Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.431
PIJUSH BASAK
1. Investigation of the relationship among climatic variables namely, temperature, vapour pressure and rainfall significantly play a predominant role in building model and prediction through modelling in the Himalayan and dooars region along with Gangetic plains but indicates limitations of the efficiency of the model due to complicated geographical topography (Pant et al., 2018: Singh et al., 2016). The statistical variations among climatic variables limit one to point out the relationships among those and are lacking over some of the regions.
{"title":"A CLIMATIC PREDICTABILITY INDEX FOR SOUTH WEST MONSOON SEASON IN DIFFERENT DISTRICTS OF WEST BENGAL WITH APPLICATION OF FRACTAL DIMENSION ANALYSIS","authors":"PIJUSH BASAK","doi":"10.54302/mausam.v74i4.431","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.431","url":null,"abstract":"1. Investigation of the relationship among climatic variables namely, temperature, vapour pressure and rainfall significantly play a predominant role in building model and prediction through modelling in the Himalayan and dooars region along with Gangetic plains but indicates limitations of the efficiency of the model due to complicated geographical topography (Pant et al., 2018: Singh et al., 2016). The statistical variations among climatic variables limit one to point out the relationships among those and are lacking over some of the regions.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.4264
SYED ROUHULLAH ALI, JUNAID N. KHAN, ROHITASHW KUMAR, FAROOQ AHMAD LONE, SHAKEEL AHMAD MIR, IMRAN KHAN
Regional climate models (RCMs) give more reliable results for a regional impact study of climate change, but they still have a significant bias that has to be corrected before they can be utilised in climate change research. In this study, two methods for local bias correction of Tmax, Tmin and precipitation data at monthly scales, namely the modified difference approach (MDA) and the linear scaling (LS) method, were applied and validated to minimize the bias between the modelled (HAD GEM2-ES-GCM) and observed climate data in Central Kashmir Valley. Tmax, Tmin and precipitation correction functions generated using the LS method on a monthly time scale were shown to be excellent than MDA for bias correction of weather data to make it close to observed data in both scenarios (RCP 4.5 & 8.5). Comparison between two scenarios was done to determine the climate change extent in Central Kashmir Valley using LS method. The past 30 years observed average temperature and precipitation was 14.17 °C and 734.06 mm, respectively considered as a baseline for comparison purpose. Annual Taverage (°C) showed increase in all the three time slices and maximum increase by 3.09 and 5.72 °C during far future (FF) (2071-2095) under RCP 4.5 & 8.5, respectively. Whereas, the results of average annual precipitation also showed increase in future scenario and maximum increase by 29.25 mm (3.98%) during mid future (2046-2070) and 215.98 mm (29.42%) during end future (2071-2095), under RCP 4.5 & 8.5 respectively. It was concluded that under RCP 8.5 scenario climate change was quite significant than RCP 4.5.
{"title":"21st Century climate change projections of temperature and precipitation in Central Kashmir Valley under RCP 4.5 and RCP 8.5","authors":"SYED ROUHULLAH ALI, JUNAID N. KHAN, ROHITASHW KUMAR, FAROOQ AHMAD LONE, SHAKEEL AHMAD MIR, IMRAN KHAN","doi":"10.54302/mausam.v74i4.4264","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.4264","url":null,"abstract":"Regional climate models (RCMs) give more reliable results for a regional impact study of climate change, but they still have a significant bias that has to be corrected before they can be utilised in climate change research. In this study, two methods for local bias correction of Tmax, Tmin and precipitation data at monthly scales, namely the modified difference approach (MDA) and the linear scaling (LS) method, were applied and validated to minimize the bias between the modelled (HAD GEM2-ES-GCM) and observed climate data in Central Kashmir Valley. Tmax, Tmin and precipitation correction functions generated using the LS method on a monthly time scale were shown to be excellent than MDA for bias correction of weather data to make it close to observed data in both scenarios (RCP 4.5 & 8.5). Comparison between two scenarios was done to determine the climate change extent in Central Kashmir Valley using LS method. The past 30 years observed average temperature and precipitation was 14.17 °C and 734.06 mm, respectively considered as a baseline for comparison purpose. Annual Taverage (°C) showed increase in all the three time slices and maximum increase by 3.09 and 5.72 °C during far future (FF) (2071-2095) under RCP 4.5 & 8.5, respectively. Whereas, the results of average annual precipitation also showed increase in future scenario and maximum increase by 29.25 mm (3.98%) during mid future (2046-2070) and 215.98 mm (29.42%) during end future (2071-2095), under RCP 4.5 & 8.5 respectively. It was concluded that under RCP 8.5 scenario climate change was quite significant than RCP 4.5.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.5909
NUR ISLAM SAIKH, SUNIL SAHA, DEBABRATA SARKAR, PROLAY MONDAL
The core purpose of this study is to investigate the spatial variation in monthly, seasonally, and yearly rainfall patterns in the Kolkata district of West Bengal, India, between 1901 and 2019. (Around 119 years). The trend's reliability and intensity were assessed non-parametrically by applying monthly rainfall data series and the Mann–Kendall and Sen's slope estimators. The data showed a considerable increase in pre-monsoon, monsoon, post-monsoon, and also annual rainfall while decreasing in winter rainfall across the district of Kolkata. The positive trend is identified in the data series of pre-monsoon, monsoon, post-monsoon, and annual rainfall, however, winter rainfall exhibited negative trends. The highest increase in rainfall was observed during the post-monsoon season (0.365091 mm year-1), with the smallest increase (0.232591 mm year-1) occurring during the pre-monsoon season. In the winter season, there was a notable rain that has declined substantially(-0.01356 mm year-1). The coefficient CV, %, was used to determine the pattern of rainfall variability. The winter rainfall exhibited the highest CV rating (72.89%), but annual rainfall showed a minimum CV value (17.68%). Generally speaking, a high variance in CV was discovered, indicating that the whole area is very sensitive to droughts and floods. For future forecasts, there is a considerable difference in monthly rainfall data between linear regression and SMOreg, while the annual rainfall is little difference between linear regression, SMOreg, and CA-ANN analysis.
本研究的核心目的是研究1901 - 2019年印度西孟加拉邦加尔各答地区月、季、年降水模式的空间变化。(大约119年)。利用月降水数据序列和Mann-Kendall和Sen's斜率估计法对趋势的可靠性和强度进行了非参数评价。数据显示，季风前、季风后、季风后以及年降雨量都有相当大的增加，而整个加尔各答地区的冬季降雨量却在减少。季风前、季风后、季风后和年降水量均呈现正趋势，而冬季降水呈现负趋势。季风季节后降雨量增加最多(0.365091 mm -1)，季风季节前降雨量增加最少(0.232591 mm -1)。在冬季，降雨量明显减少(-0.01356 mm -1)。系数CV %用于确定降雨变异的模式。冬季降雨量的CV值最高(72.89%)，而全年降雨量的CV值最低(17.68%)。总的来说，CV的方差很大，说明整个地区对旱涝非常敏感。对于未来的预测，线性回归和SMOreg对月降雨量的预测差异较大，而线性回归、SMOreg和CA-ANN对年降雨量的预测差异不大。
{"title":"Rainfall trend and variability analysis of the past 119 (1901-2019) years using statistical techniques: A case study of Kolkata, India","authors":"NUR ISLAM SAIKH, SUNIL SAHA, DEBABRATA SARKAR, PROLAY MONDAL","doi":"10.54302/mausam.v74i4.5909","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.5909","url":null,"abstract":"The core purpose of this study is to investigate the spatial variation in monthly, seasonally, and yearly rainfall patterns in the Kolkata district of West Bengal, India, between 1901 and 2019. (Around 119 years). The trend's reliability and intensity were assessed non-parametrically by applying monthly rainfall data series and the Mann–Kendall and Sen's slope estimators. The data showed a considerable increase in pre-monsoon, monsoon, post-monsoon, and also annual rainfall while decreasing in winter rainfall across the district of Kolkata. The positive trend is identified in the data series of pre-monsoon, monsoon, post-monsoon, and annual rainfall, however, winter rainfall exhibited negative trends. The highest increase in rainfall was observed during the post-monsoon season (0.365091 mm year-1), with the smallest increase (0.232591 mm year-1) occurring during the pre-monsoon season. In the winter season, there was a notable rain that has declined substantially(-0.01356 mm year-1). The coefficient CV, %, was used to determine the pattern of rainfall variability. The winter rainfall exhibited the highest CV rating (72.89%), but annual rainfall showed a minimum CV value (17.68%). Generally speaking, a high variance in CV was discovered, indicating that the whole area is very sensitive to droughts and floods. For future forecasts, there is a considerable difference in monthly rainfall data between linear regression and SMOreg, while the annual rainfall is little difference between linear regression, SMOreg, and CA-ANN analysis.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.3446
SARATHI SAHA, SAON BANERJEE, FEROZE RAHMAN
The present study was conducted aiming to evaluate the individual and combined impact of temperature and rainfall on mustard yield through detrended production index for five districts of West Bengal viz., Hooghly, Nadia, Burdwan, Mursidabad and South 24 Parganas. The crop data and weather information were collected from various stations of those five locations. The selected study areas belong to different agroclimatic zones of the state, namely old alluvial zone, new alluvial zone and coastal saline zone. Mustard growing season in these districts starts from middle of October and continues upto middle of January (Rabi season). The detailed information on yield for 18 years (1997 to 2014) was collected from Government of West Bengal and weather data were collected from India Meteorological Department. The entire growing season of mustard was divided into vegetative and reproductive stages for convenience of the study. Although a definite trend among them existed. Moreover, when all the five locations are considered, overall increase in the year-wise yield was significant with R2 value 0.63. Some R square had poor values. Higher values of R2 indicated the significance of technological trend in case of Hooghly (R2 = 0.46), Nadia (R2 = 0.65) and South 24 Parganas (R2 = 0.73) districts where as it was not significant for Burdwan and Mursidabad. A gradual decrease in yield was observed with temperature increment from 0.50C to 2.00C. The results indicated a reduction of 0.36%, 0.72%, 1.01% and 1.4% in mustard yield in 0.50C, 10 C, 1.50C and 20C increased temperature condition, respectively. Declined yield of mustard will be 908 kg ha-1 in the study location at 20C more temperature condition. Yield reduction is more if higher temperature coincides with the vegetative stage. Time of sowing should be adjusted so that vegetative stage can escape the high temperature period. But all other required management practices should be performed along with the mentioned one. Otherwise several other biotic and abiotic stresses may lower down the yield too. Thus, the results of this work strongly support the idea of engaging DPI to evaluate the impacts of prime weather parameters on crop production and generate yield forecasting models based on that
{"title":"Assessing the impact of temperature and rainfall on mustard yield through detrended production index","authors":"SARATHI SAHA, SAON BANERJEE, FEROZE RAHMAN","doi":"10.54302/mausam.v74i4.3446","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.3446","url":null,"abstract":"The present study was conducted aiming to evaluate the individual and combined impact of temperature and rainfall on mustard yield through detrended production index for five districts of West Bengal viz., Hooghly, Nadia, Burdwan, Mursidabad and South 24 Parganas. The crop data and weather information were collected from various stations of those five locations. The selected study areas belong to different agroclimatic zones of the state, namely old alluvial zone, new alluvial zone and coastal saline zone. Mustard growing season in these districts starts from middle of October and continues upto middle of January (Rabi season). The detailed information on yield for 18 years (1997 to 2014) was collected from Government of West Bengal and weather data were collected from India Meteorological Department. The entire growing season of mustard was divided into vegetative and reproductive stages for convenience of the study. Although a definite trend among them existed. Moreover, when all the five locations are considered, overall increase in the year-wise yield was significant with R2 value 0.63. Some R square had poor values. Higher values of R2 indicated the significance of technological trend in case of Hooghly (R2 = 0.46), Nadia (R2 = 0.65) and South 24 Parganas (R2 = 0.73) districts where as it was not significant for Burdwan and Mursidabad. A gradual decrease in yield was observed with temperature increment from 0.50C to 2.00C. The results indicated a reduction of 0.36%, 0.72%, 1.01% and 1.4% in mustard yield in 0.50C, 10 C, 1.50C and 20C increased temperature condition, respectively. Declined yield of mustard will be 908 kg ha-1 in the study location at 20C more temperature condition. Yield reduction is more if higher temperature coincides with the vegetative stage. Time of sowing should be adjusted so that vegetative stage can escape the high temperature period. But all other required management practices should be performed along with the mentioned one. Otherwise several other biotic and abiotic stresses may lower down the yield too. Thus, the results of this work strongly support the idea of engaging DPI to evaluate the impacts of prime weather parameters on crop production and generate yield forecasting models based on that","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.3526
B.A. GUDADE, SUBHASH BABU, A.B. AAGE, S.S. BORA, T.N. DEKA, NUTTAM SINGH, AMIT KUMAR, RAGHAVENDRA SINGH, K. DHANAPAL, A.B. REMASHREE
Among the extreme weather events, hailstorm in recent past caused significant damage of large cardamom crop in Sikkim. In high altitude area of Sikkim, hailstorms generally occurrs in the month of March and April and caused severe damage to large cardamom plantations. In this paper, a detailed account of incidence of hailstorm damage and strategies to minimize its effects on large cardamom plantations are discussed. Frequency distribution of hailstorm showed that during last eight years hailstorm in Pangthang area of Sikkim occurred between 1427 to 1532 hrs and it continued for around 37 minutes on average. However, in Kabi area of North Sikkim hailstorm generally occurs during 1621 to 1628 hrs and it continues for around 21.25 minutes. Hailstorms varied in size from 0.5 to 1.0 cm in diameter. Damage caused by the hailstorms on plant tissue depends mainly on its size, duration of the storm event and the condition of the plant tissue when the injury occurs. Large cardamom being a broad leaved plant, the lamina tears parallel to the veins. Physical damage to floral parts of large cardamom plants due to hailstorm occurred at the flowering stage and depending on the extent of damage the yield of the plant was also affected in the subsequent crop season. Frequent hail episodes are identified and measures to minimize the damage of large cardamom plantations are discussed. The information generated in this study was found to be very useful in minimizing large cardamom crop loss through operational agromet services launched by the India Meteorological Department/Ministry of Earth Sciences in collaboration with the Agromet Field Units (AMFUs) located at Gangtok and ICAR-NOFRI, Tadong through Krishi Vigyan Kendra-East Sikkim, Ranipool.
{"title":"Incidence of hailstorms damage and strategies to minimize its effects on large cardamom (Amomum subulatum Roxburgh) plantations in Sikkim, North East India","authors":"B.A. GUDADE, SUBHASH BABU, A.B. AAGE, S.S. BORA, T.N. DEKA, NUTTAM SINGH, AMIT KUMAR, RAGHAVENDRA SINGH, K. DHANAPAL, A.B. REMASHREE","doi":"10.54302/mausam.v74i4.3526","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.3526","url":null,"abstract":"Among the extreme weather events, hailstorm in recent past caused significant damage of large cardamom crop in Sikkim. In high altitude area of Sikkim, hailstorms generally occurrs in the month of March and April and caused severe damage to large cardamom plantations. In this paper, a detailed account of incidence of hailstorm damage and strategies to minimize its effects on large cardamom plantations are discussed. Frequency distribution of hailstorm showed that during last eight years hailstorm in Pangthang area of Sikkim occurred between 1427 to 1532 hrs and it continued for around 37 minutes on average. However, in Kabi area of North Sikkim hailstorm generally occurs during 1621 to 1628 hrs and it continues for around 21.25 minutes. Hailstorms varied in size from 0.5 to 1.0 cm in diameter. Damage caused by the hailstorms on plant tissue depends mainly on its size, duration of the storm event and the condition of the plant tissue when the injury occurs. Large cardamom being a broad leaved plant, the lamina tears parallel to the veins. Physical damage to floral parts of large cardamom plants due to hailstorm occurred at the flowering stage and depending on the extent of damage the yield of the plant was also affected in the subsequent crop season. Frequent hail episodes are identified and measures to minimize the damage of large cardamom plantations are discussed. The information generated in this study was found to be very useful in minimizing large cardamom crop loss through operational agromet services launched by the India Meteorological Department/Ministry of Earth Sciences in collaboration with the Agromet Field Units (AMFUs) located at Gangtok and ICAR-NOFRI, Tadong through Krishi Vigyan Kendra-East Sikkim, Ranipool.
","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.6194
Nitesh Awasthi, Jayant Nath Tripathi, Kailas Dakhore, Dileep Kumar Gupta, Y. E. Kadam
Global climate change could have a substantial negative influence on Indian agriculture and becoming more common and intense growing as a result of food security. Indeed, the examination of weather variability on agricultural growth and production is always complex. The weather variability impact on agricultural growth and production has been evaluated by Pearson correlation analysis among various weather variables (minimum temperature, maximum temperature, relative humidity, wind speed and rainfall), vegetation indices (NDVI and LAI) and crop yield (wheat and rice) on yearly and monthly basis for the time period from the year 1991 to 2020 in the present study. Initially, the temporal behavior of weather variables and vegetation indices have been explored on the monthly and yearly time scale for the long term (1991-2020) along with crop yield over Indian state of Haryana. After that a Pearson correlation analysis have been carried out among the weather variables, vegetation indices and crop yield on monthly and yearly time scale, individually to understand the relationship of NDVI-weather and LAI- weather along with the long-term weather impact on agricultural production. A significant correlation is found between NDVI- weather and LAI- weather on monthly and yearly basis. The positive impact of the temperature, relative humidity and rainfall is found on the rice crop production, while the wind speed showed the negative impact on the rice crop production during the Kharif season in Haryana state of India during the years 1998-2018. In case of wheat crop (Rabi season), the minimum temperature, rainfall and relative humidity supports the wheat crop production, while the maximum temperature and wind speed showed the negative impact on the wheat yield in Haryana during the years 1998-2018. Overall, this study has found the annual increase in wheat crop yield approximately 0.044 tons per hectare, and rice crop yield 0.029 tons per hectare.
{"title":"Assessment of climatic impact on growth and production of rice (Kharif) and wheat (Rabi) using geospatial technology over Haryana","authors":"Nitesh Awasthi, Jayant Nath Tripathi, Kailas Dakhore, Dileep Kumar Gupta, Y. E. Kadam","doi":"10.54302/mausam.v74i4.6194","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.6194","url":null,"abstract":"Global climate change could have a substantial negative influence on Indian agriculture and becoming more common and intense growing as a result of food security. Indeed, the examination of weather variability on agricultural growth and production is always complex. The weather variability impact on agricultural growth and production has been evaluated by Pearson correlation analysis among various weather variables (minimum temperature, maximum temperature, relative humidity, wind speed and rainfall), vegetation indices (NDVI and LAI) and crop yield (wheat and rice) on yearly and monthly basis for the time period from the year 1991 to 2020 in the present study. Initially, the temporal behavior of weather variables and vegetation indices have been explored on the monthly and yearly time scale for the long term (1991-2020) along with crop yield over Indian state of Haryana. After that a Pearson correlation analysis have been carried out among the weather variables, vegetation indices and crop yield on monthly and yearly time scale, individually to understand the relationship of NDVI-weather and LAI- weather along with the long-term weather impact on agricultural production. A significant correlation is found between NDVI- weather and LAI- weather on monthly and yearly basis. The positive impact of the temperature, relative humidity and rainfall is found on the rice crop production, while the wind speed showed the negative impact on the rice crop production during the Kharif season in Haryana state of India during the years 1998-2018. In case of wheat crop (Rabi season), the minimum temperature, rainfall and relative humidity supports the wheat crop production, while the maximum temperature and wind speed showed the negative impact on the wheat yield in Haryana during the years 1998-2018. Overall, this study has found the annual increase in wheat crop yield approximately 0.044 tons per hectare, and rice crop yield 0.029 tons per hectare.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.6124
HARSH SRIVASTAVA, SHIKHA VERMA, TRILOKI PANT
The present study, conducted in the most populous state of India, i.e., Uttar Pradesh, estimates the variation of air quality for the period between 2019 and 2021, taking into account the extraordinary situation of COVID-19. The Government of India imposed the four-phased complete lockdown on 25th March, 2020, which lasted until 31st May, 2020. The study deals with pollution data during these phases with the help of ground station-based pollution data as well as available satellite data. Since ground data is available at limited stations, an Inverse Distance Weighted (IDW) interpolation technique is used for the generation of phase-wise pollution maps for the whole state during the timeline of 2020. The generated maps show a sharp decline in pollution levels for PM2.5, PM10, NO2, NOx and NO, and an increase in the level of SO2 and Ozone in Phase-I (P1), justifying the effectiveness of the lockdown. Further, for station-wise analysis, a six-phase timeline for the years 2019, 2020 and 2021 has been devised to calculate mean pollution levels as well as pollution level changes. In comparison to 2019 and 2021, the mean and standard deviation in the year 2020 through P1-P4 is the least, emphasising the least spread of pollution level in 2020 due to the lockdown. The analysis is also accompanied by Sentinel-5P TROPOMI satellite data, giving similar observations for NO2. Regarding correlation, data from ground stations and satellites correlate most for NO2 and least for SO2. In addition, empirical relations between pollution data (dependent) and meteorological data (independent) are generated, which reveal that the power to explain the pollution level variability has further increased by using binary lockdown variables along with meteorological data.
{"title":"Analysis of the effect of COVID-19 lockdown on air pollutants using multi-source pollution data and meteorological variables for the state of Uttar Pradesh, India","authors":"HARSH SRIVASTAVA, SHIKHA VERMA, TRILOKI PANT","doi":"10.54302/mausam.v74i4.6124","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.6124","url":null,"abstract":"The present study, conducted in the most populous state of India, i.e., Uttar Pradesh, estimates the variation of air quality for the period between 2019 and 2021, taking into account the extraordinary situation of COVID-19. The Government of India imposed the four-phased complete lockdown on 25th March, 2020, which lasted until 31st May, 2020. The study deals with pollution data during these phases with the help of ground station-based pollution data as well as available satellite data. Since ground data is available at limited stations, an Inverse Distance Weighted (IDW) interpolation technique is used for the generation of phase-wise pollution maps for the whole state during the timeline of 2020. The generated maps show a sharp decline in pollution levels for PM2.5, PM10, NO2, NOx and NO, and an increase in the level of SO2 and Ozone in Phase-I (P1), justifying the effectiveness of the lockdown. Further, for station-wise analysis, a six-phase timeline for the years 2019, 2020 and 2021 has been devised to calculate mean pollution levels as well as pollution level changes. In comparison to 2019 and 2021, the mean and standard deviation in the year 2020 through P1-P4 is the least, emphasising the least spread of pollution level in 2020 due to the lockdown. The analysis is also accompanied by Sentinel-5P TROPOMI satellite data, giving similar observations for NO2. Regarding correlation, data from ground stations and satellites correlate most for NO2 and least for SO2. In addition, empirical relations between pollution data (dependent) and meteorological data (independent) are generated, which reveal that the power to explain the pollution level variability has further increased by using binary lockdown variables along with meteorological data.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.3880
CHAOJIU DA, TAI CHEN, BINGLU SHEN, JIAN SONG
In this paper, preliminary theoretical research on abrupt change induced by the forcing term in a dynamical system is described. Taking the Lorenz equationtrajectoryasthe research object, the trajectory response to different pulse forcing terms is studied based on the stability theorem of differential equations and numerical methods. From the perspective of a dynamical system, abrupt changecan be classified as internal or external. The former reflectstrajectory self-adjustment inside the attractor, whereasthe latter represents the bizarre behaviorof the trajectoryin its deviation from the attractor. This classification helps in understanding the physical mechanisms of different manifestations of atmospheric abrupt change. For different intensities and durations of the pulse forcing term,which are simplified to the magnitude and width of a rectangular wave, respectively, the corresponding abrupt change is analyzed quantitatively. It is established that the larger the amplitude of the pulse forcing term, the greater the deviation of thetrajectory from the attractor and the more violent theabrupt change. Moreover, the greater the width of the pulse forcing term, the longer the duration over which the trajectory deviates from the attractor. Finally, two simple but meaningful linear relationships are obtained: one between the amplitude of the pulse forcing term and the distance of trajectory deviation from the attractor, and the other between the width of the pulse forcing term and the duration over which the trajectory dwells outside of the attractor. These relationships indicate that nonlinear systems have some linear properties.
{"title":"Classification and characteristics of abrupt change based on the Lorenz equation","authors":"CHAOJIU DA, TAI CHEN, BINGLU SHEN, JIAN SONG","doi":"10.54302/mausam.v74i4.3880","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.3880","url":null,"abstract":"In this paper, preliminary theoretical research on abrupt change induced by the forcing term in a dynamical system is described. Taking the Lorenz equationtrajectoryasthe research object, the trajectory response to different pulse forcing terms is studied based on the stability theorem of differential equations and numerical methods. From the perspective of a dynamical system, abrupt changecan be classified as internal or external. The former reflectstrajectory self-adjustment inside the attractor, whereasthe latter represents the bizarre behaviorof the trajectoryin its deviation from the attractor. This classification helps in understanding the physical mechanisms of different manifestations of atmospheric abrupt change. For different intensities and durations of the pulse forcing term,which are simplified to the magnitude and width of a rectangular wave, respectively, the corresponding abrupt change is analyzed quantitatively. It is established that the larger the amplitude of the pulse forcing term, the greater the deviation of thetrajectory from the attractor and the more violent theabrupt change. Moreover, the greater the width of the pulse forcing term, the longer the duration over which the trajectory deviates from the attractor. Finally, two simple but meaningful linear relationships are obtained: one between the amplitude of the pulse forcing term and the distance of trajectory deviation from the attractor, and the other between the width of the pulse forcing term and the duration over which the trajectory dwells outside of the attractor. These relationships indicate that nonlinear systems have some linear properties.
","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.5903
GAURAV YADAV, GEETA SINGH, S.D. ATTRI
The continuous change in climatic conditions has created a very difficult situation for the people living all over the world. The cities with higher population and poor air quality have been hard hit by the rising temperature and humidity, bad air quality of an urban environment plays a significant role in affecting climatic variables. As Delhi, the capital of India, tops the list of air pollution hotspots among all top polluted cities around the world is selected for this study. Through this study a relationship was assessed, among criteria air pollutants and meteorological parameters. It was hypothesized that criteria air pollutants will positively predict the change in temperature and relative humidity (pillars of climate change) during daily dataset(January 01, 2015 – December 31, 2021) and average annual dataset (2000 to 2021) in Delhi. To test this hypothesis, elastic net-applied regularization has been used in model exploration and coefficient estimation using EVIEWS 12. It was found that during the selected study period, most of the criteria air pollutants were playing a significant role in increasing the changes in climatic conditions of Delhi. This research further explains about the interlinkage between air pollution and climate change with the help of available literature.
{"title":"Poor air quality as an important predictor of climate change in Delhi","authors":"GAURAV YADAV, GEETA SINGH, S.D. ATTRI","doi":"10.54302/mausam.v74i4.5903","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.5903","url":null,"abstract":"The continuous change in climatic conditions has created a very difficult situation for the people living all over the world. The cities with higher population and poor air quality have been hard hit by the rising temperature and humidity, bad air quality of an urban environment plays a significant role in affecting climatic variables. As Delhi, the capital of India, tops the list of air pollution hotspots among all top polluted cities around the world is selected for this study. Through this study a relationship was assessed, among criteria air pollutants and meteorological parameters. It was hypothesized that criteria air pollutants will positively predict the change in temperature and relative humidity (pillars of climate change) during daily dataset(January 01, 2015 – December 31, 2021) and average annual dataset (2000 to 2021) in Delhi. To test this hypothesis, elastic net-applied regularization has been used in model exploration and coefficient estimation using EVIEWS 12. It was found that during the selected study period, most of the criteria air pollutants were playing a significant role in increasing the changes in climatic conditions of Delhi. This research further explains about the interlinkage between air pollution and climate change with the help of available literature.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.5381
RAKHEE SHARMA, ARCHANA SINGH, MAMTA MITTAL
For the timely planning and management of water resources, evaporation prediction must be estimated properly, especially in regions that are prone to drought and where evaporation directly affects the pest population. Changes in meteorological variables such as temperature, relative humidity, solar radiation, rainfall have a great impact on the evaporation process. In order to forecast the variable, ensemble feature selection techniques along with various machine learning techniques were investigated. Meteorological weekly weather data were collected from the ICRISAT location over a period from 1974 to 2021. The reliability of these developed models was based on statistical approaches namely Mean Absolute Error, Root Mean Square Error, Coefficient of Determination, Nash–Sutcliffe Efficiency coefficient, and Willmott’s Index of agreement along with several graphical aids. The results indicate that lasso regression outperforms all other machine learning approaches and the results are validated using current data (2020-2021). For a better understanding of the results, these validated results were also compared with results obtained from the established linear regression method and artificial neural network. It was further found that lasso regression shows an improved performance (R2 = 0.929) over linear regression (R2 = 0.871) and artificial neural network (R2 = 0.889).
{"title":"Efficient prediction of evaporation using ensemble feature selection techniques","authors":"RAKHEE SHARMA, ARCHANA SINGH, MAMTA MITTAL","doi":"10.54302/mausam.v74i4.5381","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.5381","url":null,"abstract":"For the timely planning and management of water resources, evaporation prediction must be estimated properly, especially in regions that are prone to drought and where evaporation directly affects the pest population. Changes in meteorological variables such as temperature, relative humidity, solar radiation, rainfall have a great impact on the evaporation process. In order to forecast the variable, ensemble feature selection techniques along with various machine learning techniques were investigated. Meteorological weekly weather data were collected from the ICRISAT location over a period from 1974 to 2021. The reliability of these developed models was based on statistical approaches namely Mean Absolute Error, Root Mean Square Error, Coefficient of Determination, Nash–Sutcliffe Efficiency coefficient, and Willmott’s Index of agreement along with several graphical aids. The results indicate that lasso regression outperforms all other machine learning approaches and the results are validated using current data (2020-2021). For a better understanding of the results, these validated results were also compared with results obtained from the established linear regression method and artificial neural network. It was further found that lasso regression shows an improved performance (R2 = 0.929) over linear regression (R2 = 0.871) and artificial neural network (R2 = 0.889).","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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