The present study encompasses the performance of Land Surface Model (LSM) physics on simulation of Tropical Cyclones (TCs) key characteristics - track, mean sea level pressure (MSLP), maximum sustained wind (MSW) and rainfall. The impact of four LSM schemes - Thermal Diffusion, Noah, RUC and Noah-MP, is evaluated for the simulation of Severe Cyclonic Storm (SCS) ‘Vardah’ that crossed Tamil Nadu coast, near Chennai on 12 December, 2016 and Extremely Severe Cyclonic Storms (ESCS) ‘Fani’ that crossed Odisha coast, close to Puri on 03 May, 2019. For this purpose, the Advanced Weather Research and Forecasting (ARW) model, configured with a single domain of 9 km horizontal resolution covering the Bay of Bengalis considered. The initial and lateral boundary conditions to the model integration are taken from National Centers for Environmental Prediction (NCEP) Final Analysis (FNL). The model simulated track is verified with India Meteorological Department (IMD) observed track for both the cases. The model simulated MSW and MSLP at the landfall location is validated with IMD best estimation along with fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis (ERA5) products. The rainfall associated with both the cyclones are compared with ERA5 and Global Precipitation Measurement (GPM) rainfall for its validation. The track of TCs Vardah and Fani are well simulated with all the four land surface schemes with reasonable accuracy in landfall position and time of landfall of the systems. The Along Track Error (ATE) and Cross Track Error (CTE) are minimal for the unified Noah LSM scheme. The landfall position error (about 2 km only) is significantly improved with the unified Noah scheme. In case of rainfall forecast, LSMs tend to overestimate the rainfall during landfall of both systems. It is also noticed that overestimation is more towards inland than on the coast. Out of all four LSMs, rainfall estimation from the RUC is closest to the GPM and ERA5 rainfall estimates during landfall. In addition to this, RUC scheme intensifies the cyclones in terms of MSLP and MSW during the landfall of the system as compared to the other parameterization schemes.
{"title":"Performance of land surface schemes on simulation of land falling tropical cyclones over Bay of Bengal using ARW model","authors":"PUSHPENDRA JOHARI, SUSHIL KUMAR, SUJATA PATTANAYAK, DIPAK KUMAR SAHU, ASHISH ROUTRAY","doi":"10.54302/mausam.v74i4.5861","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.5861","url":null,"abstract":"The present study encompasses the performance of Land Surface Model (LSM) physics on simulation of Tropical Cyclones (TCs) key characteristics - track, mean sea level pressure (MSLP), maximum sustained wind (MSW) and rainfall. The impact of four LSM schemes - Thermal Diffusion, Noah, RUC and Noah-MP, is evaluated for the simulation of Severe Cyclonic Storm (SCS) ‘Vardah’ that crossed Tamil Nadu coast, near Chennai on 12 December, 2016 and Extremely Severe Cyclonic Storms (ESCS) ‘Fani’ that crossed Odisha coast, close to Puri on 03 May, 2019. For this purpose, the Advanced Weather Research and Forecasting (ARW) model, configured with a single domain of 9 km horizontal resolution covering the Bay of Bengalis considered. The initial and lateral boundary conditions to the model integration are taken from National Centers for Environmental Prediction (NCEP) Final Analysis (FNL). The model simulated track is verified with India Meteorological Department (IMD) observed track for both the cases. The model simulated MSW and MSLP at the landfall location is validated with IMD best estimation along with fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis (ERA5) products. The rainfall associated with both the cyclones are compared with ERA5 and Global Precipitation Measurement (GPM) rainfall for its validation. The track of TCs Vardah and Fani are well simulated with all the four land surface schemes with reasonable accuracy in landfall position and time of landfall of the systems. The Along Track Error (ATE) and Cross Track Error (CTE) are minimal for the unified Noah LSM scheme. The landfall position error (about 2 km only) is significantly improved with the unified Noah scheme. In case of rainfall forecast, LSMs tend to overestimate the rainfall during landfall of both systems. It is also noticed that overestimation is more towards inland than on the coast. Out of all four LSMs, rainfall estimation from the RUC is closest to the GPM and ERA5 rainfall estimates during landfall. In addition to this, RUC scheme intensifies the cyclones in terms of MSLP and MSW during the landfall of the system as compared to the other parameterization schemes.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934952","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}
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.
{"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":null,"pages":null},"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}
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.6302
Shikha G. Anand, Pravendra Kumar, Manish Kumar
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%.
{"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":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":"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}
Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.1923
A. Vashisth, K. S. Aravind, B. Das, P. Krishnan
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.
{"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":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":"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}
Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.6304
Prem Deep, ML Khichar, R. Niwas, Madho Singh
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. The 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. Daily 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).
{"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":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":"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}
Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.174
A. Gupta, K. Sarkar, D. Dhakre, D. Bhattacharya
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.
{"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":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":"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}
Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.5598
M. Peki̇n, N. Demirbağ, K. Khawar, Halit Apaydin
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.
{"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":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":"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}
Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.5940
R. Bhatla, Manas Pant, Soumik Ghosh, S. Verma, Nishant Pandey, Sanjay Bist
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.
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Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.5888
KHALEDS.M. Essa, H. Taha
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.
在本文中，本文对平流扩散方程(ADE)的一维时变和稳态三维进行了解析求解，在假定边界层高度(h)被划分为若干子层和每个矩形区域内顺风距离被划分为若干区间的情况下，对大气边界层(ABL)的浓度进行了估计，并在假定风速和涡的平均值的情况下，采用拉普拉斯变换方法对ADE进行了估计扩散系数。将提出的模型、高斯羽流模型和以前的工作(Essa et al.2019)与在埃及开罗Inshas的埃及原子能管理局核研究反应堆测量的碘-135浓度进行了比较。统计分析表明，提出的浓度值与实验值吻合较好。
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