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":null,"pages":null},"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.v74i3.828
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":null,"pages":null},"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.v74i3.5608
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":null,"pages":null},"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.v74i3.3495
Y. Garde, K. Banakara, H. Pandya
Agriculture plays very important role in development of country. Rice is a staple food for more than half of world’s population. Timely and reliable forecasting provides vital and appropriate input, foresight and informed planning. The present investigation was carried out to forecast Kharif rice yield using two different statistical techniques, viz., discriminant function analysis and logistic regression analysis. The statistical models were developed using data from 1990 to 2012 and validation of developed models was done by using remaining data, i.e., 2013 to 2016. It was observed that value of adjusted R2 varied from 73.00 per cent to 93.30 per cent in different models. The best forecast model was selected based on high value of adjusted R2, Forecast error and RMSE. Based on obtained results in Navsari district, the discriminant function analysis technique (Model-5) was found better than logistic regression analysis (Model-12) for pre-harvest forecasting of rice crop yield. The results revealed that Model-5 showed comparatively low forecast error (%) along with highest value of Adj. R2 (93.30) and lowest value of RMSE (120.07). Also Model-5 is able to generate yield forecast a week earlier (39thSMW) than Model-12 (40thSMW).
{"title":"Different statistical models based on weather parameters in Navsari district of Gujarat","authors":"Y. Garde, K. Banakara, H. Pandya","doi":"10.54302/mausam.v74i3.3495","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.3495","url":null,"abstract":"Agriculture plays very important role in development of country. Rice is a staple food for more than half of world’s population. Timely and reliable forecasting provides vital and appropriate input, foresight and informed planning. The present investigation was carried out to forecast Kharif rice yield using two different statistical techniques, viz., discriminant function analysis and logistic regression analysis. The statistical models were developed using data from 1990 to 2012 and validation of developed models was done by using remaining data, i.e., 2013 to 2016. It was observed that value of adjusted R2 varied from 73.00 per cent to 93.30 per cent in different models. The best forecast model was selected based on high value of adjusted R2, Forecast error and RMSE. Based on obtained results in Navsari district, the discriminant function analysis technique (Model-5) was found better than logistic regression analysis (Model-12) for pre-harvest forecasting of rice crop yield. The results revealed that Model-5 showed comparatively low forecast error (%) along with highest value of Adj. R2 (93.30) and lowest value of RMSE (120.07). Also Model-5 is able to generate yield forecast a week earlier (39thSMW) than Model-12 (40thSMW).\u0000 ","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47659946","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}
The present investigation provides a modeling solution to downscale MODIS-based evapotranspiration (ET) at a 30 m spatial resolution from its original 500 m spatial resolution using meteorological and Landsat 8 (Operational Land Imager, OLI) data by employing downscaling models. The nine indices namely Surface Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Infrared Index for Band 7 (NDIIB7) were calculated from Lansat 8 data at 30 m spatial resolution. The multiple linear regression (MLR) and Least Square Support Vector Machine (LS-SVM) models were developed to generate the relationship between MODIS 500 m ET and Landsat indices at 500 m scale. Further, these develop models were used to estimate 30 m ET based on 30 m Landsat 8 indices. The performance of developed models (MLR and LS-SVM) was carried out using correlation coefficient (CC), Nash-Sutcliffe coefficient (NASH) efficiency, Root Mean Square Error (RMSE) and Normalised Mean Square Error (NMSE). Penman–Monteith (PM) method was used to estimate the ET using observed station data. The results show that lowest ETO was observed in the month of December while it was maximum in the month of May. Using the performances indices, it was found that LS-SVM model slightly outperformed than MLR model. However, the downscaled model overestimates ET in comparison to the Penman-Monteith method. Further, the significant correlation was found between MODIS ET and LS-SVM ET at all the stations.
本研究利用气象和Landsat 8 (Operational Land Imager, OLI)数据，通过采用降尺度模型，提供了基于modis的蒸散发(ET)从原来的500 m空间分辨率降尺度到30 m空间分辨率的建模解决方案。利用30 m空间分辨率的Lansat 8数据，计算了地表反照率、地表温度、归一化植被指数(NDVI)、土壤校正植被指数(SAVI)、改良土壤校正植被指数(MSAVI)、归一化建筑指数(NDBI)、归一化水分指数(NDWI)、归一化水分指数(NDMI)和7波段归一化红外指数(NDIIB7) 9个指数。建立多元线性回归(MLR)和最小二乘支持向量机(LS-SVM)模型，生成500 m尺度MODIS 500 m ET与Landsat指数之间的关系。此外，利用这些开发模型估算了基于30 m Landsat 8指数的30 m ET。采用相关系数(CC)、NASH - sutcliffe系数(NASH)效率、均方根误差(RMSE)和归一化均方误差(NMSE)对所开发模型(MLR和LS-SVM)的性能进行评估。采用Penman-Monteith (PM)方法，利用台站观测资料估算ET。结果表明:12月ETO最小，5月ETO最大;利用这些性能指标，发现LS-SVM模型的性能略优于MLR模型。然而，与Penman-Monteith方法相比，缩小模型高估了ET。各站MODIS ET与LS-SVM ET存在显著相关。
{"title":"Modeling medium resolution evapotranspiration using downscaling techniques in north-western part of India","authors":"Arvind Dhaloiya, Darshana Duhan, D. Denis, Dharmendra Singh, Mukesh Kumar, Manender Singh","doi":"10.54302/mausam.v74i3.5112","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5112","url":null,"abstract":"The present investigation provides a modeling solution to downscale MODIS-based evapotranspiration (ET) at a 30 m spatial resolution from its original 500 m spatial resolution using meteorological and Landsat 8 (Operational Land Imager, OLI) data by employing downscaling models. The nine indices namely Surface Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Infrared Index for Band 7 (NDIIB7) were calculated from Lansat 8 data at 30 m spatial resolution. The multiple linear regression (MLR) and Least Square Support Vector Machine (LS-SVM) models were developed to generate the relationship between MODIS 500 m ET and Landsat indices at 500 m scale. Further, these develop models were used to estimate 30 m ET based on 30 m Landsat 8 indices. The performance of developed models (MLR and LS-SVM) was carried out using correlation coefficient (CC), Nash-Sutcliffe coefficient (NASH) efficiency, Root Mean Square Error (RMSE) and Normalised Mean Square Error (NMSE). Penman–Monteith (PM) method was used to estimate the ET using observed station data. The results show that lowest ETO was observed in the month of December while it was maximum in the month of May. Using the performances indices, it was found that LS-SVM model slightly outperformed than MLR model. However, the downscaled model overestimates ET in comparison to the Penman-Monteith method. Further, the significant correlation was found between MODIS ET and LS-SVM ET at all the stations.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44361745","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.v74i3.2976
Rishika Shah, RK Pandit, MK Gaur
The study aims to develop artificial neural networks for prediction of outdoor thermal comfort using meteorological parameters as input parameters. Universal Thermal Climate Index (UTCI) is used as the target parameter. For this purpose, a total number of 5088 hours of field monitoring data was considered from four representative urban streets of Gwalior city, India. First, linear association was determined between meteorological parameters. Mean radiant temperature was to be in high correlation with globe temperature and surface temperature. Second, Adaptive Neuro Fuzzy Inference System (ANFIS) was used to rank the meteorological parameters in order of their impact on UTCI. Air temperature was found to be having highest influence. Third, ANN models are developed to predict UTCI with air temperature as the only meteorological parameter in input layer. The developed ANN models for all four streets show remarkable predictive ability for both summer (R2 = 0.852, 0.986, 0.962, 0.955) and winter season (R2 = 0.976, 0.870, 0.941, 0.950). Additionally, the success index of the developed models is found to be in range 0.73 – 1, 0.88 – 1, 0.86 – 1, 0.87 – 1 for summer season and 0.78 – 0.99, 0.61 – 0.98, 0.55 – 0.98, 0.87 – 0.99 for winter season. The study contributes to the smart city initiatives for future urban designing by establishing that outdoor thermal comfort can be easily predicted using air temperature when other microclimatic parameters are difficult to record using machine learning approach.
{"title":"Determining the influence of meteorological parameters on outdoor thermal comfort using ANFIS and ANN","authors":"Rishika Shah, RK Pandit, MK Gaur","doi":"10.54302/mausam.v74i3.2976","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.2976","url":null,"abstract":"The study aims to develop artificial neural networks for prediction of outdoor thermal comfort using meteorological parameters as input parameters. Universal Thermal Climate Index (UTCI) is used as the target parameter. For this purpose, a total number of 5088 hours of field monitoring data was considered from four representative urban streets of Gwalior city, India. First, linear association was determined between meteorological parameters. Mean radiant temperature was to be in high correlation with globe temperature and surface temperature. Second, Adaptive Neuro Fuzzy Inference System (ANFIS) was used to rank the meteorological parameters in order of their impact on UTCI. Air temperature was found to be having highest influence. Third, ANN models are developed to predict UTCI with air temperature as the only meteorological parameter in input layer. The developed ANN models for all four streets show remarkable predictive ability for both summer (R2 = 0.852, 0.986, 0.962, 0.955) and winter season (R2 = 0.976, 0.870, 0.941, 0.950). Additionally, the success index of the developed models is found to be in range 0.73 – 1, 0.88 – 1, 0.86 – 1, 0.87 – 1 for summer season and 0.78 – 0.99, 0.61 – 0.98, 0.55 – 0.98, 0.87 – 0.99 for winter season. The study contributes to the smart city initiatives for future urban designing by establishing that outdoor thermal comfort can be easily predicted using air temperature when other microclimatic parameters are difficult to record using machine learning approach. ","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49478022","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.v74i3.6295
Editor Mausam
During 2022, in all 15 cyclonic disturbances (CDs) formed over the Indian seas. These included; two severe cyclonic storms (ASANI & MANDOUS), one cyclonic storm (SITRANG), four deep depressions, six depressions and two land depressions. Out of these 15 CDs, ten formed over the Bay of Bengal (BoB), three over the Arabian Sea (AS) and two over land. One severe cyclonic storm, one deep depression and two depressions formed over BoB in pre-monsoon season. Monsoon season witnessed development of one depression and one deep depression over the BoB, two land depressions and two depressions over the AS. During the post monsoon season, one severe cyclonic storm, one cyclonic storm & two depressions formed over the BoB and one deep depression over the AS.
{"title":"Cyclonic storms and depressions over the North Indian Ocean during 2022","authors":"Editor Mausam","doi":"10.54302/mausam.v74i3.6295","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.6295","url":null,"abstract":"During 2022, in all 15 cyclonic disturbances (CDs) formed over the Indian seas. These included; two severe cyclonic storms (ASANI & MANDOUS), one cyclonic storm (SITRANG), four deep depressions, six depressions and two land depressions. Out of these 15 CDs, ten formed over the Bay of Bengal (BoB), three over the Arabian Sea (AS) and two over land. One severe cyclonic storm, one deep depression and two depressions formed over BoB in pre-monsoon season. Monsoon season witnessed development of one depression and one deep depression over the BoB, two land depressions and two depressions over the AS. During the post monsoon season, one severe cyclonic storm, one cyclonic storm & two depressions formed over the BoB and one deep depression over the AS.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139364205","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.v74i3.4936
Jitendra Rajput, N. Kushwaha, D. Sena, DK Singh, I. Mani
Understanding rainfall and temperature’s spatio-temporal variations at the local, regional, and global scale is vital for planning soil and water conservation structures and making irrigation decisions. The present investigation attempts to observe the rainfall and temperature variability and trend over 31 years (1990-2020) in the National Capital Region (NCR), Delhi, India, obtained from IARI meteorological station, Pusa, New Delhi. The statistical trend analyses Mann-Kendall (MK) test followed by Theil Sen slope estimator test was used for annual and monthly analysis to assess the trend direction and magnitude of the change over time. Pettitt's test detected the inflection point in the variable time series. The annual Tmax, Tmin, and rainfall showed no trend in the time series data. However, Tmax indicated a statistically significant decreasing trend in January and December. This implies a dip in the temperature during the winter months of January and December. Similarly, Tmin revealed a statistically significant decreasing trend in January and December. But a statistically increasing trend for Tmin was observed in April, which may cause a harsh environment for cultivating the Zaid season crops due to increased warming. The Pettitt test showed no change point in the time series trend in the annual Tmax and Tmin data series. For January Tmax data, the trend change point occurred in 1998. However, it was observed that Tmin in April showed a change point in the time series trend in 1999. The change point in the annual average rainfall data was marked in 2012. A didactic implication of these changes on hydrologic design and crop irrigation decisions was discussed in this paper.
{"title":"Trend assessment of rainfall, temperature and relative humidity using non-parametric tests in the national capital region, Delhi","authors":"Jitendra Rajput, N. Kushwaha, D. Sena, DK Singh, I. Mani","doi":"10.54302/mausam.v74i3.4936","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.4936","url":null,"abstract":"Understanding rainfall and temperature’s spatio-temporal variations at the local, regional, and global scale is vital for planning soil and water conservation structures and making irrigation decisions. The present investigation attempts to observe the rainfall and temperature variability and trend over 31 years (1990-2020) in the National Capital Region (NCR), Delhi, India, obtained from IARI meteorological station, Pusa, New Delhi. The statistical trend analyses Mann-Kendall (MK) test followed by Theil Sen slope estimator test was used for annual and monthly analysis to assess the trend direction and magnitude of the change over time. Pettitt's test detected the inflection point in the variable time series. The annual Tmax, Tmin, and rainfall showed no trend in the time series data. However, Tmax indicated a statistically significant decreasing trend in January and December. This implies a dip in the temperature during the winter months of January and December. Similarly, Tmin revealed a statistically significant decreasing trend in January and December. But a statistically increasing trend for Tmin was observed in April, which may cause a harsh environment for cultivating the Zaid season crops due to increased warming. The Pettitt test showed no change point in the time series trend in the annual Tmax and Tmin data series. For January Tmax data, the trend change point occurred in 1998. However, it was observed that Tmin in April showed a change point in the time series trend in 1999. The change point in the annual average rainfall data was marked in 2012. A didactic implication of these changes on hydrologic design and crop irrigation decisions was discussed in this paper.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43832487","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.v74i3.818
G. Rao, A. Sowjanya, D. Shekhar, BNSandeep Naik, Bvs Kiran
Climate change and variability, particularly which of the annual rainfall, has received a great deal of interest to researchers worldwide. The extent of the variability of rainfall varies according to locations. Consequently, investigating the dynamics of rainfall variable in the perspective of changing climate is important to evaluate the impact of climate change and adapt potential mitigation strategies. To gain insight, trend analysis has been employed to inspect and quantify the rainfall distribution in the Chintapalli, Visakhapatnam district of Andhra Pradesh, India. Thirty-one years for a period of 1990–2020 long historical rainfall data series for different temporal scales (Monthly, Seasonal and Annual) of the study region was used for the analysis. Statistical trend analysis techniques namely Mann–Kendall (MK) test was used to detect the trend. To compute trend magnitude, Theil–Sen approach (TSA) was used for calculation of Sen’s slope. The detailed analysis of the data for 31 years indicates positive increasing trend with 2.13mm per year derived from the linear regression. MK test detected that there were rising and falling trends for various time scales in the study area. Departure analysis of rainfall indicated that a possible chance of normal rainfall, more frequently in the area. Rainfall Anomaly Index (RAI) analysis revealed that normal for most of the years, however, 2002 is the very dry year. While last ten years, the frequency of drought occurrence is thrice, but the magnitude is low. The study results will help in persuading the rainfall risks with effective use of water resources which can increase crop productivity and likely to manage natural resources for sustainability at HAT zone of Andhra Pradesh.
{"title":"Rainfall analysis over 31 years of Chintapalle, Visakhapatnam, High Altitude and Tribal zone, Andhra Pradesh, India","authors":"G. Rao, A. Sowjanya, D. Shekhar, BNSandeep Naik, Bvs Kiran","doi":"10.54302/mausam.v74i3.818","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.818","url":null,"abstract":"Climate change and variability, particularly which of the annual rainfall, has received a great deal of interest to researchers worldwide. The extent of the variability of rainfall varies according to locations. Consequently, investigating the dynamics of rainfall variable in the perspective of changing climate is important to evaluate the impact of climate change and adapt potential mitigation strategies. To gain insight, trend analysis has been employed to inspect and quantify the rainfall distribution in the Chintapalli, Visakhapatnam district of Andhra Pradesh, India. Thirty-one years for a period of 1990–2020 long historical rainfall data series for different temporal scales (Monthly, Seasonal and Annual) of the study region was used for the analysis. Statistical trend analysis techniques namely Mann–Kendall (MK) test was used to detect the trend. To compute trend magnitude, Theil–Sen approach (TSA) was used for calculation of Sen’s slope. The detailed analysis of the data for 31 years indicates positive increasing trend with 2.13mm per year derived from the linear regression. MK test detected that there were rising and falling trends for various time scales in the study area. Departure analysis of rainfall indicated that a possible chance of normal rainfall, more frequently in the area. Rainfall Anomaly Index (RAI) analysis revealed that normal for most of the years, however, 2002 is the very dry year. While last ten years, the frequency of drought occurrence is thrice, but the magnitude is low. The study results will help in persuading the rainfall risks with effective use of water resources which can increase crop productivity and likely to manage natural resources for sustainability at HAT zone of Andhra Pradesh.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44798014","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.v74i3.872
A. Keskiner, M. Cetin
Identification of spatiotemporal tendencies of climate types may help water managers mitigate the negative impacts of droughts on water-demanding sectors. The primary objective of this study was to figure out the spatiotemporal tendencies of climatetypes in Sanliurfa province by using Erinc’s aridity index (EDI). To that end, long-term (1965-2018) annual precipitation and average annual maximum temperature series of meteorological stations were obtained and utilized to calculate the EDI series on a yearly basis. The EDI series of each station was divided into three periods, non-overlapping and successive, i.e., P1 (1965-1981), P2 (1982-1999) and P3 (2000-2018). Outliers were detected, andremoved from the EDI series; missing data were completed by regression analysis. The Markov transition probability matrix of the climate classes for the three periods was estimated for each station. Maps of the initial probability vectors and steady-state probabilities for the three periods of each climate class were generated by the inverse distance-weighted technique. Hypsometric curves for each climate class, as well as period, were developed and areal coverage of occurrence probabilities (OP) was determined. Results indicated that, as time progressed, the areal extent of severe-arid and arid climatic classes continued consistently to spread from the south to the north. Areas of semi-arid climate type showed a slight tendency towards the arid-climate type. Construction of large dams in the region could not prevent the shifts in the climate in favour of developing arid zones. The humid climate class is likely to vanish away in the future. Research led us to conclude that the expansion of the aridzone from south to northhas been alarming in terms of the adequacy of water resources. It is strongly recommended that spatiotemporal climate change studies should be periodically conducted in tandem with forest management practices for the region.
{"title":"Modelling spatiotemporal tendencies of climate types by Markov chain approach : A case study in Sanliurfa province in the south-eastern of Turkey","authors":"A. Keskiner, M. Cetin","doi":"10.54302/mausam.v74i3.872","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.872","url":null,"abstract":"Identification of spatiotemporal tendencies of climate types may help water managers mitigate the negative impacts of droughts on water-demanding sectors. The primary objective of this study was to figure out the spatiotemporal tendencies of climatetypes in Sanliurfa province by using Erinc’s aridity index (EDI). To that end, long-term (1965-2018) annual precipitation and average annual maximum temperature series of meteorological stations were obtained and utilized to calculate the EDI series on a yearly basis. The EDI series of each station was divided into three periods, non-overlapping and successive, i.e., P1 (1965-1981), P2 (1982-1999) and P3 (2000-2018). Outliers were detected, andremoved from the EDI series; missing data were completed by regression analysis. The Markov transition probability matrix of the climate classes for the three periods was estimated for each station. Maps of the initial probability vectors and steady-state probabilities for the three periods of each climate class were generated by the inverse distance-weighted technique. Hypsometric curves for each climate class, as well as period, were developed and areal coverage of occurrence probabilities (OP) was determined. Results indicated that, as time progressed, the areal extent of severe-arid and arid climatic classes continued consistently to spread from the south to the north. Areas of semi-arid climate type showed a slight tendency towards the arid-climate type. Construction of large dams in the region could not prevent the shifts in the climate in favour of developing arid zones. The humid climate class is likely to vanish away in the future. Research led us to conclude that the expansion of the aridzone from south to northhas been alarming in terms of the adequacy of water resources. It is strongly recommended that spatiotemporal climate change studies should be periodically conducted in tandem with forest management practices for the region.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44864191","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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