A Simple Framework for Incorporating Seasonal Streamflow Forecasts into Existing Water Resource Management Practices1

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Abstract: Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility.     

A review of recent substance flow analyses of phosphorus to identify priority management areas at different geographical scales

The dwindling global reserves of extractable phosphorus (P) and its growing demand to produce the required food for a burgeoning global population (the global P crisis) necessitate the sustainable use of this crucial resource. To advert the crisis requires informed policy decisions which can only be obtained by a better understanding of the nature and magnitude of P flow through different systems at different geographical scales. Through a systematic and in-depth review of twenty one recent substance flow analyses of P, we have assessed the key P inflows, outflows, stocks, internal flows, and recycling flows at the city, regional, and country scales. The assessment has revealed, the main inflow and outflow of P at the city scale occurs through food and wastewater respectively, while the main stock of P occurs in landfill. At the regional scale, mineral ore is the main P inflow and chemical P fertilizer is the main outflow particularly in the regions that have P fertilizer production sector. In contrast, either chemical P fertilizer or animal feed is the key inflow and either food and agricultural products or soil losses (erosion, runoff, and/or leaching) is the major outflow especially in the regions without P fertilizer production sector. At the country scale, the key P inflow occurs either through mineral ore or chemical P fertilizer and the key outflow takes place either as food and agricultural products, waste (both solid and liquid), or soil losses (erosion, runoff, and/or leaching). The main stock of P both at the regional and country scales occurs in the soil of the agricultural production sector. As identified in this assessment, the key unproductive outflows and stocks at different geographical scales indicate that there is a potential scope to improve P management through the increased P recovery and recycling, and by the utilization of available soil P stocks. In many of the studies at all the geographical scales, P recycling flow has been found to be less than 20% of the total inflow, and even in some studies at the country scale, P recycling has been found to be entirely absent, which is a clear indication of poor P management. This study has also identified, there is a clear knowledge gap in relation to understanding the P flow over multiple years at the regional scale. The information about the key flows and stocks at different geographical scales as we identified can be utilized to make better P policy and management decisions for a city, region, or country. The information can also be used to guide future research that aims to analyze P flow at the city, regional, and country scales.     

PLANNING METHODOLOGY FOR ANALYSIS AND MANAGEMENT ON LAKE EUTROPHICATION1

ABSTRACT: The effects of changing nutrient inputs through land use management, waste water treatment, or effluent diversion are not clear, and managers are discovering that decisions which were effective in reversing eutrophication for one lake are often unsuccessful when applied to another. Simple empirical relationships are often used to predict the impact of management decisions. Errors in estimation could result in either substantial costs for overdesign or failure to meet desired eutrophication levels. This paper presents and illustrates a methodology to evaluate the impact of land use and water resource management decisions on lake eutrophication. The problems of worth of additional information, and uncertainty of estimates were handled within a cost-effectiveness framework. The probability of exceeding a critical level of eutrophication was considered as a measure of effectiveness. The cost criterion is the expected value of opportunity costs, costs of analysis and costs of additional information. Uncertainty analysis techniques were used to estimate the effectiveness of various management alternatives. Bayesian methods can be utilized to determine the worth of additional information. The methodology was applied to Beseck Lake, Connecticut, and the cost and effectiveness measures estimated for a number of land management alternatives. Worth of additional information was not determined in this initial effort in uncertainty analysis for lake eutrophication management.     

Trade-offs in assessing environmental impacts

Summary The evolution of the environmental issue has moved through the early stage of technological fixes and estimable economic costs into an area of larger uncertainty and higher information costs.The economic costs of pollution control are about one percent of the GNP. The technological fixes have improved air and water quality somewhat; but in air quality little reduction in nitrogen oxides has been achieved, and in water quality about 25 percent of the water is poor or worse, with high fecal coliform bacteria.The next stage of environmental improvement is to reduce hazardous chemical elements in the environment. In these areas, the health and biological effects are still uncertain, information costs are high, and subjective judgments are common.As uncertainty and qualitative judgments have become more prominent in environmental decisions, delays have been introduced into the decision loops, raising regulatory costs, and heightening adversary stresses between business and environmentalists. The stresses place an aura of arbitrariness over regulations in a milieu where the governability of society is already questioned. This is not the time to retreat from environmental improvement, but to try to simplify the decision process. Two possible alternatives are to codify court precedents into a more rigorous reliance on agency administration law, or to revert to the common rules of civil law that complainants must show damage and cause before an award is allowable by an administrative agency. Either of these options would free the decision making process of delays and allow it to function prospectively, while allowing environmental damages from inadequate decisions to be remedied retroactively. Potential liabilities may increase, but at least the decision process could move forward without being frustrated by regulatory delays.     

RESERVOIR OPERATION ANI EVALUATION OF DOWNSTREAM FLOW AUGMENTATION1

ABSTRACT: Operation of a storage‐based reservoir modifies the downstream flow usually to a value higher than that of natural flow in dry season. This could be important for irrigation, water supply, or power production as it is like an additional downstream benefit without any additional investment. This study addresses the operation of two proposed reservoirs and the downstream flow augmentation at an irrigation project located at the outlet of the Gandaki River basin in Nepal. The optimal operating policies of the reservoirs were determined using a Stochastic Dynamic Programming (SDP) model considering the maximization of power production. The modified flows downstream of the reservoirs were simulated by a simulation model using the optimal operating policy (for power maximization) and a synthetic long‐term inflow series. Comparing the existing flow (flow in river without reservoir operation) and the modified flow (flow after reservoir operation) at the irrigation project, the additional amount of flow was calculated. The reliability analysis indicated that the supply of irrigation could be increased by 25 to 100 percent of the existing supply over the dry season (January to April) with a reliability of more than 80 percent.     

Importance of scientific uncertainty in decision making

Uncertainty in environmental decision making should not be thought of as a problem that is best ignored. In fact, as is illustrated in a simple example, we often informally make use of awareness of uncertainty by hedging decisions away from large losses. This hedging can be made explicit and formalized using the methods of decision analysis. While scientific uncertainty is undesirable, it can still be useful in environmental management as it provides a basis for the need to fund additional monitoring, experimentation, or information acquisition to improve the scientific basis for decisions.     

Assessing and communicating data quality in policy-relevant research

The quality of scientific information in policy-relevant fields of research is difficult to assess, and quality control in these important areas is correspondingly difficult to maintain. Frequently there are insufficient high-quality measurements for the presentation of the statistical uncertainty in the numerical estimates that are crucial to policy decisions. We propose and develop a grading system for numerical estimates that can deal with the full range of data quality—from statistically valid estimates to informed guesses. By analyzing the underlying quality of numerical estimates, summarized as spread and grade, we are able to provide simple rules whereby input data can be coded for quality, and these codings carried through arithmetical calculations for assessing the quality of model results. For this we use the NUSAP (numeral, unit, spread, assessment, pedigree) notational system. It allows the more quantitative and the more qualitative aspects of data uncertainty to be managed separately. By way of example, we apply the system to an ecosystem valuation study that used several different models and data of widely varying quality to arrive at a single estimate of the economic value of wetlands. The NUSAP approach illustrates the major sources of uncertainty in this study and can guide new research aimed at the improvement of the quality of outputs and the efficiency of the procedures.     

A geophysical perspective of value of information: examples of spatial decisions for groundwater sustainability

The value of information (VOI) can be used to determine what kind of spatial information maybe relevant and useful for groundwater sustainability decisions. In this paper, the unique challenges for applying VoI to spatial information from geophysical data are described. The uncertainty regarding the spatial structure or continuity of the subsurface properties can be described with geostatistical sample models. Using these models, one can quantify the prior value given our present state of uncertainty and a set of decision alternatives and outcomes. Because geophysical techniques are a type of remote-sensing data, assuming “perfect” information is not realistic since the techniques usually are indirectly sampling the aquifer properties. Therefore, the focus of this paper is describing how the data reliability (the measure of imperfectness) can be quantified. One of the foremost considerations is the non-unique relationship between geological parameters (which determine groundwater flow) and geophysical observables (what determines the response of the technique). Another is to have the information in a form such that it is useful for spatial decisions. This will often require inversion and interpretation of the geophysical data. Inversion reconstructs an image of the subsurface from the raw geophysical data. How closely the image reproduces the true subsurface structure or property of interest depends on the particular technique’s resolution, depth of investigation and sensor locations. Lastly, in some cases, interpretation of the geophysical data or inversion will be necessary to link the data to the variables that determine the outcome of the decision. Three examples are provided that illustrate different approaches and methods for addressing these challenges. In the examples, time-domain electromagnetic and electrical resistivity techniques are evaluated for their ability to assist in spatial decisions for aquifer management. The examples considered address these three situations: aquifer vulnerability to surface–borne contaminants, managed aquifer recharge and CO₂/brine leakage (related to CO₂ geologic sequestration activities). The methods presented here are transferable to other subsurface sciences and decisions that involve risk. Recent work has been applied to geothermal well-siting using electromagnetic techniques. These approaches can also be applied for oil and mining spatial decisions, and they offer advantages over previous VOI work done for oil applications: they explicitly include the geologic uncertainty modeling and simulate the physics of the considered geophysical technique.     

A MULTICOMPONENT MODEL OF PHOSPHORUS DYNAMICS IN RESERVOIRS1

The models available for simulating phosphorus dynamics and trophic state in impoundments vary widely. The simpler empirically derived phosphorus models tend to be appropriate for long-term, steady or near steady state analyses. The more complex ecosystem models, because of computational expense and the importance of input parameter uncertainty, are impractical for very long-term simulation and most applicable for time-variable water quality simulations generally of short to intermediate time frames. An improved model for time variable, long-term simulation of trophic state in reservoirs with fluctuating inflow and outflow rates and volume is needed. Such a model is developed in this paper representing the phosphorus cycle in two-layer (i.e., epilimnion and hypolimnion) reservoirs. The model is designed to simulate seasonally varying reservoir water quality and eutrophication potential by using the phosphorus state variable as the water quality indicator. Long-term simulations with fluctuating volumes and variable influent and effluent flow rates are feasible and practical. The model utility is demonstrated through application to a pumped storage reservoir characteristic of these conditions.     

Rough Set Rule Induction for Suitability Assessment

The data that characterize an environmental system are a fundamental part of an environmental decision-support system. However, obtaining complete and consistent data sets for regional studies can be difficult. Data sets are often available only for small study areas within the region, whereas the data themselves contain uncertainty because of system complexity, differences in methodology, or data collection errors. This paper presents rough-set rule induction as one way to deal with data uncertainty while creating predictive if–then rules that generalize data values to the entire region. The approach is illustrated by determining the crop suitability of 14 crops for the agricultural soils of the Willamette River Basin, Oregon, USA. To implement this method, environmental and crop yield data were spatially related to individual soil units, forming the examples needed for the rule induction process. Next, four learning algorithms were defined by using different subsets of environmental attributes. ROSETTA, a software system for rough set analysis, was then used to generate rules using each algorithm. Cross-validation analysis showed that all crops had at least one algorithm with an accuracy rate greater than 68%. After selecting a preferred algorithm, the induced classifier was used to predict the crop suitability of each crop for the unclassified soils. The results suggest that rough set rule induction is a useful method for data generalization and suitability analysis.     

CONFLICTS IN ATTAINING NATIONAL GOALS FOR BOTH WATER QUALITY AND ENERGY PRODUCTION1

ABSTRACT: The “policy environment” is defined herein as the institutional setting in which planning is conducted and policy decisions are made with regard to meeting two of the Nation's high priority goals: water quality protection and energy independence. The simultaneous pursuit of these goals has resulted in numerous conflicts among the energy industry, environmentalists, and government. An analysis of selected energy development-water quality conflicts shows that these conflicts can be described in terms of one or more of the following policy environment characteristics: resource scarcity, sense of urgency, lack of experience, administrative complexity, uncertainty about future policies and regulations, technological complexity, and uncertainty about impacts. These characterics provide a useful framework for formulating potential strategies for the resolution of energy development-water quality conflicts.     

Predicting soil fumigant air concentrations under regional and diverse agronomic conditions

SOFEA (SOil Fumigant Exposure Assessment system; Dow AgroSciences, Indianapolis, IN) is a new stochastic numerical modeling tool for evaluating and managing human inhalation exposure potential associated with the use of soil fumigants. SOFEA calculates fumigant concentrations in air arising from volatility losses from treated fields for large agricultural regions using multiple transient source terms (treated fields), geographical information systems (GIS) information, agronomic specific variables, user-specified buffer zones, and field reentry intervals. A modified version of the USEPA Industrial Source Complex Short Term model (ISCST3) is used for air dispersion calculations. Weather information, field size, application date, application rate, application type, soil incorporation depth, pesticide degradation rates in air, tarp presence, field retreatment, and other sensitive parameters are varied stochastically using Monte Carlo techniques to mimic region and crop specific agronomic practices. Regional land cover, elevation, and population information can be used to refine source placement (treated fields), dispersion calculations, and risk assessments. This paper describes the technical algorithms of SOFEA and offers comparisons of simulation predictions for the soil fumigant 1,3-dichloropropene (1,3-D) to actual regional air monitoring measurements from Kern, California. Comparison of simulation results to daily air monitoring observations is remarkable over the entire concentration distribution (average percent deviation of 44% and model efficiency of 0.98), especially considering numerous inputs such as meteorological conditions for SOFEA were unavailable and approximated by neighboring regions. Both current and anticipated and/or forecasted fumigant scenarios can be simulated using SOFEA to provide risk managers and product stewards the necessary information to make sound regulatory decisions regarding the use of soil fumigants in agriculture.     

Exergy and extended exergy accounting of very large complex systems with an application to the province of Siena, Italy

This paper describes the application of exergy and extended exergy analyses to large complex systems. The system to be analysed is assumed to be at steady state, and the input and output fluxes of matter and energy are expressed in units of exergy. Human societies of any reasonable extent are indeed Very Large Complex Systems and can be represented as interconnected networks of N elementary "components", their Subsystems; the detail of the disaggregation depends on the type and quality of the available data. The structural connectivity of the "model" of the System must correctly describe the interactions of each mass or energy flow with each sector of the society: since it is seldom the case that all of these fluxes are available in detail, some preliminary mass- and energy balances must be completed and constitute in fact a part of the initial assumptions. Exergy accounting converts the total amount of resources inflow into their equivalent exergetic form with the help of a table of "raw exergy data" available in the literature. The quantification of each flow on a homogeneous exergetic basis paves the way to the evaluation of the efficiency of each energy and mass transfer between the N sectors and makes it possible to quantify the irreversible losses and identify their sources. The advantage of the EEA, compared to a classical exergy accounting, is the inclusion in the system balance of the exergetic equivalents of three additional "Production Factors": human Labour, Capital and Environmental Remediation costs. EEA has an additional advantage: it allows for the calculation of the efficiency of the domestic sector (impossible to evaluate with any other energy- or exergy-based method) by considering the working hours as its product. As implied in the title, an application of the method was made to a model of the province of Siena (on a year 2000 database): the results show that the sectors of this Province have values of efficiency close to the Italian average, with the exception of the commercial and energy conversion sectors that are more efficient, in agreement with the rather peculiar socio-economic situation of the Province. The largest inefficiency is found to be in the transportation sector, which has an efficiency lower then 30% in EEA and lower than 10% in classical exergy accounting.     

Simulation of Temperature Mitigation by a Stormwater Detention Pond1

Abstract: A numerical model has been developed to simulate the hydraulic and heat transfer properties of a stormwater detention pond, as part of a simulation tool to evaluate thermal pollution of coldwater streams from stormwater runoff. The model is dynamic (unsteady) and based on principles of fluid mechanics and heat transfer. It is driven by hourly weather data, and specified inflow rates and temperatures. To calibrate and validate the pond model field data were collected on a commercial site in Woodbury, Minnesota. The relationship between pond inflow and outflow rates to precipitation was effectively calibrated using continuously recorded pond levels. Algorithms developed for surface heat transfer in lakes were found to be applicable to the pond with some modification, resulting in agreement of simulated and observed pond surface temperature within 1.0°C root mean square error. The use of an unshaded pond for thermal mitigation of runoff from paved surfaces was evaluated using the pond model combined with simulated runoff from an asphalt parking lot for six years of observed rainfall events. On average, pond outflow temperature was 1.2°C higher than inflow temperature, but with significant event‐to‐event variation. On average, the pond added heat energy to runoff from an asphalt parking lot. Although the pond added total heat energy to runoff, it did reduce the rate of heat outflow from the pond by an order of magnitude due to reductions in volumetric outflow rate compared with the inflow rate. By reducing the rate of heat flow, the magnitude of temperature impacts in a receiving stream were also reduced, but the duration of impacts was increased.     

Quantifying sources of climate uncertainty to inform risk analysis for climate change decision-making

Quantitative estimates of future climate change and its various impacts are often based on complex climate models which incorporate a number of physical processes. As these models continue to become more sophisticated, it is commonly assumed that the latest generation of climate models will provide us with better estimates of climate change. Here, we quantify the uncertainty in future climate change projections using two multi-model ensembles of climate model simulations and divide it into different components: internal, scenario and model. The contributions of these sources of uncertainty changes as a function of variable, temporal and spatial scale and especially lead time in the future. In the new models, uncertainty intervals for each of the components have increased. For temperature, importance of scenario uncertainty is the largest over low latitudes and increases nonlinearly after the mid-century. It has a small importance for precipitation simulations on all time scales, which hampers estimating the effect which any mitigation efforts might have. In line with current state-of-the-art adaptation approaches, we argue that despite these uncertainties climate models can provide useful information to support adaptation decision-making. Moreover, adaptation decisions should not be postponed in the hope that future improved scientific understanding will result in more accurate predictions of future climate change. Such simulations might not become available. On the contrary, while planning adaptation initiatives, a rational framework for decision-making under uncertainty should be employed. We suggest that there is an urgent need for continued development and use of improved risk analysis methods for climate change adaptation.     

Trends and applications of multi-criteria decision analysis: use in government agencies

Government agencies are responsible for making complex, high-stake decisions, which require them to balance political, technical, and economic considerations. Pressure from stakeholders and administrative requirements necessitate a traceable and transparent method for decision making. Multi-criteria decision analysis (MCDA) methods are available to decision makers to facilitate systematic treatment of the information and factors necessary to make informed and effective decisions in complex circumstances. A survey of gray and academic literature was conducted to gauge the level of application and awareness of MCDA methods by US government agencies and determine if the tools’ benefits are being realized. Results show an increase in awareness and consideration of MCDA from 2000 to the present, and that agencies are especially considering and using tools to engage with stakeholders. Government agencies would benefit from extending the application of MCDA to strategic planning and congressional engagement, as well as by standardizing MCDA use to better enable inter-agency collaboration and communication.     

INFORMATION TECHNOLOGY IN WATERSHED MANAGEMENT DECISION MAKING1

ABSTRACT: Watershed management decision making is a complex process. Cooperation and communication among federal, state, and local stakeholders is required while balancing biophysical and socioeconomic concerns. The public is taking part in environmental decisions, and the need for technology transfer from public agencies to stakeholders is increasing. Information technology has had a profound influence on watershed management over the past decade. Advances in data acquisition through remote sensing, data utilization through geographic information systems (GIS), and data sharing through the Internet have provided watershed managers access to more information for management decisions. In the future, applications incorporating hydrologic simulation models, GIS, and decision support systems will be deployed through the Internet. In addition to challenges in making complex modeling technology available to diverse audiences, new information technology issues, such as interoperability, Internet access, and security, are introduced when GIS, simulation models, and decision support systems are integrated in an Internet environment. This paper presents a review of current use of information technology in watershed management decision making and a discussion of issues created when developing Internet based, integrated watershed management decision support systems. A prototype spatial decision support system (SDSS) for rangeland watershed management was developed using web services, which are components that communicate using text based messages, thus eliminating proprietary protocols. This new framework provides an extensible, accessible, and interoperable approach for SDSS.     

A dynamic hydrological Monte Carlo simulation model to inform decision-making at Lake Toolibin,Western Australia

Lake Toolibin, an ephemeral lake in the agricultural zone of Western Australia, is under threat from secondary salinity due to land clearance throughout the catchment. The lake is extensively covered with native vegetation and is a Ramsar listed wetland, being one of the few remaining significant migratory bird habitats in the region. Currently, inflow with salinity greater than 1000 mg/L TDS is diverted from the lake in an effort to protect sensitive lakebed vegetation. However, this conservative threshold compromises the frequency and extent of lake inundation, which is essential for bird breeding. It is speculated that relaxing the threshold to 5000 mg/L may pose negligible additional risk to the condition of lakebed vegetation. To characterise the magnitude of improvement in the provision of bird breeding habitat that might be generated by relaxing the threshold, a dynamic water and salt balance model of the lake was developed and implemented using Monte Carlo simulation. Results from best estimate model inputs indicate that relaxation of the threshold increases the likelihood of satisfying habitat requirements by a factor of 9.7. A second-order Monte Carlo analysis incorporating incertitude generated plausible bounds of [2.6, 37.5] around the best estimate for the relative likelihood of satisfying habitat requirements. Parameter-specific sensitivity analyses suggest the availability of habitat is most sensitive to pan evaporation, lower than expected inflow volume, and higher than expected inflow salt concentration. The characterisation of uncertainty associated with environmental variation and incertitude allows managers to make informed risk-weighted decisions.     

PUBLIC INFORMATION AND CITIZEN INVOLVEMENT1

ABSTRACT: Public information, an important contributer to citizen involvement in the governmental decision making process, has traditionally been given a low priority by government agencies. However, citizens are becoming increasingly concerned about governmental decisions that affect their lives and are demanding more information about governmental activities, including information about water resource issues, Because of this active citizen interest and involvement, the role of public information in increasing citizen awareness is becoming more important. Government officials and professionals should recognize that public information efforts also play an important role in achieving increased credibility and respect for their agencies. Once an agency recognizes the importance of public information and decides to initiate a public information program, careful planning is required to develop a program that addresses the needs of both the citizens and the agency. Two of the most flexible and cost effective ways to get information to the publié are through publications and public meetings. Both can be easily adapted to suit the needs of specific audiences and projects. A successful public information program can be carried out at a relatively low cost, but it requires a substantial amount of time and energy. The commitment of time and energy for this purpose is a good investment, however, because an effective public information program can play a significant role in improving the quality of governmental decisions through the increased involvement of the citizenry.     

A heuristic approach to stochastic cutoff grade optimization for open pit mining complexes with multiple processing streams

Cutoff grade specifies the available supply of metallic ore from an open pit mine to the multiple processing streams of an open pit mining complex. An optimal cutoff grade strategy maximizes the net present value (NPV) of an open pit mining operation subject to the mining, processing, and marketing/refining capacity constraints. Even though, the quantities of material flowing from the mine to the market are influenced by the expected variation in the available metal content or inherent uncertainty in the supply of ore, the majority of cutoff grade optimization models not only disregard this aspect and may lead to unrealistic cash flows, but also they are limited in application to an open pit mining operation with single processing facility. The model proposed herein determines the optimal cutoff grade policy based on a stochastic framework that accounts for uncertainty in supply of ore to the multiple ore processing streams. An application on a large-scale open pit mining operation develops a unique cutoff grade policy along with a portfolio of mining, processing, and marketing/refining rates. Owing to the geological uncertainty, the approach addresses risk by showing a difference of 14% between the minimum and maximum production rates, cash flows and NPV.