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":" ","pages":""},"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}
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":" ","pages":""},"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}
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":" ","pages":""},"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.v74i3.6295
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":"56 1","pages":""},"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":" ","pages":""},"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":" ","pages":""},"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":" ","pages":""},"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}
Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.5993
N. Nissanka, E. Lokupitiya, Shiromani Jayawardena
Climate change-related changes in temperature and precipitation trends must be investigated at local, regional and global levels. Temperature and precipitation trends in two selected regions having tropical wet and tropical montane climates (i.e., Colombo and Nuwara Eliya respectively) in Sri Lanka were studied for a 30 year period from 1989 to 2019, to evaluate the temporal dynamics of climate change. Precipitation trends were analyzed on annual, monthly, and seasonal scales, while the trends in mean, minimum, and maximum temperatures were examined on annual and monthly scales. Decadal time series plots were used to study decadal variations in average temperature and precipitation. The trends in extreme temperature and precipitation events were also evaluated. In addition, the trends in diurnal temperature range (DTR), cool and warm nights, and heat index (HI) were studied. The significance of trends was evaluated using the Mann-Kendall test, while the magnitude of the slope was assessed by Sen’s slope estimator. Clear statistically significant increasing trends were observed for the mean annual temperatures under the tropical wet and tropical montane climates, and no clear trends were observed in annual precipitation in both districts. There were decreasing trends in south-west monsoon rainfall, with a significant decrease in Nuwara Eliya under the tropical montane climate. Increasing trends were observed for the average monthly precipitation in November (i.e., during the inter-monsoonal rains) and average monthly temperature in April (i.e., the hottest month) over the last decade (i.e., 2010-2019) in Colombo. The DTR has significantly decreased over the last three decades in Colombo. A significant upward trend was observed for HI values during the last decade in Colombo. Colombo also showed a statistically significant decreasing trend in the number of cool nights and a statistically significant decreasing trend in the number of warm nights over the last decade.
{"title":"Trends in climate change observed under tropical wet and tropical montane climates; A case study from Sri Lanka","authors":"N. Nissanka, E. Lokupitiya, Shiromani Jayawardena","doi":"10.54302/mausam.v74i3.5993","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5993","url":null,"abstract":"Climate change-related changes in temperature and precipitation trends must be investigated at local, regional and global levels. Temperature and precipitation trends in two selected regions having tropical wet and tropical montane climates (i.e., Colombo and Nuwara Eliya respectively) in Sri Lanka were studied for a 30 year period from 1989 to 2019, to evaluate the temporal dynamics of climate change. Precipitation trends were analyzed on annual, monthly, and seasonal scales, while the trends in mean, minimum, and maximum temperatures were examined on annual and monthly scales. Decadal time series plots were used to study decadal variations in average temperature and precipitation. The trends in extreme temperature and precipitation events were also evaluated. In addition, the trends in diurnal temperature range (DTR), cool and warm nights, and heat index (HI) were studied. The significance of trends was evaluated using the Mann-Kendall test, while the magnitude of the slope was assessed by Sen’s slope estimator. Clear statistically significant increasing trends were observed for the mean annual temperatures under the tropical wet and tropical montane climates, and no clear trends were observed in annual precipitation in both districts. There were decreasing trends in south-west monsoon rainfall, with a significant decrease in Nuwara Eliya under the tropical montane climate. Increasing trends were observed for the average monthly precipitation in November (i.e., during the inter-monsoonal rains) and average monthly temperature in April (i.e., the hottest month) over the last decade (i.e., 2010-2019) in Colombo. The DTR has significantly decreased over the last three decades in Colombo. A significant upward trend was observed for HI values during the last decade in Colombo. Colombo also showed a statistically significant decreasing trend in the number of cool nights and a statistically significant decreasing trend in the number of warm nights over the last decade.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"47 8","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41262251","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.v74i3.5898
M. Gundalia
Rain is a meager and crucial hydrological variable in arid and semi-arid region. Junagadh (Gujarat-India) reels under monsoon rainfall uncertainties and thereby the agriculture and other water resources management activities suffer. Therefore, urgent attention is needed to address water resources conservation and crop damage issues due to deficits or excess rainfall. Water resources development of any locality depends on amount of runoff generated and rainfall received. Appropriate probability distributions need to be selected and fitted to the historical time series of rainfall for better frequency analysis and forecasting of the rainfall. The daily rainfall data was collected for a period of 38 years i.e., from 1984 to 2021. This research attempts to fit eightdifferent theoretical probability distributions to the monthly and annual maximum rainfall for one to five consecutive days to select the best one for the better prediction of maximum rainfall. For determination of goodness of fit Chi-Square and Nash-Sutcliffe Efficiency were carried out by comparing the expected values with the observed values. The results indicated that the Gumbel distribution emerged to be the best fit for the prediction of monthly and annual maximum rainfall of Junagadh Region.
{"title":"Best Fitting of Probability Distribution for Monthly and Annual Maximum Rainfall Prediction in Junagadh Region (Gujarat-India)","authors":"M. Gundalia","doi":"10.54302/mausam.v74i3.5898","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5898","url":null,"abstract":"Rain is a meager and crucial hydrological variable in arid and semi-arid region. Junagadh (Gujarat-India) reels under monsoon rainfall uncertainties and thereby the agriculture and other water resources management activities suffer. Therefore, urgent attention is needed to address water resources conservation and crop damage issues due to deficits or excess rainfall. Water resources development of any locality depends on amount of runoff generated and rainfall received. Appropriate probability distributions need to be selected and fitted to the historical time series of rainfall for better frequency analysis and forecasting of the rainfall. The daily rainfall data was collected for a period of 38 years i.e., from 1984 to 2021. This research attempts to fit eightdifferent theoretical probability distributions to the monthly and annual maximum rainfall for one to five consecutive days to select the best one for the better prediction of maximum rainfall. For determination of goodness of fit Chi-Square and Nash-Sutcliffe Efficiency were carried out by comparing the expected values with the observed values. The results indicated that the Gumbel distribution emerged to be the best fit for the prediction of monthly and annual maximum rainfall of Junagadh Region.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43021892","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 : 2023-07-01DOI: 10.54302/mausam.v74i3.5331
S. Sandhu, P. Kaur
Rainfall is an important part of hydrological cycle and any alteration in its pattern influence water resources. In Punjab, the monsoon season of 77 days extending during three months July, August and September, receives rainfall at an average rate of 6 mm/day. In the present study, monsoon rainfall data for three parts of the state, viz., the north eastern region (1984-2020), Central plain region (1970-2020) and the south western region (1977-2020) of the state have been analyzed using non-parametric tests, i.e., descriptive statistics, trend analysis, Mann Kendall test and Sen’s slope. Though, the duration of the monsoon season has increased over the last two decades at 0.8 day/year, the rate of rainfall has decreased as rainfall has been less than normal during 17 of the past 20 years. The monsoon rainfall analysis for the five decades indicates a significant decrease in rainfall at 0.7 mm/year which has mainly been due to a decline in rainfall in the north eastern region. The Sen’s slope value of -4.77 (Ballowal) and -0.60 (Bathinda) indicate a decreasing trend of rainfall in the region. The decreasing trend in rainfall received during the July-August months with Sen’s slope values ranging between -0.04 to -2.50 and -0.24 to -3.14, indicates that the months which contribute 70 percent to total rainfall are not a good signal for the agriculture sector in the state.
{"title":"A case study on the changing pattern of monsoon rainfall duration and its amount during recent five decades in different agroclimatic zones of Punjab state of India","authors":"S. Sandhu, P. Kaur","doi":"10.54302/mausam.v74i3.5331","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5331","url":null,"abstract":"Rainfall is an important part of hydrological cycle and any alteration in its pattern influence water resources. In Punjab, the monsoon season of 77 days extending during three months July, August and September, receives rainfall at an average rate of 6 mm/day. In the present study, monsoon rainfall data for three parts of the state, viz., the north eastern region (1984-2020), Central plain region (1970-2020) and the south western region (1977-2020) of the state have been analyzed using non-parametric tests, i.e., descriptive statistics, trend analysis, Mann Kendall test and Sen’s slope. Though, the duration of the monsoon season has increased over the last two decades at 0.8 day/year, the rate of rainfall has decreased as rainfall has been less than normal during 17 of the past 20 years. The monsoon rainfall analysis for the five decades indicates a significant decrease in rainfall at 0.7 mm/year which has mainly been due to a decline in rainfall in the north eastern region. The Sen’s slope value of -4.77 (Ballowal) and -0.60 (Bathinda) indicate a decreasing trend of rainfall in the region. The decreasing trend in rainfall received during the July-August months with Sen’s slope values ranging between -0.04 to -2.50 and -0.24 to -3.14, indicates that the months which contribute 70 percent to total rainfall are not a good signal for the agriculture sector in the state.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44833167","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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