Do natural resources and economic components exhibit differential quantile environmental effects?

In view of the resource curse assumption, the environmental aspects of resource utilization are arguably posing more dangers to human existence. In the Middle East and North Africa (MENA) region, the region that holds more than 60% and 50% of the world's oil and gas reserves respectively, the need to examine the contribution of natural resources to environmental quality among other factors cannot be overemphasized. By leveraging on the novelty of observing the differential impact of natural resources and other economic components such as income and primary energy utilizations across the quantiles of carbon emission, this study implements the quantile regression approach alongside other relevant techniques to analyze data between 1990 and 2018 for selected countries in the MENA region including Saudi Arabia, Iran, Kuwait, Qatar, Algeria, Morocco, Oman, Egypt, and the United Arab Emirates (UAE). The result posits that natural resource utilization generally hampers the environment across the quantiles. However, this negative effect decreases until the 50th quantile before starting to rise again toward the upper quantiles. Additionally, primary energy utilization and globalization respectively worsen and improve environmental quantile, especially toward the upper quantiles while income affirms the inverted U-shaped hypothesis across the entire quantiles. Moreover, there is a statistically significant one-way directional causality from natural resources, economic expansion, primary energy use, and globalization to carbon emission levels. Hence, the study offers environmentally friendly resource utilization policies to the MENA economies and other resource-rich states by extension.     

FLOOD FREQUENCY ANALYSIS FOR EVALUATING WATERSHED CONDITIONS WITh RAINFALL-RUNOFF MODELS1

ABSTRACT: Many rainfall-runoff modeling studies compare flood quantiles for different land-use and/or flood mitigation scenarios. However, when flood quantiles are estimated using conventional statistical methods, comparisons may be misleading because the estimates often misrepresent the quantile relationship between scenarios. An alternate statistical procedure is proposed, in which rainfall-runoff modeling is used to evaluate an approximate relationship between flood quantiles for different scenarios. Monte Carlo experiments show that the proposed method produces flood quantile estimates that better reflect the differences between scenarios. The ratio between quantiles for different scenarios is more accurate, so comparisons of the scenarios using flood quantiles are more reliable.     

Application of Index Procedures to Flood Frequency Analysis in Turkey1

Abstract: This study investigates the regional analysis of annual maximum flood series of 48 stream gauging stations in the basins of the West Mediterranean Region in Turkey. The region is divided into three homogeneous subregions according to both Student‐t test and Dalrymple homogeneity test. The regional relationships of mean annual flood per unit area‐drainage area and coefficient of skew‐coefficient of variation are obtained. Two statistically meaningful relationships of the mean flood per unit area‐drainage area and a unique relationship between skewness and variation coefficients exist. Results show that the index‐flood method may be applicable to each homogenous subregion to estimate flood quantiles in the study area.     

CONFIDENCE INTERVALS FOR FLOOD EVENTS UNDER A PEARSON 3 OR LOG PEARSON 3 DISTRIBUTION1

ABSTRACT: The Pearson type 3 (P3) and log Pearson type 3 (LP3) distributions are very frequently used in flood frequency analysis. Existing methods for constructing confidence intervals for quantiles (X_p) of these two distributions are very crude. Most of these methods are based on the idea of adjusting confidence intervals for quantiles Y_p of the normal distribution to obtain approximate confidence inervals for quantiles X_p of the P3/LP3 distribution. Since there is no theoretical reason why this “base” distribution, Y, should be taken to be normal, we search in the present study for the best possible base distribution for producing confidence intervals for P3/LP3 quantiles. We consider a group of base distributions such as the normal, log normal, Weibull, Gumbel, and exponential. We first assume that the skew coefficient, γ of X, to be known, and develop a method for adjusting confidence intervals for Y_p to produce approximate confidence intervals for X_p. We then compare this method (Method A) with another method (Method B) introduced by Stedinger. Simulation shows that the performance of each of these two methods depends on the base distribution Y that is being used, but as a whole, the normal distribution appears to be the best-fit distribution for producing confidence intervals for P3/LP3 quantiles when γ is assumed to be known. We then extend our method (Method A) to the more important case of unknown coefficient of skewness. It is shown that by taking Y to be Weibull, fairly accurate confidence intervals for P3/LP3 quantiles can be obtained for quite a wide range of sample sizes and coefficients of skewness commonly found in hydrology. The case of the P3 distribution with negative skewness needs further research.     

ESTIMATING LOW-FLOW QUANTILES FROM DRAINAGE-BASIN CHARACTERISTICS IN NEW HAMPSHIRE AND VERMONT1

ABSTRACT: This study systematically develops, validates, and compares alternative approaches for estimating quantiles of the distribution of annual minimum seven-day-average flows (7Q) for ungaged, unregulated drainage basins in New Hampshire and Vermont via regression on map-measurable drainage-basin characteristics. At 47 gaging stations in the region, the hypotheses that 7Q is log normally distributed and serially independent are not rejected, and the regional average spatial correlation is R= 0.35. Step-forward examination of a suite of potential predictor variables revealed that logarithm of drainage area, mean elevation, and fraction of basin covered with sand and gravel deposits are significant predictors of quantiles of 7Q. The regression equations were incorporated into four approaches to estimate the 7Q value with a nonexceedence probability of 0.1, 7Q10. Comparison of observed values and values predicted via a delete-one jackknife resampling validation indicates that one of the approaches gives estimates with acceptable bias and precision, with median relative error of 33 percent and prediction error of 64 percent. This is equivalent to the precision obtainable with only one to two years of gaging records. In spite of this limited precision, the approaches developed herein are useful for predicting 7Q quantiles at ungaged sites. Further improvement in precision will likely be possible only by exploiting the spatial correlation of annual 7Q.     

COMPARISON OF MODELS FOR ESTIMATING FLOOD QUANTILES IN NEW HAMPSHIRE AND VERMONT1

We develop and compare three regression models for estimating flood quantiles at ungaged stream reaches in New Hampshire and Vermont. These models emerge from systematic analysis and validation of relations between flood magnitude and six candidate predictors reflecting basin size, topography, and climate and channel size at 36 gaging stations with record lengths exceeding 20 years. Of the candidate predictors, bank full width is most highly correlated with flood magnitude and the best prediction equation is based on width. Thus channel geometry is closely related to the current hydrologic regime in spite of geologically recent glaciation and apparently non-alluvial bank materials. We also develop models that use information obtainable from maps or GIS. The best of these uses drainage area and drainage-basin elevation as predictors, but it is substantially less precise than the width-based relation. A third relation using only drainage area as a predictor is even less precise but may be useful for some purposes. No other single predictors or combinations yielded useful predictions, although some had been included in previously-established models for the region. Model comparison included examination of residuals generated by regression using one-at-a-time suppression of data points and comparison with precision obtainable with gaging records of varying lengths.     

ANTIDEGRADATION WATER QUALITY CRITERIA FOR THE DELAWARE RIVER: A DISTRIBUTION-FREE STATISTICAL APPROACH1

ABSTRACT: Existing water quality for the Middle Delaware Scenic and Recreational River is significantly better than is required by current standards, leaving a potential for degradation. A method is presented for deriving candidate antidegradation water quality criteria for this segment of the Delaware River using statistical analysis of historic (ambient) water quality data. Data for 34 water quality parameters are first evaluated for data density, serial correlation, trend, seasonality, and other factors. These preliminary analyses are based on observation of data plots and application of distribution-free statistical techniques that are insensitive to outliers and are robust to relatively mild violations of basic assumptions. Data for 12 of the parameters have sufficient density for further analysis and can reasonably be modeled as independent and identically distributed over time (either seasonally or for the entire data sets). For these 12 parameters, distribution-free statistical methods are developed and used to derive intervals within which there is high confidence (usually greater than 95 percent) that the quantiles with potential use as anti-degradation criteria (the 0.85^th, 0.90^th, and 0.95^th quantiles in this study) for a particular parameter lie.     

ANTIDEGRADATION WATER QUALITY CRITERIA FOR THE DELAWARE RIVER: A DISTRIBUTION-FREE STATISTICAL APPROACH1

ABSTRACT: Existing water quality for the Middle Delaware Scenic and Recreational River is significantly better than is required by current standards, leaving a potential for degradation. A method is presented for deriving candidate antidegradation water quality criteria for this segment of the Delaware River using statistical analysis of historic (ambient) water quality data. Data for 34 water quality parameters are first evaluated for data density, serial correlation, trend, seasonality, and other factors. These preliminary analyses are based on observation of data plots and application of distribution-free statistical techniques that are insensitive to outliers and are robust to relatively mild violations of basic assumptions. Data for 12 of the parameters have sufficient density for further analysis and can reasonably be modeled as independent and identically distributed over time (either seasonally or for the entire data sets). For these 12 parameters, distribution-free statistical methods are developed and used to derive intervals within which there is high confidence (usually greater than 95 percent) that the quantiles with potential use as anti-degradation criteria (the 0.85^th, 0.90^th, and 0.95^th quantiles in this study) for a particular parameter lie.     

Nature of property rights and motivation for blue growth: An empirical evidence from the fisheries industry

If unauthorized resource use is prevented, managing marine resources by allocating property rights may match economic and environmental conservation incentives. However, because of the developing exploitation of marine resources and accompanying pollution, species' living conditions in Europe's waters are changing more quickly than before. By considering the roles of fisheries productivity, intellectual property rights, intellectual capital rights, market size, governance, and economic growth from 1990 to 2022, this paper aims to investigate the dynamic effect of property rights factors on the sustainability of the fisheries industry in 27 European countries. At higher quantiles, the findings showed a significant positive association between governance and fisheries sustainability adopting a new method, the MMQR with fixed effects, the Method of Moments Quantile Regression. In addition, in EU27 nations, the impact of intellectual property rights was favorable and statistically significant from the first to ninth quantiles. The findings show that the EU14 developed nations have more excellent governance and intellectual capital rights than the EU13 developing countries, significantly benefiting fisheries sustainability. In the same way that market size and economic growth condense fisheries sustainability in EU14 developed and EU13 developing countries, it has been discovered that intellectual property rights do the same across all quantiles, supporting the growth hypothesis for fisheries-producing countries. The findings specifically show that the beneficial solid impact of intellectual property rights, market size, and economic development on the sustainability of fisheries is more significant in EU13 developing nations than in EU14 developed countries. These results provide policymakers with helpful information for promoting property rights aspects in EU14 and EU13 nations via effective green technologies in the fisheries sector to meet sustainable development objectives.     

Evidence for Changing Flood Risk in New England Since the Late 20th Century1

Abstract: Long‐term flow records for watersheds with minimal human influence have shown trends in recent decades toward increasing streamflow at regional and national scales, especially for low flow quantiles like the annual minimum and annual median flows. Trends for high flow quantiles are less clear, despite recent research showing increased precipitation in the conterminous United States over the last century that has been brought about primarily by an increased frequency and intensity of events in the upper 10th percentile of the daily precipitation distribution – particularly in the Northeast. This study investigates trends in 28 long‐term annual flood series for New England watersheds with dominantly natural streamflow. The flood series are an average of 75 years in length and are continuous through 2006. Twenty‐five series show upward trends via the nonparametric Mann‐Kendall test, 40% (10) of which are statistically significant (p < 0.1). Moreover, an average standardized departures series for 23 of the study gages indicates that increasing flood magnitudes in New England occurred as a step change around 1970. The timing of this is broadly synchronous with a phase change in the low frequency variability of the North Atlantic Oscillation, a prominent upper atmospheric circulation pattern that is known to effect climate variability along the United States east coast. Identifiable hydroclimatic shifts should be considered when the affected flow records are used for flood frequency analyses. Special treatment of the flood series can improve the analyses and provide better estimates of flood magnitudes and frequencies under the prevailing hydroclimatic condition.     

Daily Precipitation Extremes in Iran: Decadal Anomalies and Possible Drivers

This study focuses on the empirical statistical analysis of the anomalies in daily precipitation extremes by applying the quantile perturbation method (QPM) to data from 31 Iranian weather stations during the period between 1961 and 2005. The possible causes behind the anomalies in precipitation extremes are identified by analyzing their relationship with the anomalies in eight atmospheric indices (i.e., NAO, SOI, PDO, AMO, NCP, DMI, WeMO, SSN). In terms of decadal oscillations, the country was generally wet in the 1960s and 1970s with most stations exhibiting periods of higher quantile perturbations, whereas lower quantile perturbations were dominant in the 1980s and 1990s. The highest perturbation in extreme precipitation quantiles prevails in Central Iran during the early 1980s, in which the quantiles are about 50% higher than the ones based on the full time series. The frequency of significant precipitation anomalies for winter season was greater than that for spring and autumn seasons. For the summer season, the humid region in North Iran demonstrates strong positive anomalies. The results highlight the noticeable role of large‐scale climatic factors in the anomalous behavior of precipitation extremes in Iran. The atmospheric drivers of the quantile anomalies in extreme precipitation were found to differ from one season to another.     

Bayesian Nonstationary Frequency Analysis of Hydrological Variables1

Ouarda, T.B.M.J. and S. El‐Adlouni, 2011. Bayesian Nonstationary Frequency Analysis of Hydrological Variables. Journal of the American Water Resources Association (JAWRA) 47(3):496‐505. DOI: 10.1111/j.1752‐1688.2011.00544.x Abstract: The present paper provides a discussion of nonstationary frequency analysis models in hydrology with a focus on the Bayesian approach. The Bayesian model provides an efficient estimation framework of hydrological quantiles in the presence of nonstationarity. In nonstationary frequency analysis models, the parameters are functions of covariates, allowing for dependent parameters and trends. The use of the nonstationary Generalized Maximum Likelihood Estimation method in hydrologic frequency analysis is discussed. This model allows using prior information concerning the variables under study and considering a number of models (linear, quadratic, etc.) of the dependence of the parameters on covariates. A discussion is also provided concerning the use of the reversible jump Monte Carlo Markov Chain procedure which allows carrying out the estimation of the posterior distributions of the parameters and the selection of the Bayesian model at the same time. An application to a case study is presented to illustrate the potential of the model.     

A GIS Approach to Prioritizing Habitat for Restoration Using Neotropical Migrant Songbird Criteria

Restoration efforts to increase wildlife habitat quality in agricultural landscapes have limited funding and are typically done on a first come, first serve basis. In order to increase the efficiency of these restoration efforts, a prioritized ranking system is needed to obtain the greatest increase in habitat quality possible for the fewest amount of hectares restored. This project examines the use of a GIS based multi-criteria approach to prioritize lands for reforestation along the Kaskaskia River in Illinois. Loss of forested area and corresponding increase in forest fragmentation has decreased songbird habitat quality across the Midwestern United States. We prioritized areas for reforestation based on nine landscape metrics: available agricultural land, forest cover gaps, edge density, proximity to river, 200 m corridor area, total forest core area, fringe core area, distance to primary core value, and primary core area. The multi-criteria analysis revealed that high priority areas for reforestation were most likely to be close to the riparian corridor and existing large blocks of forest. Analysis of simulated reforestation (0, 0.5, 1.0, 5.0 10.0, 25.0, and 50.0% of highest priority parcels reforested) revealed different responses for multiple landscape metrics used to quantify forest fragmentation following reforestation, but indicated that the study area would get the greatest rate of return on reforestation efforts by reforesting 10.0% of the highest priority areas. This project demonstrates how GIS and a multi-criteria analysis approach can be used to increase the efficiency of restoration projects. This approach should be considered by land managers when attempting to identify the location and quantity of area for restoration within a landscape.     

Establishing backcountry use quotas: An example from Mineral King,California

Increasing levels of visitor use and consequent resource damage have necessitated that backcountry use restrictions be established in the Mineral King area of Sequoia National Park, California. In this paper we review the steps taken in developing a trailhead quota system. The availability of acceptable campsites, based on a detailed inventory of site distribution and impact, was used to quantitatively derive use capacities for each camp area. Wilderness permit data on visitor dispersal patterns from the major trailheads, including length of stay at each camp area, were then used to translate the area capacities into daily trailhead quotas that would assure that these capacities were not surpassed. The general approach is applicable to any backcountry area, although large complex areas may require the use of available computer simulation models.     

EFFICIENTLY REDUCING NONPOINT PHOSPHORUS LOADS TO LAKES AND RESERVOIRS1

ABSTRACT: Several problems that make it difficult to deal with water pollution from cropland are identified. The most immediate need is for a rational framework for determining where conservation programs can make an impact on eutrophication problems in reservoirs draining rural watersheds. This includes estimating the level of control that would be required for each local farm situation and the economic impacts for the planning area. A modeling approach is suggested for a planning area in Southeastern Pennsylvania.     

The perplexing dispute over oil

Territorial disputes in the South China Sea have been exacerbated by the great potential for oil reserves in the area. The Spratly Islands have been a major object of these disputes. While China and other countries have softened their positions to a degree, questions remain regarding how each country would cooperate in oil exploration in this area. The ultimate resolution of the dispute over the Spratly Islands could set an example for the future handling of rival claims, but whether the approach will be one of cooperation or conflict is unclear.     

Transforming the Integrated Conservation and Development Project (ICDP) Approach: Observations from the Ranomafana National Park Project, Madagascar

Preservation of the biological diversity and ecosystems in protected areas can be achieved through projects linking conservation of the protected areas with improved standards of living for resident peoples within surrounding buffer zones. This is the hypothetical claim of the integrated conservation and development project (ICDP) approach to protected area management. This paper, based on several years of experience with the Ranomafana National Park Project in Madagascar, questions the major assumptions of this approach from ethical and practical perspectives. The four basic strategies available to ICDPs – protected areas, buffer zones, compensation, and economic development – are analyzed and shown to be deficient or untested in the case of Ranomafana. Recommendations are made to explore conservation models other than the western conception of the national park, to modify the notion of a buffer zone outside the protected area, to redistribute money or other resources directly to the poor people living in and around the protected areas, and to eliminate the middle men in the development business. An appeal is made to focus on local education, organization and discipline in order to promote self-determination and self-reliance among resident peoples of protected areas. The paper argues that a public works program, similar to the Roosevelt administration's Civilian Conservation Corps of the 1930s, funded through a hard-currency endowment or other innovative financing mechanism, should be tried as a replacement for the currently questionable ICDP approach at Ranomafana.     

Assessing landscape dynamics in a protected area

This article describes an approach to assessing spatial and temporal land-use and land-cover changes in and adjacent to protected areas and to the measurement of landscape stability within a protected area. Methods employed include aerial photographic interpretation and GIS technology. Odum's four-compartment ecosystem model provides the conceptual framework for assessing landscape stability. The study area is a selected sample of the Upper San Pedro National Riparian Conservation Area in the high desert grassland of southeastern Arizona. Significant changes were observed in the landscape matrix and riparian ecosystem. However, when these changes were assessed in the context of Odum's model, the change was nonsignificant. Implications of the approach and potential applications in protected area management are discussed.     

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS1

ABSTRACT: A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that includsion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.