Integration of Geospatial Technology and Simulation Modelling for Climate Change Studies

Abstract

According to the Intergovernmental Panel on Climate Change (IPCC), the likely increase in global mean surface temperature by the end of the twenty-first century relative to 1986–2005 is 0.3–1.7 C under the most stringent mitigation of greenhouse gases emissions scenario (RCP2.6), 1.1–2.6 C under intermediate scenario RCP4.5, 1.4–3.1 C under RCP6.0 and 2.6–4.8 C under very high emissions scenario RCP8.5 (IPCC, in: Core Writing Team, Pachauri and Meyer, eds., Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change, IPCC, Geneva, 151 pp., 2014). As agricultural production is largely influenced by the climatic parameters, any changes in climate would pose a high risk to the global food security. Therefore, an accurate assessment of the impact of climate change is required for development of the best adaptation and mitigation strategies and proper management of natural resources. Geospatial technologies such as remote sensing (RS), geographic information system (GIS), Global Positioning System (GPS) and computer-based dynamic crop simulation models (CSMs) have been widely used for monitoring the growth and yield parameters of crops and simulating the plant processes under different climatic, soil and management conditions. Crop simulation models that simulate the relationship between plants and their environments are one of the most important tools used to study the potential impacts of climate change on crop production. But a major limitation of these models is the lack of spatial information, which reduces their actual application on the regional scale because most of these models are simulated at the field scale. The application of such models can be improved if they are integrated with geospatial technologies that provide spatial information on crop conditions at larger scale. This chapter describes the different methods by which geospatial technology and crop growth models can be integrated for improved decision-making under current and future climate.