A Software Tool for Earth Surface Modeling of Environmental variables

A software tool for earth surface modeling of climate change through high-accuracy and high-speed methods are presented here. Environmental variables including temperature, rainfall and soil property constitute critical driving factors of ecosystem modeling. However, there are a couple of challenges behind modeling environmental factors including heterogeneity of multiple data sets. To handle compatibility between heterogeneous data sets, the modeling process need to represent environmental data in grid cells with earth surface modeling. However, there are error problems in classical methods of surface modeling. Furthermore, there is still limited experience in developing multi-scale surface modeling tool of climate change for ecosystem modeling. High-accuracy and high-speed methods for surface modeling (HASM) were used to solve error problems, improve the accuracy of spatial distribution simulation of environmental variables and handling muti-scale information. However, HASM which is more theoretically perfect and more complicated than other classical methods of surface modeling. This tool decompose the HASM modeling process into several components including HASM initial field, HASM optimistic controller and HASM iterative solver .HASM can opt remote sensing images or existing short precision grid data for initial field and can also simulate filed when lacking of initial fields .HASM optimistic controller need to get knowledge from environmental database to generate controlling condition rulers ensuring modeling more reasonable and more precision. HASM iterative solver is responsible for solution of HASM by means of integration of various solve methods which we have developed. Rather than linking environmental data with these modeling components, a supermatic surface modeling approach ensuring effective information exchange components is also designed for HASM. This software is easily applied to the development of ecosystem modeling integrated with surface modeling. This study from this integration is also helpful for future researches that aim to integrate surface modeling and ecosystem modeling.