Koiliaris River watershed is a Critical Zone Observatory that represents severely degraded soils due to intensive agricultural activities and biophysical factors. It has typical Mediterranean soils under the imminent threat of desertification which is expected to intensify due to projected climate change. High frequency hydro-chemical monitoring with targeted sampling for Rare Earth Elements (REE) analysis of different water bodies and geochemical characterization of soils were used for the identification of hydrologic and geochemical pathways.

This paper assesses strategic water availability and use under different development pathways on a basin scale using remote sensing (RS), geographical information systems (GIS) and a spatial water budget model (SWBM). The SWBM was applied to the Upper Ing Basin in northern Thailand to investigate the spatial and temporal variations in the location of streams and water yields from different parts of the basin. The base simulation was carried out for the years 1998–2007 using a DEM and actual land-use data at 100-m resolution.

Drought-induced, regional-scale dieback of forests has emerged as a global concern that is expected to escalate under model projections of climate change. Since 2000, drought of unusual severity, extent, and duration has affected large areas of western North America, leading to regional-scale dieback of forests in the southwestern US. We report on drought impacts on forests in a region farther north, encompassing the transition between boreal forest and prairie in western Canada.

In megacities water quantity and quality are threatened by complex and interrelated processes caused by population growth, land use change, unsustainable agricultural practices, deforestation, erosion, destruction of ecosystems, lack of planning, laissez-faire policies, unsustainable water management, political conflicts, and increasingly also by the impacts of climate change.

A GIS-based Soil and Water Assessment Tool (SWAT) model is used to assess the impacts of climate change on the hydrological regime of the Cauvery river basin. First, the impact of changes in land-management practices on water availability under present conditions is modelled. Then, the same analysis is carried out under the future climatic scenarios. Finally, annual and monthly precipitation variability is compared under present, as well as future, climate-change scenarios.

Land degradation is a global issue on par with climate change and loss of biodiversity, but its extent and severity are only roughly known and there is little detail on the immediate processes – let alone the drivers. Earth-observation methods enable monitoring of land degradation in a consistent, physical way and on a global scale by making use of vegetation productivity and/or loss as proxies. Most recent studies indicate a general greening trend, but improved data sets and analysis also show a combination of greening and browning trends.

Using data at a high spatial resolution, we estimate a cereal yield response function conditional upon climatological and topographical features using a recently developed estimator for spatial process models when sample selection is of concern. We control for localized spatial correlationin unobserved disturbances affecting both the selection to plant cereals as well as in the resulting conditional yield response.

Land use change, natural disturbance, and climate change directly alter ecosystem productivity and carbon stock level. The estimation of ecosystem carbon dynamics depends on the quality of land cover change data and the effectiveness of the ecosystem models that represent the vegetation growth processes and disturbance effects. We used the Integrated Biosphere Simulator (IBIS) and a set of 30- to 60-m resolution fire and land cover change data to examine the carbon changes of California's forests, shrublands, and grasslands.

Concerns about climate change and water quality make it necessary to have a better understanding of the cycling of carbon (C) and nitrogen (N) within landscapes. In New Zealand, pastoral farming on hill country is a major land use, and there is little information available at a landscape level on the cycling of C and N within these systems, particularly the impacts of land use intensification.

Questions: Can the climatic adaptive capacity of natural ecosystems be estimated with using landscape indicators based on vegetation or land-cover data? Can species distribution model (SDM) outputs be enhanced using such indicators? What are the data requirements and optimal parameter values of potential indicators? Location: Indicator framework: unspecified. Case study: Kiskunság, Hungary. Methods: (1) We define a general framework for handling adaptation in ecological climate change impact assessments based on IPCC definitions.

Land use/cover change (LUCC) is a major indicator of the impact of climate change and human activity, particularly in the Sahel, where the land cover has changed greatly over the past 50 years. Aerial and satellite sensors have been taking images of the Earth's surface for several decades. These data have been widely used to monitor LUCC, but many questions remain concerning what type of pre-processing should be carried out on image resolutions and which methods are most appropriate for successfully mapping patterns and dynamics in both croplands and natural vegetation.

In this paper is presented a methodological approach which integrates statistic modelling and 2-D cellular automata (CA) in order to describe tree species shifts responding to the climate changes foreseen for Italy in the 21st century. Five Italian tree species populations of Abies alba, Pinus sylvestris, Fagus sylvatica, Acer campestris and Quercus suber and their actual potential distributions (PDs) – represented by Importance Value (IV), have been considered.