GCAM-CA

A cellular automata based method to spatio-temporally discrete land use and land cover sequence data under the integrated assessment framework of GCAM

Cellular automataSpatio-temporally discreteland use and land cover change
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contributed at 2018-07-22

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Earth System SubjectEarth Surface System Synthesis

Detailed Description

Brief Introduction

      Our newly proposed GCAM-CA model is an integrated model for spatio-temporally discrete land use and land cover sequence data under the integrated assessment framework of GCAM. GCAM is a global integrated model that represents the behavior of five systems including energy, water, agriculture and land use, economy, and climate. Since the projections of land-cover and land-use change in GCAM are estimated every 5 years at 283 agro-ecological zones, downscaling these estimates is necessary to align the land-cover and land-use change estimates with other local environmental variables (e.g., soil attributes, weather data, land management statistics), thereby facilitating analysis of the impacts of potential land-cover and land-use change on surrounding local environments. GCAM-CA model attempts to preserve the spatial details of land-use patterns at 1-km resolution, and provide the publicly available land-use/land cover products to feed all kinds of global environmental change assessments.

Our GCAM-CA model is developed based on the theory of Cellular Automaton (CA), but with several great improvements over the traditional CA. In our GCAM-CA model, some details worth paying attention to.

lThe demand amount of urban land use was modified by using population and socioeconomic data calculated by GCAM model.

lAn artificial neural network (ANN) is used to find the complex relationships between land use pattern and various human and natural driving forces. The well-trained neural network models are used to estimate the initial probabilities of land conversion on a specific grid cell in the different AEZ-based spatial units.

lThe conversion weight matrix of different land types, which indicates the difficulty for the conversion between two types of land use, is also another important factor influencing the land cover and land use change. The conversion weight matrix indicates the probability of the land use conversion from the current type to the target type in a specific AEZ-based region, and it is estimated based on the every two periods of land use data.

 In addition, our GCAM-CA model also takes the multiple scenario simulations into consideration.The GCAM model has designed three policy scenarios: REF, G26 and G45, output changes in global land use area from 2005 to 2100 under three scenarios. REF is reference scenario, do not consider the intervention of climate policy, but consider factors such as technological progress. G26, G45 are selected from the four “Representative Concentration Pathway” (RCP). G45 scenario is based on the same population and income drivers as the GCAM reference scenario but applies greenhouse gas emissions valuation policies to stabilize atmospheric radiative forcing at 4.5 W m-2 in 2100. And stabilize temperature within 2ºC, is a widely accepted scenario that most likely to appear in the future. G26 is a scenario that stabilizes radiative forcing at 2.6 W m-2 in the year 2100 without ever exceeding that value, and global average temperature rises no more than 2℃. A limit of W m-2 is exceedingly tight, so this is a radical reduction scenario. At present, the scenario is considered to be very difficult to achieve. 

Author

Min Cao, Mengxue Huang, Yanhui Zhu, Jinling Quan, Sheng Zhou, Guonian Lü, Min Chen

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contributed at 2018-07-22

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