Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.6302
Shikha G. Anand, Pravendra Kumar, Manish Kumar
The rainfall intensity-duration-frequency (IDF) relationship plays an important toolin planning,designing, evaluatingand operatingof water resource projects, water resources development and management.It is necessary to examine the location-specific relationship between rainfall components, intensity, duration and frequency due to their spatiotemporal variation. In this article, weinvestigate the relationship between the rainfall intensity and its components and develop nomographs for Washim, Chandrapur and Yeotmal districts in Maharashtra, India. We also studied the rainfall charts of various stations for maximum annual rainfall intensities of selected duration. The frequency lines are computed for the above locations.. An empirical studyisconducted to determine the value of constants ‘a’ and ‘b’ and that of ‘K’ (Nemec, 1973).The nomographs are also developed for the rainfall intensity-duration-frequency (IDF) relationships (Luzzadar, 1964). Adequacy of the results istested by statistical indices such as integral square error, correlation coefficient, percent absolute deviation and root mean square error. The variation between nomographic solutions and mathematical equations lies within the permissible limit, less than 20%.
{"title":"Development of rainfall intensity-duration-frequency relationships and nomographs for selected stations in Maharashtra, India","authors":"Shikha G. Anand, Pravendra Kumar, Manish Kumar","doi":"10.54302/mausam.v74i3.6302","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.6302","url":null,"abstract":"The rainfall intensity-duration-frequency (IDF) relationship plays an important toolin planning,designing, evaluatingand operatingof water resource projects, water resources development and management.It is necessary to examine the location-specific relationship between rainfall components, intensity, duration and frequency due to their spatiotemporal variation. In this article, weinvestigate the relationship between the rainfall intensity and its components and develop nomographs for Washim, Chandrapur and Yeotmal districts in Maharashtra, India. We also studied the rainfall charts of various stations for maximum annual rainfall intensities of selected duration. The frequency lines are computed for the above locations.. An empirical studyisconducted to determine the value of constants ‘a’ and ‘b’ and that of ‘K’ (Nemec, 1973).The nomographs are also developed for the rainfall intensity-duration-frequency (IDF) relationships (Luzzadar, 1964). Adequacy of the results istested by statistical indices such as integral square error, correlation coefficient, percent absolute deviation and root mean square error. The variation between nomographic solutions and mathematical equations lies within the permissible limit, less than 20%.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42519230","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.1923
A. Vashisth, K. S. Aravind, B. Das, P. Krishnan
Wheat is second most consumed staple food grain after rice, cultivated nearly 26 Mha areas in the northern part of India. Weather variables like Maximum temperature, Minimum temperature, Relative humidity, Rainfall, Bright sunshine hours, Evaporation etc. have a great impact on crop yield. Weather based pre harvest crop yield estimation is helpful for deciding marketing, pricing, import-export and policy making etc. Wheat yield and weather variable data were collected for last 35 years from Hisar, Ludhiana, Amritsar, Patiala and IARI, New Delhi. Multistage wheat yield estimation was done at tillering, flowering and grain filling stage of the crop by considering weather variables from 46th to 4th, 46th to 8th and 46th to 11th standard meteorological week for model development. Model was developed using stepwise multiple linear regression (SMLR), Principal component analysis in combination with SMLR (PCA-SMLR), Artificial Neural Network (ANN) alone and in combination with principal components analysis (PCA-ANN), Least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques. Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these multivariate models for stage-wise estimation of wheat yield, percentage deviation of estimated yield by observed yield was ranged between -0.1 to 25.6, 0.9 to 22.8, -0.7 to 22.5% during tillering, flowering, and grain filling stage respectively. On the basis of percentage deviation and model accuracy Elastic net and LASSO model was found better and can be used for district level wheat crop yield estimation at different crop growth stage.
{"title":"Multi stage wheat yield estimation using multiple linear, neural network and penalised regression models","authors":"A. Vashisth, K. S. Aravind, B. Das, P. Krishnan","doi":"10.54302/mausam.v74i3.1923","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.1923","url":null,"abstract":"Wheat is second most consumed staple food grain after rice, cultivated nearly 26 Mha areas in the northern part of India. Weather variables like Maximum temperature, Minimum temperature, Relative humidity, Rainfall, Bright sunshine hours, Evaporation etc. have a great impact on crop yield. Weather based pre harvest crop yield estimation is helpful for deciding marketing, pricing, import-export and policy making etc. Wheat yield and weather variable data were collected for last 35 years from Hisar, Ludhiana, Amritsar, Patiala and IARI, New Delhi. Multistage wheat yield estimation was done at tillering, flowering and grain filling stage of the crop by considering weather variables from 46th to 4th, 46th to 8th and 46th to 11th standard meteorological week for model development. Model was developed using stepwise multiple linear regression (SMLR), Principal component analysis in combination with SMLR (PCA-SMLR), Artificial Neural Network (ANN) alone and in combination with principal components analysis (PCA-ANN), Least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques. Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these multivariate models for stage-wise estimation of wheat yield, percentage deviation of estimated yield by observed yield was ranged between -0.1 to 25.6, 0.9 to 22.8, -0.7 to 22.5% during tillering, flowering, and grain filling stage respectively. On the basis of percentage deviation and model accuracy Elastic net and LASSO model was found better and can be used for district level wheat crop yield estimation at different crop growth stage.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46875302","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.6304
Prem Deep, ML Khichar, R. Niwas, Madho Singh
An experiment was conducted during rabi seasons of 2015-16 and 2016-17 to study the phenology, accumulation of growing degree days (GDD), helio-thermal unit (HTU), photo-thermal unit (PTU), Heat use efficiency (HUE), Radiation use efficiency (RUE)and to assess the effects of thermal and radiation regimes on wheat at research farm of Department of Agricultural Meteorology, Chaudhary Charan Singh Haryana Agricultural University, Hisar during rabi seasons of 2015-16 and 2016-17. The twenty-seven treatment combinations were tested in split plot design with three replications. The main plot treatments consist of three date of sowing, i.e., D1- 2nd fortnight of November, D2- 1st fortnight of December, D3- 2nd fortnight of December and sub plot treatments consist of three varieties, i.e., V1- WH 1105, V2- DPW 621-50 and V3- HD 2967. Daily meteorological data recorded at Agromet observatory near the experimental plot was used for computation of agrometerological indices, i.e., heat unit (HU), heliothermal unit (HTU), photothermal unit (PTU), heat use efficiency (HUE) and radiation use efficiency (RUE).
{"title":"Effects of sowing dates on phenology, radiation interception and yield of wheat","authors":"Prem Deep, ML Khichar, R. Niwas, Madho Singh","doi":"10.54302/mausam.v74i3.6304","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.6304","url":null,"abstract":"An experiment was conducted during rabi seasons of 2015-16 and 2016-17 to study the phenology, accumulation of growing degree days (GDD), helio-thermal unit (HTU), photo-thermal unit (PTU), Heat use efficiency (HUE), Radiation use efficiency (RUE)and to assess the effects of thermal and radiation regimes on wheat at research farm of Department of Agricultural Meteorology, Chaudhary Charan Singh Haryana Agricultural University, Hisar during rabi seasons of 2015-16 and 2016-17.\u0000The twenty-seven treatment combinations were tested in split plot design with three replications. The main plot treatments consist of three date of sowing, i.e., D1- 2nd fortnight of November, D2- 1st fortnight of December, D3- 2nd fortnight of December and sub plot treatments consist of three varieties, i.e., V1- WH 1105, V2- DPW 621-50 and V3- HD 2967.\u0000Daily meteorological data recorded at Agromet observatory near the experimental plot was used for computation of agrometerological indices, i.e., heat unit (HU), heliothermal unit (HTU), photothermal unit (PTU), heat use efficiency (HUE) and radiation use efficiency (RUE).","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"60 32","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41308820","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.5940
R. Bhatla, Manas Pant, Soumik Ghosh, S. Verma, Nishant Pandey, Sanjay Bist
The Indian summer monsoon is a large scale synoptic and dominant circulation feature which is largely restricted to the summer months from June to September. The proper understanding of rainfall pattern and its trends may help water resources development, agriculture sector and to take decisions for developmental activities. The present study is an attempt to evaluate the spatial variability in Indian summer monsoon rainfall (ISMR) over the Indian subcontinent during the climatological period (1901-2010). The long-term annual, decadal and tricadal monsoon rainfall differences are considered for the period 1901-2010 during the monsoon (June-September) and peak monsoon month (July-August). The results show concern for the major areas of upper Himalaya, Western and peninsular India where positive rainfall difference/increase rainfall with 0.2 to 1 mm/day variation have been reported during the monsoon and peak monsoon months. Also, decrease in rainfall have been reported over Western Ghats, Indo-Gangetic Plain (IGP) and some central Indian regions in the range of -0.6 to -1.5 mm/day. Further, a broad overview of the study shows an enhancement of ISMR over Western India whereas a substantial decline over Northeast Indian regions which suggests the western shift of ISMR in changing climate.
{"title":"Variations in Indian Summer Monsoon Rainfall patterns in Changing Climate","authors":"R. Bhatla, Manas Pant, Soumik Ghosh, S. Verma, Nishant Pandey, Sanjay Bist","doi":"10.54302/mausam.v74i3.5940","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5940","url":null,"abstract":"The Indian summer monsoon is a large scale synoptic and dominant circulation feature which is largely restricted to the summer months from June to September. The proper understanding of rainfall pattern and its trends may help water resources development, agriculture sector and to take decisions for developmental activities. The present study is an attempt to evaluate the spatial variability in Indian summer monsoon rainfall (ISMR) over the Indian subcontinent during the climatological period (1901-2010). The long-term annual, decadal and tricadal monsoon rainfall differences are considered for the period 1901-2010 during the monsoon (June-September) and peak monsoon month (July-August). The results show concern for the major areas of upper Himalaya, Western and peninsular India where positive rainfall difference/increase rainfall with 0.2 to 1 mm/day variation have been reported during the monsoon and peak monsoon months. Also, decrease in rainfall have been reported over Western Ghats, Indo-Gangetic Plain (IGP) and some central Indian regions in the range of -0.6 to -1.5 mm/day. Further, a broad overview of the study shows an enhancement of ISMR over Western India whereas a substantial decline over Northeast Indian regions which suggests the western shift of ISMR in changing climate.\u0000 ","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45708321","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.174
A. Gupta, K. Sarkar, D. Dhakre, D. Bhattacharya
This paper attempts to compare the weather indices based regression approach and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) approach for rice yield prediction at district level of West Bengal. The weather indices for weather variables, viz., minimum temperature, maximum temperature, rainfall, and relative humidity are used as input variables along with time variable t and yield of rice as output variable. The study reveals that the ANN approach works better than the standard regression approach in crop yield prediction. The prediction error percentages are found to be consistently less than 5% in MLP ANN approach except for one district.
{"title":"Weather based crop yield prediction using artificial neural networks: A comparative study with other approaches","authors":"A. Gupta, K. Sarkar, D. Dhakre, D. Bhattacharya","doi":"10.54302/mausam.v74i3.174","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.174","url":null,"abstract":"This paper attempts to compare the weather indices based regression approach and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) approach for rice yield prediction at district level of West Bengal. The weather indices for weather variables, viz., minimum temperature, maximum temperature, rainfall, and relative humidity are used as input variables along with time variable t and yield of rice as output variable. The study reveals that the ANN approach works better than the standard regression approach in crop yield prediction. The prediction error percentages are found to be consistently less than 5% in MLP ANN approach except for one district.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42933104","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.5598
M. Peki̇n, N. Demirbağ, K. Khawar, Halit Apaydin
Alfalfa is one of the most widely cultivated forage crops in the world. Alfalfa farming is carried out on approximately 35 million ha of land worldwide with an annual production amounting to 255 million tons. The average alfalfa cultivated area is about 637 000 ha with production of 13 million tons and yield of 2 200 kg da-1 in Turkey. It is expected that climate change will have significantly different effects on its production and yield in future. Therefore, the aim of the study was to predict the effect of climate change on the yield of alfalfa via selected Artificial Neural Network (ANN) according to RCP4.5 and RCP8.5 climate change scenarios. In line with this, first of all the best ANN structure among 176 different ANN alternatives consisting of various input parameters, learning rates, decay and neuron numbers to predicts alfalfa yield was selected. The ANN training/test dataset used in the study were composed of the alfalfa cultivation statistics, the soil parameters and the climatological data. Alfalfa yield for years 2020-2060 and 2060-2100 in 79 provinces of Turkey are predicted by using best ANN model, according to climate change projections (HadGEM2-ES RCP4.5 and RCP8.5). The ANN was able to calculate alfalfa yield with 0.827 coefficient of determination and 0.813 Nash-Sutcliff coefficient. It is understood that the alfalfa plant can resist climate change and its yield tend to increase or decrease in regions, where there will be an increase or decrease in precipitation in the same order as result of climatic change. It is predicted that the highest yield increase will be in Artvin (6%) (a province of the Eastern Anatolia region) and the maximum yield decrease will be noted in Siirt (9%) (a province of the South eastern Anatolia region). This research may be considered as a creative prediction approach for the alfalfa yield estimation.
{"title":"Estimation of Alfalfa (Medicago sativa l.) yield under RCP4.5 and RCP8.5 climate change projections with ANN in Turkey","authors":"M. Peki̇n, N. Demirbağ, K. Khawar, Halit Apaydin","doi":"10.54302/mausam.v74i3.5598","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5598","url":null,"abstract":"Alfalfa is one of the most widely cultivated forage crops in the world. Alfalfa farming is carried out on approximately 35 million ha of land worldwide with an annual production amounting to 255 million tons. The average alfalfa cultivated area is about 637 000 ha with production of 13 million tons and yield of 2 200 kg da-1 in Turkey. It is expected that climate change will have significantly different effects on its production and yield in future. Therefore, the aim of the study was to predict the effect of climate change on the yield of alfalfa via selected Artificial Neural Network (ANN) according to RCP4.5 and RCP8.5 climate change scenarios. In line with this, first of all the best ANN structure among 176 different ANN alternatives consisting of various input parameters, learning rates, decay and neuron numbers to predicts alfalfa yield was selected. The ANN training/test dataset used in the study were composed of the alfalfa cultivation statistics, the soil parameters and the climatological data. Alfalfa yield for years 2020-2060 and 2060-2100 in 79 provinces of Turkey are predicted by using best ANN model, according to climate change projections (HadGEM2-ES RCP4.5 and RCP8.5). The ANN was able to calculate alfalfa yield with 0.827 coefficient of determination and 0.813 Nash-Sutcliff coefficient. It is understood that the alfalfa plant can resist climate change and its yield tend to increase or decrease in regions, where there will be an increase or decrease in precipitation in the same order as result of climatic change. It is predicted that the highest yield increase will be in Artvin (6%) (a province of the Eastern Anatolia region) and the maximum yield decrease will be noted in Siirt (9%) (a province of the South eastern Anatolia region). This research may be considered as a creative prediction approach for the alfalfa yield estimation.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41628624","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.5608
M. Mazumdar, M. Dutta, Mrigakshi Bharadwaj
Geographic Information Systems and remote sensing, have proved to be efficient tools in delineation of drainage pattern and different geometric methodology of geomorphologic, watershed management even GIS has been widely used in several flood management, and environmental applications. The river Beki with an area of 19,354.35 sq.km2 originates at Himalayan glacier (Kula Kangri glacier in Bhutan) 26.18° N latitudes and 90.53° E longitudes and flows though the plains of Assam and finally to the mighty Brahmaputra at 26.48° N latitudes and 91.02° E longitudes has been selected for detailed morphometric analysis. Morphometric parameters via; Stream order, Stream length, Bifurcation ratio, Drainage density, Drainage frequency, Drainage texture, Form factor, Circularity ratio, Elongation ratio and Compactness ratio etc. were measured for prioritization and compound parameter values were calculated. This study will help the local people to utilize the resources in right manner for Sustainable Water Resource Development of the Basin area. Moreover, the study can also be referred as a benchmark for studies on temporal change in geomorphology due to climate change. Different Morphometric analysis provides the explanation of physical characteristics of the watershed which are useful for the areas of land use planning, soil conservation, terrain elevation and soil erosion.
{"title":"A Geographic Information System (GIS) based approach for drainage and morphometric characterization of Beki river basin, India","authors":"M. Mazumdar, M. Dutta, Mrigakshi Bharadwaj","doi":"10.54302/mausam.v74i3.5608","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5608","url":null,"abstract":"Geographic Information Systems and remote sensing, have proved to be efficient tools in delineation of drainage pattern and different geometric methodology of geomorphologic, watershed management even GIS has been widely used in several flood management, and environmental applications. The river Beki with an area of 19,354.35 sq.km2 originates at Himalayan glacier (Kula Kangri glacier in Bhutan) 26.18° N latitudes and 90.53° E longitudes and flows though the plains of Assam and finally to the mighty Brahmaputra at 26.48° N latitudes and 91.02° E longitudes has been selected for detailed morphometric analysis. Morphometric parameters via; Stream order, Stream length, Bifurcation ratio, Drainage density, Drainage frequency, Drainage texture, Form factor, Circularity ratio, Elongation ratio and Compactness ratio etc. were measured for prioritization and compound parameter values were calculated. This study will help the local people to utilize the resources in right manner for Sustainable Water Resource Development of the Basin area. Moreover, the study can also be referred as a benchmark for studies on temporal change in geomorphology due to climate change. Different Morphometric analysis provides the explanation of physical characteristics of the watershed which are useful for the areas of land use planning, soil conservation, terrain elevation and soil erosion.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47060055","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.828
Divyansh Saini, D. Lataye, V. Motghare
The objective of this study is to assess the long-term variation in concentrations of Respirable suspended particulate matter (PM10), sulphur dioxide (SO2) and nitrogen dioxide (NO2) in the ambient air of Nagpur (India) during 2011-2018. The pollution data during the above period at three locations, viz., residential (Station-I), industrial (Station-II), and commercial location (Station-III) has been analyzed. The highest daily average concentration of PM10 at residential, industrial, and commercial locations was found 154 microgm/m3, 199 microgm/m3, and 153 microgm/m3, whereas, the average annual concentration at these locations was found 101.87 microgm/m3, 115.37 microgm/m3 and 98.75 microgm/m3, respectively during the above period. The highest daily average concentration of SO2 was found at 18 microgm/m3, 22 microgm/m3 and 19 microgm/m3 and the average annual concentration was 13.25 microgm/m3, 13.5 microgm/m3, 13 microgm/m3 at respective locations. And the highest daily average concentration of NO2 was found 77 microgm/m3, 60 microgm/m3, 60 microgm/m3 and the annual average concentration was 44.125 microgm/m3, 41.825 microgm/m3 and 40.25 microgm/m3 at the respective locations. The exceedance factors for PM10 varied from 'moderate to high' at the residential and commercial locations and from 'high to moderate' at the industrial location. Planetary boundary layer height (PBLH) and ventilation coefficient (VC) were also estimated over the region for 2011-2018. The maximum PBLH and VC observed during the study period was in the summer season, and the minimum was in the post-monsoon season. Annual and Seasonal Air quality index analysis shows that the level of pollution was in the range of SATIFACTORY to MODERATE. A study of seasonal analysis of PM10, SO2 and NO2 showed that the higher concentrations were found in winter relative to summer with the least concentration occurring during the monsoon season. A regression analysis was performed to check PM10's interdependence with other contaminants. A positive association was found between PM10 and SO2 for all seasons. A negative association was found between PM10 and NO2 in summer for all the stations and winter at Station-I and Station-III. Similarly, the correlation between PM10 and meteorological parameters such as wind speed and temperature was found to be negative whereas it was positive for relative humidity.
{"title":"Studies on the variation in concentrations of respirable suspended particulate matter (PM10), NO2 and SO2 in and around Nagpur","authors":"Divyansh Saini, D. Lataye, V. Motghare","doi":"10.54302/mausam.v74i3.828","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.828","url":null,"abstract":"The objective of this study is to assess the long-term variation in concentrations of Respirable suspended particulate matter (PM10), sulphur dioxide (SO2) and nitrogen dioxide (NO2) in the ambient air of Nagpur (India) during 2011-2018. The pollution data during the above period at three locations, viz., residential (Station-I), industrial (Station-II), and commercial location (Station-III) has been analyzed. The highest daily average concentration of PM10 at residential, industrial, and commercial locations was found 154 microgm/m3, 199 microgm/m3, and 153 microgm/m3, whereas, the average annual concentration at these locations was found 101.87 microgm/m3, 115.37 microgm/m3 and 98.75 microgm/m3, respectively during the above period. The highest daily average concentration of SO2 was found at 18 microgm/m3, 22 microgm/m3 and 19 microgm/m3 and the average annual concentration was 13.25 microgm/m3, 13.5 microgm/m3, 13 microgm/m3 at respective locations. And the highest daily average concentration of NO2 was found 77 microgm/m3, 60 microgm/m3, 60 microgm/m3 and the annual average concentration was 44.125 microgm/m3, 41.825 microgm/m3 and 40.25 microgm/m3 at the respective locations. The exceedance factors for PM10 varied from 'moderate to high' at the residential and commercial locations and from 'high to moderate' at the industrial location. Planetary boundary layer height (PBLH) and ventilation coefficient (VC) were also estimated over the region for 2011-2018. The maximum PBLH and VC observed during the study period was in the summer season, and the minimum was in the post-monsoon season. Annual and Seasonal Air quality index analysis shows that the level of pollution was in the range of SATIFACTORY to MODERATE. A study of seasonal analysis of PM10, SO2 and NO2 showed that the higher concentrations were found in winter relative to summer with the least concentration occurring during the monsoon season. A regression analysis was performed to check PM10's interdependence with other contaminants. A positive association was found between PM10 and SO2 for all seasons. A negative association was found between PM10 and NO2 in summer for all the stations and winter at Station-I and Station-III. Similarly, the correlation between PM10 and meteorological parameters such as wind speed and temperature was found to be negative whereas it was positive for relative humidity.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46332182","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.4754
R. Thapliyal, Bikram Singh
The unprecedented rainfall observed over Uttarakhand on 18th October 2021 caused landslides, debris flow and floods over the Kumaun region and adjoining districts of the Garhwal region of Uttarakhand, which resulted in huge damage to life, agriculture, transport, tourism and other sectors. The synoptic and dynamic study of the current event showed the movement of the Low-Pressure Area over central India resulting in the strong southeasterly winds (Atmospheric River) over Indo-Gangetic planes from the Bay of Bengal from 17th to 19th October. The interaction and blocking of the Atmospheric River by the deep trough of eastward-moving Western Disturbance (WD) caused extreme rainfall over Uttarakhand. However, the X-band Doppler Weather Radar and 123 Automatic Weather/raingauge Stations data suggest that the hourly rainfall rate was of light to moderate intensity (10-20 mm/h) over most of the area and at most of the time. The rainfall rate was extremely intense (50-100 m/hour) for around 1-hour duration in 7 stations of Udham Singh Nagar, Champawat, Nainital and Pauri districts. Unlike the June 2013 extremely heavy rainfall event over Uttarakhand which impacted the whole Uttarakhand state, the present event was concentrated over the Kumaun region and the highest ever 24-hours accumulated rainfall was observed on 18th October, 2021 in Kumaon region of Uttarakhand. The expected rainfall as well as the impact of the event over Uttarakhand was forecasted 5 days in advance with good accuracy based on the synoptic analysis and NWP model guidance. The predictability of the IMD-GFS (T-1534) NWP model was found to be up to 10 days for this extreme rainfall event.
{"title":"A case study of exceptionally heavy rainfall event over Uttarakhand, India on 18th October, 2021 and its forecasting","authors":"R. Thapliyal, Bikram Singh","doi":"10.54302/mausam.v74i3.4754","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.4754","url":null,"abstract":"The unprecedented rainfall observed over Uttarakhand on 18th October 2021 caused landslides, debris flow and floods over the Kumaun region and adjoining districts of the Garhwal region of Uttarakhand, which resulted in huge damage to life, agriculture, transport, tourism and other sectors. The synoptic and dynamic study of the current event showed the movement of the Low-Pressure Area over central India resulting in the strong southeasterly winds (Atmospheric River) over Indo-Gangetic planes from the Bay of Bengal from 17th to 19th October. The interaction and blocking of the Atmospheric River by the deep trough of eastward-moving Western Disturbance (WD) caused extreme rainfall over Uttarakhand. However, the X-band Doppler Weather Radar and 123 Automatic Weather/raingauge Stations data suggest that the hourly rainfall rate was of light to moderate intensity (10-20 mm/h) over most of the area and at most of the time. The rainfall rate was extremely intense (50-100 m/hour) for around 1-hour duration in 7 stations of Udham Singh Nagar, Champawat, Nainital and Pauri districts. Unlike the June 2013 extremely heavy rainfall event over Uttarakhand which impacted the whole Uttarakhand state, the present event was concentrated over the Kumaun region and the highest ever 24-hours accumulated rainfall was observed on 18th October, 2021 in Kumaon region of Uttarakhand. The expected rainfall as well as the impact of the event over Uttarakhand was forecasted 5 days in advance with good accuracy based on the synoptic analysis and NWP model guidance. The predictability of the IMD-GFS (T-1534) NWP model was found to be up to 10 days for this extreme rainfall event.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42047105","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.5888
KHALEDS.M. Essa, H. Taha
In this paper, one dimension time dependent and the steady state three dimensions of advection-diffusion equation (ADE) have been solved analytically to estimate the concentration in the atmospheric boundary layer (ABL) taking into account the assumption that the ABL height (h) is divided into sub-layers and the downwind distance is also divided into intervals within each rectangular area the ADE is estimated by using the Laplace transform method assuming that the mean values of wind speed and eddy diffusivity. The proposed model, Gaussian plume model and previous work (Essa et al.2019) was compared with the observed concentration of Iodine-135 which was measured at Egyptian Atomic Energy Authority, Nuclear Research Reactor, Inshas, Cairo Egypt. The statistical analysis shows that there is a good agreement between the proposed and experimental values of concentration.
在本文中，本文对平流扩散方程(ADE)的一维时变和稳态三维进行了解析求解，在假定边界层高度(h)被划分为若干子层和每个矩形区域内顺风距离被划分为若干区间的情况下，对大气边界层(ABL)的浓度进行了估计，并在假定风速和涡的平均值的情况下，采用拉普拉斯变换方法对ADE进行了估计扩散系数。将提出的模型、高斯羽流模型和以前的工作(Essa et al.2019)与在埃及开罗Inshas的埃及原子能管理局核研究反应堆测量的碘-135浓度进行了比较。统计分析表明，提出的浓度值与实验值吻合较好。
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