Clustered heavy rains （CHRs） defined using hierarchical cluster analysis based on daily observations of precipitation in China during 1960-2008 are investi- gated in this paper. The geographical pattern of CHRs in C...Clustered heavy rains （CHRs） defined using hierarchical cluster analysis based on daily observations of precipitation in China during 1960-2008 are investi- gated in this paper. The geographical pattern of CHRs in China shows three high-frequency centers--South China, the Yangtze River basin, and part of North China around the Bohai Sea. CHRs occur most frequently in South China with a mean annual frequency of 6.8 （a total of 334 times during 1960-2008）. June has the highest monthly frequency （2.2 times/month with a total of 108 times dur- ing 1960-2008）, partly in association with the Meiyu phenomenon in the Yangtze River basin. Within the past 50 years, the frequency of CHRs in China has increased significantly from 13.5 to 17.3 times per year, which is approximately 28%. In the 1990s, the frequency of CHRs often reached 19.1 times per year. The geographical extent of CHR has expanded slightly by 0.5 stations, and its average daily rainfall intensity has increased by 3.7 mm d-1. The contribution of CHRs to total rainfall amount and the frequency of daily precipitation have increased by 63.1% and 22.7%, respectively, partly due to a significant decrease in light rains. In drying regions of North and Northeast China, the amounts of minimal CHRs have had no significant trend in recent years, probably due to warming in these arid regions enhancing atmospheric conveetivity at individual stations.展开更多
Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized g...Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized groundwater level monitoring networks are rarely designed to build up a strong groundwater level data base, and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation(GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology(LiRDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System(GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyses have been performed separately for all kriging methods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyses conclude that the exponential semivariogram model obtained from ordinary kriging gives the best fit model. Study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering multiple parameters such as GWLF and LiRDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area.展开更多
基金supported by the NationalBasic Research Program of China (Grant No. 2009CB421401)the Chinese Meteorological Administration Program (Grant No.GYHY200906009)
文摘Clustered heavy rains （CHRs） defined using hierarchical cluster analysis based on daily observations of precipitation in China during 1960-2008 are investi- gated in this paper. The geographical pattern of CHRs in China shows three high-frequency centers--South China, the Yangtze River basin, and part of North China around the Bohai Sea. CHRs occur most frequently in South China with a mean annual frequency of 6.8 （a total of 334 times during 1960-2008）. June has the highest monthly frequency （2.2 times/month with a total of 108 times dur- ing 1960-2008）, partly in association with the Meiyu phenomenon in the Yangtze River basin. Within the past 50 years, the frequency of CHRs in China has increased significantly from 13.5 to 17.3 times per year, which is approximately 28%. In the 1990s, the frequency of CHRs often reached 19.1 times per year. The geographical extent of CHR has expanded slightly by 0.5 stations, and its average daily rainfall intensity has increased by 3.7 mm d-1. The contribution of CHRs to total rainfall amount and the frequency of daily precipitation have increased by 63.1% and 22.7%, respectively, partly due to a significant decrease in light rains. In drying regions of North and Northeast China, the amounts of minimal CHRs have had no significant trend in recent years, probably due to warming in these arid regions enhancing atmospheric conveetivity at individual stations.
基金Under the auspices of the Visvesvaraya National Institute of Technology(Nagpur)Centrally Funded Technical Institution Under the Ministry of Human Resource Development(No.l7-2/2014-TS.I)Department of Science and Technology,Government of India(No.SR/S9/Z-09/2012)
文摘Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized groundwater level monitoring networks are rarely designed to build up a strong groundwater level data base, and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation(GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology(LiRDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System(GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyses have been performed separately for all kriging methods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyses conclude that the exponential semivariogram model obtained from ordinary kriging gives the best fit model. Study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering multiple parameters such as GWLF and LiRDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area.
