DATA ANALYSIS OF WATER QUALITY TIME SERIES IN LAKE ERIE1

ABSTRACT: A comprehensive data analysis study is carried out for detecting trends and other statistical characteristics in water quality time series measured in Long Point Bay, Lake Erie. In order to glean an optimal amount of useful information from the available data, the exploratory and confirmatory data anslysis stages are adhered to. To test a range of hypotheses regarding the statistical properties of the time series, a wide variety of both parametric and nonparametric techniques are employed. A particularly useful nonparametric method for discovering trends is the seasonal Mann-Kendall test.     

A HYBRID OPTIMIZATION APPROACH TO THE ESTIMATION OF DISTRIBUTED PARAMETERS IN TWO-DIMENSIONAL CONFINED AQUIFERS1

ABSTRACT: In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.     

Scenario realism and welfare estimates in choice experiments--a non-market valuation study on the European water framework directive

Using choice experiment data for economic valuation we analyse how disbelief in survey information could affect the retrieved welfare estimates. We distinguish between two types of survey information to the respondents. The first type of information concerns the current environmental status of a water body. This information is provided prior to the valuation questions and the corresponding beliefs in the provided information are also elicited before valuation. The second type of information concerns the proposed improvements in the environmental status of the water body. We find that average welfare measures differ considerably according to whether respondents who disagree with the status quo levels and find proposed scenarios unlikely are included or not.     

Beta estimates of shares on the JSE Top 40 in the context of reference-day risk

A topic of interest in the finance world is measuring systematic risk. Accurately measuring the systematic risk component—or Beta—of an asset or portfolio is important in many financial applications. In this work, we consider the efficiency of a range of Beta estimation methods commonly used in practice from a reference-day risk perspective. We show that, when using the industry standard data sample of 5 years of monthly returns, the choice of reference-day used to calculate underlying returns has a significant impact on all of the Beta estimation methods considered. Driven by this finding, we propose and test an alternative nonparametric bootstrap approach for calculating Beta estimates which is unaffected by reference-day risk. Our primary goal is to determine a point-estimate of Beta, independent of reference-day.     

Tasmanian landowner preferences for conservation incentive programs: a latent class approach

An empirical model of landowners' conservation incentive program choice is developed in which information about landowners' socio-economic and property characteristics and their attitudes, is combined with incentive program attributes. In a Choice survey landowners were presented with the choice of two incentive programs modelled as 'bundles of attributes' mimicking a voluntary choice scenario. Landowner behaviour and decision and the type of conditions and regulations they preferred were analyzed. Based on choice survey data, landowner heterogeneity was accounted for using a latent class approach to estimate the preference parameters. Three latent classes of landowners with different attitudes to the role and outcome of establishing conservation reserves on private land were identified: multi-objective owners; environment owners; and production owners. Only a small proportion of landowners, mostly environment owners, would voluntarily join a program. Although compensation funding contributed to voluntary program choice for multi-objective owners and environment owners, welfare losses were around 4000 AUD per hectare, which is less than the average agricultural land value in Tasmania. Landowners for whom compensation funding contributed to voluntary program choice were also most likely to set aside land for conservation without payment. This raises the possibility that the government's compensation expenditure could potentially be either reduced or re-allocated to landowners who will not voluntarily take conservation action. Increasing participation in conservation incentive programs and minimizing the welfare losses associated with meeting conservation targets may be best achieved by offering programs that allow flexibility in terms of legal arrangements and other program attributes.     

Choice of stakeholder groups and members in multicriteria decision models

Simplistic economic objectives such as maximisation of producer profits are of little relevance in generating information to assist in the management of natural resources beyond the individual firm level. To provide data and information to support decision-making in natural resource management, it is necessary to take into account the views of various stakeholder groups and the multiple objectives of each group, through the use of some form of multicriteria analysis (MCA). Important decisions arise in the choice of stakeholder, since this will influence the management advice generated. Many groups and individuals can be affected by resource management decisions, but it would be impractical to attempt to identify the objectives and estimate their importance for each group. Also, questions arise concerning whether or not to include government agencies (which represent the broader community) and researchers as stakeholders. A further issue concerns choosing representative samples of stakeholder groups, from which to obtain preference data. Discussions with modellers and a reading of the literature would suggest that the choice of stakeholder groups and representatives is conducted haphazardly and is perhaps biased, and that a more systematic approach is needed. This article explores the above issues with reference to a number of multicriteria analyses, including local studies.     

Near-miss events,risk messages,and decision making

Decades of research have sought to understand how disaster preparedness decisions are made. We believe one understudied factor is the impact of near-miss events. A near-miss occurs when an event (such as a hurricane or terrorist attack) has some non-trivial probability of ending in disaster (loss of life, property damage), but the negative outcome is avoided largely by chance (e.g., at the last minute, the storm dissipates or the bomb fails to detonate). In the first of two experiments, we study reactions to a hurricane threat when participants are told about prior near-miss events. We find that people with information about a prior near-miss event that has no negative consequences are less likely to take protective measures than those with either no information or information about a prior near-miss event that has salient negative information. Similar results have been shown in prior research, but we seek to understand people’s reasoning for the different reactions. We examine the role of an individual’s risk propensity and general level of optimism as possible explanatory variables for the “near-miss” effect. We find risk propensity to be stable across conditions, whereas general optimism is influenced by the type of prior near-miss information, so that optimism mediates how near-miss information impacts protective decisions. People who experience a potentially hazardous near-miss but escape without obvious cues of damage will feel more optimistic and take less protective action. In the second study, we test messages about the hazard’s risk and examine the impact of these messages to offset the influence of near-misses. We end by discussing the implications of near-misses for risk communication.     

Adoption of sustainable agriculture practices: Evidence from a semi-arid region of Ethiopia

In the wake of the resource constraints for external farm inputs faced by farmers in developing countries, sustainable agriculture practices that rely on renewable local or farm resources present desirable options for enhancing agriculture productivity. In this study, plot-level data from the semi-arid region of Ethiopia, Tigray are used to investigate the factors influencing farmers' decisions to adopt agriculture practices, with a particular focus on conservation tillage, compost and chemical fertilizer. A trivariate probit model is used to analyze the determinants of adoption of these practices. In addition, stochastic dominance analysis is used to compare the productivity impacts of compost with that of chemical fertilizer based on a six-year cross-sectional farm-level dataset. Our results indicate heterogeneity with regard to the factors that influence adoption decisions of the three practices and the importance of both plot and household characteristics on influencing adoption decisions. In particular, we found that household endowments and access to information, among other factors, impact the choice of sustainable farming practices significantly. Furthermore, the use of stochastic dominance analysis supported the contention that sustainable farming practices enhance productivity. They even proved to be superior to the use of chemical fertilizers — justifying the need to investigate factors that influence adoption of these practices and to use this knowledge to formulate policies that encourage adoption.     

Analysing Conflict between Cultural Heritage and Nature Conservation in the Australian Alps: A CVM Approach

The contingent valuation method (CVM) can be useful for assessing the non-market economic values associated with government land use decisions. A particular variant of CVM, which is based on dichotomous choice responses, has become widely used. Previous studies have employed a variety of techniques for analysing dichotomous choice CVM data to produce estimates of economic welfare changes. This paper summarizes these analytical options, and illustrates their application in a case study concerning cattle grazing on the Bogong High Plains in the Australian Alps. This case study is one of the few to assess the non-market economic value of cultural heritage conservation using contingent valuation, and is also unusual in that the competing values are both non-market in character. Nature conservation and heritage values were separately measured using two independent surveys. Mean willingness to pay for nature conservation and cultural heritage were estimated using a range of parametric and nonparametric methods.     

EVALUTING STREAM STANDARD VIOLATIONS USING A WATER QUALITY DATA BASE1

ABSTRACT: Cumulative density functions (c.d.f.'s) for water quality random variables may be estimated using data from a routine grab sampling program. The c.d.f. may then be used to estimate the probability that a single grab sample will violate a given stream standard and to determine the anticipated number of violations in a given number of samples. Confidence limits about a particular point on the c.d.f. may be used to reflect the accuracy with which the sample estimate represents the true c.d.f. Methods are presented here for calculating such confidence limits using both a normal model and a nonparametric model. Examples are presented to illustrate the usefulness of an estimated c.d.f. and associated confidence limits in assessing whether an observed number of standard violations is the result of natural variability or represents real degradation in water quality.     

Nonparametric Monte Carlo Simulation for Flood Frequency Curve Derivation: An Application to a Korean Watershed1

Abstract: A mix of causative mechanisms may be responsible for flood at a site. Floods may be caused because of extreme rainfall or rain on other rainfall events. The statistical attributes of these events differ according to the watershed characteristics and the causes. Traditional methods of flood frequency analysis are only adequate for specific situations. Also, to address the uncertainty of flood frequency estimates for hydraulic structures, a series of probabilistic analyses of rainfall‐runoff and flow routing models, and their associated inputs, are used. This is a complex problem in that the probability distributions of multiple independent and derived random variables need to be estimated to evaluate the probability of floods. Therefore, the objectives of this study were to develop a flood frequency curve derivation method driven by multiple random variables and to develop a tool that can consider the uncertainties of design floods. This study focuses on developing a flood frequency curve based on nonparametric statistical methods for the estimation of probabilities of rare floods that are more appropriate in Korea. To derive the frequency curve, rainfall generation using the nonparametric kernel density estimation approach is proposed. Many flood events are simulated by nonparametric Monte Carlo simulations coupled with the center Latin hypercube sampling method to estimate the associated uncertainty. This study applies the methods described to a Korean watershed. The results provide higher physical appropriateness and reasonable estimates of design flood.     

LONG-RANGE STREAMFLOW FORCASTING USING NONPARAMETRIC REGRESSION1

ABSTRACT: A class of nonparametric procedures is developed for producing long-range streamflow forecasts. The forecasting procedures, which are based solely on daily streamflow data, utilize nonparametric regression to relate a forecast variable to a covariate variable. The forecast variable is a function of future streamflow and can take a wide variety of forms. The covariate variable is a function of antecedent streamflow. The forecasting procedures are quite flexible, both in terms of the duration of the forecast period and the types of forecast variables that can be considered. The procedures are used to develop long-term (1–4 months) forecasts of minimum daily flow of the Potomac River at Washington, D.C. This forecast information is an integral component of water management activities for the Washington, D.C. metropolitan area.     

Problems in Extinction Model Selection and Parameter Estimation

It is a vexing problem to achieve a consensus about the proper scientific way to assess population viability for habitat conservation plans. Rather than a hypothesis-testing approach, here it is proposed to select population models, estimate extinction parameters, and assess prediction uncertainty using a pragmatic, empirical Bayesian approach. The simplest usable models include the effects of population growth, r; carrying capacity, K; Allee threshold, N(A); and environmental stochasticity, v(r). Analytic predictions of expected extinction times are available for such models. Models that are more complex can be elaborated from this basis. Selection from a hierarchy of nesting population models can often be done through the evaluation of parameters. The estimation of the most important extinction parameters can be undertaken in a variety of ways. Time series can be analyzed to estimate r(d), v(r), rho, and K. Habitat models and individualistic population models may help estimate N(A) and K and demographic stochasticity. Fine-scale biogeography and climatological data may be useful in the estimation of a variety of parameters. Because it takes many years to estimate extinction parameters accurately for a given population of interest, the most efficient estimation procedures are desirable. I propose the use of prior information from an (as yet nonexistent) population biology database. The accumulation of local information through monitoring will improve our estimates allowing adaptive management. Uncertainty in the estimates will always remain, but it may be quantified by the posterior distributions. A crude example is discussed using treefrog population data. Although the motivations, beliefs, and biases of competing stakeholders will differ, a habitat conservation plan could accommodate this variation in the prior distributions. Field experience from monitoring will increasingly clear up any discrepancies between the opposing beliefs and the real ecosystem. As the world is an uncertain place and because there is no universal scientific method, there will always be controversy and surprises. The best we can do is (1) agree about our prior information, (2) agree about the strategy of model selection and parameter estimation, and (3) agree about our strategy for adaptive management. Perhaps the greatest impediment to such prior agreements for HCPs is the likely paranoia inspired by the use of unfamiliar statistical methodology. We need to train students of ecology in a more flexible and deeper understanding of statistics and philosophy of science.     

NONPARAMETRIC STATISTICAL ANALYSIS OF ANNUAL PEAK FLOW DATA FROM A RECENTLY URBANIZED WATERSHED1

ABSTRACT. In order to demonstrate the feasibility of a nonparametric statistical application in investigating the hydrologic impact of the rapid land use change accompanying intense urbanization, annual maximum peak flow data from an actual example (the Northeast Branch Basin, a recently urbanized Washington, D.C., suburban watershed) were analyzed. Annual peak flow data from the Patuxent Basin above Unity, Maryland, a rural watershed in close proximity to the study area, were compared to data from the Northeast Branch for the Same period utilizing the Wilcoxon matched-pairs signed-ranks test. A change in central tendency of each series was noted at the 0.01 significance level; however, the change was negative in the rural basin and positive in the urbanized Northeast Branch Basin. This central tendency change was considered indicative of an average decrease in the size of rainstorms producing annual maximum peak discharges. Rainstorm data from the Northeast Branch Basin were divided into two equal periods (before urbanization and after) and the Wilcoxon test was applied. It was found that rainstorms producing maximum annual peak discharges in the urbanized period were indeed smaller than those in the prior period (0.01 level of significance); however, larger annual peaks were produced. It was concluded that nonparametric statistical methods can be used readily with conventional methods to isolate and clearly analyze the various problems in an actual urban hydrologic study.     

Economic simulation modeling of reprocessing alternatives for corn masa byproducts

Increasing production of corn masa for tortillas, chips, and related snack foods is resulting in large quantities of organic residuals requiring environmentally sound management. These byproduct streams appear suitable for use as livestock feed material, thus eliminating landfilling costs. Possibilities for developing livestock feed include direct shipping to livestock feeding facilities, blending prior to shipping, extrusion processing, pellet mill processing, and dehydration. To assess the viability of these options for reprocessing masa byproducts as livestock feed materials, an economic model was developed and applied to each of these alternatives. Through a series of simulation runs with this model, it was determined that direct shipping was by far the most inexpensive means of recycling masa processing residuals (10–57 $/Mg). Other alternatives examined in increasing order of costs included blending prior to shipping, extrusion, pellet mill processing (3–15, 5–18, and 4–18 times greater than direct shipping, respectively), while dehydration was clearly cost-prohibitive (33–81 times greater). Bagged feed was slightly more expensive to produce than bulk feed (1.1 times greater), and reprocessing costs increased as delivery distance increased, due to increased labor, equipment, and fuel costs, but decreased as byproduct generation rate increased, due to the development of the economies of scale. Alternately, based on a tipping fee of 50 $/Mg, the total estimated cost to landfill ranged from 65 to 112 $/Mg. Based on this cost analysis, direct shipping and feeding to livestock is the recycling option of choice for masa processing byproducts. Although specific details of process configurations and associated costs will vary, similar results are likely for other high moisture food processing residuals destined for utilization as livestock feed or components thereof.     

Identifying Influences on Model Uncertainty: An Application Using a Forest Carbon Budget Model

Uncertainty is an important consideration for both developers and users of environmental simulation models. Establishing quantitative estimates of uncertainty for deterministic models can be difficult when the underlying bases for such information are scarce. We demonstrate an application of probabilistic uncertainty analysis that provides for refinements in quantifying input uncertainty even with little information. Uncertainties in forest carbon budget projections were examined with Monte Carlo analyses of the model FORCARB. We identified model sensitivity to range, shape, and covariability among model probability density functions, even under conditions of limited initial information. Distributional forms of probabilities were not as important as covariability or ranges of values. Covariability among FORCARB model parameters emerged as a very influential component of uncertainty, especially for estimates of average annual carbon flux.     

HEDONIC PRICE ANALYSIS OF THE EFFECT OF A FLOODPLAIN ON PROPERTY VALUES1

ABSTRACT: The effect of a floodplain location on the market value for property is controversial, with the conventional wisdom being that buyers are myopic. Previous parametric and nonparametric statistical tests have proven inconclusive in deciding the issue. Herein, a theoretical model, that of hedonic price indexes, is posited and tested, using housing sales characteristics data obtained from a Multi-List Service cooperative. This study suggests that home buyers do adjust the purchase price for houses within a floodplain, and this amounts to, on average, just over 12 percent. The data are from a small midwestern town in which the last severe flooding occurred a decade ago.     

REGIONAL FLOOD EQUATIONS FOR THE PROVINCES OF ONTARIO AND QUEBEC1

ABSTRACT: When nonparametric frequency analysis was performed on 183 stations from Ontario and Quebec, unimodal and multimodal maximum annual flood density functions were discovered. In order to determine generating mechanisms, a monthly partitioning of the annual maximum floods was undertaken. The timing of the floods revealed that the unimodal distributions reflected a single flood generating mechanism while the multi-modal densities reflected two or more mechanisms. Based on the division of the flood series by mechanisms, nine homogeneous regions were delineated. L-moment distributional homogeneity tests along with smaller standard errors for the regional equations supported the delineation.     

The role of respondents’ comfort for variance in stated choice surveys: evidence from a SCUBA diving case

Preference elicitation among outdoor recreational users is subject to measurement errors that depend, in part, on survey planning. This study uses data from a choice experiment survey on recreational SCUBA diving to investigate whether self-reported information on respondents’ comfort when they complete surveys correlates with the error variance in stated choice models of their responses. Comfort-related variables are included in the scale functions of the scaled multinomial logit models. The hypothesis was that higher comfort reduces error variance in answers, as revealed by a higher scale parameter and vice versa. Information on, e.g., sleep and time since eating (higher comfort) correlated with scale heterogeneity, and produced lower error variance when controlled for in the model. That respondents’ comfort may influence choice behavior suggests that knowledge of the respondents’ activity patterns could be used to plan the timing of interviews to decrease error variance in choices and, hence, generate better information.     

Assessing Uncertainty in Estimates of Nitrogen Loading to Estuaries for Research, Planning, and Risk Assessment

Parametric (propagation for normal error estimates) and nonparametric methods (bootstrap and enumeration of combinations) to assess the uncertainty in calculated rates of nitrogen loading were compared, based on the propagation of uncertainty observed in the variables used in the calculation. In addition, since such calculations are often based on literature surveys rather than random replicate measurements for the site in question, error propagation was also compared using the uncertainty of the sampled population (e.g., standard deviation) as well as the uncertainty of the mean (e.g., standard error of the mean). Calculations for the predicted nitrogen loading to a shallow estuary (Waquoit Bay, MA) were used as an example. The previously estimated mean loading from the watershed (5,400 ha) to Waquoit Bay (600 ha) was 23,000 kg N yr⁻¹. The mode of a nonparametric estimate of the probability distribution differed dramatically, equaling only 70% of this mean. Repeated observations were available for only 8 of the 16 variables used in our calculation. We estimated uncertainty in model predictions by treating these as sample replicates. Parametric and nonparametric estimates of the standard error of the mean loading rate were 12–14%. However, since the available data include site-to-site variability, as is often the case, standard error may be an inappropriate measure of confidence. The standard deviations were around 38% of the loading rate. Further, 95% confidence intervals differed between the nonparametric and parametric methods, with those of the nonparametric method arranged asymmetrically around the predicted loading rate. The disparity in magnitude and symmetry of calculated confidence limits argue for careful consideration of the nature of the uncertainty of variables used in chained calculations. This analysis also suggests that a nonparametric method of calculating loading rates using most frequently observed values for variables used in loading calculations may be more appropriate than using mean values. These findings reinforce the importance of including assessment of uncertainty when evaluating nutrient loading rates in research and planning. Risk assessment, which may need to consider relative probability of extreme events in worst-case scenarios, will be in serious error using normal estimates, or even the nonparametric bootstrap. A method such as our enumeration of combinations produces a more reliable distribution of risk.