A NEW LOOK AT FLOOD RISK DETERMINATION1

ABSTRACT: The probability distributions of annual peak flows used in flood risk analysis quantify the risk that a design flood will be exceeded. But the parameters of these distributions are themselves to a degree uncertain and this uncertainty increases the risk that the flood protection provided will in fact prove to be inadequate. The increase in flood risk due to parameter uncertainty is small when a fairly long record of data is available and the annual flood peaks are serially independent, which is the standard assumption in flood frequency analysis. But standard tests for serial independence are insensitive to the type of grouping of high and low values in a time series, which is measured by the Hurst coefficient. This grouping increases the parameter uncertainty considerably. A study of 49 annual peak flow series for Canadian rivers shows that many have a high Hurst coefficient. The corresponding increase in flood risk due to parameter uncertainty is shown to be substantial even for rivers with a long record, and therefore should not be neglected. The paper presents a method of rationally combining parameter uncertainty due to serial correlation, and the stochastic variability of peak flows in a single risk assessment. In addition, a relatively simple time series model that is capable of reproducing the observed serial correlation of flood peaks is presented.     

Evaluation of methods for determining adverse impacts of air pollution on terrestrial ecosystems

Methods used to determine adverse impacts of air pollution on four levels of biological organization of terrestrial ecosystems were evaluated for their use in decision making by federal land managers of class I areas and as guidelines for scientists employed to design field studies in these areas. At the level of the individual, visible injury, biomass, and sulfur uptake were the most often used components; at the population level, natality and mortality; at the community level, diversity; and at the ecosystem level, biogeochemical cycling. Most studies focused on structural responses of individual organisms. These components are relatively sensitive and are easy and inexpensive to measure; however, linkages of these parameters to adverse impacts on populations, communities, or ecosystems are lacking. Measurements of effects of air pollution at the higher levels of organization are confounded by natural variability, long response times, climatic variation, pathogens, and other factors. In addition, the lack of replication and of true control areas creates severe problems for design of monitoring programs and testing of hypotheses concerning effects.     

UNCERTAINTY ANALYSIS OF BMP EFFECTWENESS FOR CONTROLLING NITROGEN FROM URBAN NONPOINT SOURCES1

ABSTRACT: The effectiveness of urban Best Management Practices (BMPs) in achieving the No-Net-Increase Policy (NNTP), a policy designed to limit nonpoint nitrogen loading to Long Island Sound (US), is analyzed. A unit loading model is used to simulate annual nitrogen exported from the Norwalk River watershed (Connecticut) under current and future conditions. A probabilistic uncertainty analysis is used to incorporate uncertainty in nitrogen export coefficients and BMP nitrogen removal effectiveness. The inclusion of uncertainty in BMP effectiveness and nitrogen export coefficients implies that additional BMPs, or BMPs with a greater effectiveness in nitrogen removal, will be required to achieve the NNIP. Even though including uncertainty leads to an increase in BMP implementation rates or BMP effectiveness, this type of analysis provides the decision maker with a more realistic assessment of the likelihood that implementing BMPs as a management strategy will be successful. Monte Carlo simulation results indicate that applying BMPs to new urban developments alone will not be sufficient to achieve the NNIP since BMPs are not 100 percent effective in removing the increase in nitrogen caused by urbanization. BMPs must also be applied to selected existing urban areas. BMPs with a nitrogen removal effectiveness of 40–60 percent, probably the highest level of removal that can be expected over an entire watershed, must be applied to at least 75 percent of the existing urban area to achieve the NNIP This high rate of application is not likely to be achieved in urbanized watersheds in the LIS watershed; therefore, additional point source control will be necessary to achieve the NNIP     

Designating Air Quality Management Areas (AQMAs) in the UK Implications for Securing UK Air Quality Objectives

Across the United Kingdom, the majority of local authoritieshave now completed their first phase of local air qualityreview and assessment work, as required under the AirQuality Strategy for England, Scotland, Wales and NorthernIreland (DETR, 2000a). Emerging from this first phasework is an anticipated suite of over 110 Air QualityManagement Areas (AQMAs). These areas are identifiedlocations where one or more of the national air qualityobjectives are predicted to exceed by specific target dates,and their spatial extent and shape is emerging as highlyvariable. Local authorities are guided to use a variety ofscientific tools to underpin the scientific assessments, anda consideration of uncertainty in both the tools used andsubsequent delineation of AQMAs is likely to affect theemerging management areas significantly. With subsidiarity underpinning the process of local air qualitymanagement (LAQM), local decision-making is anticipated toinfluence the outcome of the LAQM process in its entirety,with the declaration of AQMAs necessitating the preparationand implementation of air quality action plans. UKexperience of the effective management of local air quality,through the designation of AQMAs, demonstrates a valuableframework for other European countries developing mechanismsto manage air quality locally.     

ACCURACY AND RELIABILITY OF NUMERICAL RIVER MODELS1

ABSTRACT: Over the last 20 years, our possibilities to model river flows numerically have increased enormously. In this paper, the question is addressed whether a more sophisticated model is always better than a simpler one. Increased detail, both in finer resolution and in physical processes taken into account, has its price in more computer time, higher data need, and perhaps more unknown coefficients to be calibrated. Moreover, uncertainty in actual physical conditions (e.g., bottom roughness), inflow and parameters remains, which may dominate the uncertainty of the results. Also, the questions asked by a decision maker may not always be very precise. For a schematic but relevant example, we show that a better model does not necessarily give more reliable results because some of the basic uncertainties remain. It is concluded that we should use the simplest model that will answer the question as to the accuracy needed, taking into account uncertainties in the data     

A REGIONAL APPROACH TO PROJECT EVALUATION1

ABSTRACT. Water and related land resource planning has generally been characterized by project evaluation in isolation. Feasible alternatives have been ignored because the economic analysis did not include the interdependence of such subregional and regional variables as factor and product prices and production possibilities. This paper presents an economic framework, consistent with a regional development objective and an efficiency criterion, for proposing and evaluating resource projects. Subregional and regional derived demand curves for water are developed under alternative assumptions of subregional competition, regional market restraints, and yield and price uncertainty. The derivation of water demand curves using the proposed regional framework as compared to sub regional isolation has the desirable properties of (1) a marginal analysis that is more flexible over time is substituted for a static average analysis; (2) range estimates incorporating probabilities are substituted for point estimates; (3) approximations to functional demand curves are substituted for “needs”; and (4) fewer resources are required to meet a regional market restraint.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

A Proactive Approach Can Make Site Remediation Less Expensive

Without any incentive to clean up a contaminated site, remediation is often delayed until the site owner is compelled to act by regulatory agencies. In such a context, the selected technology is typically the one that will reach the remediation goals as quickly as possible. Unfortunately, this criterion is often met by overly expensive technologies, resulting in high and sometimes unaffordable total remediation costs, leading to a remediation with a negative net benefit. This study examines the effects of time constraint and benefit value on the optimal remediation strategy for a diesel-contaminated site. This strategy is developed using the technico-economic model METEORS, which takes into account the technology’s effectiveness, the uncertainty of the level of contamination, and the possibility of reducing this uncertainty through either an additional characterization (before selecting and applying a technology) or the monitoring of the remediation technology (during its use). Results of simulations with both economic and temporal constraints support a proactive approach to site remediation.     

Prioritizing Wetland Restoration for Sediment Yield Reduction: A Conceptual Model

In a climate of limited resources, it is often necessary to prioritize restoration efforts geographically. The synoptic approach is an ecologically based tool for geographic prioritization of wetland protection and restoration efforts. The approach was specifically designed to incorporate best professional judgment in cases where information and resources are otherwise limited. Synoptic assessments calculate indices for functional criteria in subunits (watersheds, counties, etc.) of a region and then rank the subunits. Ranks can be visualized in region-scale maps which enable managers to identify areas where efforts optimize functional performance on a regional scale. In this paper, we develop a conceptual model for prioritizing watersheds whose wetlands can be restored to reduce total sediment yield at the watershed outlet. The conceptual model is designed to rank watersheds but not individual wetlands within a watershed. The synoptic approach is valid for applying the sediment yield reduction model because there is high demand for prioritizing disturbed wetlands for restoration, but there is limited, quantitative, accurate information available with which to make decisions. Furthermore, the cost of creating a comprehensive database is prohibitively high. Finally, because the model will be used for planning purposes, and, specifically, for prioritizing based on multiple decisions rather than optimizing a single decision, the consequence of prioritization errors is low. Model results cannot be treated as scientific findings. The conclusions of an assessment are based on judgement, but this judgement is guided by scientific principles and a general understanding of relevant ecological processes. The conceptual model was developed as the first step towards prioritizing of wetland restoration for sediment yield reduction in US EPA Region 4.