Pub Date : 2024-01-01DOI: 10.54302/mausam.v75i1.803
Dr Neeti Singh, T. C S, Gajendra Kumar, A. S H, Dinesh Sankhala
This study examines the temporal variation of rainfall on monthly, seasonal, annual and decadal scale over Konkan & Goa, India during 1901-2020. Trend analysis of rainfall data is carried out by using Man-Kendall and t-test. A significant increasing trend has been observed in annual rainfall data. A significant increasing trend of 32mm/year is present in annual rainfall. Southwest monsoon showed significant increasing rainfall trends over Konkan & Goa during the last 120 years. On the monthly scale, rainfall indicate significant increasing trend during the month of June, August, September and October showed and significant decreasing trend during January & February. During the period of 120 year rainfall is highest in period of 1931-1960. Decadal rainfall analysis shows total 18 excess years and 15 deficit years observed annually over the period of study.
{"title":"Temporal variations of Rainfall over Konkan & Goa during 1901-2020","authors":"Dr Neeti Singh, T. C S, Gajendra Kumar, A. S H, Dinesh Sankhala","doi":"10.54302/mausam.v75i1.803","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.803","url":null,"abstract":"This study examines the temporal variation of rainfall on monthly, seasonal, annual and decadal scale over Konkan & Goa, India during 1901-2020. Trend analysis of rainfall data is carried out by using Man-Kendall and t-test. A significant increasing trend has been observed in annual rainfall data. A significant increasing trend of 32mm/year is present in annual rainfall. Southwest monsoon showed significant increasing rainfall trends over Konkan & Goa during the last 120 years. On the monthly scale, rainfall indicate significant increasing trend during the month of June, August, September and October showed and significant decreasing trend during January & February. During the period of 120 year rainfall is highest in period of 1931-1960. Decadal rainfall analysis shows total 18 excess years and 15 deficit years observed annually over the period of study.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"48 16","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139126608","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 : 2023-12-31DOI: 10.54302/mausam.v75i1.6080
H. N. Sowmya, Channabasavaraj Wollur, G. P. Shivashankara, H. K. Ramaraju
The data of Particulate matter PMs (PM2.5, PM10) and Gaseous Pollutants such as carbon monoxide (CO), methane (CH4), oxides of nitrogen (NOx: NO and NO2), non-methane hydrocarbons (NMHCs), sulfur dioxide (SO2), along with ammonia (NH3) at five different locations across Bengaluru from 1st January, 2017 to 20th March, 2018 were collected. The primary objective of this research work is to identify the sources of atmospheric particulate matter and gaseous pollutants using receptor models in Bengaluru, India. To execute this, receptor models, namely Conditional Bivariate Probability Function (CBPF) and Concentrated Weighted Trajectory (CWT) Analysis, are applied. Conditional Bivariate Probability Function (CBPF) shows that, annually, the maximum concentrations of PMs over receptor sites were detected during low wind speed (< 2 knots) along the north-east direction specifying that the long-range transport does not play an essential role in the transportation of higher concentrations of PM and their primary source region may be localized. Concentrated Weighted Trajectory (CWT) analysis shows that, seasonally, the highest air mass contribution of about 37% was noticed in summer, whereas the lowest was in the post-monsoon season (13%). The significant contribution of PM2.5 transported from long distances was during monsoon, and in the case of PM10, it was in summer. The study suggests that the long-range transport of PMs and gaseous Pollutants was not vital and was observed to be localized.
{"title":"Identifying source apportionment of atmospheric particulate matter and gaseous pollutants using receptor models : A case study of Bengaluru, India","authors":"H. N. Sowmya, Channabasavaraj Wollur, G. P. Shivashankara, H. K. Ramaraju","doi":"10.54302/mausam.v75i1.6080","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.6080","url":null,"abstract":"The data of Particulate matter PMs (PM2.5, PM10) and Gaseous Pollutants such as carbon monoxide (CO), methane (CH4), oxides of nitrogen (NOx: NO and NO2), non-methane hydrocarbons (NMHCs), sulfur dioxide (SO2), along with ammonia (NH3) at five different locations across Bengaluru from 1st January, 2017 to 20th March, 2018 were collected. The primary objective of this research work is to identify the sources of atmospheric particulate matter and gaseous pollutants using receptor models in Bengaluru, India. To execute this, receptor models, namely Conditional Bivariate Probability Function (CBPF) and Concentrated Weighted Trajectory (CWT) Analysis, are applied. Conditional Bivariate Probability Function (CBPF) shows that, annually, the maximum concentrations of PMs over receptor sites were detected during low wind speed (< 2 knots) along the north-east direction specifying that the long-range transport does not play an essential role in the transportation of higher concentrations of PM and their primary source region may be localized. Concentrated Weighted Trajectory (CWT) analysis shows that, seasonally, the highest air mass contribution of about 37% was noticed in summer, whereas the lowest was in the post-monsoon season (13%). The significant contribution of PM2.5 transported from long distances was during monsoon, and in the case of PM10, it was in summer. The study suggests that the long-range transport of PMs and gaseous Pollutants was not vital and was observed to be localized.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" 946","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139136420","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 : 2023-12-31DOI: 10.54302/mausam.v75i1.5899
K. Essa, S. Etman, M. El-Otaify, M. Embaby
The mathematical formulation of the concentration of the diffusing particles in air was derived by solving analytically the advection-diffusion equation taking into consideration: (1) the vertical variation of wind speed and eddy diffusivity with height above ground. (2) the vertical diffusion is limited by an elevated impenetrable inversion layer located at the top of the atmospheric boundary layer (ABL) of height h. (3) the dry deposition of the diffusing particles at the ground surface which was included through the boundary conditions. A power law profile is used to describe the vertical variation of eddy diffusivity with height, while the sum of power law profile and logarithmic law is used to describe the vertical variation of wind speed with height above ground surface. The decay distance of a pollutant along the wind direction was derived. The present solution was evaluated against the dataset from Hanford diffusion experiment in stable conditions. The results are discussed and presented in illustrative figures.
通过对平流-扩散方程进行分析求解，得出了空气中扩散粒子浓度的数学公式，其中考虑到：(1) 风速和涡流扩散率随地面高度的垂直变化。(2) 垂直扩散受到位于高度为 h 的大气边界层（ABL）顶部的高空不可穿透反转层的限制。幂律曲线用于描述涡扩散率随高度的垂直变化，而幂律曲线和对数定律之和用于描述风速随地面高度的垂直变化。得出了污染物沿风向的衰减距离。 在稳定条件下，根据汉福德扩散实验的数据集对本解决方案进行了评估。对结果进行了讨论，并给出了说明性数字。
{"title":"Analytical concentration of pollutants with deposition using wind speed as power and logarithmic law","authors":"K. Essa, S. Etman, M. El-Otaify, M. Embaby","doi":"10.54302/mausam.v75i1.5899","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.5899","url":null,"abstract":"The mathematical formulation of the concentration of the diffusing particles in air was derived by solving analytically the advection-diffusion equation taking into consideration: (1) the vertical variation of wind speed and eddy diffusivity with height above ground. (2) the vertical diffusion is limited by an elevated impenetrable inversion layer located at the top of the atmospheric boundary layer (ABL) of height h. (3) the dry deposition of the diffusing particles at the ground surface which was included through the boundary conditions. A power law profile is used to describe the vertical variation of eddy diffusivity with height, while the sum of power law profile and logarithmic law is used to describe the vertical variation of wind speed with height above ground surface. The decay distance of a pollutant along the wind direction was derived. The present solution was evaluated against the dataset from Hanford diffusion experiment in stable conditions. The results are discussed and presented in illustrative figures.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"99 41","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139131681","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 : 2023-12-31DOI: 10.54302/mausam.v75i1.5398
Chetan R. Patel, R. Singhal
In this research firstly, the rainfall pattern of Ahmedabad and Surat, the fast-growing urban areas of Gujarat state of India have been studied and compared. It is detected that what makes Surat city more prone to floods. Then, analysis for rainfall shift in Surat over the last three decades has been carried out. It is interesting to observe that the rainfall pattern of Surat is following the local calendar, i.e. Indian calendar rather Gregorian calendar. This relation of rainfall pattern with Indian calendar shows that the prediction and the climatic condition responsible for rain is following the local calendar based on the planetary position. For the Water Sensitive Urban Design, four different wards in Surat Municipal Corporation (SMC) named Adajan, Piplod, Anjana and Pandesara are studied. These wards are selected based on land use, having the highest area in commercial, residential, industrial and institutional in total SMC area. For each ward, the previous and impervious area is calculated, and the runoff is determined. Planning interventions for water sensitive urban design at a building level, street level and ward level have been given for the study area. The study will be definitely helpful for the decision-makers to prepare a policy to follow the local calendar to operate the monsoon protocol and to manage water resource infrastructure, including the planning of harvesting activities.
在这项研究中，首先对印度古吉拉特邦快速发展的城市地区艾哈迈达巴德和苏拉特的降雨模式进行了研究和比较。研究发现了苏拉特市更容易遭受洪灾的原因。然后，对苏拉特过去三十年的降雨量变化进行了分析。值得注意的是，苏拉特的降雨模式遵循的是当地日历，即印度历而不是公历。 降雨模式与印度历的关系表明，降雨的预测和气候条件是根据行星位置按照当地历法进行的。为了进行水敏感型城市设计，苏拉特市政公司（Surat Municipal Corporation，SMC）研究了四个不同的区，分别名为 Adajan、Piplod、Anjana 和 Pandesara。这些选区是根据土地使用情况选出的，在苏拉特市政公司总面积中，商业、住宅、工业和机构占地面积最大。每个区都计算了以前的面积和不透水面积，并确定了径流量。对研究区域的建筑物、街道和选区层面的水敏感城市设计进行了规划干预。这项研究必将有助于决策者制定政策，按照当地日历执行季风协议，管理水资源基础设施，包括规划收集活动。
{"title":"Policy Interventions to Address Urban Water Problems of highly urbanised area due to Climate Change","authors":"Chetan R. Patel, R. Singhal","doi":"10.54302/mausam.v75i1.5398","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.5398","url":null,"abstract":"In this research firstly, the rainfall pattern of Ahmedabad and Surat, the fast-growing urban areas of Gujarat state of India have been studied and compared. It is detected that what makes Surat city more prone to floods. Then, analysis for rainfall shift in Surat over the last three decades has been carried out. It is interesting to observe that the rainfall pattern of Surat is following the local calendar, i.e. Indian calendar rather Gregorian calendar. This relation of rainfall pattern with Indian calendar shows that the prediction and the climatic condition responsible for rain is following the local calendar based on the planetary position. For the Water Sensitive Urban Design, four different wards in Surat Municipal Corporation (SMC) named Adajan, Piplod, Anjana and Pandesara are studied. These wards are selected based on land use, having the highest area in commercial, residential, industrial and institutional in total SMC area. For each ward, the previous and impervious area is calculated, and the runoff is determined. Planning interventions for water sensitive urban design at a building level, street level and ward level have been given for the study area. The study will be definitely helpful for the decision-makers to prepare a policy to follow the local calendar to operate the monsoon protocol and to manage water resource infrastructure, including the planning of harvesting activities.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"119 35","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139133228","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 : 2023-12-31DOI: 10.54302/mausam.v75i1.5099
R. M, Dr. Geeta Agnihotri
Daily Average Areal Precipitation (AAP) data of South West Monsoon Season for 2012 to 2020 in respect of sub-basins of Cauvery river basin were collected alongwith synoptic systems causing rainfall in the sub-basins. Five synoptic systems namely Depression/Deep Depression, low/well marked low(WML) pressure area, Upper air cyclonic circulations(UAC), off-shore trough(OST)/OST with embedded cyclonic circulations, east-west shear zone are considered in the study. Rainfall(AAP) caused by these systems considered are 11-25mm, 26-50mm, 51-100mm and > 100mm. Number of days for which these systems caused rainfall under each range was computed. The rainfall range with highest frequency for the particular system is taken as Synoptic Analogue Model. OST/OST with embedded cyclonic circulation has contributed significantly to rainfall in all the sub-basins. Depression/Deep Depression over Rayalaseema, Tamil Nadu and Pondicherry, South Interior Karnataka or North Interior Karnataka provides > 50mm rainfall in Harangi basin. Depression/Deep Depression over Rayalaseema, Tamil Nadu and Pondicherry, South Interior Karnataka or North Interior Karnataka provides > 50mm rainfall in Harangi basin. Low/Well Marked Low over Telangana provides > 50mm rainfall in Hemavathy basin. Upper Air Cyclonic circulation(UAC) over Rayalaseema provides > 50mm rainfall in Kabini basin. UAC over Rayalaseema, South East Bay of Bengal or West Central Bay of Bengal off Coastal Andhra Pradesh leads to >100 mm rain in Harangi. UAC over Coastal Karnataka and North Interior Karnataka or OST from Konkan Goa/Maharashtra to Karnataka leads to >100 mm rain in Upper Vaigai. Key words- Aerial Average Precipitation, QPF, Cauvery river basin, Synoptic Analogue Model
{"title":"Development of Synoptic Analogue Model for Quantitative Precipitation Forecast over Cauvery basin, India","authors":"R. M, Dr. Geeta Agnihotri","doi":"10.54302/mausam.v75i1.5099","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.5099","url":null,"abstract":"Daily Average Areal Precipitation (AAP) data of South West Monsoon Season for 2012 to 2020 in respect of sub-basins of Cauvery river basin were collected alongwith synoptic systems causing rainfall in the sub-basins. Five synoptic systems namely Depression/Deep Depression, low/well marked low(WML) pressure area, Upper air cyclonic circulations(UAC), off-shore trough(OST)/OST with embedded cyclonic circulations, east-west shear zone are considered in the study. Rainfall(AAP) caused by these systems considered are 11-25mm, 26-50mm, 51-100mm and > 100mm. Number of days for which these systems caused rainfall under each range was computed. The rainfall range with highest frequency for the particular system is taken as Synoptic Analogue Model. OST/OST with embedded cyclonic circulation has contributed significantly to rainfall in all the sub-basins. Depression/Deep Depression over Rayalaseema, Tamil Nadu and Pondicherry, South Interior Karnataka or North Interior Karnataka provides > 50mm rainfall in Harangi basin. Depression/Deep Depression over Rayalaseema, Tamil Nadu and Pondicherry, South Interior Karnataka or North Interior Karnataka provides > 50mm rainfall in Harangi basin. Low/Well Marked Low over Telangana provides > 50mm rainfall in Hemavathy basin. Upper Air Cyclonic circulation(UAC) over Rayalaseema provides > 50mm rainfall in Kabini basin. UAC over Rayalaseema, South East Bay of Bengal or West Central Bay of Bengal off Coastal Andhra Pradesh leads to >100 mm rain in Harangi. UAC over Coastal Karnataka and North Interior Karnataka or OST from Konkan Goa/Maharashtra to Karnataka leads to >100 mm rain in Upper Vaigai. Key words- Aerial Average Precipitation, QPF, Cauvery river basin, Synoptic Analogue Model","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" 443","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139137109","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 : 2023-12-31DOI: 10.54302/mausam.v75i1.6140
P. Kaur, S. S. Sandhu, Abhishek Dhir
Crop production is a direct output of manageable (agronomic) and unmanageable (weather) inputs. A farmer can cut down losses in crop production due to aberrant weather conditions by following weather forecast. India Meteorological Department is providing weather forecast on eight weather parameters at district and block level. Under All India Coordinated Research Project on Agrometeorology-National Innovations in Climate Resilient Agriculture, an Agromet Advisory Bulletin (AAB) is prepared by using this forecast for coming five days and disseminated to farmers. To evaluate the impact of AAB in three selected villages Badoshe Kalan and Bauranga Zer (district Fatehgarh Sahib) and Rampur Fasse (district Rupnagar) a survey from 110 farmers was conducted. Amongst the 110 farmer, 70 were marginal/small farmers (landholding <2.0ha) and 40 were medium farmers (landholding 2-10ha) who adopted the information given in AAB in crop cultivation. The analysis revealed that by following AAB in rice and wheat crops 65-93% farmers benefitted by managing biotic stresses, 65-85% farmers by irrigation management, 75-78% farmers by adjusting sowing and 62-65% farmers by nutrient management. The farmers who scheduled irrigations to their crop by adopting AAB in rice-wheat cropping system reduced ~34.2 metric tonnes of CO2 emissions by preventing wasteful burning of diesel. The adopters of AAB in rice and wheat crop were able to harness an average yield increase of 2.25-3.75q/ha and 1.75-4.50 q/ha, respectively and save nearly Rs 4100 to 7000/ha and Rs 3200-9200/ha, respectively with lesser expenditure. Hence, AAB can help boost crop productivity as well as help reduce carbon footprints and make agriculture an eco-friendly and profitable venture.
{"title":"Mitigation and risk management of climate change in crop cultivation through the adoption of Agromet Advisory Bulletin (AAB) in NICRA adopted villages in Punjab","authors":"P. Kaur, S. S. Sandhu, Abhishek Dhir","doi":"10.54302/mausam.v75i1.6140","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.6140","url":null,"abstract":"Crop production is a direct output of manageable (agronomic) and unmanageable (weather) inputs. A farmer can cut down losses in crop production due to aberrant weather conditions by following weather forecast. India Meteorological Department is providing weather forecast on eight weather parameters at district and block level. Under All India Coordinated Research Project on Agrometeorology-National Innovations in Climate Resilient Agriculture, an Agromet Advisory Bulletin (AAB) is prepared by using this forecast for coming five days and disseminated to farmers. To evaluate the impact of AAB in three selected villages Badoshe Kalan and Bauranga Zer (district Fatehgarh Sahib) and Rampur Fasse (district Rupnagar) a survey from 110 farmers was conducted. Amongst the 110 farmer, 70 were marginal/small farmers (landholding <2.0ha) and 40 were medium farmers (landholding 2-10ha) who adopted the information given in AAB in crop cultivation. The analysis revealed that by following AAB in rice and wheat crops 65-93% farmers benefitted by managing biotic stresses, 65-85% farmers by irrigation management, 75-78% farmers by adjusting sowing and 62-65% farmers by nutrient management. The farmers who scheduled irrigations to their crop by adopting AAB in rice-wheat cropping system reduced ~34.2 metric tonnes of CO2 emissions by preventing wasteful burning of diesel. The adopters of AAB in rice and wheat crop were able to harness an average yield increase of 2.25-3.75q/ha and 1.75-4.50 q/ha, respectively and save nearly Rs 4100 to 7000/ha and Rs 3200-9200/ha, respectively with lesser expenditure. Hence, AAB can help boost crop productivity as well as help reduce carbon footprints and make agriculture an eco-friendly and profitable venture.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"62 2","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139132605","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}
Region wetness variability was assessed across the Tripura state of North east India (1971 to 2016). Multiple Change point detection tests confirmed the high degree of spatiotemporal variability for the identified shifts in wetness pattern over study period. The periodicity of different wetness time-series varied between 2-128 months for the calculated SPI time scales over variable time series for the selected rain gauge stations. The periodicity pattern became more prominent with an increasing temporal domain of calculated SPI time series. Hierarchical clustering and Principle component analysis (PCA) accounted for the variability in randomness, trend and periodicity of all the SPI time series. Our present study identified the homogeneous clusters of raingauge stations suitable for real-time drought monitoring and reversible use of missing dataset on rainfall in near future across the Tripura state.
{"title":"Shifts in wetness pattern and periodicity across Tripura state in north east India","authors":"Saurav Saha, Gulav Singh Yadav, Dhiman Daschaudhuri, Mrinmoy Datta, Debasish Chakraborty, Sandip Sadhu, Bappa Das, Samik Chowdhury, V. Dayal, Anup Das, Basant Kandpal, Ingudam Shakuntala","doi":"10.54302/mausam.v75i1.4536","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.4536","url":null,"abstract":"Region wetness variability was assessed across the Tripura state of North east India (1971 to 2016). Multiple Change point detection tests confirmed the high degree of spatiotemporal variability for the identified shifts in wetness pattern over study period. The periodicity of different wetness time-series varied between 2-128 months for the calculated SPI time scales over variable time series for the selected rain gauge stations. The periodicity pattern became more prominent with an increasing temporal domain of calculated SPI time series. Hierarchical clustering and Principle component analysis (PCA) accounted for the variability in randomness, trend and periodicity of all the SPI time series. Our present study identified the homogeneous clusters of raingauge stations suitable for real-time drought monitoring and reversible use of missing dataset on rainfall in near future across the Tripura state.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"31 8","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139131232","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 : 2023-12-31DOI: 10.54302/mausam.v75i1.3576
P. Setiya, A. Nain, Anurag Satpathi
The study was aimed to develop the yield forecast model for rice crop yield. Four different techniques i.e. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ELNET)were used to build the prediction models. Dataset of meteorological data and crop yield data of 15 years have been used to develop the forecast models. The developed models were also validated on the dataset of three years. The assessment of the developed models wasdone by using root mean square error (RMSE),normalized root mean square error (nRMSE),Mean Absolute Error (MAE) and on the basis of coefficient of determination (R2). The experimental analysis suggested that the performance for Artificial Neural Network (R2=0.99, RMSE=0.07, nRMSE=2.20, MAE=0.06) is better as compared to SMLR(R2=0.97, RMSE=0.08, nRMSE=2.34, MAE=0.05), LASSO (R2=0.62, RMSE=0.26, nRMSE=7.81, MAE=0.24) and ELNET (R2=0.54, RMSE=0.38, nRMSE=11.41, MAE=0.37) for the predictionof rice crop yield for Udham Singh Nagar (USN) district of Uttarakhand. Therefore, for the prediction of rice yield, ANN technique can be well utilised for Udham Singh Nagar district of Uttarakhand.
{"title":"Comparative analysis of SMLR, ANN, Elastic net and LASSO based models for rice crop yield prediction in Uttarakhand","authors":"P. Setiya, A. Nain, Anurag Satpathi","doi":"10.54302/mausam.v75i1.3576","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.3576","url":null,"abstract":"The study was aimed to develop the yield forecast model for rice crop yield. Four different techniques i.e. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ELNET)were used to build the prediction models. Dataset of meteorological data and crop yield data of 15 years have been used to develop the forecast models. The developed models were also validated on the dataset of three years. The assessment of the developed models wasdone by using root mean square error (RMSE),normalized root mean square error (nRMSE),Mean Absolute Error (MAE) and on the basis of coefficient of determination (R2). The experimental analysis suggested that the performance for Artificial Neural Network (R2=0.99, RMSE=0.07, nRMSE=2.20, MAE=0.06) is better as compared to SMLR(R2=0.97, RMSE=0.08, nRMSE=2.34, MAE=0.05), LASSO (R2=0.62, RMSE=0.26, nRMSE=7.81, MAE=0.24) and ELNET (R2=0.54, RMSE=0.38, nRMSE=11.41, MAE=0.37) for the predictionof rice crop yield for Udham Singh Nagar (USN) district of Uttarakhand. Therefore, for the prediction of rice yield, ANN technique can be well utilised for Udham Singh Nagar district of Uttarakhand.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"117 44","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139133470","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 : 2023-12-31DOI: 10.54302/mausam.v75i1.765
P. Naskar, Dushmanta R. Pattanaik
This study has been undertaken to find out the variation of central pressure (intensity) of intense Tropical Cyclones (TCs) with Sea Surface Temperature (SST), Mid-tropospheric Relative Humidity (MRH), Mid-tropospheric Instability (MI), Vertical Wind Shear (VWS), 200-hPa divergence, and Surface Latent Heat Flux (SLHF) during the lifetime intense TCs. This study also aims to determine the most crucial parameter which shows the highest correlation with central pressure (intensity) of intense TCs during their lifetime. Out of all these parameters, SLHF is highly correlated (R = 0.74) with the central pressure (intensity) of intense TCs. Increase and decrease of SLHF correspond to decrease and increase of TCs central pressure (increase and decrease in TCs intensity). The highest SLHF corresponds to the lowest central pressure (highest intensity).
{"title":"Variations in intensity of Bay of Bengal tropical cyclones with surface latent heat flux and other parameters","authors":"P. Naskar, Dushmanta R. Pattanaik","doi":"10.54302/mausam.v75i1.765","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.765","url":null,"abstract":"This study has been undertaken to find out the variation of central pressure (intensity) of intense Tropical Cyclones (TCs) with Sea Surface Temperature (SST), Mid-tropospheric Relative Humidity (MRH), Mid-tropospheric Instability (MI), Vertical Wind Shear (VWS), 200-hPa divergence, and Surface Latent Heat Flux (SLHF) during the lifetime intense TCs. This study also aims to determine the most crucial parameter which shows the highest correlation with central pressure (intensity) of intense TCs during their lifetime. Out of all these parameters, SLHF is highly correlated (R = 0.74) with the central pressure (intensity) of intense TCs. Increase and decrease of SLHF correspond to decrease and increase of TCs central pressure (increase and decrease in TCs intensity). The highest SLHF corresponds to the lowest central pressure (highest intensity).","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" 961","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139136389","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 : 2023-12-31DOI: 10.54302/mausam.v75i1.6133
Vineet Ahuja, Chhavi P. Pandey, L. K. Joshi, H. Nandan, Parmanand P. Pathak
Climate change has become a major issue for the world today. Small changes in the climate in the Himalayan region can have a significant impact on the delicate ecosystem, which is very sensitive to such changes. Recent investigations into climate change in the Western Himalayas have provided compelling evidence that these regions are especially susceptible to a wide variety of catastrophic occurrences. In the current scenario, the threat posed by climate change to human existence in Jammu and Kashmir (J&K), as well as the region of Ladakh, has grown more tangible and evident. Temperature and precipitation statistics could be used to observe this regional climatic shift. This study analyses and forecasts long-term spatio-temporal variations in precipitation and temperature using a century-long dataset from 1901 to 2002 over 14 districts of Jammu and Kashmir and Ladakh. The Augmented Dickey-Fuller (ADF) test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test of stationarity on the data show that the time series is stationary. Extreme Value Theory (EVT), which is an outstanding statistical method to interpret the records for the estimation of the future probability of the occurrence of extremes, is utilised in this study. Further, precipitation and temperature extremes are forecasted for 50, 80, 100, 120, 200, 250, 300, and 500 year return periods respectively and results reveal that the districts- Jammu, Rajouri, Leh, Srinagar, Baramulla and Poonch will be more prone to extreme weather events phenomenon.
{"title":"Extreme value analysis of precipitation and temperature over Jammu & Kashmir and Ladakh in western Himalaya, India","authors":"Vineet Ahuja, Chhavi P. Pandey, L. K. Joshi, H. Nandan, Parmanand P. Pathak","doi":"10.54302/mausam.v75i1.6133","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.6133","url":null,"abstract":"Climate change has become a major issue for the world today. Small changes in the climate in the Himalayan region can have a significant impact on the delicate ecosystem, which is very sensitive to such changes. Recent investigations into climate change in the Western Himalayas have provided compelling evidence that these regions are especially susceptible to a wide variety of catastrophic occurrences. In the current scenario, the threat posed by climate change to human existence in Jammu and Kashmir (J&K), as well as the region of Ladakh, has grown more tangible and evident. Temperature and precipitation statistics could be used to observe this regional climatic shift. This study analyses and forecasts long-term spatio-temporal variations in precipitation and temperature using a century-long dataset from 1901 to 2002 over 14 districts of Jammu and Kashmir and Ladakh. The Augmented Dickey-Fuller (ADF) test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test of stationarity on the data show that the time series is stationary. Extreme Value Theory (EVT), which is an outstanding statistical method to interpret the records for the estimation of the future probability of the occurrence of extremes, is utilised in this study. Further, precipitation and temperature extremes are forecasted for 50, 80, 100, 120, 200, 250, 300, and 500 year return periods respectively and results reveal that the districts- Jammu, Rajouri, Leh, Srinagar, Baramulla and Poonch will be more prone to extreme weather events phenomenon.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"18 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139130232","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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