Pub Date : 2024-03-24DOI: 10.54302/mausam.v75i2.6189
D. VARGHESE G. S., M. Chadaga, L. U A, S. Salim, Roopali Shantha Pai
The steep topographical setting of Kerala, traversing from Western Ghats in the east to the sandy beaches on the west, demands the use of precipitation data at a very fine spatio-temporal resolution for a range of hydrological and hydrometeorological studies. The limitation of the existing rain gauge network data in representing the variability in the monsoon showers received, across the physiographic divisions of the state, could be overcome using satellite rainfall dataset offered at a finer resolution. In this paper, a statistical evaluation of the satellite derived CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) precipitation data for the Kidangoor sub-catchment was performed by comparing it with station rainfall data and IMD gridded data sets. The homogeneity test at 95 % confidence level classified the station data under ‘useful’ category. Additionally, the statistical performance matrices suggested that the CHIRPS data slightly underestimated the observed station rainfall data. However, the coefficient of determination R2 values (0.95-0.97) in the monthly series and (0.37 - 0.64) in the annual series demonstrated a strong to moderate positive correlation between the datasets. To summarize, the quantitative statistical performance matrices, evaluated for the first time in the study area, proposed that the CHIRPS rainfall estimates could very well reproduce the ground-based monthly rainfall datasets and could also serve as a good replacement for IMD gridded data.
{"title":"Statistical evaluation of satellite-based CHIRPS precipitation data averaged over the midland and highland regions of Kidangoor sub-catchment, Kerala","authors":"D. VARGHESE G. S., M. Chadaga, L. U A, S. Salim, Roopali Shantha Pai","doi":"10.54302/mausam.v75i2.6189","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.6189","url":null,"abstract":"The steep topographical setting of Kerala, traversing from Western Ghats in the east to the sandy beaches on the west, demands the use of precipitation data at a very fine spatio-temporal resolution for a range of hydrological and hydrometeorological studies. The limitation of the existing rain gauge network data in representing the variability in the monsoon showers received, across the physiographic divisions of the state, could be overcome using satellite rainfall dataset offered at a finer resolution. In this paper, a statistical evaluation of the satellite derived CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) precipitation data for the Kidangoor sub-catchment was performed by comparing it with station rainfall data and IMD gridded data sets. The homogeneity test at 95 % confidence level classified the station data under ‘useful’ category. Additionally, the statistical performance matrices suggested that the CHIRPS data slightly underestimated the observed station rainfall data. However, the coefficient of determination R2 values (0.95-0.97) in the monthly series and (0.37 - 0.64) in the annual series demonstrated a strong to moderate positive correlation between the datasets. To summarize, the quantitative statistical performance matrices, evaluated for the first time in the study area, proposed that the CHIRPS rainfall estimates could very well reproduce the ground-based monthly rainfall datasets and could also serve as a good replacement for IMD gridded data.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140386096","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.5873
Srinivasan Reddy, G. S. Keerthy, N. G. Challa, G. K. Naidu, Srinivasan Reddy
At the regional level, climate change has significant influences on crop productivity and food security. A climate change study was carried out using different parametric indices like rainfall attributes, temperature, and humidity from 58 years of climatic data (1964-2017) in Karnataka. The climatic period was divided into the Pre-climate change period- P1 (1964-1990) and the climate change period- P2 (1991-2017) with 27 years. The result shows annual rainfall and rainy days were increased in South Interior Karnataka (SIK) and Malnad regions and reduced in North Interior Karnataka (NIK) and Coastal regions. Dakshina Kannada, Yadgir, Kalabarugi, Udupi and Kodagu districts showed a significant reduction in receiving rainfall and an increase in Shivamogga, Hassan, Kolar and Chitradurga districts from the P1 to P2 period. NIK and SIK regions are highly prone to drought vulnerability compared to Malnad and Coastal regions. The occurrence of droughts wasincreasing,the temperature trend is increased and the relative humidity trend is decreasing in the P2 period. Assessment of climate variability in P1 and P2 helps to adopt preciseuse of water, nutrient and different crop-specific management in different zones of Karnataka.
{"title":"Assessment of climate change in different regions of Karnataka state","authors":"Srinivasan Reddy, G. S. Keerthy, N. G. Challa, G. K. Naidu, Srinivasan Reddy","doi":"10.54302/mausam.v75i2.5873","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.5873","url":null,"abstract":"At the regional level, climate change has significant influences on crop productivity and food security. A climate change study was carried out using different parametric indices like rainfall attributes, temperature, and humidity from 58 years of climatic data (1964-2017) in Karnataka. The climatic period was divided into the Pre-climate change period- P1 (1964-1990) and the climate change period- P2 (1991-2017) with 27 years. The result shows annual rainfall and rainy days were increased in South Interior Karnataka (SIK) and Malnad regions and reduced in North Interior Karnataka (NIK) and Coastal regions. Dakshina Kannada, Yadgir, Kalabarugi, Udupi and Kodagu districts showed a significant reduction in receiving rainfall and an increase in Shivamogga, Hassan, Kolar and Chitradurga districts from the P1 to P2 period. NIK and SIK regions are highly prone to drought vulnerability compared to Malnad and Coastal regions. The occurrence of droughts wasincreasing,the temperature trend is increased and the relative humidity trend is decreasing in the P2 period. Assessment of climate variability in P1 and P2 helps to adopt preciseuse of water, nutrient and different crop-specific management in different zones of Karnataka.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385830","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.6147
Mukhtar Ahmed, S. Lotus, Bappa Das, F. Bhat, Amir Hassan Kichloo, Shivinder Singh
A study has been conducted on Extreme Weather Events (EWEs) induced mortalities in Jammu and Kashmir, India during 2010-2022. In the present study, we used the frequency of heavy rain, heavy snow, lightning/thunderstorm, Hailstorm and squall during the period 2010 to 2022 of 10 stations of J&K from India Meteorological Department. The mortalities occurred due to these extreme weather events for each district were collected from the Meteorological Centre Srinagar. The mean monthly precipitation and number of rainy days for each month was calculated for each station based on 40 years data (1982 to 2022). During the past 12 years, (2010-2022) a total of 2863 EWEs occurred over J&K in which 552 deaths occurred till 31st December 2022. Among the various EWEs, lightning (1942) and heavy rainfall (409) events were more frequent. When we compare the mortality per event, the heavy snow was more destructive compared to any other EWEs. The mortality per event due to heavy snow was highest (4.33) as compared to other extreme events, although the number of events of heavy snow is less (42) as compared to heavy rain (409), flash floods (168) and lightning (1942). District wise results of EWEs results revealed the highest deaths due to heavy snow were observed over Kupwara, Bandipora, Baramulla and Ganderbal. Similarly for flash floods, the highest deaths were observed over Kishtwar, Anantnag, Ganderbaland Doda. The Pearson correlation results revealed highest correlation of total deaths for heavy rain (0.77) and heavy snow (0.69) (significant at p value p<0.01) followed by flash floods (0.492) (significant at p value p<0.05). Negative correlation result was observed between heavy snow and windstorm (0.584) (significant at p value p<0.05). The present study has shown that, for the union territory as a whole, the heavy rain and heavy snow have been two major disasters causing mortality, though flashfloods, thunderstorms and windstorms are gaining importance. The trend analysis results also revealed that there is a significant increase in mortality over the years particularly due to flash floods (R2 value 0.434) and windstorm (R2 value 0.371).
{"title":"Extreme weather events induced mortalities in Jammu and Kashmir, India during 2010-2022","authors":"Mukhtar Ahmed, S. Lotus, Bappa Das, F. Bhat, Amir Hassan Kichloo, Shivinder Singh","doi":"10.54302/mausam.v75i2.6147","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.6147","url":null,"abstract":"A study has been conducted on Extreme Weather Events (EWEs) induced mortalities in Jammu and Kashmir, India during 2010-2022. In the present study, we used the frequency of heavy rain, heavy snow, lightning/thunderstorm, Hailstorm and squall during the period 2010 to 2022 of 10 stations of J&K from India Meteorological Department. The mortalities occurred due to these extreme weather events for each district were collected from the Meteorological Centre Srinagar. The mean monthly precipitation and number of rainy days for each month was calculated for each station based on 40 years data (1982 to 2022). During the past 12 years, (2010-2022) a total of 2863 EWEs occurred over J&K in which 552 deaths occurred till 31st December 2022. Among the various EWEs, lightning (1942) and heavy rainfall (409) events were more frequent. When we compare the mortality per event, the heavy snow was more destructive compared to any other EWEs. The mortality per event due to heavy snow was highest (4.33) as compared to other extreme events, although the number of events of heavy snow is less (42) as compared to heavy rain (409), flash floods (168) and lightning (1942). District wise results of EWEs results revealed the highest deaths due to heavy snow were observed over Kupwara, Bandipora, Baramulla and Ganderbal. Similarly for flash floods, the highest deaths were observed over Kishtwar, Anantnag, Ganderbaland Doda. The Pearson correlation results revealed highest correlation of total deaths for heavy rain (0.77) and heavy snow (0.69) (significant at p value p<0.01) followed by flash floods (0.492) (significant at p value p<0.05). Negative correlation result was observed between heavy snow and windstorm (0.584) (significant at p value p<0.05). The present study has shown that, for the union territory as a whole, the heavy rain and heavy snow have been two major disasters causing mortality, though flashfloods, thunderstorms and windstorms are gaining importance. The trend analysis results also revealed that there is a significant increase in mortality over the years particularly due to flash floods (R2 value 0.434) and windstorm (R2 value 0.371).","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385924","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 : 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":null,"pages":null},"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.v75i1.5899
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":null,"pages":null},"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.v75i1.6140
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":null,"pages":null},"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}
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":null,"pages":null},"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.v75i1.6080
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":null,"pages":null},"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.v75i1.5099
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":null,"pages":null},"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}
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":null,"pages":null},"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}
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