Modeling human-induced climatic change: A summary for environmental managers

The rapid increase in atmospheric concentrations of greenhouse gases has caused concern because of their potential to alter the earth's radiation budget and disrupt current climate patterns While there are many uncertainties associated with use of general circulation models (GCMs), GCMs are currently the best available technology to project changes in climate associated with elevated gas concentrations. Results indicate increases in global temperature and changes in global precipitation patterns are likely as a result of doubled CO₂. GCMs are not reliable for use at the regional scale because local scale processes and geography are not taken into account. Comparison of results from five GCMs in three regions of the United States indicate high variability across regions and among models depending on season and climate variable. Statistical methods of scaling model output and nesting finer resolution models in global models are two techniques that may improve projections. Despite the many limitations in GCMs, they are useful tools to explore climate-earth system dynamics when used in conjunction with water resource and ecosystem models. A variety of water resource models showed significant alteration of regional hydrology when run with both GCM-generated and hypothetical climate scenarios, regardless of region or model complexity. Similarly, ecological models demonstrate the sensitivity of ecosystem production, nutrient dynamics, and distribution to changes in climate and CO₂ levels. We recommend the use of GCM-based scenarios in conjunction with water resource and ecosystem models to guide environmental management and policy in a “no-regrets” framework or as part of a precautionary approach to natural resource protection.     

MODEL PREDICTIONS OF WATERSHED HYDROLOGIC COMPONENTS: COMPARISON AND VERIFICATIION1

ABSTRACT: Model predictions of the relatively simple soil compartment model SESOIL are compared with those of the more data-intensive terrestrial ecosystem hydrology model AGTEHM. Comparisons were performed using data from a deciduous forest stand watershed, a grassland watershed, and two agricultural field plots. Good agreement was obtained between model predictions for annual values of infiltration, evapotranspiration, surface runoff, and groundwater runoff. SESOIL model predictions also compare well with empirical measurements at the forest stand and the grassland watersheds.     

A SEMI-DISTRIBUTED ADAPTIVE MODEL FOR REAL-TIME FLOOD FORECASTING1

ABSTRACT: A semi-distributed deterministic model for real-time flood forecasting in large basins is proposed. Variability of rainfall and losses in space is preserved and the effective rainfall-direct runoff model segment based on the Clark procedure is incorporated. The distribution of losses in space is assumed proportional to rainfall intensity and their evolution in time is represented by the φ-index; furthermore, an initial period without production of effective rainfall is considered. The first estimation of losses and the associated forecasts of flow are performed at the time corresponding to the first rise observed in the hydrograph. Then the forecasts of flow are corrected at each subsequent time step through the updating of the φ-index. The model was tested by using rainfall-runoff events observed on two Italian basins and the predictions of flow for lead times up to six hours agree reasonably well with the observations in each event. For example, for the coefficient of persistence, which compares the model forecasts with those generated by the no-model assumption, appreciable positive values were computed. In particular, for the larger basin with an area of 4,147 km², the mean values were 0.4, 0.4 and 0.5 for forecast lead times of two hours, four hours and six hours, respectively. Good performance of the model is also shown by a comparison of its flow predictions with those derived from a unit hydrograph based model     

ASSESSMENT OF REMOTE SENSING INPUT TO HYDROLOGIC MODELS1

Remotely sensed variables such as land cover type and snow-cover extent can currently be used directly and effectively in a few specific hydrologic models. Regression models can also be developed using physiographic and snow-cover data to permit estimation of discharge characteristics over extended periods such as a season or year. Most models, however, are not of an appropriate design to readily accept as input the various types of remote sensing parameters that can be obtained now or in the future. Because this new technology has the potential for producing hydrologic data that has significant information content on an areal basis, both inexpensively and repetitively, effort should be devoted now to either modifying existing models or developing new models that can use these data. Minor modifications would at least allow the remote sensing data to be used in an ancillary way to update the model state variables, whereas major structural modifications or new models would permit direct input of the data through remote sensing compatible algorithms. Although current remote sensing inputs to hydrologic models employ only visible and near infrared data, model modification or development should accommodate microwave and thermal infrared data that will be more widely available in the future.     

The use of computer models in planning upland land use

The British Ecological Society has suggested that computer‐based techniques could be used in the coordination of policies across different land uses in upland planning. This paper looks briefly at one such technique — a linear programming model — and describes its use at a regional scale for the Sedburgh area in north‐west England. The paper concludes with consideration of why such models are not in more common use.     

SENSITIVITY ANALYSIS OF A DRAINAGE SIMULATION MODEL1

A sensitivity analysis of a computer model, simulating major water and nitrogen processes of a soil-water-plant-climatic system on an annual basis, was conducted to determine how the model reacts to the variations in selected hydrologic and nitrogen parameters. Two major output variables (namely, total subsurface drain volume and cumulative nitrate loss with subsurface drain water) were selected for the sensitivity analysis. Model sensitivity analysis shows that the model is most sensitive to hydrologic parameters. The model is very sensitive to variations in the initial water content in the soil profile.     

Assessing Potential for Recovery of Biotic Richness and Indicator Species due to Changes in Acidic Deposition and Lake ph in Five Areas of Southeastern Canada

Biological damage to sensitive aquatic ecosystems is among the most recognisable, deleterious effects of acidic deposition. We compiled a large spatial database of over 2000 waterbodies across southeastern Canada from various federal, provincial and academic sources. Data for zooplankton, fish, macroinvertebrate (benthos) and loon species richness and occurrence were used to construct statistical models for lakes with varying pH, dissolved organic carbon content and lake size. pH changes, as described and predicted using the Integrated Assessment Model (Lam et al., 1998; Jeffries et al., 2000), were based on the range of emission reductions set forth in the Canada/US Air Quality Agreement (AQA). The scenarios tested include 1983, 1990, 1994 and 2010 sulphate deposition levels. Biotic models were developed for five regions in southeastern Canada (Algoma, Muskoka, and Sudbury, Ontario, southcentral Québec, and Kejimkujik, Nova Scotia) using regression tree, multiple linear regression and logistic regression analyses to make predictions about recovery after emission reductions. The analyses produced different indicator species in different regions, although some species showed consistent trends across regions. Generally, the greatest predicted recovery occurred during the final phase of emission reductions between 1994 and 2010 across all taxonomic groups and regions. The Ontario regions, on average, were predicted to recover to a greater extent than either southcentral Québec or the Kejimkujik area of Nova Scotia. Our results reconfirm that pH 5.5–6.0 is an important threshold below which damage to aquatic biota will remain a major local and regional environmental problem. This damage to biodiversity across trophic levels will persist well into the future if no further reductions in sulphate deposition are implemented.     

JSEM: A Framework for Identifying and Evaluating Indicators

There are two issues in indicator development that have not been adequately addressed: (1) how to select an optimal combination of potentially redundant indicators that together best represent an endpoint, given cost constraints; (2) how to identify and evaluate indicators when the endpoint is unmeasured. This paper presents an approach to identifying and evaluating combinations of indicators when the mathematical relationships between the indicators and an endpoint may not be quantified, a limitation common to many ecological assessments. The approach uses the framework of Structural Equation Modeling (SEM), which combines path analysis withmeasurement models, to formalize available informationabout potential indicators and to evaluate their potential adequacy for representing an endpoint. Unlike traditional applications of SEM which require data on all variables, our approach – judgement-based SEM (JSEM) – can utilize expert judgement regarding the strengths and shapes of indicator-endpoint relationships. JSEM is applied in two stages. First, a conceptual model that relates variables in a network of direct and indirect linkages is developed, and is used to identify indicators relevant to an endpoint. Second, an index of indicator strength – i.e., the strength of the relationship between the endpoint and a set of indicators – is calculated from estimates of correlation between the modeled variables, and is used to compare alternative sets of indicators. The second stage is most appropriate for large, long-term assessments. Although JSEM is not a statistical technique, basing JSEM on SEM provides a structure for validating the conceptual model and for refining the index of indicator strength as data become available. Our main objective is to contribute to a rigorous and consistent selection of indicators even when knowledgeabout the ability of indicators to represent an endpoint is limited to expert judgement.     

Limitations of multimedia models for use in environmental decision making

The United States currently is engaged in a complex,multi-billion dollar effort to cleanup a legacy ofboth privately- and federally-owned hazardous wastesites. Decisions regarding the best approach forremediation of these sites often are based on theanalysis of potential risks to human health and theenvironment. A cornerstone of such analysis is thefrequent use of computerized multimedia environmentaltransport models, to evaluate the large quantities ofinformation necessary to understand the present andfuture implications of contamination at a site. Onebarrier to wide-spread use of this analyticalprocedure is the view that results obtained usingcomputer models are highly dependent on user input,and therefore, subject to manipulation. It is widelyrecognized that for decisions to be both credible andimplementable, the public must have confidence in boththe scientific basis for judgments involved and thedecision processes employed (NRC, 1983). Our purposein this article is to overview the difficultiesassociated with application of multimedia models toreal world problems and the contribution these modelscan make to technically sound estimates of exposure and risk.