An assessment methodology for successional systems. I. Null models and the regulatory framework

Standard procedures for evaluating environmental impact involve comparison between before and after conditions or scenarios or between treatment and control site pairs. In many cases, however, endogenous directional change (natural succession) is expected to occur at a significant rate over the period of concern, particularly for manmade systems such as impoundments. Static evaluations do not provide an adequate approach to such problems. A new evaluation frame is proposed. Nominal system behavior over time is characterized by a stochastic envelope around a nominal trajectory. We show that both the state variance and the sampling variance can change over time. In this context, environmental regulations can be framed as constraints, targets, or conformance to ideal trajectories. Statistical tests for determining noncompliance are explored relative to process variance, sample error, and sample size. Criteria are elucidated for choosing properties to monitor, sample size, and sampling interval.     

Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA

This paper presents an approach to modeling land-cover change as a function of land-use change. We argue that, in order to model the link between socio-economic change and changes in forest cover in a region that is experiencing residential and recreational development and agricultural abandonment, land-use and land-cover change need to be represented as separate processes. Forest-cover change is represented here using two transition probabilities that were calculated from Landsat imagery and that, taken together, describe a Markov transition matrix between forest and non-forest over a 10-year period. Using a three-date land-use data set, compiled and interpreted from digitized parcel boundaries, and scanned aerial photography for 136 sites (c. 2500 ha) sampled from the Upper Midwest, USA, we test functional relationships between forest-cover transition probabilities, standardized to represent changes over a decade, and land-use conditions and changes within sample sites. Regression models indicated that about 60% of the variation in the average forest-cover transition probabilities (i.e. from forest to non-forest and vice versa) can be predicted using three variables: amount of agricultural land use in a site; amount of developed land use; and the amount of area increasing in development. In further analysis, time lags were evaluated, showing that agricultural abandonment had a relatively strong time-lag effect but development did not. We demonstrate an approach to using forest-cover transition probabilities to develop spatially-constrained simulations of forest-cover change. Because the simulations are based on transition probabilities that are indexed to a particular time and place, the simulations are improved over previous applications of Markov transition models. This modeling approach can be used to predict forest-cover changes as a result of socio-economic change, by linking to models that predict land-use change on the basis of exogenous human-induced drivers.     

Modeling the Influence of Dynamic Zoning of Forest Harvesting on Ecological Succession in a Northern Hardwoods Landscape

Dynamic zoning (systematic alteration in the spatial and temporal allocation of even-aged forest management practices) has been proposed as a means to change the spatial pattern of timber harvest across a landscape to maximize forest interior habitat while holding timber harvest levels constant. Simulation studies have established that dynamic zoning strategies produce larger tracts of interior, closed canopy forest, thus increasing the value of these landscapes for interior-dependent wildlife. We used the simulation model LANDIS to examine how the implementation of a dynamic zoning strategy would change trajectories of ecological succession in the Great Divide Ranger District of the Chequamegon–Nicolet National Forest in northern Wisconsin over 500 years. The components of dynamic zoning strategies (number of zones in a scenario and the length of the hiatus between successive entries into zones) and their interaction had highly significant impacts on patterns of forest succession. Dynamic zoning scenarios with more zones and shorter hiatus lengths increased the average amount of the forest dominated by early successional aspen (Populus sp.). Dynamic zoning scenarios with two zones produced more late successional mature northern hardwoods than scenarios with four zones. Dynamic zoning scenarios with very short (30 years) or very long (120 years) hiatus lengths resulted in more late successional mature northern hardwoods than scenarios with intermediate hiatus lengths (60 and 90 years). However, none of the dynamic scenarios produced as much late successional mature northern hardwoods as the static alternative. Furthermore, the amounts of all habitat types in all dynamic zoning scenarios fluctuated greatly in time and space relative to static alternatives, which could negatively impact wildlife species that require a stable amount of habitat above some minimum critical threshold. Indeed, implementing dynamic zoning scenarios of different designs would have both positive and negative effects on wildlife species and for other objectives of forest management.     

Assessment of environmental impacts part two: Data collection

Intervention analysis is a relatively new branch of time series analysis. The power of this technique, which gives the probability that changes in mean level can be distinguished from natural data variability, is quite sensitive to the way the data are collected. The principal independent variables influenced by the data collection design are overall sample size, sampling frequency, and the relative length of record before the occurrence of the event (intervention) that is postulated to have caused a change in mean process level.For three of the four models investigated, data should be collected so that the post-intervention record is substantially longer than the pre-intervention record. This is in conflict with the intuitive approach, which would be to collect equal amounts of data before and after the intervention. The threshold (minimum) level of change that can be detected is quite high unless sample sizes of at least 50 and preferably 100 are available; this minimum level is dependent on the complexity of the model required to describe the response of the process mean to the intervention. More complex models tend to require larger sample sizes for the same threshold detectable change level.Uniformity of sampling frequency is a key consideration. Environmental data collection programs have not historically been oriented toward data analysis using time series techniques, thus eliminating a potentially powerful tool from use in many environmental assessment applications.     

APPLICATIONS OF A GIS FOR MODELING THE SENSITIVITY OF WATER RESOURCES TO ALTERATIONS IN CLIMATE IN THE GUNNISON RIVER BASIN,COLORADO1

ABSTRACT: The Gunnison River drains a mountainous basin in western Colorado, and is a large contributor of water to the Colorado River. As part of a study to assess water resource sensitivity to alterations in climate in the Gunnison River basin, climatic and hydrologic processes are being modeled. A geographic information system (GIS) is being used in this study as a link between data and modelers - serving as a common data base for project personnel with differing specialties, providing a means to investigate the effects of scale on model results, and providing a framework for the transfer of parameter values among models. Specific applications presented include: (1) developing elevation grids for a precipitation model from digital elevation model (DEM) point-elevation values, and visualizing the effects of grid resolution on model results; (2) using a GIS to facilitate the definition and parameterization of a distributed-parameters, watershed model in multiple basins; and (3) nesting atmospheric and hydrologic models to produce possible scenarios of climate change.     

A PRELIMINARY QUANTIFICATION OF THE IMPACTS OF ASPEN TO CONIFER SUCCESSION ON WATER YIELD- II. MODELING RESULTS1

ABSTRACT: Heat pulse velocity techniques were developed for effective monitoring of water movement in aspen (Populus tremuloides), subalpine fir (Abies lasiocarpa), and Englemann spruce (Picea engelmannif). Water loss was monitored in replicated trees of each species for one year. These data were used to modify the plant activity index (a reflection of the ability of plants to transpire water at various times during a year) and the crop coefficient (a reflection of differences in consumptive use rates of water by different vegetation types when all other factors are held constant) for each species within the model ASPCON, a deterministic, lumped-parameter model describing the hydrology of aspen to conifer succession. Results of the modeling in dicate 18.6 cm net loss of moisture available for streamflow when spruce replaced aspen, and a loss of 7.2 cm when fir forests replaced aspen. The aspen to conifer successional trend appears, therefore, to be significantly reducing water yields in the western United States.     

Modelling land use change with generalized linear models--a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana

This paper develops an approach to modelling land use change that links model selection and multi-model inference with empirical models and GIS. Land use change is frequently studied, and understanding gained, through a process of modelling that is an empirical analysis of documented changes in land cover or land use patterns. The approach here is based on analysis and comparison of multiple models of land use patterns using model selection and multi-model inference. The approach is illustrated with a case study of rural housing as it has developed for part of Gallatin County, Montana, USA. A GIS contains the location of rural housing on a yearly basis from 1860 to 2000. The database also documents a variety of environmental and socio-economic conditions. A general model of settlement development describes the evolution of drivers of land use change and their impacts in the region. This model is used to develop a series of different models reflecting drivers of change at different periods in the history of the study area. These period specific models represent a series of multiple working hypotheses describing (a) the effects of spatial variables as a representation of social, economic and environmental drivers of land use change, and (b) temporal changes in the effects of the spatial variables as the drivers of change evolve over time. Logistic regression is used to calibrate and interpret these models and the models are then compared and evaluated with model selection techniques. Results show that different models are 'best' for the different periods. The different models for different periods demonstrate that models are not invariant over time which presents challenges for validation and testing of empirical models. The research demonstrates (i) model selection as a mechanism for rating among many plausible models that describe land cover or land use patterns, (ii) inference from a set of models rather than from a single model, (iii) that models can be developed based on hypothesised relationships based on consideration of underlying and proximate causes of change, and (iv) that models are not invariant over time.     

Modelling the conversion of Colombian lowland ecosystems since 1940: drivers, patterns and rates

In biologically mega-diverse countries that are undergoing rapid human landscape transformation, it is important to understand and model the patterns of land cover change. This problem is particularly acute in Colombia, where lowland forests are being rapidly cleared for cropping and ranching. We apply a conceptual model with a nested set of a priori predictions to analyse the spatial and temporal patterns of land cover change for six 50-100 km(2) case study areas in lowland ecosystems of Colombia. Our analysis included soil fertility, a cost-distance function, and neighbourhood of forest and secondary vegetation cover as independent variables. Deforestation and forest regrowth are tested using logistic regression analysis and an information criterion approach to rank the models and predictor variables. The results show that: (a) overall the process of deforestation is better predicted by the full model containing all variables, while for regrowth the model containing only the auto-correlated neighbourhood terms is a better predictor; (b) overall consistent patterns emerge, although there are variations across regions and time; and (c) during the transformation process, both the order of importance and significance of the drivers change. Forest cover follows a consistent logistic decline pattern across regions, with introduced pastures being the major replacement land cover type. Forest stabilizes at 2-10% of the original cover, with an average patch size of 15.4 (+/-9.2)ha. We discuss the implications of the observed patterns and rates of land cover change for conservation planning in countries with high rates of deforestation.     

STOCHASTIC ANALYSIS OF TIME-AGGREGATED HYDROLOGIC DATA1

Stochastic models fitted to hydrologic data of different time scales are interrelated because the higher time scale data (aggregated data) are derived from those of lower time scale. Relationships between the statistical properties and parameters of models of aggregated data and of original data are examined in this paper. It is also shown that the aggregated data can be more accurately predicted by using a valid model of the original data than by using a valid model of the aggregated data. This property is particularly important in forecasting annual values because only a few annual values are usually available and the resulting forecasts are relatively inaccurate if models based only on annual data are used. The relationships and forecasting equations are developed for general aggregation time and can be used for hourly and daily, daily and monthly or monthly and yearly data. The method is illustrated by using monthly and yearly streamflow data. The results indicate that various statistical characteristics and parameters of the model of annual data can be accurately estimated by using the monthly data and forecasts of annual data by using monthly models have smaller one step ahead mean square error than those obtained by using annual data models.     

Diversity in Current Ecological Thinking: Implications for Environmental Management

Current ecological thinking emphasizes that systems are complex, dynamic, and unpredictable across space and time. What is the diversity in interpretation of these ideas among today’s ecologists, and what does this mean for environmental management? This study used a Policy Delphi survey of ecologists to explore their perspectives on a number of current topics in ecology. The results showed general concurrence with nonequilibrium views. There was agreement that disturbance is a widespread, normal feature of ecosystems with historically contingent responses. The importance of recognizing multiple levels of organization and the role of functional diversity in environmental change were also widely acknowledged. Views differed regarding the predictability of successional development, whether “patchiness” is a useful concept, and the benefits of shifting the focus from species to ecosystem processes. Because of their centrality to environmental management, these different views warrant special attention from both managers and ecologists. Such divergence is particularly problematic given widespread concerns regarding the poor linkages between science (here, ecology) and environmental policy and management, which have been attributed to scientific uncertainty and a lack of consensus among scientists, both jeopardizing the transfer of science into management. Several suggestions to help managers deal with these differences are provided, especially the need to interpret broader theory in the context of place-based assessments. The uncertainty created by these differences requires a proactive approach to environmental management, including clearly identifying environmental objectives, careful experimental design, and effective monitoring.     

TEMPORAL AND GEOMORPHIC VARIATIONS OF STREAM STABILITY AND MORPHOLOGY: MAHOGANY CREEK,NEVADA1

ABSTRACT: Detailed studies of long-term management impacts on rangeland streams are few because of the cost of obtaining detailed data replicated in time. This study uses government agency aquatic habitat, stream morphologic, and ocular stability data to assess land management impacts over four years on three stream reaches of an important rangeland watershed in northwestern Nevada. Aquatic habitat improved as riparian vegetation reestablished itself with decreased and better controlled livestock grazing. However, sediment from livestock disturbances and road crossings and very low stream flows limited the rate of change. Stream type limited the change of pool variables and width/depth ratio, which are linked to gradient and entrenchment. Coarse woody debris removal due to previous management limited pool recovery. Various critical-element ocular stability estimates represented changes with time and differences among reaches very well. Ocular stability variables tracked the quantitative habitat and morphologic variables well enough to recommend that ocular surveys be used to monitor changes with time between more intensive aquatic surveys.     

EFFECT OF RAINFALL EXCESS CALCULATIONS ON MODELED HYDROGRAPH ACCURACY AND UNIT-HYDROGRAPH PARAMETERS1

ABSTRACT: Two methods of computing rainfall excess in the U.S. Army Corps of Engineers’flood hydrograph package (HEC-1), the Initial and Uniform method and the Exponential method, are compared to evaluate the effects on modeled hydrograph accuracy. Two computed unit-hydrograph parameters, time of concentration and storage coefficient, were also compared. Rainfall and runoff data from 209 storms in 32 gaged basins in Illinois were used to calibrate the HEC-1 model. Three hydrograph characteristics - sum of incremental flows, peak discharge, and time of peak discharge - were used to evaluate modeled hydrograph accuracy. Mean percent error for each basin and hydrograph characteristic was computed. An evaluation of the mean errors indicates that, although some bias in modeled hydrograph accuracy is evident, rainfall excess computed using either method results in a computed hydrograph accuracy that is within generally accepted limits. Application of a linear-regression model shows no significant differences in computed values of unit-hydrograph parameters.     

What is a static head measurement? Evaluating structural error and implications for regional groundwater modeling

Most groundwater modelers avoid using static heads measured from active production wells because they can introduce a bias into model calibration. However, in the deep confined Cambrian-Ordovician Sandstone Aquifer System in the Central Midcontinent of North America, dedicated observation wells are sparse and remote from areas of most concentrated pumping. As a result, in areas where drawdown is the greatest and modeling is most needed, only static heads from production wells are available for calibration. This paper evaluates two leading sources of discrepancies in using production well data, spatial and temporal structural error (S.E.). A simple Theis solution is used to evaluate the potential magnitude of spatial S.E. when calibrating a regional MODFLOW model with coarse cell resolution. Despite theoretical analyses indicating that spatial S.E. could be significant, statistical analysis of the model results suggests that temporal S.E. is dominant. Long (ranging over decades) or frequent (monthly) head datasets are key in understanding temporal S.E., to better capture water-level variability. In this study, the range in static head observations impacted estimates of the remaining time a well could extract water from the aquifer by 0.1 to 16.0 years. This uncertainty in future water supply is highly relevant to stakeholders and must be assessed in hydrographs depicting risk.     

A COMPARATIVE STUDY OF A SWEDISH AND A CHINESE HYDROLOGICAL MODEL1

ABSTRACT: There are a large number of conceptual hydrological models available today. It is not easy to immediately identify the similarities and differences between the different models. The Swedish HBV model and the Chinese Xinanjiang model are two examples of conceptual, semi-distributed, rainfall-runoff models. The Xinanjiang model was designed for use in humid and semi-humid regions, with no routine for the snowmelt runoff, whereas the snow routine is an important part of the HBV model in many applications. The model structures of the two models may be described in four routines, compared in this paper. The integral structures of them are similar, but there are some differences, especially in the runoff production routine. The physical significance and physical definitions of some model parameters were analyzed. Both models were tested in two basins. Both models gave similar results, and both models performed well in the application. The similarity of the results obtained by different model structures leads to the following two conclusions. First, more effort should probably be spent on the improvement of input data quality and coverage than on the development of more detailed model structures only. Second, inference about basin behavior and characteristics from the values of calibrated model parameters must be made with great caution.     

INTERDISCIPLINARY MODELING IN THE ANALYSIS OF THE SALINITY PROBLEMS OF THE SAFFORD VALLEY1

ABSTRACT: A groundwater quality change of +0.13 millimhos electrical conductivity was documented between 1940 and 1 972 in the Safford Valley. The change is attributable to four principal mechanisms: pumping-encouraged saline artesian aquifer leakage, natural recharge of the water table aquifer by saline waters, leaching of agricultural waters into the aquifer and the lateral flow of groundwater through saline lacustrine beds. A hydrologic study of the area has shown the first of these mechanisms to be predominant. Salinity modeling has shown three regions of salinity change, and salinity increase projections for each are determined. An economic analysis and an economic model are then combined with the physical model, yielding information as to when certain economic conditions are reached with respect to the salinity increase. This combined model shows that, based on projected salinity trends, cotton, the principal agricultural crop of the valley, will remain economical to cultivate for a significant time beyond the model's limit of prediction. Alfalfa, on the other hand, should go out of production in large areas of the valley by 1990, and not be under economical cultivation by 2040. A sociologic model, based on the cluster analysis of questionnaire data, shows an awareness of the salinity problems of the area but little concern over them. Interdisciplinary model based salinity control regulations are made.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

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     

Differences among model simulations of climate change on the scale of resource regions

The climate simulations from atmospheric general circulation models (GCMs) are often used to analyze the potential effects of climate change on environmental resources. It has been demonstrated that there are differences among the simulations from various GCMs, on spatial scales ranging from global to regional. This paper quantifies the differences in temperature and precipitation simulated by three major GCMs for four specific regions: an agricultural region (the North American winter wheat belt), a hydrologic region (the Great Basin), a demographic region (the high-density population corridor of the northeast United States), and a political region (the state of Texas). Both the current (control) climate and the climatic response to a doubling of atmospheric carbon dioxide (CO₂) are consideredIn each region, even when the data are averaged on a seasonal basis, marked differences occurred in the areal average climate simulated by the different GCMs for both the control climate and the doubled-CO₂ climate. Thus, climate impact studies based on the simulations of more than one GCM could easily yield a range of possible results