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Arid and semiarid grasslands of southwestern North America have changed dramatically over the last 150 years as a result of woody plant encroachment. Overgrazing, reduced fire frequency, and climate change are known drivers of woody plant encroachment into grasslands. In this study, relatively sim- ple algorithms for encroachment factors (i.e., grazing, grassland fires, and seed dispersal by grazers) are proposed and implemented in the ecohydrological Cellular-Automata Tree Grass Shrub Simulator (CAT- GraSS). CATGraSS is used in a 7.3 km 2 rectangular domain located in central New Mexico along a zone of grassland to shrubland transition, where shrub encroachment is currently active. CATGraSS is calibrated and used to investigate the relative contributions of grazing, fire frequency, seed dispersal by herbivores and climate change on shrub abundance over a 150-year period of historical shrub encroachment. The impact of future climate change is examined using a model output that realistically represents current vegetation cover as initial condition, in a series of stochastic CATGraSS future climate simulations. Model simulations are found to be highly sensitive to the initial distribution of shrub cover. Encroachment fac- tors more actively lead to shrub propagation within the domain when the model starts with randomly distributed individual shrubs. However, when shrubs are naturally evolved into clusters, the model re- sponse to encroachment factors is muted unless the effect of seed dispersal by herbivores is amplified. The relative contribution of different drivers on modeled shrub encroachment varied based on the ini- tial shrub cover condition used in the model. When historical weather data is used, CATGraSS predicted loss of shrub and grass cover during the 1950 s drought. While future climate change is found to amplify shrub encroachment ( ∼13% more shrub cover by 2100), grazing remains the dominant factor promoting shrub encroachment. When we modeled future climate change, however, encroachment still occurred at a reduced rate in the absence of grazing along with pre-grazing fire frequency because of lower shrub water stress leading to reduced shrub mortality which increases the probability of shrub establishment.

The Mediterranean area is historically characterized by high human pressure on water resources. Today, while climate is projected to be modified in the future, through precipitation decrease and temperature increase, that jointly and non-linearly may affect runoff, concerns about water availability are increasing. For these reasons, quantitative assessment of future modifications in the mean annual water availability are important; likewise, the description of the future interannual variability of some hydrological components such as runoff and evapotranspiration are highly wished for water management and ecosystems dynamics analyses. This study investigates at basin spatial scale future runoff and evapotranspiration, exploring their probability density functions and their interdependence as functions of climatic changes. In order to do that, a parsimonious conceptual lumped model is here used. The model is forced by different future climate scenarios, generated through a weather generator based on a stochastic downscaling of an ensemble of General Circulation Models (GCMs) realizations. The use of the adopted hydrological model, under reliable stochastic future climate scenarios, allows to project future values of evapotranspiration and runoff in a probabilistic framework and, at the same time, the evaluation of their bivariate frequency distributions for changes through the Multivariate Kernel Density Estimation method. As a case study, a benchmark Mediterranean watershed has been proposed (Imera Meridionale, Italy). Results suggest a radical shift and shape modification of the annual runoff and evapotranspiration probability density functions. Possible implications and impacts on water resources management are here addressed and discussed.

Regions of vegetation transitions (ecotones) are known to be highly sensitive to climate fluctuations. In this study, the Cellular-Automata Tree Grass Shrub Simulator (CATGraSS) has been modified, calibrated and used with downscaled future climate scenarios to examine the role of climate change on vegetation patterns in a steep mountainous catchment (1.3 km2) located in Sicily, Italy. In the catchment, north-facing slopes are mostly covered by trees and grass, and south-facing slopes by Indian Fig opuntia and grass, with grasses dominating as elevation grows. CATGraSS simulates solar radiation, evapotranspiration, and soil moisture in space and time. Each model cell can hold a single plant type or can be bare soil. Plant competition is modeled explicitly through mortality and the establishment of individual plants in open spaces. In this study, CATGraSS is modified to account for heterogeneity in soil thickness and tested in the study catchment using the historical climate of the region. Predicted vegetation patterns are compared with those obtained from satellite images. Results of model under current climate underscore the importance of solar irradiance and soil thickness, especially in the uplands where soil is shallow, in determining vegetation composition over complex terrain. A stochastic weather generator is used to generate future climate change scenarios for the catchment by downscaling GCM realizations in space and time. Future increase in atmospheric CO2 concentration was considered through modifying the vegetation water use efficiency and stomatal resistance for our study site. Model results suggest that vegetation pattern is highly sensitive to temperature and rainfall variations provided by climate scenarios (30% reduction of the annual precipitation and a 2.8 °C increase of the mean annual temperature). Future climate change is predicted to bring a considerable reorganization of the plant composition following topographic patterns, leading to a decrease of trees cover at the expenses of a grass expansion, which will cause loss of landscape vegetation diversity.