MAUSAM最新文献

{"title":"Effect of spatial resolution of climatological data on streamflow simulations using the SWAT : A case study","authors":"PRIYANKA MOHAPATRA, DWARIKA MOHAN DAS, BHARAT CHANDRA SAHOO, JAGADISH PADHIARY, JAGADISH CHANDRA PAUL, SANJAY KUMAR RAUL, CHINMAYA PANDA","doi":"10.54302/mausam.v74i4.4931","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.4931","url":null,"abstract":"Data quality always affects the accuracy of model output. Rainfall is the basic data required in hydrological modelling as rainfall to runoff conversion is the core of all such models. Regional modelling studies required high resolution spatio-temporal data and availability of data at appropriate resolution also greatly affect the modelling results. Therefore, efforts have been started to record climatic variables at finer resolution so that they will be useful for block level and gram Panchayat level studies. In this study, an effort has been made to identify the effect of using various resolution climatic data on streamflow simulation in the Kesinga catchment of the Mahanadi river basin. Three types of rainfall sets with spatial resolution of 0.25° × 0.25° and 1° × 1° from IMD and one set of recorded rainfall data of the Special Relief Commissioner (SRC), Govt. of Odisha is used in combination with IMD 1° × 1° gridded temperature to simulate streamflow at the Kesinga gauging station using the Soil and Water Assessment Tool (SWAT) keeping other parameters constant. The three simulations were analyzed using NSE, R2, RMSE, PBIAS, P-factor and R-factor. The results depicted that IMD gridded rainfall data sets predicted similar flows compared to the SRC recorded rainfall data which proves the fairness of IMD gridded data is at par with the recorded rainfall data of SRC, Govt. of Odisha.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"43 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":"134934752","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}

{"title":"A long-term drought assessment over India using CMIP6 framework : present and future perspectives","authors":"AASHNA VERMA, AKASH VISHWAKARMA, SANJAY BIST, SUSHIL KUMAR, RAJEEV BHATLA","doi":"10.54302/mausam.v74i4.6198","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.6198","url":null,"abstract":"Research on the characteristics and spread of droughts has progressed significantly for future climate scenarios. However, studies on drought mitigation in relation to climate change have been largely inadequate. This study focuses on the severity and frequency of drought events based on meteorological properties of drought under two climate change scenarios: Shared Socioeconomic Pathway (SSP2 4.5 and SSP5 8.5). We utilized the Sixth International Coupled Model Inter-comparison Project sixth phase (CMIP6) ensemble General Circulation Models (GCMs) to collect historical (1901-2014) and future (2025-2100) precipitation data. IMD gridded precipitation was used as a reference data for comparative studies. We constructed the Standardized Precipitation Index (SPI) under two different Socioeconomic Shared Pathways (SSPs) to analyze future drought scenarios in the Indian region. Our results show a gradual increase in SPI values for future years, indicating an increase in the severity of drought events in the Indian region. The increase is more pronounced under the SSP5 8.5 scenario, which assumes high greenhouse gas emissions and limited climate change mitigation efforts. Furthermore, our results suggest that major dry spells are likely to occur in the first half of the future period, particularly in the case of ACCESS-ESM, one of the GCMs used in our analysis. In contrast, the NOR-ESM-MM model indicates that dry spells are anticipated throughout the entire future period. Overall, our study provides valuable insights into the potential impacts of climate change on drought events in the Indian region.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"26 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":"134934403","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}

{"title":"Development of rainfall intensity-duration-frequency relationships and nomographs for selected stations in Maharashtra, India","authors":"Shikha G. Anand, Pravendra Kumar, Manish Kumar","doi":"10.54302/mausam.v74i3.6302","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.6302","url":null,"abstract":"The rainfall intensity-duration-frequency (IDF) relationship plays an important toolin planning,designing, evaluatingand operatingof water resource projects, water resources development and management.It is necessary to examine the location-specific relationship between rainfall components, intensity, duration and frequency due to their spatiotemporal variation. In this article, weinvestigate the relationship between the rainfall intensity and its components and develop nomographs for Washim, Chandrapur and Yeotmal districts in Maharashtra, India. We also studied the rainfall charts of various stations for maximum annual rainfall intensities of selected duration. The frequency lines are computed for the above locations.. An empirical studyisconducted to determine the value of constants ‘a’ and ‘b’ and that of ‘K’ (Nemec, 1973).The nomographs are also developed for the rainfall intensity-duration-frequency (IDF) relationships (Luzzadar, 1964). Adequacy of the results istested by statistical indices such as integral square error, correlation coefficient, percent absolute deviation and root mean square error. The variation between nomographic solutions and mathematical equations lies within the permissible limit, less than 20%.","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":"42519230","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}

{"title":"Multi stage wheat yield estimation using multiple linear, neural network and penalised regression models","authors":"A. Vashisth, K. S. Aravind, B. Das, P. Krishnan","doi":"10.54302/mausam.v74i3.1923","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.1923","url":null,"abstract":"Wheat is second most consumed staple food grain after rice, cultivated nearly 26 Mha areas in the northern part of India. Weather variables like Maximum temperature, Minimum temperature, Relative humidity, Rainfall, Bright sunshine hours, Evaporation etc. have a great impact on crop yield. Weather based pre harvest crop yield estimation is helpful for deciding marketing, pricing, import-export and policy making etc. Wheat yield and weather variable data were collected for last 35 years from Hisar, Ludhiana, Amritsar, Patiala and IARI, New Delhi. Multistage wheat yield estimation was done at tillering, flowering and grain filling stage of the crop by considering weather variables from 46th to 4th, 46th to 8th and 46th to 11th standard meteorological week for model development. Model was developed using stepwise multiple linear regression (SMLR), Principal component analysis in combination with SMLR (PCA-SMLR), Artificial Neural Network (ANN) alone and in combination with principal components analysis (PCA-ANN), Least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques. Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these multivariate models for stage-wise estimation of wheat yield, percentage deviation of estimated yield by observed yield was ranged between -0.1 to 25.6, 0.9 to 22.8, -0.7 to 22.5% during tillering, flowering, and grain filling stage respectively. On the basis of percentage deviation and model accuracy Elastic net and LASSO model was found better and can be used for district level wheat crop yield estimation at different crop growth stage.","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":"46875302","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}

{"title":"Effects of sowing dates on phenology, radiation interception and yield of wheat","authors":"Prem Deep, ML Khichar, R. Niwas, Madho Singh","doi":"10.54302/mausam.v74i3.6304","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.6304","url":null,"abstract":"An experiment was conducted during rabi  seasons of 2015-16 and 2016-17 to study the phenology, accumulation of growing degree days (GDD), helio-thermal unit (HTU), photo-thermal unit (PTU), Heat use efficiency (HUE), Radiation use efficiency (RUE)and to assess the effects of thermal and radiation regimes on wheat at research farm of Department of Agricultural Meteorology, Chaudhary Charan Singh Haryana Agricultural University, Hisar during rabi seasons of 2015-16 and 2016-17.\u0000The twenty-seven treatment combinations were tested in split plot design with three replications. The main plot treatments consist of three date of sowing, i.e., D1- 2nd fortnight of November, D2- 1st fortnight of December, D3- 2nd  fortnight of December and sub plot treatments consist of three varieties, i.e., V1- WH 1105, V2- DPW 621-50 and   V3- HD 2967.\u0000Daily meteorological data recorded at Agromet observatory near the experimental plot was used for computation of agrometerological indices, i.e., heat unit (HU), heliothermal unit (HTU), photothermal unit (PTU), heat use efficiency (HUE) and radiation use efficiency (RUE).","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"60 32","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41308820","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}

{"title":"Variations in Indian Summer Monsoon Rainfall patterns in Changing Climate","authors":"R. Bhatla, Manas Pant, Soumik Ghosh, S. Verma, Nishant Pandey, Sanjay Bist","doi":"10.54302/mausam.v74i3.5940","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5940","url":null,"abstract":"The Indian summer monsoon is a large scale synoptic and dominant circulation feature which is largely restricted to the summer months from June to September. The proper understanding of rainfall pattern and its trends may help water resources development, agriculture sector and to take decisions for developmental activities. The present study is an attempt to evaluate the spatial variability in Indian summer monsoon rainfall (ISMR) over the Indian subcontinent during the climatological period (1901-2010). The long-term annual, decadal and tricadal monsoon rainfall differences are considered for the period 1901-2010 during the monsoon (June-September) and peak monsoon month (July-August). The results show concern for the major areas of upper Himalaya, Western and peninsular India where positive rainfall difference/increase rainfall with 0.2 to 1 mm/day variation have been reported during the monsoon and peak monsoon months. Also, decrease in rainfall have been reported over Western Ghats, Indo-Gangetic Plain (IGP) and some central Indian regions in the range of -0.6 to -1.5 mm/day. Further, a broad overview of the study shows an enhancement of ISMR over Western India whereas a substantial decline over Northeast Indian regions which suggests the western shift of ISMR in changing climate.\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":"45708321","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}

{"title":"Weather based crop yield prediction using artificial neural networks: A comparative study with other approaches","authors":"A. Gupta, K. Sarkar, D. Dhakre, D. Bhattacharya","doi":"10.54302/mausam.v74i3.174","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.174","url":null,"abstract":"This paper attempts to compare the weather indices based regression approach and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) approach for rice yield prediction at district level of West Bengal. The weather indices for weather variables, viz., minimum temperature, maximum temperature, rainfall, and relative humidity are used as input variables along with time variable t and yield of rice as output variable. The study reveals that the ANN approach works better than the standard regression approach in crop yield prediction. The prediction error percentages are found to be consistently less than 5% in MLP ANN approach except for one district.","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":"42933104","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}

{"title":"Estimation of Alfalfa (Medicago sativa l.) yield under RCP4.5 and RCP8.5 climate change projections with ANN in Turkey","authors":"M. Peki̇n, N. Demirbağ, K. Khawar, Halit Apaydin","doi":"10.54302/mausam.v74i3.5598","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5598","url":null,"abstract":"Alfalfa is one of the most widely cultivated forage crops in the world. Alfalfa farming is carried out on approximately 35 million ha of land worldwide with an annual production amounting to 255 million tons. The average alfalfa cultivated area is about 637 000 ha with production of 13 million tons and yield of 2 200 kg da-1 in Turkey. It is expected that climate change will have significantly different effects on its production and yield in future. Therefore, the aim of the study was to predict the effect of climate change on the yield of alfalfa via selected Artificial Neural Network (ANN) according to RCP4.5 and RCP8.5 climate change scenarios. In line with this, first of all the best ANN structure among 176 different ANN alternatives consisting of various input parameters, learning rates, decay and neuron numbers to predicts alfalfa yield was selected. The ANN training/test dataset used in the study were composed of the alfalfa cultivation statistics, the soil parameters and the climatological data. Alfalfa yield for years 2020-2060 and 2060-2100 in 79 provinces of Turkey are predicted by using best ANN model, according to climate change projections (HadGEM2-ES RCP4.5 and RCP8.5). The ANN was able to calculate alfalfa yield with 0.827 coefficient of determination and 0.813 Nash-Sutcliff coefficient. It is understood that the alfalfa plant can resist climate change and its yield tend to increase or decrease in regions, where there will be an increase or decrease in precipitation in the same order as result of climatic change. It is predicted that the highest yield increase will be in Artvin (6%) (a province of the Eastern Anatolia region) and the maximum yield decrease will be noted in Siirt (9%) (a province of the South eastern Anatolia region). This research may be considered as a creative prediction approach for the alfalfa yield estimation.","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":"41628624","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}

{"title":"Study Gaussian plume model and the Gradient Transport (K) of the advection-diffusion equation and its applications","authors":"KHALEDS.M. Essa, H. Taha","doi":"10.54302/mausam.v74i3.5888","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5888","url":null,"abstract":"In this paper, one dimension time dependent and the steady state three dimensions of advection-diffusion equation  (ADE) have been solved analytically to estimate the concentration in the atmospheric boundary layer (ABL) taking into account the assumption that the ABL height (h) is divided into sub-layers and the downwind distance is also divided into intervals within each rectangular area the ADE is estimated by using the Laplace transform method assuming that the mean values of wind speed and eddy diffusivity. The proposed model, Gaussian plume model and previous work (Essa et al.2019) was compared with the observed concentration of Iodine-135 which was measured at Egyptian Atomic Energy Authority, Nuclear Research Reactor, Inshas, Cairo Egypt. The statistical analysis shows that there is a good agreement between the proposed and experimental values of concentration.","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":"48347982","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}

{"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":" ","pages":""},"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}