autoregressive models. The model parameter distributions are determined using the Metropolis-Hastings algorithm, one of the Monte Carlo Markov Chain methods. Such an approach, successful when dealing with water quality control, should also be very powerful for rare events modeling in hydrology or in other fields such as ecology.

INTRODUCTION

The bacterial pollution indicators are understood here as the coliforms, a group of bacteria that is “public enemy number one” for water suppliers. Their occurrence in domestic distributed waters is a major concern for many utility companies. The coliform group includes many different species, the most famous one being Escherichia coli. Part of the bacteria belonging to the coliforms group are fecal bacteria and may provoke gastroenteritis or other digestive problems. The other inoffensive part is generally considered as an indicator of a possible presence of their more dangerous cousins.

ABSTRACT

Effective protection of a drinking water well against pollution by persistent compounds requires the knowledge of the well's capture zone. This zone can be computed by means of groundwater flow models. However, because the accuracy and uniqueness of such models is very limited, the outcome of a deterministic modeling exercise may be unreliable. In this case stochastic modeling may present an alternative to delimit the possible extension of the capture zone. In a simplified example two methods are compared: the unconditional and the conditional Monte Carlo simulation. In each case realizations of an aquifer characterized by a recharge rate and a transmissivity value are produced. By superposition of capture zones from each realization, a probability distribution can be constructed which indicates for each point on the ground surface the probability to belong to the capture zone. The conditioning with measured heads may both shift the mean and narrow the width of this distribution. The method is applied to the more complex example of a zoned aquifer. Starting from an unconditional simulation with recharge rates and transmissivities randomly sampled from given intervals, observation data of heads are successively added. The transmissivities in zones that do not contain head data are generated stochastically within boundaries typical for the zone, while the remaining zonal transmissivities are now determined in each realization through inverse modeling. With a growing number of conditioning data the probability distribution of the capture zones is shown to narrow. The approach also allows the quantification of the value of data. Data are the more valuable the larger the decrease of uncertainty they lead to.

ABSTRACT

Management of water resources is inherently subject to uncertainties due to data inadequacy and errors, modeling inaccuracy, randomness of natural phenomena, and operational variability. Uncertainties are associated with each of the contributing factors or components of a water resources system and with the system as a whole. Uncertainty can be measured in terms of the probability density function, confidence interval, or statistical moments such as standard deviation or coefficient of variation. In this chapter, available methods for uncertainty analysis are briefly reviewed.

INTRODUCTION

The planning, design, and operation of water resources systems usually involve many components or contributing factors. Each of the components or factors individually and the system as a whole are always subject to uncertainties. For example, the reliability of flood forecast depends not only on the uncertainty of the prediction model itself but also on the uncertainties on the input data. The design of a storm drain is subject to the uncertainty on the runoff simulation model used, uncertainties on the design storm determination, as well as uncertainties on the materials, construction, and maintenance used. Water supply is always subject to uncertainties on the demand, availability of the sources of water, and the performance of the distribution network. Safety of a dam depends not only on the magnitude of the flood but also on the waves, earthquake, conditions of the foundation, and maintenance and appropriateness of the operational procedure. Numerous other examples can be cited. In short, decisions on water resources are always subject to uncertainties.

Knowledge of uncertainties is useful for rational decision making, for cost-effective design, for safe operation, and for improved public awareness of water resources risks and reliability.

ABSTRACT

The primary objective of this chapter is the development of an appropriate social indicator which represents society's robustness against disaster risk. With a focus on the risk of drought, a sociopsychological approach based on the concept of social representation is presented. As the working hypothesis, the society's perceived level of readiness against drought (SPRD) is defined in terms of the social message of relevant newspaper articles by its article area. Using actual drought cases for the cities of Takamatsu and Fukuoka, this working hypothesis has been examined and reexamined from two different analytical viewpoints – that is, a contextual analysis and an analysis of the water-saving phenomenon. It is shown that the results are basically positive in support of our working hypothesis.

INTRODUCTION

The Great Hanshin-Awaji Earthquake, which struck the heart of the Kobe-centered metropolis on January 17, 1995, has demonstrated the often forgotten fact that the citizens of a large city are bound to coexist with the risks of an urban disaster. Though not so catastrophic as this earthquake, several metropolitan regions in Japan experienced a drought of unprecedented scale during the preceding summer. Among them are the cities of Fukuoka (on Kyushu Island) and Takamatsu (on Shikoku Island), which underwent the most serious socioeconomic damage. This must have strengthened the awareness that in society, large cities are bound to coexist with the risks of urban disaster. In both cases, citizens seemed to have learned that the community's disaster risk awareness makes a difference when examining the criticality of the disaster damage.

ABSTRACT

A critical contemplation is offered of what is known and what is not known about the hydrological, economic, and societal uncertainties that are eagerly and routinely subjected to all kinds of mathematical prestidigitation in the process of risk analysis of water resources systems. An illustration of the issues is provided by a reality check on some real-life situations, mostly based on the author's own experience from the past forty years. Three simple recommendations are offered that may bring risk analysis down to earth.

INTRODUCTION

Spurred by the newly available computing technology, the influx of mathematical statistics and probability theory into the analysis of uncertainty in hydrology and water resources in the 1960s was powerful and unprecedented. Also unprecedented has been its side effect – an outburst of “applications” of various spurious theories suddenly made so easy by the computer. By the mid-1970s the danger of this malignant growth was already evident and solitary warning voices could be heard. One of the clearest and most passionate was that of the late Myron Fiering of Harvard University: “Fascination with automatic computation has encouraged a new set of mathematical formalisms simply because they now can be computed; we have not often enough asked ourselves whether they ought to be computed or whether they make any difference … we build models to serve models to serve models to serve models, and with all the computation, accumulated truncation, roundoff, sloppy thinking, and sources of intellectual slippage, there is some question as to how reliable are the final results” (Fiering 1976).

Motto: Sustainable is what people agree is sustainable.

Abstract

This chapter introduces the field of integrated regional risk assessment and safety management for energy and other complex industrial systems. The international initiative includes compilation of methods and guidelines, and development of various models and decision support systems to assist implementation of various tasks of risk assessment at the regional level. The merit of GIS methodology is highlighted.

INTRODUCTION

Almost ten years after the UNCED (United Nations Conference on Environment and Development), Rio de Janeiro, Brazil, 1992, some progress has been achieved in relation to the protection of the environment, development policies, and strategical future topics. A number of issues were addressed by UNCED – Agenda 21 that were connected with the topic of this chapter.

1. Issue 1. Achieving sustainable development, environmental protection shall constitute an integral part of the development process.

2. Issue 2. Environmental issues are best handled with the participation of all concerned citizens.

3. Issue 3. National authorities should endeavor to promote the internalization of environmental costs.

4. Issue 4. Information for decision making would involve:

• the data gap;

• availability of information.

1. Issue 5. Emergency planning and preparedness are integral parts of a coherent sustainable development.

Regional risk assessment and safety planning is a coordinated strategy for risk reduction and safety management in a spatially defined region, across a broad range of hazard sources. It deals equally with normal operation of plants as well as with accidental situations, including synergetic effects.

ABSTRACT

The problem of water quality management under uncertain emission levels, reaction rates, and pollutant transport is considered. Three performance measures – reliability, resiliency, and vulnerability – are taken into account. A general methodology for finding a cost-effective water quality management program is developed. The approach employs a new stochastic branch and bound method that combines random estimates of the performance for subsets of decisions with iterative refinement of the most promising subsets.

INTRODUCTION

Devising successful and cost-effective water quality management strategies can be difficult because the inputs to, and the behavior of, the system being managed are never entirely predictable. Decision makers do not know what conditions will exist in the future nor how these conditions will affect the impact of their decisions on the environment. Vincens, Rodriguez-Iturbe, and Schaake (1975) classify uncertainty in modeling hydrologic systems into three categories: uncertainty in the model structure (Type I uncertainty); uncertainty in the model parameters (Type II uncertainty); and uncertainty resulting from natural variability (Type III uncertainty). For water quality systems, uncertainty in the pollutant transport model, the model reaction rates, and the natural variability of emission rates and receiving water conditions, such as streamflow, temperature, and background pollutant loadings from unregulated pollution sources, contribute to difficulties in predicting the future behavior of the system (Beck 1987). This chapter develops an approach for identifying water quality management solutions under Type II and Type III uncertainty. It is based on an application of the stochastic branch and bound method of Norkin, Ermoliev, and Ruszczyński (1994) to water quality management, which is modified to account for the performance indicators of reliability, resiliency, and vulnerability.

Natural and anthropogenic effects combine to result in the maximum erosional stress on the barrier islands located near the mouth of the Mississippi River. These islands include Grand Isle, Timbalier, and Isle Dernieres to the west of the active delta and the Chandeleur Islands to the east. Natural effects include the subsidence resulting from the weight of the delta and the soft unconsolidated underlying muds. However, in this instance, the anthropogenic effects probably dominate and include additional subsidence resulting from withdrawal of hydrocarbons and the reduction of sediment supply from the river by the construction of upstream impoundments and jetties that direct the riverine sediment offshore to deep water. Additionally, in some areas, inlets and jetties have disturbed local sediment transport pathways. The total relative rise in sea level in the region of the Mississippi River Delta is on the order of 1 cm per year – eight times the worldwide rate. The resulting erosion rate from the combined effects is so great that entire islands can move and vanish within periods of decades. See, for example, Nummedal and Cuomo (1984) for an historical perspective on some of this erosion.

INTRODUCTION

The processes that shape shorelines can be examined with many different time scales. The beach changes constantly under the action of the individual waves that suspend the beach sediment and move it about; however, it is the erosion on the scale of hours and days that is responsible for the cumulative damage by a storm.

In the early 1970s, much of Miami Beach, Florida, was badly eroded, lined with seawalls, and compartmented by long steel groins, making it impossible for tourists to walk along the beach. The numbers of visitors to Miami Beach and hotel occupancy rates were declining and, at high tide during storms, the impact of large waves against the seawalls and the resulting vibrations were reported to be worrisome to guests in the upper (more expensive) rooms.

From 1976 to 1981, the Miami Beach Nourishment Project was undertaken, widening the beach by 100 m over a length of 16 km. The project extends from Bakers Haulover Inlet at the north to Government Cut Inlet at the south. Both of these termini have large, relatively impermeable jetty structures to prevent end losses from the project. The project required in excess of 10 million m3 of sand obtained from offshore borrow areas by large hydraulic dredges, and it cost $64 million (1980 USD).

The construction of the nourishment project took place in the midst of rising concern over the effects and effectiveness of shoreline projects. The project attracted negative publicity by receiving the Golden Fleece Award that was given annually by a popular senator to the project deemed to be the greatest waste of public funds. There were other outspoken claims that the project would wash away with the first major storm and was a waste of money.

Hurricane Hugo, a Category 4 storm, made landfall slightly north of the entrance to Charleston Harbor, South Carolina, in September 1989. The storm tide, based on high water marks, peaked in excess of 20 ft, which is second only to the 22.4-ft. storm tide caused by Hurricane Camille (1969) at Pass Christian, Mississippi.

The development along the impacted area of South Carolina had taken place over several decades under generally inadequate structural and setback controls. Moreover, the erosion rates on some portions of the South Carolina shoreline are moderately high, causing older structures to be near the shoreline.

Although no structures built to modern standards suffered major damage, Hurricane Hugo destroyed or caused severe damage to in excess of 15,000 structures. Of these, about 150 were considered to be unbuildable under the management policies in place at the time. (Shore & Beach, 58, 4, 1990, has a number of articles about Hugo's effects.) There has been considerable litigation over whether and under what conditions these structures should be rebuilt. The total damages caused by Hugo exceeded $4.0 billion.

With most of the U.S. shoreline heavily developed, the preceding scenario will recur, and it is important that national and local management policies be in place to regulate construction near the shoreline and reconstruction following storms so that future disaster liabilities and personal hardships will be minimized.

INTRODUCTION

The current pressure for development along much of the nation's shoreline, coupled with the prognosis for increased rates of sea level rise (Chapter 3) and the possibility of increased storminess due to the Greenhouse effect, have raised questions about the most appropriate policies for coastal zone management.

Some of the most ambitious and daring field programs occurred during World War II with the University of California at Berkeley's field studies (directed by M.P. O'Brien) of the beaches of northern California, Oregon, and Washington. The studies were intended to examine the nature of beaches and waves to provide information for amphibious landings on enemy beaches. The field surveys consisted of beach profiles made using military amphibious vehicles (DUKW) in surf sometimes exceeding 6 m – so high that sometimes the 10-m-long vehicles would surf the waves. The measurements were made by lead line casts along profiles separated by about 300 m. Triangulation of the DUKW by a transit operator located onshore provided the position information. Fortunately, no one was killed during these studies! (A further description of these studies can be found in Bascom 1980.)

INTRODUCTION

The elevations– and contours of a shoreline are a response to the forcing of waves, winds, and currents and depend on the supply of sediment to the beach. By measuring the dimensions of a beach repeatedly, the amounts of erosion or accretion can be determined over the period of the surveys as well as some indications of where the material may be going.

This chapter will review methods of measuring the beach and present several tools for determining beach changes such as the empirical orthogonal eigenfunction method, which is a convenient method to resolve the nature of the changes in a beach profile, and the sand budget analysis.

On the southeastern shore of the Chesapeake Bay, parts of the shoreline are receding at very high rates of up to 10 m per year! Many of the shorelines in this region consist of narrow sand beaches backed by small bluffs several meters in height. The bluff, however, comprises considerably finer material than the beach and does not contain much sand-sized or coarser material. At Taylor Island, Maryland, for example, the mean diameter of the beach sand is greater than 0.5 mm, the bluff material is 99 percent silt, and the sand fraction has a mean diameter of 0.12 mm. Dalrymple et al. (1986) hypothesized that the high erosion rate there occurs because the erosion of the bluff yields only fine materials, which are easily transported away by the waves and tides, and contributes very little sand to the beach. Had the upland been composed of coarser material, bluff erosion would have produced more sand, resulting in a wider beach that would have slowed the bluff erosion.

INTRODUCTION

A beach can be composed of a wide variety of materials of many sizes and shapes. Remarkably, however, there is a very narrow range over which most beach materials vary, both in composition and size. Along the U.S. East Coast, for example, despite the variation in exposure to waves and the composition of the upland, the mean grain size of most of the beaches varies by a factor of only five (U.S. Army Shore Protection Manual 1973).

Most waves are formed by relatively steady winds blowing over the water surface, but, in some cases, waves can be formed by unusual meteorological events. As an example, squall line surges are caused by the movement of a relatively small atmospheric pressure perturbation at the approximate speed of the waves being generated. This results in energy being transferred to the same waves continuously, causing them to build up at a relatively rapid rate in a type of resonance. Examples include an event in 1954 that caused seven fatalities along the Lake Michigan shoreline (Ewing, Press, and Donn 1954) and one in 1929 that also caused fatalities (Proudman 1929, 1952). Some surprising characteristics of these types of waves are the small number of substantial waves produced (one to three), the height of the waves (1 to 3 m), and the small magnitude of the pressure change required to cause this phenomena (1 to 3 mbar).

On the evening of July 3, 1992, at Daytona Beach, Florida, a freak wave about 2 m high surged up the beach on an otherwise calm night. Driving on the beach is allowed in this area, and several cars were carried up the beach by the wave and jammed under the boardwalk. A park ranger driving down the beach was overtaken by the wave, which floated his jeep up the beach on the uprush and then back down; the vehicle was then set down near where it had been picked up. The ranger continued on his drive.

SUPERTANK may conjure up in your mind the title of a grade B movie but actually refers to one of the largest cooperative physical model studies ever carried out in coastal engineering. It was organized by the U.S. Army Corps of Engineers for the purpose of determining storm-induced erosion effects on coastlines in a wave tank large enough that modeling scale effects were not important.

The 8-week experiment, involving investigators from numerous universities, the Corps, and other institutions began in July 1991 in the O.H. Hinsdale Wave Research Laboratory at Oregon State University. The tank is 104 m long, 3.7m wide, and 4.6 m deep with a hydraulic hinged wave paddle at one end capable of generating random or monochromatic waves up to 1.5 m and absorbing reflected waves. A sand beach of 0.22 mm diameter was constructed over 76 m of the tank, utilizing approximately 1700 metric tons of beach sand trucked in from the Oregon coast.

The instrumentation in the tank included wave gages, current meters, pressure gauges, video cameras, and sediment concentration gauges to measure the hydrodynamics, and beach and dune response. More details about the experiments are given in Kraus and Smith (1994) and Smith and Kraus (1995).

INTRODUCTION

A major goal of coastal engineering is to develop models for the reliable prediction of short- and long-term nearshore evolution. Ideally, these models, with appropriate wave and sediment information, would allow prediction of the behavior of the shoreline and the offshore bathymetry over some given period of time.