Groundwater Vulnerability Assessment Combining the Drastic and Dyna-Clue Model in the Argentine Pampas

Vulnerability assessment is considered an effective tool in establishing monitoring networks required for controlling potential pollution. The aim of this work is to propose a new integrated methodology to assess actual and forecasted groundwater vulnerability by including land-use change impact on groundwater quality. Land-use changes were simulated by applying a spatial dynamics model in a scenario of agricultural expansion. Groundwater vulnerability methodology DRASTIC-P, was modifyed by adding a land-use parameter in order to assess groundwater vulnerability within a future scenario. This new groundwater vulnerability methodology shows the areas where agricultural activities increase the potential level of groundwater vulnerability to pollution. The Dulce Creek Basin was the study case proposed for the application of this methodology. The study revealed that the area with Very High vulnerability would increase 20% by the year 2020 in the Dulce Creek Basin. This result can be explained by analyzing the land-use map simulated by the Dyna-CLUE model for the year 2020, which shows that the areas with increments in crop and pasture coincide with the area defined by the Very High aquifer vulnerability category in the year 2020. Through scenario analysis, land-use change models can help to identify medium or long term critical locations in the face of environmental change.     

Exploring changes in Ecuadorian land use for food production and their effects on natural resources

A model is used for the dynamic and spatially explicit exploration of near future agricultural land-use changes. In a case study for Ecuador, different plausible scenarios are formulated, taking into account possible developments in national food demand until the year 2010. The protection of nature parks and restrictions due to land degradation are evaluated with respect to their possible spatial impacts on the land-use change dynamics within the country. Under the assumptions of the demand scenarios, agricultural land-use expands significantly, resulting in more use of land in existing agricultural areas and frontier-type expansion into relatively undisturbed natural areas. The patterns of change depend on the increase in demand, competition between land-use types, changes in driving factors of land use, and the area and characteristics of land that is excluded from agricultural use. The modelled land-use dynamics are related to their possible impacts on the natural resource base, specifically soil fertility. The results indicate potential negative effects of land-use changes on the soil nutrient balance and biodiversity. It is argued that spatial and temporal quantification of land-use dynamics at the landscape level can support research and policies aimed at understanding the driving factors of land-use change and the behaviour of complex agro-ecosystems under changing conditions at different scales. In this way, issues dealing with sustainable food production and the management of the natural resource base can be addressed in a more integrated and quantitative manner.     

Trade-off analysis in the Northern Andes to study the dynamics in agricultural land use

In this paper we hypothesize that land use change can be induced by non-linearities and thresholds in production systems that impact farmers' decision making. Tradeoffs between environmental and economic indicators is a useful way to represent dynamic properties of agricultural systems. The Tradeoff Analysis (TOA) System is software designed to implement the integrated analysis of tradeoffs in agricultural systems. The TOA methodology is based on spatially explicit econometric simulation models linked to spatially referenced bio-physical simulation models to simulate land use and input decisions. The methodology has been applied for the potato-pasture production system in the Ecuadorian Andes. The land use change literature often describes non-linearity in land use change as a result of sudden changes in the political (e.g. new agricultural policies) or environmental setting (e.g. earthquakes). However, less attention has been paid to the non-linearities in production systems and their consequences for land use change. In this paper, we use the TOA system to study agricultural land use dynamics and to find the underlying processes for non-linearities. Results show that the sources of non-linearities are in the properties of bio-physical processes and in the decision making-process of farmers.     

Assessing Land-Use Impacts on Natural Resources

/ Much information is available on changes that occur in natural resources from both spatially-explicit data on environmental conditions and models of the interactions of these conditions and resources with human activities. The strategy for assessing land-use impacts on natural resources developed in this paper provides a framework for using relevant data and models to address questions of how management practices can promote both use and protection of resources. This assessment strategy integrates spatially explicit environmental data using geographic information systems (GIS) with computer models that simulate changes in land cover in response to land-use impacts. The computer models also simulate susceptibility of species to changes in habitat suitability and landscape patterns. The approach is applied to management of limestone barrens on the Oak Ridge Reservation in East Tennessee. Potential limestone barrens habitats are identified by overlaying appropriate soils, geology, slope, and land-use/land-cover conditions. Their validity is tested against known sites containing rare species that occur in these habitats. The location of habitats at risk in the aftermath of human activities is determined by using an available area model that identifies the size and proximity of sites that particular types of species can no longer use as habitat. The resulting risk map can be used in land management planning. The approach uses readily available in situ and remotely sensed data and is applicable to a wide range of locations and land-use scenarios. This approach can be refined based on needs identified by land managers and on the sensitivity of the results to the resolution of available resource information.KEY WORDS: Land management; Assessment; Habitat characterization; Limestone barrens; Ecological modeling; Geographic information systems     

Carbon dynamics and land-use choices: building a regional-scale multidisciplinary model

Policy enabling tropical forests to approach their potential contribution to global-climate-change mitigation requires forecasts of land use and carbon storage on a large scale over long periods. In this paper, we present an integrated modeling methodology that addresses these needs. We model the dynamics of the human land-use system and of C pools contained in each ecosystem, as well as their interactions. The model is national scale, and is currently applied in a preliminary way to Costa Rica using data spanning a period of over 50 years. It combines an ecological process model, parameterized using field and other data, with an economic model, estimated using historical data to ensure a close link to actual behavior. These two models are linked so that ecological conditions affect land-use choices and vice versa. The integrated model predicts land use and its consequences for C storage for policy scenarios. These predictions can be used to create baselines, reward sequestration, and estimate the value in both environmental and economic terms of including C sequestration in tropical forests as part of the efforts to mitigate global climate change. The model can also be used to assess the benefits from costly activities to increase accuracy and thus reduce errors and their societal costs.     

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.     

Policy-Oriented Integrated Analysis of Land-Use Change: An Analysis of Data Needs

Integrated analysis of land-use change in support of informed policy-making for sustainable land use at various spatial levels makes heavy requirements for quality data. This paper aims to structure the principal methodological and practical issues of data needs for this task. The data needs for local level analyses of land-use change are given special attention. The paper adopts an integrated economic–environmental modeling framework and suggests the main types of data needed. Four main data dimensions are distinguished: system of spatial reference, system of temporal reference, definitions, and data collection procedures (sources, collection instruments and techniques, recording and retrieval systems, data format, frequency of data collection). To evaluate the suitability of current data sets as well as to guide the collection of new ones, a scheme based on substantive and practical criteria is employed. The former include compatibility, consistency, and reliability of data, while the latter include availability, ease and cost of data collection/retrieval, and transferability of data. The evaluation indicates the broad gaps in current data sets along the four main data dimensions. Based on this evaluation, the paper suggests framework guidelines to address the main data issues identified. RID=" ID=" <SUP>1</SUP>CLAUDE: Coordinating Land Use and Land Cover Data and Analyses in Europe. European Commission Concerted Action project (DG-XII); DAPLARCH: Data Plan Development for Land Use and Land Cover Change Research. Program sponsored by IGBP-IHDP (LUCC and DIS). Street, Durham, North Carolina 27707, USA     

Land-Cover Change Trajectories in Northern Ghana

Land-cover change trajectories are an emergent property of complex human–environment systems such as the land-use system. An understanding of the factors responsible for land change trajectories is fundamental for land-use planning and the development of land-related policies. The aims of this study were to characterize and identify the spatial determinants of agricultural land-cover change trajectories in northern Ghana. Land-cover change trajectories were defined using land-cover maps prepared from Landsat Thematic Mapper dataset acquired in 1984, 1992, and 1999. Binary logistic regression was used to model the probability of observing the trajectories as a function of spatially explicit biophysical and socioeconomic independent variables. Population densities generally increased along the continuum of land-use intensity, whereas distance from market and roads generally decreased along this continuum. Apparently, roads and market serve as incentives for settlement and agricultural land use. An increase in population density is an important spatial determinant only for trajectories where the dominant change process is agricultural extensification. A major response to population growth is an increase in cultivation frequency around the main market. Agricultural intensification is highly sensitive to accessibility by roads. The increase in land-use intensity is also associated with low soil quality. These results suggest the need for policies to restore soil fertility for agricultural sustainability. The models also provide a means for identifying functional relationships for in-depth analyses of land-use change in Ghana.     

Multi-criteria risk management with the use of <Emphasis Type="Italic">DecernsMCDA</Emphasis>: methods and case studies

Emerging challenges of risk management, environmental protection, and land-use planning requires integration of stakeholder values and expert judgment. The process of decision making in situation of high uncertainty can be assisted through the use of decision support systems (DSSs). Such DSSs are often based on tools for spatial data representation (GIS) and environmental models that are integrated using multi-criteria decision analysis (MCDA). This paper presents DecernsMCDA implementing all major types of multi-criteria methods and tools (AHP, MAUT, Outranking) under the same user interface. In addition to providing ability for testing model uncertainty associated with selection of specific MCDA algorithms, DecernsMCDA implements new algorithms for parameter uncertainty analysis based on probabilistic approaches and fuzzy sets. The paper illustrates application of DecernsMCDA for selecting remedial alternative at radiologically contaminated sites.     

A GIS-based approach for assessing the regional conservation status of genetic diversity: an example from the southern Appalachians

Conserving genetic diversity requires an assessment of the distribution of genetic variants in relation to patterns of land use and environmental variation at a regional scale. This assessment requires a novel approach to integrating and analyzing the genetic and environmental data across spatial scales. To explore the integration of genetic data with other geospatial data sets, we developed a GIS-based approach for examining patterns of genetic diversity for several species of salamanders in southern Appalachians. The genetic data, from allozyme surveys in the genetics literature, were integrated into a GIS database along with related attributes including population identifications and spatial locations. Using existing geospatial data, we classified sample locations as being either protected from anthropogenic disturbance (e.g., National Parks, Wilderness Areas) or as unprotected (e.g., private lands, multiple-use lands in National Forests). We used multidimensional scaling of allelic frequencies and contributions of populations to interpopulation differences in allelic richness to determine which populations had genetic characteristics most different from other populations in the sample. Measures of genetic differentiation were integrated into the GIS database to facilitate spatial analysis and visualization of the indices in relation to land use. This approach was useful for both identification of populations with components of genetic variation that were not well represented at protected sites and for identifying areas of species distributions where more genetic sampling would be necessary to make informed management decisions. Our approach could be readily adapted for use by managers and geneticists working with other species and types of genetic markers.