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.     

OPTIMAL RESERVOIR MANAGEMENT AND CROP PLANNING UNDER DETERMINISTIC AND STOCHASTIC INFLOWS1

ABSTRACT: This study analyzes planning under deterministic and stochastic inflows for the Mayurakshi project in India. Models are developed to indicate the optimal storage of reservoir water, the transfer of water to the producing regions, and the spillage of water from the reservoir, if needed. A deterministic programming model was first formulated to represent the existing situation. A chance-constrained model then was constructed to evaluate potential violations of the deterministic model. Both models were quantified for the command area. Data were collected from surveys of the area and from government agencies. Both the deterministic and change-constrained models suggest a more intensive cropping program in the region. Both emphasize more dependence on rabi and less on kharif crops. The chance-constrained especially suggests use of more water in the rabi season. Important chances in cropping programs and labor use take place under the chance-constrained model.     

COMPARISON OF TWO AUTOREGRESSIVE MODELS FOR MONTHLY STREAM FLOW GENERATION1

ABSTRACT: Two autoregressive models for monthly stream flow generation are compared based on the reproduction of the historical record in terms of several important statistics such as the mean, standard deviation, skewness coefficient, correlation coefficient, and the reservoir storage components. In the comparison, both theoretical considerations and practical applications are employed to evaluate the performance of each model.     

STOCHASTIC MODELING OF MONTHLY AND DAILY RAINFALL SEQUENCES1

ABSTRACT: A monthly model and two daily models (I and II) are presented for the purpose of generating monthly and daily rainfall sequences in the Quae Yai river basin in Thailand. Performance of the models are evaluated by comparing the statistical parameters of the generated sequences with those from historical data. For monthly generation, Thomas-Fiering model worked satisfactorily in spite of the monthly correlations being weak, if any. Daily Model I, which assumes no persistence between daily rainfall amounts within the wet spells, could not preserve some important parameters regardless of the simplicity in model construction. Application of multi-state transition probability matrix model gave good results, although the user has to modify some parameters looking at the performance of the model for each historical record.     

Energy analysis and the coupling of man and estuaries

New concepts and insights concerning human and natural systems in the coastal zone are emerging from recent energy analysis and synthesis studies. By using new concepts for measuring the quality of energy, one can express the work of ecosystems and human economies in equivalent terms. From energy models and new energy quality evaluations, one can learn what kinds of coastal systems maximize power, are competitive, economically vital, and likely to have a survival advantage.Energy analysis and synthesis is aided by energy circuit diagrams. Models of the coastal zone that emphasize the change in external driving functions related to world energy sources provide insights and some predictive abilities that are not found in economic studies, since money flows alone do not evaluate external driving energies.This paper suggests four procedures for coastal planning: 1) calculation of investment ratio in units of equal quality to determine which projects are economic in a broad sense, 2) development of energy signatures for coastal ecosystems, 3) determination of which interface ecosystems develop the best energy flows, and 4) development of regional models that include the main features of human and natural ecosystems.     

A MANGANESE OXIDATION MODEL FOR RIVERS1

ABSTRACT: The presence of manganese in natural waters (>0.05 mg/L) degrades water-supply quality. A model was devised to predict the variation of manganese concentrations in river water released from an impoundment with the distance downstream. The model is one-dimensional and was calibrated using dissolved oxygen, biochemical oxygen demand, pH, manganese, and hydraulic data collected in the Duck River, Tennessee. The results indicated that the model can predict manganese levels under various conditions. The model was then applied to the Chattahoochee River, Georgia. Discrepancies between observed and predicted may be due to inadequate pH data, precipitation of sediment particles, unsteady flow conditions in the Chattahoochee River, inaccurate rate expressions for the low pH conditions, or their combinations.     

Evaluating the impacts of global environmental assessments

There are currently no widely accepted procedures for comparing the performance of global environmental assessments (GEAs) and this may be a barrier to improving their methodology. To encourage greater self-reflection within the GEA community, it is proposed to introduce consistent evaluation approaches. Two elements from current evaluation practice are reviewed here that could be particularly useful for evaluating GEAs. The first are logic models which provide a transparent visual mapping of how activities in a GEA are intended to have impacts on policies. The second are performance metrics. It is proposed that GEAs adopt two kinds of metrics: (i) A common generic set for use in all GEAs to provide a basis for comparing the performance of GEAs, and (ii) a specific set of measureable metrics for each particular GEA derived from/linked to the generic set. Although many issues arise in applying these and other elements from evaluation theory and practice to GEAs, the potential benefits are greater comparability of GEA performance and new knowledge about how to improve them. This Short Communication is part of a Special Issue on solution-oriented GEAs.     

Calibrating a Basin‐Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ET_g) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ET_g. An iterative approach is provided that calibrates GWM to ET_g derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11‐year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ET_g to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM‐grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first‐order second moment analysis shows ET_g to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ET_g despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ET_g appears to bias ET_g during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness.     

The evolution of mass balance models of persistent organic pollutant fate in the environment

Current approaches to modelling the fate of persistent organic pollutants (POPs) in the environment have evolved in response to four dominant characteristics of these substances; namely: (1) the presence of POPs in virtually all environmental phases and the ease with which they move from one to the other requires multi-compartmental modelling. Describing transport across phase boundaries becomes as, or even more, important as quantifying transport within the phases; (2) POPs may persist in the environment for many decades. For chemicals that 'have time', concepts such as equilibrium partitioning and steady-state become more important than for short-lived substances whose fate is more controlled by the rates of transformation; (3) measuring POPs is difficult and expensive and observed concentrations of POPs are not available in high spatial or temporal resolution. Consequently, high resolution tends not to be a high priority in POP models; and (4) detrimental effects of POPs often manifest themselves in top predators, which has led to a focus on modelling biotic uptake and transfer within food chains. The task of building a POPs model is viewed as combining the four 'building blocks' of partitioning, transport, transformation and source data with the help of the law of the conservation of mass. Process models, evaluative models, models of real local, regional and global fate, as well as biological uptake models are presented and references to numerous examples are provided. An attempt is made to forecast future directions in the field of POPs modelling. It is expected that modelling techniques that do not rely on quantitative emission estimates as well as approaches that take into account spatial, temporal and climatic variability as well as parameter uncertainty will increase in importance. Finally, the relationship between modelling POPs and models of other pollutant issues is addressed, as are potential interactions between POPs and pollutant issues such as eutrophication, acidification and global climate change.     

Conceptual Models as Hypotheses in Monitoring Urban Landscapes

Many problems and challenges of ecosystem management currently are driven by the rapid pace and spatial extent of landscape change. Parks and reserves within areas of high human population density are especially challenged to meet the recreational needs of local populations and to preserve valued environmental resources. The complex problem of managing multiple objectives and multiple resources requires an enormous quantity of information, and conceptual models have been proposed as tools for organizing and interpreting this information. Academics generally prefer a bottom-up approach to model construction that emphasizes ecologic theory and process, whereas managers often use a top-down approach that takes advantage of existing information to address more pragmatic objectives. The authors propose a formal process for developing, applying, and testing conceptual models to be used in landscape monitoring that reconciles these seemingly opposing perspectives. The four-step process embraces the role of hypothesis testing in the development of models and evaluation of their utility. An example application of the process to a network of national parks in and around Washington, DC illustrates the ability of the approach to systematically identify monitoring data that would both advance ecologic theory and inform management decisions.