IF 0.6 4区地球科学

MAUSAM Pub Date : 2023-12-31 DOI: 10.54302/mausam.v75i1.6037

Priyanka Singh, R. Mall, K. K. Singh, A. K. Das

引用次数: 0

Importance of PAR interception and radiation use efficiency on growth and yield of Potatoes under different microclimates in the upper Brahmaputra valley zone of Assam 阿萨姆邦布拉马普特拉河流域上游地区不同小气候条件下 PAR 截获量和辐射利用效率对马铃薯生长和产量的影响

IF 0.6 4区地球科学

MAUSAM Pub Date : 2023-12-31 DOI: 10.54302/mausam.v75i1.3892

Raktim Jyoti Saikia, P. Neog, R. L. Deka, K. Medhi

{"title":"Importance of PAR interception and radiation use efficiency on growth and yield of Potatoes under different microclimates in the upper Brahmaputra valley zone of Assam","authors":"Raktim Jyoti Saikia, P. Neog, R. L. Deka, K. Medhi","doi":"10.54302/mausam.v75i1.3892","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.3892","url":null,"abstract":"A field experiment was conducted at Assam Agricultural University, Jorhat, Assam during rabi 2018-19 for assessing the PAR interception and radiation use efficiency in potato variety Kufri Jyoti under different microclimates, which was planted in split plot design with 4 dates of plantings and three mulching treatments with water hyacinth, black polythene and without mulching. The incident, reflected and transmitted PAR were measured periodically over the crop with line quantum sensor and daily incident radiation were calculated from incident PAR and bright sunshine hours. The interception of PAR (iPAR) varied considerably among different treatments, while highest iPAR was recorded under first date of planting and mulching treatment with water hyacinth. The leaf area index (LAI) and biomass production was highest in crop planted in first date planting and grown under water hyacinth mulch. The RUE for tuber yield was highest under water hyacinth (2.35 g MJ-1) followed by black polythene (2.03 g MJ-1) and non-mulched (1.67 g MJ-1) condition, while among planting dates it was highest in case of first date of planting. The LAI, biomass production and yield of potato were found to be significantly correlated with iPAR and RUE. The predictive models were developed by using stepwise regression method to predict tuber yield from iPAR and REU, which have R2 value of 0.96 and 0.99, respectively.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"78 24","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139130530","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}

引用次数: 0

STUDY ON STATISTICAL DISTRIBUTION OF MONTHLY RAINFALL IN PUNJAB, INDIA 印度邦贾巴邦月降雨量统计分布研究

IF 0.6 4区地球科学

MAUSAM Pub Date : 2023-12-31 DOI: 10.54302/mausam.v75i1.6016

T. S. Bajirao, D. Madane

引用次数: 0

Climate and its variability over Tarai region of Uttarakhand 北阿坎德邦塔赖地区的气候及其变异性

IF 0.6 4区地球科学

MAUSAM Pub Date : 2023-12-31 DOI: 10.54302/mausam.v75i1.5015

Shubhika Goel, Jaya Dhami, S. R K

引用次数: 0

Homogenizing Monthly Rainfall and Temperature Data Series in Maharashtra & Goa 马哈拉施特拉邦和果阿邦月降雨量和温度数据序列的同质化

IF 0.6 4区地球科学

MAUSAM Pub Date : 2023-12-31 DOI: 10.54302/mausam.v75i1.5886

Nilesh Wagh, P. Guhathakurta

{"title":"Homogenizing Monthly Rainfall and Temperature Data Series in Maharashtra & Goa","authors":"Nilesh Wagh, P. Guhathakurta","doi":"10.54302/mausam.v75i1.5886","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.5886","url":null,"abstract":"Annual rainfall and temperature data series of all climate stations in Maharashtra & Goa are statistically tested for data homogeneity. To inspect homogeneity of a station, a two-step approach is followed. First, four homogeneity tests Standard normal homogeneity test, Pettit’s test, Buishand’s range test and Von Neumann ration test at 5% level of significance are used to determine test hypothesis for homogeneity on testing parameters of annual rainfall and temperature. Second, results from all these four tests aggregated together into three different classes as ‘useful’, ‘doubtful’ and ‘suspect’. Here 30 rainfall, 29 maximum and minimum temperature climate stations were tested. The results showed 80% stations as ‘useful’, 7% as ‘suspect’ and 13% as ‘doubtful’ for rainfall, for maximum temperature series these results are 17% as ‘useful’, 7% as ‘suspect’ and 76% as ‘doubtful’, while for minimum temperature series these results are 21% as ‘useful’, 10% as ‘suspect’ and 69% as ‘doubtful’. Further, in this study an attempt is also made to correct the monthly rainfall and temperature data series for homogeneity. Stations categorised as ‘useful’ are used as reference series to remove inhomogeneities from ‘suspect’ and ‘doubtful’ stations. To correct rainfall series ratio’s method is used while for temperature series addition method is used. Correction results showed significant improvement in ‘suspect’ category stations. After correction of inhomogeneous series, the results shows all 100% of rainfall stations and more than 65% of temperature stations are now in ‘useful’ category. The corrected stations may be included in further climate research studies.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"120 38","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139134064","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}

引用次数: 0

Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala 喀拉拉邦不同地区相对湿度和风速的随机模拟与预报

4区地球科学

MAUSAM Pub Date : 2023-10-01 DOI: 10.54302/mausam.v74i4.5603

GOKUL KRISHNAN B., VISHAL MEHTA, V. N. RAI

{"title":"Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala","authors":"GOKUL KRISHNAN B., VISHAL MEHTA, V. N. RAI","doi":"10.54302/mausam.v74i4.5603","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.5603","url":null,"abstract":"The variations in climatic conditions depend on seasonal changes throughout the year. Modelling and prediction of climatic conditions can help to determine the impacts of seasonal changes in climate over a specific period of time. Climate change can directly and indirectly affect agricultural, industrial, geographical and technological sectors in our society. Agriculture and the allied sector are seriously affected by changes in climate since it leads to complete destruction of cultivated crops. In this study, in order to model and forecast relative humidity and wind speed for northern, central and southern zones of Kerala, stochastic approach using SARIMA (Seasonal Autoregressive Integrated Moving Average) model was employed. The monthly weather data for the northern zone and the central zone of Kerala was taken from the location of RARS Pilicode and RARS Pattambi for a period of 39 years (1982-2020) whereas for southern zone, data was collected from the location of RARS, Vellayani for a period of 36 years (1985-2020) with the help of data access viewer. The model validation was done using MSE (mean square error), RMSE (root mean square error), MAE (mean absolute error) and RMAPE (relative mean absolute percentage error). The RMAPE values of relative humidity and wind speed in different zones of Kerala was less than 10 per cent which indicated that fitted model is showing accurate performance. The best selected SARIMA model is used in attaining anticipated values of relative humidity and wind speed for the next 5 years.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934172","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}

引用次数: 0

New method of precipitation forecast model and validation 降水预报模型的新方法及验证

4区地球科学

MAUSAM Pub Date : 2023-10-01 DOI: 10.54302/mausam.v74i4.4359

KUMARASWAMY KANDUKURI, BHATRACHARYULU N. CH.

{"title":"New method of precipitation forecast model and validation","authors":"KUMARASWAMY KANDUKURI, BHATRACHARYULU N. CH.","doi":"10.54302/mausam.v74i4.4359","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.4359","url":null,"abstract":"There is a lot of time series data in many realistic sectors with different forecast techniques over the years. However there is no unanimous conclusion on forecast techniques such as individual forecasts Autoregressive, Moving averages, Autoregressive Moving average, Autoregressive Integrated Moving average, Artificial Neural Network, Long Short Term Memory network and Auto-Regressive Conditional Heteroscedasticity / Generalized Autoregressive Conditional Heteroskedasticity and combination of forecast (simple Average of forecasts, Minimum variance method, and Regression method of the combine). The most empirical hydrological time series models do not accurately forecast the weather. This paper focuses on a comparative study of different existing individual and combination forecasts with the proposed Hybrid Stochastic Model (HSM) forecast procedure. For this we consider a hydrological time series data of the Indian subcontinent to test the proposed forecast model. As a whole in comparison to all other traditional model's contributions accuracy, the proposed model performed well, and also we examined the model's dimension reduction approach to choose an optimum number of forecast techniques to be included in the model to yield the best forecasts.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934396","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}

引用次数: 0

Convective weather event monitoring with multispectral image analysis of INSAT-3D/3DR over Indian domain 基于INSAT-3D/3DR多光谱图像分析的印度地区对流天气事件监测

4区地球科学

MAUSAM Pub Date : 2023-10-01 DOI: 10.54302/mausam.v74i4.6176

C. S. TOMAR, RAJIV BHATLA, V. K. SONI, R. K. GIRI

{"title":"Convective weather event monitoring with multispectral image analysis of INSAT-3D/3DR over Indian domain","authors":"C. S. TOMAR, RAJIV BHATLA, V. K. SONI, R. K. GIRI","doi":"10.54302/mausam.v74i4.6176","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.6176","url":null,"abstract":"Pre-monsoon season (March to May) is very challenging as convective activities prevails almost throughout the country. Most of the Rabi crops harvesting affected and sometimes suffer great losses due to sudden rain or high winds. INSAT-3D/3DR satellite images and derived products provides continuous support to the forecasters and end users in monitoring such events and thereafter significant value addition improves the prediction. This information was found to be very useful where actual ground based or upper air observations are limited or especially over data sparse or difficult terrain regions. In this work, we have examined three weather events at different Geographical locations (i) Rainfall over Bihar-24-26 June, 2020 (ii) Delhi & NCR region on 17 June, 2022 (iii) NE region activity in 16-18 June, 2022. The Real Time Analysis of Products and Information Dissemination (RAPID) web based tool was utilized in monitoring and diagnosing the convective weather events based on the brightness temperature & derived products like Outgoing longwave radiation, upper tropospheric humidity, insolation etc & RGB imagery composite in terms of day & night time microphysics daily operational products. The time series of the wind derived products for Delhi NCR rainfall and NE rainfall products also generated through RAPID. The synoptic model analysis provides valuable inputs for these mesoscale convective weather events. The southerly wind flow (at 925 hPa) and velocity convergence (at 500 hPa) analysis of European Centre for Medium Range Weather Forecasting (ECMWF) supports the severity of NE event occurred on 16-18 June, 2022. Therefore, utilization of near real time INSAT-3D/3DR products along with appropriate synoptic model analysis can help the forecasters to understand better about such mesoscale convective events & accurate forecast with sufficient lead time can save the life and property.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934951","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}

引用次数: 0

Drought analysis in southern region of Tamil Nadu using meteorological and remote sensing indices 利用气象和遥感指数分析泰米尔纳德邦南部地区的干旱

4区地球科学

MAUSAM Pub Date : 2023-10-01 DOI: 10.54302/mausam.v74i4.6040

B. LALMUANZUALA, NK. SATHYAMOORTHY, S. KOKILAVANI, R. JAGADEESWARAN, BALAJI KANNAN

{"title":"Drought analysis in southern region of Tamil Nadu using meteorological and remote sensing indices","authors":"B. LALMUANZUALA, NK. SATHYAMOORTHY, S. KOKILAVANI, R. JAGADEESWARAN, BALAJI KANNAN","doi":"10.54302/mausam.v74i4.6040","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.6040","url":null,"abstract":"Drought is a natural phenomenon caused due to inadequate rainfall over a region as compared to the expected amount, which when sustained over an extended period of time, eventually leads to shortage of water to sustain various human activities. One-month SPI showed that the southern zone is highly prone to moderate drought conditions. The seasonal analysis of SPI showed that the region faced more drought instances during the South West Monsoon compared with North East Monsoon season. Thoothukudi, Dindigul, Pudukkottai and Virudhunagar showed the high occurrences of drought at seasonal and annual scale. The weekly MAI calculated indicated a risk in the rainfed cropping season. Tirunelveli and Tenkasi showed highly vulnerable to moderate drought. NDVI during the NEM 2016, 2017 and 2018 showed that more than 80 per cent of the total area in the southern districts was under drought stress. NDVI analysis showed that Thoothukudi, Ramanathapuram, Pudukkottai, Sivagangai and Virudhunagar districts are highly vulnerable to drought. NDWI analysis during the NEM 2016, 2017 and 2018 showed high drought stresses with more than 90 per cent of the area showing drought stress during these three years. NDVI and NDWI analysis showed that the Southern Zone of Tamil Nadu was most vulnerable to Moderate and Severe droughts. The comparison of NDVI and NDWI and 3-, 6-, 9- and 12-month SPI showed that the three indices are fairly accurate with each other and hence are useful in the analysis of drought. However, just a single drought index cannot clearly define accurately the spatial and temporal extent of drought. Thus, a combination of meteorological and remote sensing indices gave a detailed idea about the spatio-temporal extent of drought.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934175","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}

引用次数: 0

Quantitative precipitation forecast for the Godavari basin using the Synoptic analogue method 用天气模拟方法定量预报哥达瓦里盆地降水

4区地球科学

MAUSAM Pub Date : 2023-10-01 DOI: 10.54302/mausam.v74i4.5267

DR. A. SRAVANI, DR. K. NAGA RATNA, R. SUDHEER KUMAR, N. REKHA

引用次数: 0

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