Geoespacial Analysis and Modelling to Map Socioenvironmental Sensitivity of Landscape to Impact of Extreme Events in the Usumacinta Watershed

Daniel Lopez Lopez

Centro de Investigación en geografía y Geomatica Ing. Jorge L. Tamayo A. C., México

Abstract: Climate change continues to be a major research thrust within global environmental change and sustainability research, but with increasing emphasis on how society may adapt to future stresses. There is an increasing recognition of the importance to consider the social vulnerability equally with the biophysical vulnerability, thus presenting vulnerability on the whole as a function both of physical characteristics of climate change and of social system’s inherent sensitivity. Sensitivity is regarded as the potential for and the probable magnitude of change within a physical system in response to external effects and the ability of this system to resist the change. One of the major concerns over a potential change in climate is that it will cause an increase in extreme weather events. In Mexico, the exposure factors as well as the sensitivity to the extreme weather events have increased during the last three or four decades. From the biophysical point of view, the extreme weather events, particularly heavy rains lead to flooding, increase soil erosion and landslide. In this study spatial analysis and modeling were used to assess and map socio-biophysical sensitivity of Landscape to extreme weather events in the Usumacinta watershed. Indices for Hydric erosion susceptibility, landslide susceptibility, flooding susceptibility and land use intensity were calculated and combined using a decision model to construct a biophysical sensitivity index. Disabled population, population higher than 65 years older, population less than five years old, indigenous population, population without access to health services, and population in households with famine head, were used to constructs a social sensitivity index. The social sensitivity index and the environmental sensitivity index were combined by decision model to construct a landscape sensitivity index. The final results indicate that the biophysical sensitivity is higher in the lowlands, but the social sensitivity is higher in the highlands. The landscape sensitivity index indicates that around 815.000 (24.2%) have a high social-environmental sensitivity index, 1,540.000 (45.7%) moderate and 1,010.000 has (30.0%) low.

Key words: Geospatial analysis, socio-environmental sensitivity, Landscape