Uncertainty,Irreversibility and the Timing Problems of Environmental Policy

Most environmental issues and policy designing are uncertain and irreversible;therefore,the timing of environmental policy implementation becomes especially important.This paper establishes a random dynamic programming model and analyzes the optimal timing problems in environmental policy under uncertain variables.This model results indicate that two variables have a significant impact on the timing of environmental policy implementation and they work in opposite directions:on one hand,the more uncertain the economy is,the higher the cost of policies implementation will be,and consequently the incentive to immediately adopt the policy will be stronger.On the other hand,the higher the uncertainty of the environment is,the stronger the irreversibility of ecological harm caused by pollutants per unit will be.Therefore,the government should implement new environmental policies as early as possible in order to gain more ecological benefits.     

DETERMINING NUTRIENT EXPORT COEFFICIENTS AND SOURCE LOADING UNCERTAINTY USING IN‐STREAM MONITORING DATA1

ABSTRACT: Over a three‐year period, flow and nutrients were monitored at 13 sites in the upper North Bosque River watershed in Texas. Drainage areas above sampling sites differed in percent of dairy waste application fields, forage fields, wood/range, and urban land area. A multiple regression approach was used to develop total phosphorus (TP) and total nitrogen (TN) export coefficients for the major land uses in these heterogeneous drainage areas. The largest export coefficients were associated with dairy waste application fields followed by urban, forage fields, and wood/range. An empirical model was then established to assess nutrient contribution by major sources using developed export coefficients and point source loadings from municipal wastewater treatment. This model was verified by comparison of estimated loadings to measured in‐stream data. Monte Carlo simulation techniques were applied to provide an uncertainty analysis for nutrient loads by source, based on the variance associated with each export coefficient. The largest sources of nutrients contributing to the upper North Bosque River were associated with dairy waste application fields and forage fields, while the greatest relative uncertainty in source contribution was associated with loadings from urban and wood/range land uses.     

TROPICAL EUTROPHIC LAKE MODELING AND PARAMETER DISCRIMINATION1

ABSTRACT: Water quality data collected between 1978 and 1981 in a highly lake in Northern Venezuela, Lake Valencia, were analyzed to detect spatial and temporal trends. Based on the results of the analyses, an appropriate nutrient-algae dynamics model was formulated. Because many parameters, such as the algae concentration were constant over time, and the model is time dependent, the model had to be calibrated with the use of a large and structured trial-and-error calibration process. Through the calibration process, the most sensitive parameters of the model were identified, and are in order of importance: the chlorophyll-to-nitrogen ratio for algae, the algae settling velocity, the phosphorus release rate from the sediments, the chlorophyll-to-phosphorus ratio for algae, and the exchange coefficient in the upper layer of the lake. Model simulations showed that a reduction in the nitrogen load to the lake as well as a reduction in the phosphorus load will decrease the algae population. These model simulations had a high degree of uncertainty associated with them, making additional sampling directed towards the measurement of the sensitive parameters desirable.     

The Effect of Nitrogen Enrichment on the Carbon Sink in Coniferous Forests: Uncertainty and Sensitivity Analyses of Three Ecosystem Models

Estimates of the global carbon sink induced by nitrogen enrichment range vary widely, from nearly zero to 2.3 Gt C year^-1. It is necessary to reduce this uncertainty if we are to make accurate predictions of the future magnitude of the terrestrial carbon sink. Here, we present a Monte Carlo approach to uncertainty and sensitivity analysis of three ecosystem models, Century,BGCand Hybrid. These models were applied to a coniferous forest ecosystem in Sweden. The best estimate of the change in total carbon content of the ecosystem with the cumulative change in nitrogen deposition over 100 years, C_total/N_deposition was 20.1 kg C (kg N)^-1 using the pooled mean, with a pooled standard deviation of 13.8 kg C (kg N)^-1. Variability in parameters accounted for 92% of the total uncertainty in C_total/N_deposition, and only 8% was attributable to differences between models. The most sensitive parameters were those which controlled the allocation of assimilate between leaves, roots and stem. In particular, an increase in allocation to fine roots led to a large reduction in C_total/N_deposition in all models, because the fine roots have a very high turnover rate, and extra carbon allocated there is soon lost through mortality and decomposition.     

NPS MODEL PARAMETER UNCERTAINTY ANALYSIS FOR AN OFF-STREAM RESERVOIR1

ABSTRACT: Parameter uncertainties exert a significant effect on nonpoint source pollution (NPS) modeling results. A decision made on the basis of such results may thereby be inappropriate. In this work, the parameter uncertainty is analyzed to explore an improved modeling procedure. Drainage patterns generated from digital elevation data and rainfall are the major parameters examined. A case study for the watershed of the Posan off-stream reservoir is implemented. A significant spatial variation of NPS distribution simulated with a drainage pattern generated from varied methods is observed. The effects of rainfall randomness on the spatial loading distribution are assessed and computed based on a Monte Carlo simulation. The proposed procedure is capable of improving the quality of modeling results and the decision for an appropriate control strategy.     

USE OF STATE ESTIMATION TECHNIQUES IN WATER RESOURCE SYSTEM MODELING1

ABSTRACT. A relatively straightforward illustration of the potential uses of State Estimation techniques in water resources modeling is given. Background theory for Linear and Extended Kalman Filters is given; application of the filter techniques to modeling BOD and oxygen deficit in a stream illustrates the importance of model conceptualization, model completeness, uncertainty in model dynamics and incorporation of measurements and measurement errors. Potential applications of state estimation techniques to measurement system design; model building, assessment and calibration; and data extension are explored.     

APPLICATION OF COMPUTER INTENSIVE STATISTICS TO PARAMETER UNCERTAINTY IN STREAMFLOW SYNTHESIS1

ABSTRACT: This paper introduces the application of Computer Intensive Statistics (CIS) to the evaluation of parameter uncertainty with specific reference to the parameters in the ARMA models used in the synthesis of streamflows. The CIS provides an empirically derived joint distribution for all parameters. Random values from this distribution can be drawn for application in the model for the generation of a specific synthetic sample. The CIS originally proposed by Efron and discussed herein is of general applicability in the evaluation of uncertainty in any statistic derived from a set of data. Some comments and limitations of the CIS are discussed in the concluding remarks.     

UNCERTAINTY ANALYSIS OF RUNOFF ESTIMATES FROM A RUNOFF CONTOUR MAP1

ABSTRACT: The U.S. Environmental Protection Agency (EPA) in cooperation with the U.S. Geological Survey (USGS) conducted an analysis to quantify the uncertainty associated with interpolating runoff to specific sites using a runoff contour map. We interpolated runoff to 93 gaged watersheds from a runoff contour map using (1) hand interpolation to the watershed outlet, (2) a computer interpolation to the watershed outlet, and (3) hand interpolation to the watershed centroid. We compared the interpolated values to the actual gaged values and found that there was a bias in the average interpolated value for runoff estimated at basin outlets, with interpolated values being less than the actual. We found no significant difference between the hand interpolation method and the computer interpolation method except that the computer method tended to have higher variability due to factors inherent to the software used. There were no strong spatial correlations or regional patterns in the runoff interpolations, which indicates that there are no regional biases introduced in the development of the contour map. We determined that we could estimate runoff, on the average, within approximately 8.9 cm (3.5 in; 15 percent) of the measured value using the three methods. The results of this work indicate that runoff contour maps can he used in regional studies to estimate runoff to ungaged systems with quantifiable uncertainty.     

National Greenhouse Gas Inventories: Understanding Uncertainties versus Potential for Improving Reliability

We investigated the Austrian national greenhouse gas emission inventory to review the reliability and usability of such inventories. The overall uncertainty of the inventory (95% confidence interval) is just over 10% of total emissions, with nitrous oxide (N₂O) from soils clearly providing the largest impact. Trend uncertainty – the difference between 2 years – is only about five percentage points, as important sources like soil N₂O are not expected to show different behavior between the years and thus exhibit a high covariance. The result is very typical for industrialized countries – subjective decisions by individuals during uncertainty assessment are responsible for most of the discrepancies among countries. Thus, uncertainty assessment cannot help to evaluate whether emission targets have been met. Instead, a more rigid emission accounting system that allows little individual flexibility is proposed to provide harmonized evaluation uninfluenced by the respective targets. Such an accounting system may increase uncertainty in terms of greenhouse gas fluxes to the atmosphere. More importantly, however, it will decrease uncertainty in intercountry comparisons and thus allow for fair burden sharing. Setting of post-Kyoto emission targets will require the independent evaluation of achievements. This can partly be achieved by the validation of emission inventories and thorough uncertainty assessment.     

Approaches to Evaluate Water Quality Model Parameter Uncertainty for Adaptive TMDL Implementation1

Abstract: The National Research Council recommended Adaptive Total Maximum Daily Load implementation with the recognition that the predictive uncertainty of water quality models can be high. Quantifying predictive uncertainty provides important information for model selection and decision‐making. We review five methods that have been used with water quality models to evaluate model parameter and predictive uncertainty. These methods (1) Regionalized Sensitivity Analysis, (2) Generalized Likelihood Uncertainty Estimation, (3) Bayesian Monte Carlo, (4) Importance Sampling, and (5) Markov Chain Monte Carlo (MCMC) are based on similar concepts; their development over time was facilitated by the increasing availability of fast, cheap computers. Using a Streeter‐Phelps model as an example we show that, applied consistently, these methods give compatible results. Thus, all of these methods can, in principle, provide useful sets of parameter values that can be used to evaluate model predictive uncertainty, though, in practice, some are quickly limited by the “curse of dimensionality” or may have difficulty evaluating irregularly shaped parameter spaces. Adaptive implementation invites model updating, as new data become available reflecting water‐body responses to pollutant load reductions, and a Bayesian approach using MCMC is particularly handy for that task.