This paper aims to detect the short-term as well as long-term change point in the surface air temperature time series for Asansol weather observation station, West Bengal, India. Temperature data for the period from 1...This paper aims to detect the short-term as well as long-term change point in the surface air temperature time series for Asansol weather observation station, West Bengal, India. Temperature data for the period from 1941 to 2010 of the said weather observatory have been collected from Indian Meteorological Department, Kolkata. Variations and trends of annual mean temperature, annual mean maximum temperature and annual minimum temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping were used for the detection of abrupt changes in the time series data set. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperatures occurred around 1986 (0.57°C) at the period of 25 years in the long-term regional scale. On the other side, the annual mean maximum and annual mean minimum temperatures have distinct change points at level 1. There are abrupt changes in the year 1961 (Confidence interval 1961, 1963) for the annual mean maximum and 1994 (Confidence interval 1993, 1996) for the annual mean minimum temperatures at a confidence level of 100% and 98%, respectively. Before the change, the annual mean maximum and annual mean minimum temperatures were 30.90°C and 23.99°C, respectively, while after the change, the temperatures became 33.93°C and 24.84°C, respectively. Over the entire period of consideration (1941-2010), 11 forward and backward changes were found in total. Out of 11, there are 3 changes (1961, 1986 and 2001) in annual mean temperatures, 4 changes (1957, 1961, 1980 and 1994) in annual mean maximum temperatures, and rest 4 changes (1968, 1981, 1994 and 2001) are associated with annual mean minimum temperature data set.展开更多
The study aimed to assess the heavy metals(K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, As, Pb, Sr, Zr) contamination in the soil of mine affected Singaran river basin and to analyse spatial variation in the contamination level...The study aimed to assess the heavy metals(K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, As, Pb, Sr, Zr) contamination in the soil of mine affected Singaran river basin and to analyse spatial variation in the contamination level considering 32 soil samples. Elemental analysis of soil samples has been performed through Energy Dispersive X-ray Analysis(EDX) to quantify the elemental concentration(mg kgà1). Heavy metal concentrations have been assessed through geo-accumulation index(Igeo) and enrichment factor(EF).Indices showed soils have moderate accumulation of most of the metals with moderate enrichment of Sr,Zr, Zn, Cu and Ni. Soil contamination level assessment has been carried out using indices like Contamination Factor(CF), degree of contamination(C_(deg)), modified degree of contamination(m C_(deg)) and Pollution Load Index(PLI). CF shows moderate to considerable contamination by Sr, Zr, Ca, Cu, Mn, Zn and Ni. Mean indices values(m C_(deg)and PLI for the entire basin are 3.38 and 2.23 respectively) show low to moderate level of soil contamination. These indices result have been mapped and analysed in GIS platform to get spatial variation of pollution level. Opencast mines dominate middle catchment area and so is comparatively contaminated. Sample sites 11, 18 and 25 evidenced high values of all indices of pollution load. From the ecological standpoint Ecological Risk Factor(Er) and Potential Ecological Risk Index(RI) have been estimated to assess regional threat to native soil environment and it shows low ecological risk potential. Analysis shows that mine dominated soil of the entire Singaran basin is less contaminated in all respect but tends to the moderate contamination level at the mid-catchment area,especially by Sr, Zr, Zn, Cu and Ni.展开更多
Shortage of water in the river in relation to rainfall change plays a pivotal role in water sharing like Ganga. In attempt to understand the rainfall changes, Mann-Kendall test and Sen’s slope estimation on hundred y...Shortage of water in the river in relation to rainfall change plays a pivotal role in water sharing like Ganga. In attempt to understand the rainfall changes, Mann-Kendall test and Sen’s slope estimation on hundred years’ (1901-2000) rainfall data of 236 districts in entire Ganga basin were run. Half of the districts showed a decreasing trend in annual rainfall in which 39 districts were statistically significant. During pre-monsoon (Jan.-May), 78% of the total districts showed the decreasing trend with the significance of 54 districts. A majority of the districts under the Kosi, Gandak and Sone sub-basins showed the significant negative trend in annual, pre-monsoon and post-monsoon season. So, there need some districts’ and sub-basins’ wise strategies to cope with the situation in the context of climate change.展开更多
Researches are being carried out world-wide to understand the nature of temperature change during recent past at different geographical scales so that comprehensive inferences can be drawn about recent temperature tre...Researches are being carried out world-wide to understand the nature of temperature change during recent past at different geographical scales so that comprehensive inferences can be drawn about recent temperature trend and future climate. Detection of turning points in time series of meteorological parameters puts challenges to the researches. In this work, the temperature time series from 1941 to 2010 for Asansol observatory, West Bengal, India, has been considered to understand the nature, trends and change points in the data set using sequential version of Mann-Kendall test statistic. Literatures suggest that use of this test statistic is the most appropriate for detecting climatic abrupt changes as compared to other statistical tests in use. This method has been employed upon monthly average temperatures recorded over the said 70 years for detection of abrupt changes in the average temperature of each of the months. The approximate potential trend turning points have been calculated separately for each month (January to December). Sequential version of Mann-Kendall test statistic values for the months of July and August is significant at 95% confidence level (p 0.05). The average temperature for most of the other months has shown an increasing trend but more significant rise in July and August temperature has been recognized since 1960s.展开更多
The present study aims to analyze the shift in shoreline due to coastal processes and formulate available for best estimate of future shoreline positions based on precedent shorelines. Information on rates and trends ...The present study aims to analyze the shift in shoreline due to coastal processes and formulate available for best estimate of future shoreline positions based on precedent shorelines. Information on rates and trends of shoreline change can be used to improve the understanding of the underlying causes and potential effects of coastal erosion which can support informed coastal management decisions. In this paper, researchers go over the changes in the recent positions of the shoreline of the Balasore coast for the 38 years from 1975 through 2013. The study area includes the Balasore coastal region from Rasalpur to Udaypur together with Chandipur, Choumukh, Chandrabali as well as Bichitrapur. Transects wise shoreline data base were developed for approximately 67 kilometers of shoreline and erosional/accretional scenario has also been analysed by delineating the shoreline from Landsat imageries of 1975, 1980, 1990, 1995, 2000, 2005, 2010 and 2013. A simple Linear Regression Model and End Point Rate (EPR) have been adopted to take out the rate of change of shoreline and its future positions, based on empirical observations at 67 transects along the Balasore coast. It is found that the north eastern part of Balasore coast in the vicinity of Subarnarekha estuary and Chandrabali beach undergo high rates of shore line shift. The shoreline data were integrated for long- (about 17 years) and short-term (about 7 years) shift rates analysis to comprehend the shoreline change and prediction. For the prediction of future shoreline, the model has been validated with the present shoreline position (2013). The rate of shoreline movement calculated from the fixed base line to shoreline position of 1975, 1980, 1990, 1995, 2000, 2005 and 2010 and based on this, the estimated shoreline of 2013 was calculated. The estimated shoreline was compared with the actual shoreline delineated from satellite imagery of 2013. The model error or positional shift at each sample point is observed. The positional error varies from??4.82 m to 212.41 m. It has been found that model prediction error is higher in the left hand side of river Subarnarekha. The overall error for the entire predicted shoreline was found to be 41.88 m by Root Mean Square Error (RMSE). In addition, it was tested by means difference between actual and predicted shoreline positions using “t” test and it has been found that predicted shore line is not significantly different from actual shoreline position at (t132 = 0.278) p < 0.01.展开更多
To prepare a landslide susceptibility map of Shivkhola watershed,one of the landslide prone parts of Darjeeling Himalaya,remote sensing and GIS tools were used to integrate 10 landslide triggering parameters:lithology...To prepare a landslide susceptibility map of Shivkhola watershed,one of the landslide prone parts of Darjeeling Himalaya,remote sensing and GIS tools were used to integrate 10 landslide triggering parameters:lithology,slope angle,slope aspect,slope curvature,drainage density,upslope contributing area(UCA),lineament,settlement density,road contributing area(RCA),and land use and land cover(LULC).The Analytical Hierarchy Process(AHP) was applied to derive factor weights using MATLAB with reasonable consistency ratio(CR).The frequency ratio(FR) model was used to derive class frequency ratio or class weights that indicate the relative importance of individual classes for each factor.The weighted linear combination(WLC) method was used to determine the landslide susceptibility index value(LSIV) on a GIS platform,by incorporating both factor weights and class weights.The Shiv-khola watershed is classified into five landslide susceptibility zones.The overall classification accuracy is 99.22 and Kappa Statistics is 0.894.展开更多
文摘This paper aims to detect the short-term as well as long-term change point in the surface air temperature time series for Asansol weather observation station, West Bengal, India. Temperature data for the period from 1941 to 2010 of the said weather observatory have been collected from Indian Meteorological Department, Kolkata. Variations and trends of annual mean temperature, annual mean maximum temperature and annual minimum temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping were used for the detection of abrupt changes in the time series data set. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperatures occurred around 1986 (0.57°C) at the period of 25 years in the long-term regional scale. On the other side, the annual mean maximum and annual mean minimum temperatures have distinct change points at level 1. There are abrupt changes in the year 1961 (Confidence interval 1961, 1963) for the annual mean maximum and 1994 (Confidence interval 1993, 1996) for the annual mean minimum temperatures at a confidence level of 100% and 98%, respectively. Before the change, the annual mean maximum and annual mean minimum temperatures were 30.90°C and 23.99°C, respectively, while after the change, the temperatures became 33.93°C and 24.84°C, respectively. Over the entire period of consideration (1941-2010), 11 forward and backward changes were found in total. Out of 11, there are 3 changes (1961, 1986 and 2001) in annual mean temperatures, 4 changes (1957, 1961, 1980 and 1994) in annual mean maximum temperatures, and rest 4 changes (1968, 1981, 1994 and 2001) are associated with annual mean minimum temperature data set.
基金the Council of Scientific and Industrial Research (CSIR), India for financial assistance (Research Fellowship)
文摘The study aimed to assess the heavy metals(K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, As, Pb, Sr, Zr) contamination in the soil of mine affected Singaran river basin and to analyse spatial variation in the contamination level considering 32 soil samples. Elemental analysis of soil samples has been performed through Energy Dispersive X-ray Analysis(EDX) to quantify the elemental concentration(mg kgà1). Heavy metal concentrations have been assessed through geo-accumulation index(Igeo) and enrichment factor(EF).Indices showed soils have moderate accumulation of most of the metals with moderate enrichment of Sr,Zr, Zn, Cu and Ni. Soil contamination level assessment has been carried out using indices like Contamination Factor(CF), degree of contamination(C_(deg)), modified degree of contamination(m C_(deg)) and Pollution Load Index(PLI). CF shows moderate to considerable contamination by Sr, Zr, Ca, Cu, Mn, Zn and Ni. Mean indices values(m C_(deg)and PLI for the entire basin are 3.38 and 2.23 respectively) show low to moderate level of soil contamination. These indices result have been mapped and analysed in GIS platform to get spatial variation of pollution level. Opencast mines dominate middle catchment area and so is comparatively contaminated. Sample sites 11, 18 and 25 evidenced high values of all indices of pollution load. From the ecological standpoint Ecological Risk Factor(Er) and Potential Ecological Risk Index(RI) have been estimated to assess regional threat to native soil environment and it shows low ecological risk potential. Analysis shows that mine dominated soil of the entire Singaran basin is less contaminated in all respect but tends to the moderate contamination level at the mid-catchment area,especially by Sr, Zr, Zn, Cu and Ni.
文摘Shortage of water in the river in relation to rainfall change plays a pivotal role in water sharing like Ganga. In attempt to understand the rainfall changes, Mann-Kendall test and Sen’s slope estimation on hundred years’ (1901-2000) rainfall data of 236 districts in entire Ganga basin were run. Half of the districts showed a decreasing trend in annual rainfall in which 39 districts were statistically significant. During pre-monsoon (Jan.-May), 78% of the total districts showed the decreasing trend with the significance of 54 districts. A majority of the districts under the Kosi, Gandak and Sone sub-basins showed the significant negative trend in annual, pre-monsoon and post-monsoon season. So, there need some districts’ and sub-basins’ wise strategies to cope with the situation in the context of climate change.
文摘Researches are being carried out world-wide to understand the nature of temperature change during recent past at different geographical scales so that comprehensive inferences can be drawn about recent temperature trend and future climate. Detection of turning points in time series of meteorological parameters puts challenges to the researches. In this work, the temperature time series from 1941 to 2010 for Asansol observatory, West Bengal, India, has been considered to understand the nature, trends and change points in the data set using sequential version of Mann-Kendall test statistic. Literatures suggest that use of this test statistic is the most appropriate for detecting climatic abrupt changes as compared to other statistical tests in use. This method has been employed upon monthly average temperatures recorded over the said 70 years for detection of abrupt changes in the average temperature of each of the months. The approximate potential trend turning points have been calculated separately for each month (January to December). Sequential version of Mann-Kendall test statistic values for the months of July and August is significant at 95% confidence level (p 0.05). The average temperature for most of the other months has shown an increasing trend but more significant rise in July and August temperature has been recognized since 1960s.
文摘The present study aims to analyze the shift in shoreline due to coastal processes and formulate available for best estimate of future shoreline positions based on precedent shorelines. Information on rates and trends of shoreline change can be used to improve the understanding of the underlying causes and potential effects of coastal erosion which can support informed coastal management decisions. In this paper, researchers go over the changes in the recent positions of the shoreline of the Balasore coast for the 38 years from 1975 through 2013. The study area includes the Balasore coastal region from Rasalpur to Udaypur together with Chandipur, Choumukh, Chandrabali as well as Bichitrapur. Transects wise shoreline data base were developed for approximately 67 kilometers of shoreline and erosional/accretional scenario has also been analysed by delineating the shoreline from Landsat imageries of 1975, 1980, 1990, 1995, 2000, 2005, 2010 and 2013. A simple Linear Regression Model and End Point Rate (EPR) have been adopted to take out the rate of change of shoreline and its future positions, based on empirical observations at 67 transects along the Balasore coast. It is found that the north eastern part of Balasore coast in the vicinity of Subarnarekha estuary and Chandrabali beach undergo high rates of shore line shift. The shoreline data were integrated for long- (about 17 years) and short-term (about 7 years) shift rates analysis to comprehend the shoreline change and prediction. For the prediction of future shoreline, the model has been validated with the present shoreline position (2013). The rate of shoreline movement calculated from the fixed base line to shoreline position of 1975, 1980, 1990, 1995, 2000, 2005 and 2010 and based on this, the estimated shoreline of 2013 was calculated. The estimated shoreline was compared with the actual shoreline delineated from satellite imagery of 2013. The model error or positional shift at each sample point is observed. The positional error varies from??4.82 m to 212.41 m. It has been found that model prediction error is higher in the left hand side of river Subarnarekha. The overall error for the entire predicted shoreline was found to be 41.88 m by Root Mean Square Error (RMSE). In addition, it was tested by means difference between actual and predicted shoreline positions using “t” test and it has been found that predicted shore line is not significantly different from actual shoreline position at (t132 = 0.278) p < 0.01.
文摘To prepare a landslide susceptibility map of Shivkhola watershed,one of the landslide prone parts of Darjeeling Himalaya,remote sensing and GIS tools were used to integrate 10 landslide triggering parameters:lithology,slope angle,slope aspect,slope curvature,drainage density,upslope contributing area(UCA),lineament,settlement density,road contributing area(RCA),and land use and land cover(LULC).The Analytical Hierarchy Process(AHP) was applied to derive factor weights using MATLAB with reasonable consistency ratio(CR).The frequency ratio(FR) model was used to derive class frequency ratio or class weights that indicate the relative importance of individual classes for each factor.The weighted linear combination(WLC) method was used to determine the landslide susceptibility index value(LSIV) on a GIS platform,by incorporating both factor weights and class weights.The Shiv-khola watershed is classified into five landslide susceptibility zones.The overall classification accuracy is 99.22 and Kappa Statistics is 0.894.
