EFFICIENCY OF HYDROLOGIC DATA COLLECTION SYSTEMS ROLE OF TYPE I AND II ERRORS1

ABSTRACT The efficiency of hydrologic data collection systems is relevant to solution of environmental problems, scientific understanding of hydrologic processes, model-building and management of water resources. Because these goals may be overlapping and non-commensurate, design of data networks is not simple. Identified are four elements of error or risk in such networks: (a) choice of variables and mathematical model for the same process, (b) accuracy of model parameter estimates, (c) acceptance of wrong hypothesis or rejection of correct hypothesis and (d) economic losses associated with error. Of these four, the classical hypothesis testing problem is specifically evaluated in terms of costs of type I and II errors for simple and composite hypotheses; mathematical models for these economic analyses also include costs of sample data and costs of waiting while new data is obtained. An illustrative computational example focuses on the hypothesis that natural recharge might be augmented by a system of pumping wells along an ephemeral channel. The relationship of the hypothesis testing problem to Bayesian decision theory is discussed; it is felt that the latter theory offers a more comprehensive framework for design and use of hydrologic data networks.     

USE AND MISUSE OF COMPLEX MODELS: EXAMPLES FROM WATER DEMAND MANAGEMENT1

ABSTRACT: The power of computers has increased in recent decades, and one might expect improved management to result because decisions can be made with understanding available only via models. However, there is potential for quite the opposite: poor decisions due to unrealistic model output generated by users without access to appropriate training in the use of models. We discuss and, by reference to water demand models (IWR-MAIN, MWD-MAIN), illustrate three areas in which unintended errors of judgment by untrained personnel may cause difficulty:

• * Attributes of management models; if output from any type of model has no measure of confidence, then results may be over- or undervalued
• * Input data; with complex models, problems here typically will be difficult to detect.
• * Calibration and history-matching (verification); if these steps or data are combined, then users should be less trustful of model output than otherwise.

Because all models have weaknesses and because there always is uncertainty about output from any model, we end with suggestions for coping with complex models. Monitoring programs play a central role in such efforts because they can identify discrepancies between model predictions and actual events and because they can ensure time is available to develop solutions for problems unanticipated in the modeling effort.     

Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally‐available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally‐available spatial data could be improved by including local watershed‐specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km² watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.     

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.     

SIMULATION OF INTEGRATED SURFACE WATER AND GROUND WATER SYSTEMS - MODEL FORMULATION1

ABSTRACT: The unique characteristics of the hydrogeologic system of south Florida (flat topography, sandy soils, high water table, and highly developed canal system) cause significant interactions between ground water and surface water systems. Interaction processes involve infiltration, evapotranspiration (ET), runoff, and exchange of flow (seepage) between streams and aquifers. These interaction processes cannot be accurately simulated by either a surface water model or a ground water model alone because surface water models generally oversimplify ground water movement and ground water models generally oversimplify surface water movement. Estimates of the many components of flow between surface water and ground water (such as recharge and ET) made by the two types of models are often inconsistent. The inconsistencies are the result of differences in the calibration components and the model structures, and can affect the confidence level of the model application. In order to improve model results, a framework for developing a model which integrates a surface water model and a ground water model is presented. Dade County, Florida, is used as an example in developing the concepts of the integrated model. The conceptual model is based on the need to evaluate water supply management options involving the conjunctive use of surface water and groundwater, as well as the evaluation of the impacts of proposed wellfields. The mathematical structure of the integrated model is based on the South Florida Water Management Model (SFWMM) (MacVicar et al., 1984) and A Modular Three-Dimensional Finite-Difference Groundwater Flow Model (MODFLOW) (McDonald and Harbaugh, 1988).     

ESTIMATION OF EPISODIC STREAM ACIDIFICATION BASED ON MONTHLY OR ANNUAL SAMPLING1

ABSTRACT: Programs of monthly or annual stream water sampling will rarely observe the episodic extremes of acidification chemistry that occur during brief, unpredictable runoff events. When viewed in the context of data from several streams, however, baseflow measurements of variables such as acid neutralizing capacity, pH and NO₃· are likely to be highly correlated with the episodic extremes of those variables from the same stream and runoff season. We illustrate these correlations for a water chemistry record, nearly two years in length, obtained from intensive sampling of 13 small Northeastern U.S. streams studied during USEPA's Episodic Response Project. For these streams, simple regression models estimate episodic extremes of acid neutralizing capacity, pH, NO₃·, Ca²⁺, SO₄^2?, and total dissolved Al with good relative accuracy from statistics of monthly or annual index samples. Model performances remain generally stable when episodic extremes in the second year of sampling are predicted from first-year models. Monthly or annual sampling designs, in conjunction with simple empirical models calibrated and maintained through intensive sampling every few years, may estimate episodic extremes of acidification chemistry with economy and reasonable accuracy. Such designs would facilitate sampling a large number of streams, thereby yielding estimates of the prevalence of episodic acidification at regional scales.     

Improving watershed decisions using run-off and yield models at different simulation scales

Water managers face the daunting task of balancing limited water resources with over-subscribed water users among competing demands. They face the additional challenge of taking water planning decisions in an uncertain environment with limited and sometimes inaccurate observed and simulated hydrological data. Within South African watersheds, spatial parameterization data for hydrological models are now available at two different basin management resolutions (termed quaternary and quinary). Currently, water management decisions in the Crocodile River watershed are often made at a more coarse resolution, which may exclude crucial insights into the data. This research has the following aims (1) to explore whether model performance is improved by parameterization using a more detailed quinary-scale watershed data and (2) to explore whether quinary-scale models reduce uncertainty in allocation or restriction decisions to provide better informed water resources management and decision outcomes. This study used the Agricultural Catchments Research Unit (ACRU) agro-hydrological watershed model, to evaluate the effects of spatial discretization at the quaternary and quinary scales on watershed hydrological response and runoff within the Crocodile River basin. Model performance was evaluated using statistical comparisons of results using traditional goodness-of-fit measures such as the coefficient of efficiency (C _eff), root mean square of the error and the coefficient of determination (R ²) to compare simulated monthly flows and observed flows in six subcatchments. Traditional interpretation of these goodness-of-fit measures may be inadequate as they can be subjectively interpreted and easily influenced by the number of data points, outliers and model bias. This research utilizes a recently released model evaluation program (FITEVAL) which presents probability distributions of R ²and C _eff derived by bootstrapping, graphical representation of observed and simulated stream flows, incorporates statistical significance to detect the sufficiency of the R ²and C _eff and determines the presence of outliers and bias. While analyses indicate that the ACRU model performs marginally better when parameterized and calibrated at the quinary scale, the measurements at both scales show significant variability in predictions for both high and low flows that are endemic to southern African hydrology. The improved evaluation methods also allow for the analysis of data collection errors at monitoring sites and help determine the effect of data quality on adaptive water planning management decisions. Given that many water resource challenges are complex adaptive systems, these expanded performance analysis tools help provide deeper insights into matching watershed decision metrics and model-derived predictions.     

POLLUTANT RUNOFF MODELS: SELECTION AND USE IN DECISION MAKING1

Models for pollutant runoff can be useful in water quality management planning if appropriately structured for the problem at hand. Accordingly, a “top-down” approach is proposed for the examination of extant pollutant runoff models. The approach consists of the identification of objectives and attributes that reflect the needs of planners and decision makers when these models are used for water quality management planning. Ideally, the attributes should concern the effect of model information on improved decision making and the cost of model application. Practical difficulties with the first attribute necessitates substitution of surrogate attributes reflecting model appropriateness, resolution, and uncertainty. Common pollutant runoff models, in particular export coefficients and hydrology-driven simulation models, are found to have serious weaknesses on some of the attribute scales. The “top-down” approach leads to a set of desirable pollutant runoff model attributes; alternate modeling techniques are thus examined in order to identify promising future directions for model development. The focus of this examination is phosphorus, due to its importance in the eutrophication of surface waters. Models for both sediment-attached and dissolved phosphorus are considered. Among the conclusions is the belief that the partial contributing area concept can yield an effective yet simple simulation despite the variable and complex nature of runoff.     

AGRICULTURAL WATER MODELING AND ECONOMIC INFORMATION NEEDS UNDER THE MODEL WATER CODE1

ABSTRACT: Economic information for efficient water allocation is difficult and costly to acquire under administrative water systems evolving under the Model Water Code. One approach to obtaining more information is to use a simulator like the Florida AGWATER model. The advantage of AGWATER is the potential for realistic prediction, because it operates at the field and day levels, using detailed information for each crop and tract. Unfortunately, such simulators are complex and require large amounts of costly input data. A better solution to the information problem may be to use markets for the marketable goods associated with water, because information is inherent in such a process. This approach will allow limited modeling and management resources to be put into using water models to generate information for the goods dependent on water that are difficult to market, like wildlife services.     

THE EFFECT OF DATA INDEPENDENCE IN MODEL CALIBRATION AND MODEL TESTING1

ABSTRACT: With the increased use of models in hydrologic design, there is an immediate need for a comprehensive comparison of hydrologic models, especially those intended for use at ungaged locations (i.e., where measured data are either not available or inadequate for model calibration). But some past comparisons of hydrologic models have used the same data base for both calibration and testing of the different models or implied that the results of model calibration are indicative of the accuracy at ungaged locations. This practice was examined using both the regression equation approach to peak discharge estimation and a unit hydrograph model that was intended for use in urban areas. The results suggested that the lack of data independence in the calibration and testing of regression equations may lead to both biased results and misleading statements about prediction accuracy. Additionally, although split-sample testing is recognized as desirable, the split-samples should be selected using a systematic-random sampling scheme, rather than random sampling, because random sampling with small samples may lead to a testing sample that is not representative of the population. A systematic-random sampling technique should lead to more valid conclusions about model reliability. For models like a unit hydrograph model, which are more complex and for which calibration is a more involved process, data independence is not as critical because the data fitting error variation is not as dominant as the error variation due to the calibration process and the inability of the model structure to conform with data variability.     

Habitat Assessment of Non-Wadeable Rivers in Michigan

Habitat evaluation of wadeable streams based on accepted protocols provides a rapid and widely used adjunct to biological assessment. However, little effort has been devoted to habitat evaluation in non-wadeable rivers, where it is likely that protocols will differ and field logistics will be more challenging. We developed and tested a non-wadeable habitat index (NWHI) for rivers of Michigan, where non-wadeable rivers were defined as those of order ≥5, drainage area ≥1600 km², mainstem lengths ≥100 km, and mean annual discharge ≥15 m³/s. This identified 22 candidate rivers that ranged in length from 103 to 825 km and in drainage area from 1620 to 16,860 km². We measured 171 individual habitat variables over 2-km reaches at 35 locations on 14 rivers during 2000–2002, where mean wetted width was found to range from 32 to 185 m and mean thalweg depth from 0.8 to 8.3 m. We used correlation and principal components analysis to reduce the number of variables, and examined the spatial pattern of retained variables to exclude any that appeared to reflect spatial location rather than reach condition, resulting in 12 variables to be considered in the habitat index. The proposed NWHI included seven variables: riparian width, large woody debris, aquatic vegetation, bottom deposition, bank stability, thalweg substrate, and off-channel habitat. These variables were included because of their statistical association with independently derived measures of human disturbance in the riparian zone and the catchment, and because they are considered important in other habitat protocols or to the ecology of large rivers. Five variables were excluded because they were primarily related to river size rather than anthropogenic disturbance. This index correlated strongly with indices of disturbance based on the riparian (adjusted R² = 0.62) and the catchment (adjusted R² = 0.50), and distinguished the 35 river reaches into the categories of poor (2), fair (19), good (13), and excellent (1). Habitat variables retained in the NWHI differ from several used in wadeable streams, and place greater emphasis on known characteristic features of larger rivers.     

Spatial-Scale Effects on Relative Importance of Physical Habitat Predictors of Stream Health

A common theme in recent landscape studies is the comparison of riparian and watershed land use as predictors of stream health. The objective of this study was to compare the performance of reach-scale habitat and remotely assessed watershed-scale habitat as predictors of stream health over varying spatial extents. Stream health was measured with scores on a fish index of biotic integrity (IBI) using data from 95 stream reaches in the Eastern Corn Belt Plain (ECBP) ecoregion of Indiana. Watersheds hierarchically nested within the ecoregion were used to regroup sampling locations to represent varying spatial extents. Reach habitat was represented by metrics of a qualitative habitat evaluation index, whereas watershed variables were represented by riparian forest, geomorphology, and hydrologic indices. The importance of reach- versus watershed-scale variables was measured by multiple regression model adjusted-R² and best subset comparisons in the general linear statistical framework. Watershed models had adjusted-R² ranging from 0.25 to 0.93 and reach models had adjusted-R² ranging from 0.09 to 0.86. Better-fitting models were associated with smaller spatial extents. Watershed models explained about 15% more variation in IBI scores than reach models on average. Variety of surficial geology contributed to decline in model predictive power. Results should be interpreted bearing in mind that reach habitat was qualitatively measured and only fish assemblages were used to measure stream health. Riparian forest and length-slope (LS) factor were the most important watershed-scale variables and mostly positively correlated with IBI scores, whereas substrate and riffle-pool quality were the important reach-scale variables in the ECBP.     

Parameter Uncertainty Reduction for SWAT Using Grace,Streamflow, and Groundwater Table Data for Lower Missouri River Basin1

Abstract: This study incorporates the newly available Gravity Recovery and Climate Experiment (GRACE) water storage data and water table data from well logs to reduce parameter uncertainty in Soil and Water Assessment Tool (SWAT) calibration using a SUFI2 (sequential uncertainty fitting) framework for the Lower Missouri River Basin. Model evaluations are performed in multiple stages using a multiobjective function consisting of multisite streamflow and GRACE water storage data as well as a groundwater component. Results show that (1) a model calibrated with both streamflow and GRACE data simultaneously can maintain the water balance for the whole basin, but may improperly partition surface flow and base flow. Additional inclusion of the groundwater constraint can significantly improve the model performance in groundwater hydrological processes. In our case, the estimation of specific yield of shallow aquifers has been increased to 10^?2 from previous much underestimated level (<10^?3). (2) The daily streamflow data are needed to confine the parameters related to water flow in channels such as the Manning’s coefficient, which are less sensitive to the monthly simulations. (3) Parameters are nonuniformly sensitive for different goal variables, and thus, proper specification of a prior distribution of parameters may be the key factor for global optimization algorithms to obtain stable and realistic model performance.     

System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts

Accurate prediction of municipal water demand is critically important to water utilities in fast-growing urban regions for drinking water system planning, design, and water utility asset management. Achieving the desired prediction accuracy is challenging, however, because the forecasting model must simultaneously consider a variety of factors associated with climate changes, economic development, population growth and migration, and even consumer behavioral patterns. Traditional forecasting models such as multivariate regression and time series analysis, as well as advanced modeling techniques (e.g., expert systems and artificial neural networks), are often applied for either short- or long-term water demand projections, yet few can adequately manage the dynamics of a water supply system because of the limitations in modeling structures. Potential challenges also arise from a lack of long and continuous historical records of water demand and its dependent variables. The objectives of this study were to (1) thoroughly review water demand forecasting models over the past five decades, and (2) propose a new system dynamics model to reflect the intrinsic relationship between water demand and macroeconomic environment using out-of-sample estimation for long-term municipal water demand forecasts in a fast-growing urban region. This system dynamics model is based on a coupled modeling structure that takes into account the interactions among economic and social dimensions, offering a realistic platform for practical use. Practical implementation of this water demand forecasting tool was assessed by using a case study under the most recent alternate fluctuations of economic boom and downturn environments.     

Exploring the determinants for adopting water conservation measures. What is the tendency of landowners when the resource is already at risk?

In Finland, the current water conservation policy sets equal incentives for water conservation, regardless of the environmental condition. Before any policy reform, it is vital to investigate the tendency of landowners to adopt water conservation measures. In this study, we were interested in examining adoption if the soil quality implies a high leaching risk and if the water quality is already poor. By combining survey data with GIS data, we analysed the effect of farm and farmer characteristics and attitudes towards adoption. Our probit models indicated that financial variables were the key determinants of adoption for active farmers, whereas for passive owners, adoption was also explained by attitudes. In contrast to our expectations, adoption in areas under risk was weakly supported by our estimates. Environmental awareness, providing it increases with risk, is not strong enough to motivate adoption. Targeted agri-environmental measures, even though costly, cannot be avoided, and spatially tailored measures can attract adopters in hotspot areas.     

NATURAL EROSION RATES AND THEIR PREDICTION IN THE IDAHO BATHOLITH1

ABSTRACT: Natural rates of surface erosion on forested granitic soils in central Idaho were measured in 40 m² bordered erosion plots over a period of four years. In addition, we measured a variety of site variables, soil properties, and summer rainstorm intensities in order to relate erosion rates to site attributes. Median winter erosion rates are approximately twice summer period rates, however mean summer rates are nearly twice winter rates because of infrequent high erosion caused by summer rainstorms. Regression equation models and regression tree models were constructed to explore relationships between erosion and factors that control erosion rates. Ground cover is the single factor that has the greatest influence on erosion rates during both summer and winter periods. Rainstorm intensity (erosivity index) strongly influences summer erosion rates, even on soils with high ground cover percentages. Few summer storms were of sufficient duration and intensity to cause rilling on the plots, and the data set was too small to elucidate differences in rill vs. interrill erosion. The regression tree models are relatively less biased than the regression equations developed, and explained 70 and 84 percent of the variability in summer and winter erosion rates, respectively.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

AN INTERACTWE MODELING ENVIRONMENT FOR NON-POINT SOURCE POLLUTION CONTROL1

ABSTRACT: Non-point source pollution cuntinues to be an important environmental and water quality management problem. For the moat part, analysis of non-point source pollution in watersheds has depended on the use of distributed models to identify potential problem areas and to assess the effectiveness of alternative management practices. To effectively use these models for watershed water quality management, users depend on integrated geographic information systems (GIS)-based interfaces for input/output data management. However, existing interfaces are ad-hoc and the utility of GIS is limited to organization of input data and display of output data. A highly interactive water quality modeling interface that utilizes the functional components and analytical capability of GIS is highly desirable. This paper describes the tight coupling of the Agricultural Non-point Source (AGNPS) water quality model and ARC/INFO GIS software to provide an interactive hybrid modeling environment for evaluation of non-point source pollution in a watershed. The modeling environment is designed to generate AGNPS input parameters from user-specified GIS coverages, create AGNPS input data files, control AGNPS model simulations, and extract and organize AGNPS model output data for display. An example application involving the estimation of pesticide loading in a southern Iowa agricultural watershed demonstrates the capability of the modeling environment. Compared with traditional methods of watershed water quality modeling using the AGNPS model or other ad-hoc interfaces between a distributed model and GIS, the interactive modeling environment system is efficient and significantly reduces the task of watershed analysis using tightly coupled GIS databases and distributed models.     

ESTIMATING STREAMFLOW AND ASSOCIATED HYDRAULIC GEOMETRY,THE MID‐ATLANTIC REGION,USA1

ABSTRACT: Methods to estimate streamflow and channel hydraulic geometry were developed for unpaged streams in the Mid‐Atlantic Region. Observed mean annual streamflow and associated hydraulic geometry data from 75 gaging stations in the Appalachian Plateau, the Ridge and Valley, and the Piedmont Physiographic Provinces of the Mid‐Atlantic Region were used to develop a set of power functions that relate streamflow to drainage area and hydraulic geometry to streamflow. For all three physiographic provinces, drainage area explained 95 to 98 percent of the variance in mean annual streamflow. Relationships between mean annual streamflow and water surface width and mean flow depth had coefficients of determination that ranged from R²= 0.55 to R²= 0.91, but the coefficient of determination between mean flow velocity and mean annual streamflow was lower (R²= 0.44 to R²= 0.54). The advantages of using the regional regression models to estimate streamflow over a conceptual model or a water balance model are its ease of application and reduced input data needs. The prediction of the regression equations were tested with data collected as part of the U.S. Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). In addition, equations to transfer streamflow from gaged to ungaged streams are presented.     

NEW ZEALAND'S NATIONAL WATER QUALITY MONITORING NETWORK - DESIGN AND FIRST YEAR'S OPERATION1

ABSTRACT: The design and implementation of a national surface water quality monitoring network for New Zealand are described. Some of the lessons learned from the first year of operation are also addressed. Underpinning the design, and specified in advance, are the goal and objectives, the data quality assurance system, and the mechanism for data interpretation and reporting. Because of the difficulties associated with the use of a multitude of different agencies, only one agency is involved in field work and one laboratory undertakes the analysis. Staff training has been given a high priority. The network has been designed to give good trend detectability for regular sampling over a 5–10 year period.