A Review of the United States' Past and Projected Water Use

Good information and data on water demands are needed to perform good analyses, yet collecting and compiling spatially and temporally consistent water demand data are challenges. The objective of our work was to understand the limitations associated with water‐use estimates and projections. We performed a comprehensive literature review of national and regional United States (U.S.) water‐use estimates and projections. We explored trends in past regional projections of freshwater withdrawals and compared these values to regional estimates of freshwater withdrawals made by the U.S. Geological Survey. Our results suggest a suite of limitations exist that have the potential for influencing analyses aiming to extract explanatory variables from the data or using the data to make projections and forecasts. As we explored regional projections, we paid special attention to the two largest water demand‐side sectors — thermoelectric energy and irrigation — and found thermoelectric projections are more spread out than irrigation projections. All data related to water use have limitations, and there is no alternative to making the best use that we can of the available data; our article provides a comprehensive review of these limitations so that water managers can be more informed.     

Can Volunteers Collect Data that are Comparable to Professional Scientists? A Study of Variables Used in Monitoring the Outcomes of Ecosystem Rehabilitation

Having volunteers collect data can be a cost-effective strategy to complement or replace those collected by scientists. The quality of these data is essential where field-collected data are used to monitor progress against predetermined standards because they provide decision makers with confidence that choices they make will not cause more harm than good. The integrity of volunteer-collected data is often doubted. In this study, we made estimates of seven vegetation attributes and a composite measure of six of those seven, to simulate benchmark values. These attributes are routinely recorded as part of rehabilitation projects in Australia and elsewhere in the world. The degree of agreement in data collected by volunteers was compared with those recorded by professional scientists. Combined results showed that scientists collected data that was in closer agreement with benchmarks than those of volunteers, but when data collected by individuals were analyzed, some volunteers collected data that were in similar or closer agreement, than scientists. Both groups’ estimates were in closer agreement for particular attributes than others, suggesting that some attributes are more difficult to estimate than others, or that some are more subjective than others. There are a number of ways in which higher degrees of agreement could be achieved and introducing these will no doubt result in better, more effective programs, to monitor rehabilitation activities. Alternatively, less subjective measures should be sought when developing monitoring protocols. Quality assurance should be part of developing monitoring methods and explicitly budgeted for in project planning to prevent misleading declarations of rehabilitation success.     

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.     

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.     

Applying value of information methods to prioritize elements for water quality management with an example of linear alkylbenzene sulfonate in the Yodo River,Japan

Narrowing the decision space is crucial in water quality management at the meso-scale for developing countries, where a lack of data and financial budgets prevent the development of appropriate management plans and result in serious water quality degradation in many rivers. In this study, a framework for handling this task is proposed, comprising a lumped water quality model, with sensitivity and uncertainty analyses, and a management domain, including loss estimation and value of information analysis. Through a case study with linear alkylbenzene sulfonate (LAS) in the Yodo River, it is found that non-point sources and flow rate are factors that influence LAS concentration at the hot spot location. By considering the entire process of water quality management planning, we identify that the definition of the cost function of LAS treatment determines the appropriate estimation for the expected loss in reducing LAS under uncertain water quality conditions. The value of information analysis with “expected value of including uncertainty” and “expected value of perfect information” further helps estimate the benefit of including uncertainty in decision-making and the financial cost for obtaining more information regarding inputs that have been previously prioritized.     

Mineral reserves under price uncertainty

National reporting organizations and regulatory bodies for the minerals and mining sector are requiring publicly reported Ore-Reserve estimates to take account of uncertainties. Whilst methodologies that account for physical uncertainty appear relatively well developed, methodologies which can take account of economic uncertainty appear much less so. To counter this shortfall, we present an efficient and general methodology which can quantify the effect of price uncertainty within reserve estimates, providing both the expected reserve size and the associated distribution (box whisker plot). This statistical information can be used by interested parties to understand precisely where the reserve risks lie, which we highlight in a worked example.     

Uncertainty Models for Estimates of Physical Characteristics of River Segments Over Large Areas

Spatially comprehensive estimates of the physical characteristics of river segments over large areas are required in many large‐scale analyses of river systems and for the management of multiple basins. Remote sensing and modeling are often used to estimate river characteristics over large areas, but the uncertainties associated with these estimates and their dependence on the physical characteristics of the segments and their catchments are seldom quantified. Using test data with varying degrees of independence, we derived analytical models of the uncertainty associated with estimates of upstream catchment area (CA), segment slope, and mean annual discharge for all river segments of a digital representation of the hydrographic network of France. Although there were strong relationships between our test data and estimates at the scale of France, there were also large relative local uncertainties, which varied with the physical characteristics of the segments and their catchments. Discharge and CA were relatively uncertain where discharge was low and catchments were small. Discharge uncertainty also increased in catchments with large rainfall events and low minimum temperature. The uncertainty of segment slope was strongly related to segment length. Our uncertainty models were consistent across large regions of France, suggesting some degree of generality. Their analytical formulation should facilitate their use in large‐scale ecological studies and simulation models.     

ANALYSIS OF UNCERTAINTY IN FLOOD PLAIN MAPPING1

ABSTRACT: Components contributing to uncertainty in the location of the flood plain fringe of a mapped flood plain are identified and examined to determine their relative importance. First-order uncertainty analysis is used to provide a procedure for quantifying the magnitude of uncertainty in the location of the flood plain fringe. Application of the procedure indicated that one standard deviation of uncertainty in flood plain inundation width was about one third of the mean computed inundation width for several flood population-flood geometry combinations. Suggested mapping criteria, which directly incorporate uncertainty estimates, are given. While these criteria are more suitable for use in developing areas than in flood plains that have had extensive development, the analysis procedure can be used to accommodate property owners who challenge the validity of estimated flood fringe boundaries. Use of uncertainty analysis in flood plain mapping should enhance the credibility of the final plan.     

THE INFLUENCE OF (x,y) UNCERTAINTY ON PREDICTION ERROR AND CONTOUR LINES FROM A THREE-DIMENSIONAL SURFACE1

ABSTRACT: In geohydrology, three-dimensional surfaces are typically represented as a series of contours. Water levels, saturated thickness, precipitation, and geological formation boundaries are a few examples of this practice. These surfaces start as point measurements that are then analyzed to interpolate between the known point measurements. This first step typically creates a raster or a set of grid points. In modeling, subsequent processing uses these to represent the shape of a surface. For display, they are usually converted to contour lines. Unfortunately, in many field applications, the (x, y) location on the earth's surface is much less confidently known than the data in the z dimension. To test the influence of (x, y) locational accuracy on z dimension point predictions and their resulting contours, a Monte Carlo study was performed on water level data from northwestern Kansas. Four levels of (x, y) uncertainty were tested ranging in accuracy from one arc degree-minute (± 2384 feet in the x dimension and ± 3036 feet in the y dimension) to Global Positioning Systems (GPS) accuracy (± 20 feet for relatively low cost systems). These span the range of common levels of locational uncertainty in data available to hydrologists in the United States. This work examines the influence that locational uncertainty can have on both point predictions and contour lines. Results indicate that overall mean error exhibits a small sensitivity to locational uncertainty. However, measures of spread and maximum errors in the z domain are greatly affected. In practical application, this implies that estimates over large regions should be asymptotically consistent. However, local errors in z can be quite large and increase with (x, y) uncertainty.     

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.     

Investigations of Contaminated Fluvial Sediment Deposits: Merging of Statistical and Geomorphic Approaches

Concentrations of contaminants in sediment deposits can have large spatial variability resulting from geomorphic processes acting over long time periods. Thus, systematic (e.g., regularly spaced sample locations) or random sampling approaches might be inefficient and/or lead to highly biased results. We demonstrate the bias associated with systematic sampling and compare these results to those achieved by methods that merge a geomorphic approach to evaluating the physical system and stratified random sampling concepts. By combining these approaches, we achieve a more efficient and less biased characterization of sediment contamination in fluvial systems. These methods are applied using a phased sampling approach to characterize radiological contamination in sediment deposits in two semiarid canyons that have received historical releases from the Los Alamos National Laboratory. Uncertainty in contaminant inventory was used as a metric to evaluate the adequacy of sampling during these phased investigations. Simple, one-dimensional Monte Carlo simulations were used to estimate uncertainty in contaminant inventory. We also show how one can use stratified random sampling theory to help estimate uncertainty in mean contaminant concentrations.     

PROBABILISTIC METHODS IN STREAM QUALITY MANAGEMENT1

ABSTRACT. Recent advances in water quality modelling have pointed out the need for stochastic models to simulate the probabilistic nature of water quality. However, often all that is needed is an estimate of the uncertainty in predicting water quality variables. First order analysis is a simple method of providing an estimate in the uncertainty in a deterministic model due to uncertain parameters. The method is applied to the simplified Streeter-Phelps equations for DO and BOD; a more complete Monte Carlo simulation is used to check the accuracy of the results. The first order analysis is found to give accurate estimates of means and variances of DO and BOD up to travel times exceeding the critical time. Uncertainty in travel time and the BOD decay constant are found to be most important for small travel times; uncertainty in the reaeration coefficient dominates near the critical time. Uncertainty in temperature was found to be a negligible source of uncertainty in DO for all travel times.     

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.     

Forest Fires in Mediterranean Countries: CO2 Emissions and Mitigation Possibilities Through Prescribed Burning

Forest fires are an integral part of the ecology of the Mediterranean Basin; however, fire incidence has increased dramatically during the past decades and fire is expected to become more prevalent in the future due to climate change. Fuel modification by prescribed burning reduces the spread and intensity potential of subsequent wildfires. We used the most recently published data to calculate the average annual wildfire CO(2) emissions in France, Greece, Italy, Portugal and Spain following the IPCC guidelines. The effect of prescribed burning on emissions was calculated for four scenarios of prescribed burning effectiveness based on data from Portugal. Results show that prescribed burning could have a considerable effect on the carbon balance of the land use, land-use change and forestry (LULUCF) sector in Mediterranean countries. However, uncertainty in emission estimates remains large, and more accurate data is needed, especially regarding fuel load and fuel consumption in different vegetation types and fuel layers and the total area protected from wildfire per unit area treated by prescribed burning, i.e. the leverage of prescribed burning.     

ESTIMATING THE PROBABILITY OF EXCEEDING GROUNDWATER QUALITY STANDARDS1

ABSTRACT: A model for estimating the probability of exceeding groundwater quality standards at environmental receptors based on a simple contaminant transport model is described. The model is intended for locations where knowledge about site-specific hydrogeologic conditions is limited. An efficient implementation methodology using numerical Monte Carlo simulation is presented. The uncertainty in the contaminant transport system due to uncertainty in the hydraulic conductivity is directly calculated in the Monte Carlo simulations. Numerous variations of the deterministic parameters of the model provide an indication of the change in exceedance probability with change in parameter value. The results of these variations for a generic example are presented in a concise graphical form which provides insight into the topology of the exceedance probability surface. This surface can be used to assess the impact of the various parameters on exceedance probability.     

Making framing of uncertainty in water management practice explicit by using a participant-structured approach

By now, the need for addressing uncertainty in the management of water resources is widely recognized, yet there is little expertise and experience how to effectively deal with uncertainty in practice. Uncertainties in water management practice so far are mostly dealt with intuitively or based on experience. That way decisions can be quickly taken but analytic processes of deliberate reasoning are bypassed. To meet the desire of practitioners for better guidance and tools how to deal with uncertainty more practice-oriented systematic approaches are needed. For that purpose we consider it important to understand how practitioners frame uncertainties. In this paper we present an approach where water managers developed criteria of relevance to understand and address uncertainties. The empirical research took place in the Doñana region of the Guadalquivir estuary in southern Spain making use of the method of card sorting. Through the card sorting exercise a broad range of criteria to make sense of and describe uncertainties was produced by different subgroups, which were then merged into a shared list of criteria. That way framing differences were made explicit and communication on uncertainty and on framing differences was enhanced. In that, the present approach constitutes a first step to enabling reframing and overcoming framing differences, which are important features on the way to robust decision-making. Moreover, the elaborated criteria build a basis for the development of more structured approaches to deal with uncertainties in water management practice.     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

Robust optimization evaluation of reliance on locally produced foods

While local food production may be beneficial in terms of developing the local economy and reducing greenhouse gases from transportation, sustainability strategies focused on local food production may generate their own risks due to yield variability. We have developed a robust optimization (RO) model to determine the minimum amount of land (cropland and pasture) required to grow food items that would satisfy a local population’s (accounting for gender and age) calorie and nutrient needs. This model has been applied to Boone County, Missouri, which has a population of approximately 170,000. Boone County is 1790 km², with 16% of the land defined as cropland and 30% defined as pasture. The model includes 27 nutrients from 17 potential foods that could be produced: six fruits and vegetables, five grains and six animal-sourced foods. Yield estimates are based on the predominate methods of agriculture in the USA. We first run our model assuming no variability, using the midpoint yield estimates. Then, to quantify uncertainty in yield for different food types, we use historical yield data over 10 years to estimate this variability and run our RO model under these variability estimates. We compare the two model results to illustrate the impact of data uncertainty on meeting sustainable local food for communities. Solutions suggest that nutrition needs can be met for the Boone County population within the land area defined.     

The Effects of Population Density on Juvenile Growth Rate in White-Tailed Deer

Animal body size is driven by habitat quality, food availability, and nutrition. Adult size can relate to birth weight, to length of the ontogenetic growth period, and/or to the rate of growth. Data requirements are high for studying these growth mechanisms, but large datasets exist for some game species. In North America, large harvest datasets exist for white-tailed deer (Odocoileus virginianus), but such data are collected under a variety of conditions and are generally dismissed for ecological research beyond local population and habitat management. We contend that such data are useful for studying the ecology of white-tailed deer growth and body size when analyzed at ordinal scale. In this paper, we test the response of growth rate to food availability by fitting a logarithmic equation that estimates growth rate only to harvest data from Fort Hood, Texas, and track changes in growth rate over time. Results of this ordinal scale model are compared to previously published models that include additional parameters, such as birth weight and adult weight. It is shown that body size responds to food availability by variation in growth rate. Models that estimate multiple parameters may not work with harvest data because they are prone to error, which renders estimates from complex models too variable to detect interannual changes in growth rate that this ordinal scale model captures. This model can be applied to harvest data, from which inferences about factors that influence animal growth and body size (e.g., habitat quality and nutritional availability) can be drawn.     

A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

We evaluate and compare the performance of Bayesian Monte Carlo (BMC), Markov chain Monte Carlo (MCMC), and the Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling (HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.