Research On The Spatiotemporal Variation Of Climate Resources In Heilongjiang Province Based On GIS

In recent years, climate change, global warming, atmospheric circulation exceptions and extreme climate often appeared, attracting the world's great care and attention. Heilongjiang Province is the region where air temperature were warming highest in northeast china also as the country, which extreme weather is growing and hazards increased. Therefore, there are higher demand to technologies and work efficiency on climate change analysis, evaluation and monitoring, while the spatial information technologies such as Geographical Information System (GIS) have been more and more applied in research on spatiotemporal variation of climate changes widely and deeply.GIS is the core of "3S technology" (Remote Sensing, RS; Global Positioning System, GPS and Geographic Information Systems, GIS). Spatial information technology have been applied in research on the interpolation of climate elements and its changes supported by GIS. In order to explore the regional characteristics of climate changes in Heilongjiang province, climate elements were interpolated in this paper with the support of GIS techniques and statistical analysis, based on 10-days data from wheather stations, through the relevant models, to get the spatial surface data stored in the database. Spatial and temporal variation laws of climatic resources all over the province was found by exporing and analysing data from weather stations and its interpolated values. The results of this research are significent to methods selection of climate element interpolation in a region, geodatabase established to store climage resources, analysis and monitoring of regional climate change, improving the means to analysis of regional climate change and climate element variations, promoting the content and means of agricultural informationization, development and utilization of regional resources and decision-making for agricultural development. The detail results of this research are as follows:1. The results in spatial interpolation of climate elements. In this paper, supported by ArcGIS, using climate elements data obseved in 80 weather stations in 1997-2006 as sample data to be interpolated, considering the influecne of the regional ground elevation (DEM), slope aspect, forest cover, land-use cover and other factors on climate elements, adopting Cokriging, Radial Basis Function methods, the province's climate elements data such as 10 days of temperature, precipitation, sunshine duration, evaporation and others had been spatialy interpolated to get province-wide surface data divided by lkmxlkm space units with higher precision. Mean errors, mean standardized errors and root-mean-standardized errors in 36 10-days precipitation interpolation results are -0.007mm,-0.015 and 1.011 respectively, indicating that the interpolation method used for the interpolation of climate elements is an appropriate and credible ones. And then, climate elements data with the time-series by month and year had been work out through the map algebra methods (raster overlay).2. Result in spatial database establishment of climate elements. Arranging the interpolated climate elements and their relative data, supported by ArcGIS Geodatabase model, with the ArcCatalog software, a spatial database integrating raster datasets and vector datasets including climate elements such as 10-days air temperature, precipitation, accumulated temperature, wetness indices, ground temperature, evaporation amount calculated by 10 days, month and year, land-use areas, administrative divisions, weather stations had been established.3. Results in spatial and temporal variation analysis of climate elements. Through the spatial and temporal variation analysis of climate elements, the spatial and temporal variation laws of climate elements all over the province had been found being as follows.1) The 10-days average air temperature fluctuated over the past 10 years. From the space point of view, the air temperature changes in south-eastern and eastern areas were smaller, but larger in other regions; From the time point of view, the average air temperature during 11-13,12-14 and 19-21 ten days appeared steadily declining trend and steadily rising trend during 15-17,26-28 and 27-29 ten days; The average air temperature is a slight downward trend in July, a slight upward trend in September and November, a rise of about 1.0℃from May to September, greater rising range in plain than hilly areas. The moving average change of annual average air temperature in weather stations fluctuated between 2.5℃and 3.3℃being a mean of 2.9℃appearing a slight rise and fluctuation but no significant upward trend, no significant rising trend compared with the average air temperature for many years. The change ratios of some 10 days of average air temperature during the alternate period from spring to summer reduced to be stable but rised during the turn period of summer and autumn, indicating that there was backward delay in the actual phenological signs.2) 10 days of precipitation and their fluctuations appeared the distribution characteristics that were larger values in the middle period and smaller ones in both ends; So did the values of monthly precipitation but their flucuation degree reversed; Annual precipitation in the area of Xiaoxingan Mountain and Zhagnguangcai Mountain was the largest to reducing to surrounding area gradually, decreasing fastest toward the direction to Daqing City and Qiqihar City and slowly to other directions. The greater peak of regional precipitation for many years reduced, while precipitation differentials among regions decreasing. The value of annual precipitation all over the province interpolated and calculated was 524.6mm, reducing about 60mm compared with historical data. Precipitation in the areas of Xiaoxing'an Mountain and Zhangguangcai Mountain was relatively larger, but smaller in the western of Songnen Plain and the western slope of Daxingan Mountain, being uneven distribution among regions.3) Fluctuations of wetness indices during 13-27 ten days varied greatly in the plain areas, but smaller in hilly areas. The largest variation was in Qiqihar City, while the smallest ones being in Mudanjiang City. The minimum variation was in July, while the maximum being in May in the spring.4) The variations of accumulated temperature in 13-27 ten days in the mountainous and hilly areas and Songnen Plain were the largest, but smaller in Sanjiang Plain. There was an obvious differences between accumulated temperature in Songnen Plain and ones in Sanjiang Plain. Fluctuation variations of 10-days accumulated temperature in Daxinganling Region, Yichun City and Qiqihar City varied greatly, but less in Qitaihe City and Jiamusi City. Annual accumulated temperature of regions and cities in hilly areas was relatively low, higher in the plains and south-east. Fluctuation variations of≥0℃accumulated temperature in Qitaihe City, Jixi City, Jiamusi City and Shuangyashan City varied less, but greatly in Daxinganling Region, Yichun City, Qiqihar City and other cities; Fluctuation variations of≥10℃accumulated temperature in Qitaihe City, Jiamusi City and Jixi City varied less, but greatly in Daxinganling Region, Yichun City, Qiqihar City, Suihua City and other cities.Innovation points:1) This paper carrys on the spatial interpolation of climate elements in Heilongjiang Province by Cokriging Method for the first time, establishing the statistical relationship between climate elements and elevation, aspect, forest cover ratio to interpolate precipitation, evaporation amount, sunshine duration. The spatial varition laws of air temperature, precipitation, accumulated temperature and wetness indices have been worked out based on spatial analysis of ArcGIS software. improving de Martonne model for wetness indices calculation to calculate 10-days wetness indices, making up the defect in previous studies to 10-days climate elements by 10-days scale interpolation, arriving at the regional differentiation laws of climate elements such as air temperature, precipitation, accumulated temperature and wetness indices by spatial analysis with GIS-related software.2) In the course of calculating 10-days wetness indices, the author studied many kinds of wetness index models, considering the de Martonne Model that calculate monthly wetness indices, absorbing the idea that reflects wetness degree through the relationship between precipitation and temperature in this model, to establish a model to calculate 10-days wetness indices in Heilongjiang Province. Calculation of 10-days wetness indices have abtained good effect with the newly created model.3) The spatial interpolation of climate elements carried on mainly by 10-days climate data, acquiring much spatial data of 10-days average air temperature, precipitation, sunshine duration, evaporation amount that have closer relationship to agricultural production, and calculating the monthly and yearly climate data, and the geodatabase of climate data was established based on ArcGIS database technology to lay a foundation to study the physical crop production potential, also made up for the deficiency of spatial distribution data of 10-days climate elements in the previous research.