| Climate factors are not only significant environmental conditions for human subsistence and production activities, but also indispensable natural resource for human material production. They are important data source to study such sciences as ecology, geography, resources and agronomy etc. Traditional meteorological stations are basically limited to small scope, which means that earth systems are sampled in the form of point. Though many meteorological stations have been established round the world, the sample points of observation are finite due to the constraint of cost. In addition, the meteorological stations are symmetrical. The stations in the developed region are dense while exiguous and even absent in the regions of hard natural conditions. Therefore, how to acquire spatial climate factors according to the spatial distribution of meteorological stations and the rule behind spatial variation of different climate factors is one of important tasks during recent years in ecology, resource science, environment science etc. That is also one of issues which are urgently needed to be solved in modern ecology and global change science.Climate variables data are the data which closely related to geographical distribution. The research methods of spatial distribution of climate factors have developed to qualitive, quantitive and postioning integrated methods from traditional qualitive and semi-quantitive methods due to the introduction of GIS analysis techniques, which greatly improve the precision of spatial distribution simulation of climate factors and diminish the disadvantages such as vast and complicated computations. GIS analysis techniques have become one of important approaches that investigate the spatial distribution of climate factors. There have a large area with complicated topography and physiognomy in China. The distribution density of meteorological stations in China is much lower than that in developed country. The data of spatial climate factors with quite high precision are acquired by taking advantage of limited observation data and spatial distribution rule of different climate variables at different spatial scales. The data can provide spatial basic data platform for ecology, earth science, agricultural science, resource science and environment science etc, important basic information for decision department and production department.Net primary productivity (NPP) plays an important role on the study of global change. NPP,the direct reflection of plant community productivity for a certain natural environment, is the basis of matter and energy cycle of terrestrial ecosystem. From the view of the global change research, NPP is one of the most-modeled ecological parameters, and the central carbon-related variable summarizing the interface between plant and other processes. Climate factors are very important in NPP model. With the development of the modern ecology and global change, the spatial meteorological data with high resolution are required and applied in NPP simulation. The spatial distribution of NPP for China varies highly with complicated topography and climate. Therefore, using climate data with high spatial resolution and RS data, the distribution and seasonal change of NPP in China were studied.According to three different study areas, including Xinaju County, Zhejiang Province, and China, the spatial distribution of climate variables were studied based on GIS technology. Then the NPP for China from Apr. to Dec. in 2000 were calculated. The main contents that this research has made are summarized as follow:1) Study on the spatial distribution of climate variables based on GIS technology in Xianju County.As for the research on the spatial distribution of solar radiation factors in Xianju County, we established the spatial distribution model of solar radiation factors, including direct solar radiation, diffuse solar radiation, and sunshine duration. The topographic factors of longitude, altitude, slope and aspect etc were derived from DEM with 20-meters resolution for Xianju County. With the assistant of GIS technology, we realized the spatial distribution of solar radiation factors in Xianju County.Because of the sparse distribution of meteorological stations in Xianju County, the duration of single time series of meteorological data acquisition is usually short. So those short time series of meteorological data is prolonged to study the spatial distribution of climate factors. The adjusting method of monthly average temperature and monthly precipitation series were studied. For the adjusting of monthly average temperature series, the one-variate regression method and the difference value method were used for the data collected from 8 meteorological temporal stations, and then the fitting errors produced by the two methods were analyzed using statistical methods. The results indicated that the fitting errors produce were not significantly different between the two methods and can be applied to time series adjusting of monthly average temperature in Xianju County. For the adjusting of monthly precipitation series, firstly, the time series of yearly precipitation are adjusted, and then the time series of monthly precipitation are adjusted using the relative coefficient of monthly precipitation. The fitting errors for monthly precipitation data series were calculated using statistical methods. The results indicated that the more correlative coefficient between the adjusted stations and basic station is, the less the fitting errors. The adjusting results have relation with the temporal distribution characteristics of monthly precipitation if the difference of the correlative coefficient is insignificant. Moreover, the adjusting effect that analyzed the temporal distribution characteristics of monthly precipitation were consistently better than the ones that did not analyzed the temporal distribution characteristics of monthly precipitation. As for the research on the spatial distribution of temperature and precipitation in Xianju County, we used the adjusted monthly average temperature and monthly precipitation data, and then respectively established the topography-adjusted statistical model and multiple linear regression models. With the assistant of topographic spatial data and GIS technology, we realized the spatialdistribution of monthly average temperature and monthly precipitation in Xianju County.2) Study on the spatial distribution of climate variables based on GIS technology in Zhejiang Province.Under the circumstance of taking geographical position and topographical characteristics into consideration, geographical and topographical factors relating to the distributions of temperature and precipitation in Zhejiang Province are extracted from DEM data of Zhejiang Province. Those involve longitude, latitude, elevation, slope, aspect, distance and direction from seacoast and the ratio of area to land within given radii etc. In the same time, the statistical regression models are constructed by using mean temperature per month, mean precipitation per month over nearly 30 years and geographical and topographical factors. Finally, spatial data of mean temperature and mean precipitation in different season have been achieved. After tests with real observation data were performed, results indicate that the seasonal temperature simulation values are in accord with real data values with correlation coefficients ranged from 0.870 to 0.967. The simulation efficiency in winter is better than that in other seasons. The seasonal precipitation simulation values accord with real precipitation data values, too. The correlation coefficients are between 0.739 and 0.946. Highest precision of the simulation is spring while lowest precision of that is winter.3 ) Study on the spatial distribution of climate variables based on GIS technology in China. For calculating the net primary productivity (NPP) in 2000, we only used monthly climate data, including average temperature, average maximum and minimum temperature, precipitation, average relative humid, solar radiation, sunshine duration and wind speed, etc in 2000 in China. According to different climate factors, we built the different spatial distribution models. Through topographic data with lkm-resolution and GIS technology, the spatial data of climate factors were generated.4) Calculation of the NPP for China from Apr. to Dec. in 2000 and the analysis of result. On the basis of the spatial distribution study of monthly climate factors in 2000 in China, the NPP model (Sun, 1998) was improved. The NPP for China from Apr. to Dec. in 2000 were calculated using the improved NPP model and lkm resolution MODIS data (NDVI, PAR, LandCover). The spatial distribution and seasonal change of result was analyzed from different natural regions, climate zones, longitude zones, latitude zones, and elevation zones.
|
PDF Full Text Request