Anhui Key Laboratory of Atmospheric Science and Satellite Remote Sensing

Hefei, China

Anhui Key Laboratory of Atmospheric Science and Satellite Remote Sensing

Hefei, China
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Feng Y.,Nanjing University | Feng Y.,Anhui Institute of Meteorological science | He B.-F.,Anhui Institute of Meteorological science | He B.-F.,Anhui Key Laboratory of Atmospheric Science and Satellite Remote Sensing | And 5 more authors.
Chinese Journal of Ecology | Year: 2012

In order to understand the responses of different types of vegetation in Anhui Provinc of East China to climate elements, this paper analyzed the recent ten years spatiotemporal varia tion patterns of the vegetations and their correlations with air temperature and precipitation, base' on the monthly Normalized Difference Vegetation Index (NDVI) data from Moderate Resolutio Imaging Spectroradiometer (MODIS), the daily temperature/precipitation data from 80 meteoro logical stations, and the land cover data in 2000-2009. The results showed that in recent te years, the vegetation index of different land cover types in the Province had different chang trends. The vegetation index increased significantly in crop planting area and cities, but had les change in other areas. Forestland had the highest average vegetation index, followed by cro planting area, and then, urban area. The monthly variation of the vegetation index presented double-peak pattern in crop planting area, but a single-peak pattern for other land cover types The monthly average NDVI in the Province had a significant positive linear correlation with the monthly mean temperature, and a positive nonlinear correlation with the monthly total precipitation. A threshold value of precipitation existed in its effect on NDVI. There was a weak positive correlation between the NDVI and the inter-annual change of temperature/precipitation. The partial correlation coefficient between vegetation index and air temperature was the maximum in for-estland and the minimum in crop planting area, and was larger in natural vegetation area than in artificial vegetation area. The partial correlation coefficient between vegetation index and precipitation was contrary. In the majority area of middle Huaibei plain and northern Jianghuai (non-irrigable land), the vegetation was co-driven by air temperature and precipitation; in some minority middle Huaibei plain grids and water grids, the vegetation was solely driven by precipitation: and in the other areas, the vegetation was solely driven by air temperature, except in some water grids, it was driven by non-climate factors.


Huang Y.,Nanjing University of Information Science and Technology | Huang Y.,Anhui Key Laboratory of Atmospheric Science and Satellite Remote Sensing | Huang Y.,Anhui Institute of Meteorology Science | Wang X.,Nanjing University of Information Science and Technology | And 5 more authors.
Journal of Remote Sensing | Year: 2013

Three kinds of satellite infrared imagery from 2001 to 2009, namely, GMS-5, FY-2B, and FY-2C, were used to study the characteristics of convective cloud mergers in summer severe weather in the Huaihe and Yangtze River basin. In the 35 heavy rain and 43 hailstorm cases, the occurrence probabilities of cloud merger were 94% and 65%, respectively. The average cloud merging times were 11.6 and 1.9. In addition, five statistic factors, namely, distance of two clouds (Dis), ratio of two cloud areas (Ar), minimum of two cloud top bright temperature clouds (Tmin), difference of two cloud top lowest bright temperature (dTmin), and change of Tmin (ΔTmin), were calculated and compared in the cases of hailstorm and heavy rain. Three similar points were found for the two kinds of severe weather. The quantities of the four factors comprised the main differences of the merging process in the two kinds of severe weather. Based on the combination of Tmin and dTmin, the cloud top temperature conditions and the statistic probabilities of occurrence with different Ar were analyzed. Finally, three indicators for severe weather forecasting were revealed with Dis and Ar factors.


Lu Y.,Anhui Climate Cenler | Lu Y.,Anhui Key Laboratory of Atmospheric Science and Satellite Remote Sensing | Xe W.,Anhui Climate Cenler | Xe W.,Anhui Key Laboratory of Atmospheric Science and Satellite Remote Sensing | Tian H.,Anhui Climate Cenler
Journal of Natural Disasters | Year: 2016

Taking Dongjin River as an example, and based on temporal meteor-hydrological observation data on typical flood process, analytical workflow of critical flood causing rainfalls in medium and small rivers was established, and index comparison and effect examination were carried out. Results showed that, the change of water level in Dongjin River was obviously correlated with rainfalls and the time efficiency of the accumulated rainfalls. Based on the moving accumulative correlation analysis, the critical rainfall time efficiency was determined as 16 hours. The statistical relationship between water level and accumulative rainfall was established by regression analysis. Combined with water level for each grade of flood, the flood-causing critical rainfall index values were calculated reversely. At the same time, based on the calibrated hydrological model TOPMODEL and relationship between flood water level and flow rate, another set of critical rainfall index was obtained. Examination of actual condition indica- ted that the two sets of indices both have good applicability, but there are some difference between early warning grades and actual conditions. The statistical method tends to underestimate the actual condition and the hydrological model index may tend to overestimate the actual condition. Comprehensive application of the two indices should have good practical use effects.

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