Pub Date : 2024-03-24DOI: 10.54302/mausam.v75i2.6077
Parthsarthi Pandya, Narendra Kumar Gontia
Remote sensing technology has demonstrated its significant utility in the monitoring and mapping of agricultural drought on a global scale. This study focused on the assessment of agricultural drought in the Saurashtra region of Gujarat, India, utilizing a comprehensive dataset spanning 33 years from Landsat and Sentinel satellites. It employed various vegetation indices, including NDVI (Normalized Difference Vegetation Index), Anomaly Index (NAI), Vegetation Condition Index (VCI) and NDWI Anomaly index (NDWIA), to gauge drought conditions. The performance of these indices was evaluated through the generation of drought severity maps and their correlation analysis with major Kharif crops in the region, specifically cotton and groundnut. The analysis pinpointed major agricultural drought years, such as 1986, 1987, 1991, 2000, 2002 and 2012, which corresponded to substantial crop yield losses ranging from 37% to 76% for cotton and 66% to 95% for groundnut, varying by district. Despite VCI demonstrating equivalent or superior correlations with crop yields (ranging from 0.32 to 0.73 for cotton and 0.33 to 0.75 for groundnut) compared to NAI in various districts, it tended to underestimate drought severities, designating only 2 to 9 drought years for different districts. Consequently, this study recommends revised VCI drought severity thresholds, which enhance the categorization of agricultural drought in terms of severity levels and corresponding yield losses for cotton and groundnut in the Saurashtra region of Gujarat. Furthermore, it underscores the need to establish region-specific drought severity thresholds by identifying the most suitable vegetation index for effective quantification of agricultural drought, thereby facilitating informed drought mitigation measures.
{"title":"Improving remote sensing based agricultural drought characterization in Saurashtra, Gujarat : A region-specific threshold approach","authors":"Parthsarthi Pandya, Narendra Kumar Gontia","doi":"10.54302/mausam.v75i2.6077","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.6077","url":null,"abstract":"Remote sensing technology has demonstrated its significant utility in the monitoring and mapping of agricultural drought on a global scale. This study focused on the assessment of agricultural drought in the Saurashtra region of Gujarat, India, utilizing a comprehensive dataset spanning 33 years from Landsat and Sentinel satellites. It employed various vegetation indices, including NDVI (Normalized Difference Vegetation Index), Anomaly Index (NAI), Vegetation Condition Index (VCI) and NDWI Anomaly index (NDWIA), to gauge drought conditions. The performance of these indices was evaluated through the generation of drought severity maps and their correlation analysis with major Kharif crops in the region, specifically cotton and groundnut. The analysis pinpointed major agricultural drought years, such as 1986, 1987, 1991, 2000, 2002 and 2012, which corresponded to substantial crop yield losses ranging from 37% to 76% for cotton and 66% to 95% for groundnut, varying by district. Despite VCI demonstrating equivalent or superior correlations with crop yields (ranging from 0.32 to 0.73 for cotton and 0.33 to 0.75 for groundnut) compared to NAI in various districts, it tended to underestimate drought severities, designating only 2 to 9 drought years for different districts. Consequently, this study recommends revised VCI drought severity thresholds, which enhance the categorization of agricultural drought in terms of severity levels and corresponding yield losses for cotton and groundnut in the Saurashtra region of Gujarat. Furthermore, it underscores the need to establish region-specific drought severity thresholds by identifying the most suitable vegetation index for effective quantification of agricultural drought, thereby facilitating informed drought mitigation measures.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140386133","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.6323
V. S., Anushiya Jeganathan
This study performed the spatio-temporal analysis of drought hazards across the agro-climatic zones (ACZs) of Karnataka under historical and future climate scenarios. The India Meteorological Department’s high-resolution gridded data for1989-2019 was used for historical drought occurrence analysis. Coordinated Regional Climate Downscaling Experiment ensemble data of the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were used for analysing future drought hazards in the near (2031-2060) and end term (2061-2099) periods. The standardised precipitation index (SPI) was used to calculate the frequency of droughts at different accumulation periods of 1, 3, 6, 9, and 12 months. Subsequently, the ACZ-wise drought hazard index (DHI) was calculated and mapped geospatially using ArcGIS. The results indicated that moderate drought events have the highest frequencies of occurrence, followed by severe and extreme drought events for all accumulation periods. During 1989-2019, 54.8%, 28.3% and 16.7% of droughts were moderate, severe, and extreme, respectively. An increase of 2.6% and 2.4% in the frequency of moderate droughts is projected under the RCP4.5 and 8.5 scenarios, respectively, in the end term. Under both historical and future climate scenarios, a high frequency of extreme droughts was observed in the long accumulation periods (SPI-9 and SPI-12), whereas the frequency of moderate droughts was observed to be high in the short accumulation periods (SPI-1 and SPI-3). Under the historical scenario, the frequency of droughts in the extreme category was high in the southern transition, central dry, and north eastern dry zones, severe category in the northern dry, southern transition, and coastal zones, and moderate category in the north transition, hill, and southern dry zones. Among the 30 districts of Karnataka, Chitradurga, Udupi, Tumakuru, Ballari, Koppala, Raichuruand Gadaga districts have very high DHI. This study sheds light on the potential consequences of climate change on drought scenarios in the Karnataka state’s agro-climate zones and urges for zone specific drought adaptation and mitigation strategies to strengthen the State resilience.
{"title":"Agro-climatic zone-wise drought hazards in Karnataka under historical and future climate scenarios","authors":"V. S., Anushiya Jeganathan","doi":"10.54302/mausam.v75i2.6323","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.6323","url":null,"abstract":"This study performed the spatio-temporal analysis of drought hazards across the agro-climatic zones (ACZs) of Karnataka under historical and future climate scenarios. The India Meteorological Department’s high-resolution gridded data for1989-2019 was used for historical drought occurrence analysis. Coordinated Regional Climate Downscaling Experiment ensemble data of the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were used for analysing future drought hazards in the near (2031-2060) and end term (2061-2099) periods. The standardised precipitation index (SPI) was used to calculate the frequency of droughts at different accumulation periods of 1, 3, 6, 9, and 12 months. Subsequently, the ACZ-wise drought hazard index (DHI) was calculated and mapped geospatially using ArcGIS. The results indicated that moderate drought events have the highest frequencies of occurrence, followed by severe and extreme drought events for all accumulation periods. During 1989-2019, 54.8%, 28.3% and 16.7% of droughts were moderate, severe, and extreme, respectively. An increase of 2.6% and 2.4% in the frequency of moderate droughts is projected under the RCP4.5 and 8.5 scenarios, respectively, in the end term. Under both historical and future climate scenarios, a high frequency of extreme droughts was observed in the long accumulation periods (SPI-9 and SPI-12), whereas the frequency of moderate droughts was observed to be high in the short accumulation periods (SPI-1 and SPI-3). Under the historical scenario, the frequency of droughts in the extreme category was high in the southern transition, central dry, and north eastern dry zones, severe category in the northern dry, southern transition, and coastal zones, and moderate category in the north transition, hill, and southern dry zones. Among the 30 districts of Karnataka, Chitradurga, Udupi, Tumakuru, Ballari, Koppala, Raichuruand Gadaga districts have very high DHI. This study sheds light on the potential consequences of climate change on drought scenarios in the Karnataka state’s agro-climate zones and urges for zone specific drought adaptation and mitigation strategies to strengthen the State resilience.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140386225","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.3497
C. Nandhini, S. G. Patil
Rainfall is considered one of the most important weather parameters which helps in deciding the time of sowing, pest and disease management and harvesting. Markov chain analysis deals with predicting future values based on past values. In the present study, Markov Chain analysis was used to predict the future probability of monthly rainfall and examine the pattern and distribution of rainfall using daily rainfall data from the year 1982 to 2016 (34 years) in the Coimbatore district. This study mainly analysed the probability of rainfall in the Coimbatore district of Tamil Nadu based on Markov chain process. Based on the National Center for Hydrology and Meteorology, the intensity of rainfall per day was categorized and a day is considered as no rain if rainfall was less than 0.1 mm, low rain if rainfall was between 0.1 mm to 10 mm, moderate rain if rainfall was between 10 mm to 20 mm and heavy rain if rainfall was above 20 mm. By calculating the transition probability matrices and steady-state probability matrices for each month based on the conditional probability of rain on a particular day given that rain on the previous day which is to predict the state of rainfall on the next day. This study reported that the availability of water for crop production is higher during the winter, pre-monsoon, the onset of the southwest monsoon, and at the end of the northeast monsoon. There may be a scarcity of water from August to November for agricultural activities. Based on this study, farmers can plan for a better cropping system in advance to get a better yield.
{"title":"Markov Chain analysis of rainfall of Coimbatore","authors":"C. Nandhini, S. G. Patil","doi":"10.54302/mausam.v75i2.3497","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.3497","url":null,"abstract":"Rainfall is considered one of the most important weather parameters which helps in deciding the time of sowing, pest and disease management and harvesting. Markov chain analysis deals with predicting future values based on past values. In the present study, Markov Chain analysis was used to predict the future probability of monthly rainfall and examine the pattern and distribution of rainfall using daily rainfall data from the year 1982 to 2016 (34 years) in the Coimbatore district. This study mainly analysed the probability of rainfall in the Coimbatore district of Tamil Nadu based on Markov chain process. Based on the National Center for Hydrology and Meteorology, the intensity of rainfall per day was categorized and a day is considered as no rain if rainfall was less than 0.1 mm, low rain if rainfall was between 0.1 mm to 10 mm, moderate rain if rainfall was between 10 mm to 20 mm and heavy rain if rainfall was above 20 mm. By calculating the transition probability matrices and steady-state probability matrices for each month based on the conditional probability of rain on a particular day given that rain on the previous day which is to predict the state of rainfall on the next day. This study reported that the availability of water for crop production is higher during the winter, pre-monsoon, the onset of the southwest monsoon, and at the end of the northeast monsoon. There may be a scarcity of water from August to November for agricultural activities. Based on this study, farmers can plan for a better cropping system in advance to get a better yield.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385337","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.3893
A. Gupta, C. P. Sawant, Mukesh Kumar, R. K. Singh, K. V. R. Rao
Prevention of soil erosion requires long term assessment of rainfall erosivity and other related variables of the region. In the present study, four variables related to rainfall erosivity i.e. modified Fournier index (MFI), rainfall erosivity (R) factor, erosivity density (ED) and precipitation concentration index (PCI) were calculated. Long-term (1901-2021) daily rainfall data of Bundelkhand region (Central India) were used in the analysis. The above variables were assessed for spatial and temporal variability on annual and seasonal scale. The R-factor values range from 3010.61 MJ.mm ha-1 h-1 to 5346.53 MJ.mm ha-1 h-1, showing the region belongs to moderate to severe erosivity class. The mean annual R-factor, MFI, ED and PCI values for the Bundelkhand region were calculated as 4072.86 MJ.mm ha-1 h-1, 270.55 mm, 19.13 MJ ha-1 h-1 and 28.88, respectively. This study provides the insights of soil erosion problems of Bundelkhand region and would help in adopting the preventive measures and watershed development activities.
{"title":"Assessment of rainfall erosivity for Bundelkhand region of central India using long-term rainfall data","authors":"A. Gupta, C. P. Sawant, Mukesh Kumar, R. K. Singh, K. V. R. Rao","doi":"10.54302/mausam.v75i2.3893","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.3893","url":null,"abstract":"Prevention of soil erosion requires long term assessment of rainfall erosivity and other related variables of the region. In the present study, four variables related to rainfall erosivity i.e. modified Fournier index (MFI), rainfall erosivity (R) factor, erosivity density (ED) and precipitation concentration index (PCI) were calculated. Long-term (1901-2021) daily rainfall data of Bundelkhand region (Central India) were used in the analysis. The above variables were assessed for spatial and temporal variability on annual and seasonal scale. The R-factor values range from 3010.61 MJ.mm ha-1 h-1 to 5346.53 MJ.mm ha-1 h-1, showing the region belongs to moderate to severe erosivity class. The mean annual R-factor, MFI, ED and PCI values for the Bundelkhand region were calculated as 4072.86 MJ.mm ha-1 h-1, 270.55 mm, 19.13 MJ ha-1 h-1 and 28.88, respectively. This study provides the insights of soil erosion problems of Bundelkhand region and would help in adopting the preventive measures and watershed development activities.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385532","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.6259
Sourish Bondyopadhyay, Mani Sankar Jana
The track, intensity, and associated hazards of a cyclone are mostly pre- dicted using NWP models, satellites, and radar. Though the cyclones originate and strengthen in the ocean, they cause devastation in the populated land area over which they ultimately pass. Over the years, the accuracy of cyclone prediction has improved a lot. Yet, there is some uncertainty in the accurate prediction of track, intensity and associated hazards. In this article, we have studied super cyclone AMPHAN and its hazards for Kolkata, India. Here, we have proposed a new scheme for improving the forecast accuracy for cyclone distance, associated wind, and hazard for lead time up to 12-24 hours ahead based on curve fitting techniques and extrapolation using surface observational data. For the prediction of distance of the system from the concerned station and corresponding gusty wind speed, the accuracy of the proposed scheme is found to be better than the existing operational forecast and various reputed NWP models.
{"title":"Precursors of hazard due to super cyclone AMPHAN for Kolkata, India from surface observations","authors":"Sourish Bondyopadhyay, Mani Sankar Jana","doi":"10.54302/mausam.v75i2.6259","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.6259","url":null,"abstract":"The track, intensity, and associated hazards of a cyclone are mostly pre- dicted using NWP models, satellites, and radar. Though the cyclones originate and strengthen in the ocean, they cause devastation in the populated land area over which they ultimately pass. Over the years, the accuracy of cyclone prediction has improved a lot. Yet, there is some uncertainty in the accurate prediction of track, intensity and associated hazards. In this article, we have studied super cyclone AMPHAN and its hazards for Kolkata, India. Here, we have proposed a new scheme for improving the forecast accuracy for cyclone distance, associated wind, and hazard for lead time up to 12-24 hours ahead based on curve fitting techniques and extrapolation using surface observational data. For the prediction of distance of the system from the concerned station and corresponding gusty wind speed, the accuracy of the proposed scheme is found to be better than the existing operational forecast and various reputed NWP models.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385092","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.6271
L. Sridhar, D. S. Pai
The two major archipelagos of India, the Andaman & Nicobar Islands and the Lakshadweep situated in the climate-hazardous areas of the Bay of Bengal and Arabian Sea respectively are largely affected by weather systems developing over the sea and heavy rainfall activities. The recent two daily gridded rainfall data sets published by IMD; Rajeevan et al. (2010) at 1° × 1° spatial resolution and Pai et al. (2014) at 0.25° × 0.25° spatial resolution extending for a period of more than 100 years have been extensively used by researchers to study the rainfall characteristics at various spatiotemporal scales over the Indian mainland. However, these data sets do not include the grids over these two island meteorological subdivisions of India mainly because of the absence of daily rainfall observation for this long period. In this study, an attempt has been made to develop daily gridded rainfall data over these island subdivisions for the recent 70 years (1951 to 2020) in two spatial resolutions, viz., 1° × 1° and 0.25° × 0.25° using all the available islands station data during the period and carry out statistical analyses of various rainfall characteristics over these islands. The 0.25° × 0.25° data set was observed to be more comparable with the official rainfall time series of IMD for both these two Island subdivisions, and hence this data set has been used to carry out the trend analysis of Daily events of rainfall DER (> = 5 mm) for these two island subdivisions for the whole data period of 1951-2020 and the climate regime shift period of 1971-2020. DER was classified into two categories DMR (5-100 mm), daily moderate rainfall events and DHR (100 mm and above) daily heavy rainfall events. Signs and magnitude of the long-term trends in the frequency of DER (with DMR & DHR) showed significant changes during the recent period 1971-2020.
{"title":"Analysis of long-term trends of rainfall and extreme rainfall events over Andaman & Nicobar and Lakshadweep Islands of India","authors":"L. Sridhar, D. S. Pai","doi":"10.54302/mausam.v75i2.6271","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.6271","url":null,"abstract":"The two major archipelagos of India, the Andaman & Nicobar Islands and the Lakshadweep situated in the climate-hazardous areas of the Bay of Bengal and Arabian Sea respectively are largely affected by weather systems developing over the sea and heavy rainfall activities. The recent two daily gridded rainfall data sets published by IMD; Rajeevan et al. (2010) at 1° × 1° spatial resolution and Pai et al. (2014) at 0.25° × 0.25° spatial resolution extending for a period of more than 100 years have been extensively used by researchers to study the rainfall characteristics at various spatiotemporal scales over the Indian mainland. However, these data sets do not include the grids over these two island meteorological subdivisions of India mainly because of the absence of daily rainfall observation for this long period. In this study, an attempt has been made to develop daily gridded rainfall data over these island subdivisions for the recent 70 years (1951 to 2020) in two spatial resolutions, viz., 1° × 1° and 0.25° × 0.25° using all the available islands station data during the period and carry out statistical analyses of various rainfall characteristics over these islands. The 0.25° × 0.25° data set was observed to be more comparable with the official rainfall time series of IMD for both these two Island subdivisions, and hence this data set has been used to carry out the trend analysis of Daily events of rainfall DER (> = 5 mm) for these two island subdivisions for the whole data period of 1951-2020 and the climate regime shift period of 1971-2020. DER was classified into two categories DMR (5-100 mm), daily moderate rainfall events and DHR (100 mm and above) daily heavy rainfall events. Signs and magnitude of the long-term trends in the frequency of DER (with DMR & DHR) showed significant changes during the recent period 1971-2020.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385094","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.3416
V. Shinde, S. Ghavale, H. P. Maheswarappa, D. N. Jagtap, S. Wankhede, P. Haldankar, Lingaraj Huggi
Long term experiments (2013-14 to 2018-19) were conducted in Regional Coconut Research Station, Bhatye, a representative location of major coconut growing region of Maharashtra (Konkan region) to study the impact of changing weather parameters on growth and yield of 32 years old coconut plants (dwarf x tall, i.e., COD x WCT). Regression based trend analysis of weather parameters was conducted to check the variability of weather parameters over experimentation years. There was a decrease in maximum temperature (r2=0.034) and increase in minimum temperature (r2=0.017) and rainfall (r2=0.393), indicating change in weather parameters. Correlation studies were carried out to understand the interaction between weather parameters and coconut growth and yield. Maximum temperature had a negative impact on growth (-0.02 and -0.58 for number of leaves and annual leaf production) but had a positive impact on yield (0.41, 0.64 and 0.63 for number of bunches, number of buttons and nut yield). Minimum temperature had significant negative effect on annual leaf production (-0.88) and had a positive effect on nut yield per plant (0.95). The effect of relative humidity (morning and evening) was non-significant. Rainfall had its influence on the crop by negatively affecting the number of bunches (-0.10) and nut yield per plant (-0.48), a positively affecting number of buttons (0.08). Further, microclimate in the plantation was compared to an open field, which indicated lower maximum and minimum temperature (-3.4 and -3.1 %) and higher morning and evening relative humidity (1.6 and 1.9 %) in the coconut plantation as compared to the open field.
{"title":"Climate drives of growth, yield and microclimate variability in multistoried coconut plantation in Konkan region of Maharashtra, India","authors":"V. Shinde, S. Ghavale, H. P. Maheswarappa, D. N. Jagtap, S. Wankhede, P. Haldankar, Lingaraj Huggi","doi":"10.54302/mausam.v75i2.3416","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.3416","url":null,"abstract":"Long term experiments (2013-14 to 2018-19) were conducted in Regional Coconut Research Station, Bhatye, a representative location of major coconut growing region of Maharashtra (Konkan region) to study the impact of changing weather parameters on growth and yield of 32 years old coconut plants (dwarf x tall, i.e., COD x WCT). Regression based trend analysis of weather parameters was conducted to check the variability of weather parameters over experimentation years. There was a decrease in maximum temperature (r2=0.034) and increase in minimum temperature (r2=0.017) and rainfall (r2=0.393), indicating change in weather parameters. Correlation studies were carried out to understand the interaction between weather parameters and coconut growth and yield. Maximum temperature had a negative impact on growth (-0.02 and -0.58 for number of leaves and annual leaf production) but had a positive impact on yield (0.41, 0.64 and 0.63 for number of bunches, number of buttons and nut yield). Minimum temperature had significant negative effect on annual leaf production (-0.88) and had a positive effect on nut yield per plant (0.95). The effect of relative humidity (morning and evening) was non-significant. Rainfall had its influence on the crop by negatively affecting the number of bunches (-0.10) and nut yield per plant (-0.48), a positively affecting number of buttons (0.08). Further, microclimate in the plantation was compared to an open field, which indicated lower maximum and minimum temperature (-3.4 and -3.1 %) and higher morning and evening relative humidity (1.6 and 1.9 %) in the coconut plantation as compared to the open field.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385101","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.6099
Nitinun Pongsiri, Rhysa McNeil, R. Saelim, Benjamin Atta Owusu, Somporn Chuai-Aree
Temperature dynamics on the island of Greenland are an important factor in shaping ecological events. Investigating the land surface temperature (LST) patterns is critical for understanding ecological dynamics across different regions. Further melting of the Greenland ice sheet could deva state marine and terrestrial ecosystems. This study used data from Moderate Resolution Imaging Spectroradiometer satellites to understand the seasonal patterns and patterns of LST over the entire island. Focusing on the period between 2000 and 2019, this study used a natural cubic spline model to identify seasonal patterns for all sub-regions. The data were seasonally adjusted and filtered with a second-order autocorrelation component. The spline was fitted again to identify the LST pattern, and a multivariate regression model was then used to adjust for spatial correlation. We illustrate that most of the land surface of Greenland hasstable temperature trends. These observed patterns in LST in Greenland during the study period suggest that the observed ice-sheet melting in Greenland within the last two decades could be due to other factors, not necessarily LST patterns.
{"title":"Spatial and temporal patterns of land surface temperature in Greenland from 2000-2019","authors":"Nitinun Pongsiri, Rhysa McNeil, R. Saelim, Benjamin Atta Owusu, Somporn Chuai-Aree","doi":"10.54302/mausam.v75i2.6099","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.6099","url":null,"abstract":"Temperature dynamics on the island of Greenland are an important factor in shaping ecological events. Investigating the land surface temperature (LST) patterns is critical for understanding ecological dynamics across different regions. Further melting of the Greenland ice sheet could deva state marine and terrestrial ecosystems. This study used data from Moderate Resolution Imaging Spectroradiometer satellites to understand the seasonal patterns and patterns of LST over the entire island. Focusing on the period between 2000 and 2019, this study used a natural cubic spline model to identify seasonal patterns for all sub-regions. The data were seasonally adjusted and filtered with a second-order autocorrelation component. The spline was fitted again to identify the LST pattern, and a multivariate regression model was then used to adjust for spatial correlation. We illustrate that most of the land surface of Greenland hasstable temperature trends. These observed patterns in LST in Greenland during the study period suggest that the observed ice-sheet melting in Greenland within the last two decades could be due to other factors, not necessarily LST patterns.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385541","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 : 2024-03-24DOI: 10.54302/mausam.v75i2.5875
Sampson Twumasiankrah, W. A. Pels, S. Nadarajah
The main objective of the study was to determine the appropriate distribution for extreme rainfall along the coastal and northern sectors of Ghana. For stakeholders and policymakers to make appropriate risk-mitigating measures to lessen the damage caused by flood and drought, it is necessary to make proper inferences about extreme rainfall. In this study, we used both the multivariate and univariate extreme value data analysis approaches. The Generalized Extreme Value (GEV) with the Block Maxima approach and Generalized Pareto Distribution (GPD) with the Peak over the threshold (that is all excesses and decluster peaks approaches) were used in this study. Historical gridded monthly maximum rainfall data from 1970 to 2020 were obtained from the Climatic Research Unit and were grouped as the coastal and northern stations. The Maximum Likelihood Estimation method was used to estimate the model parameters, and both the unit root test and the Mann-Kendall tests were used to test for trend in the data. With the multivariate extreme modelling approach, the logistic bivariate GEV model was chosen as the “best” model. However, the dependence value was 0.965, so the extreme rainfall should be modelled independently using the univariate extreme value approaches. Hence, based on the information criteria and analysis of deviance approaches, the GEV distribution was considered the “best” fit for the extreme rainfall dataset for the northern part of Ghana. In contrast, the GPD distribution was the “best” fit for the coastal station. Comparatively, for the volume of rainfall in the year 2020, the extreme rainfall is expected to be higher in the coastal station of Ghana in the next two years. Also, extreme rainfall in 2 years would not exceed the maximum occurrence of rainfall (279.267), which happened in September 2020 at the northern station of Ghana.
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Pub Date : 2024-03-24DOI: 10.54302/mausam.v75i2.6003
N. SAIRAM N., Anu Varughese
Among the different inputs for the hydrological model, well distributed and precise precipitation datahas a crucial role in accurately simulating the various processes in a watershed. Poor distribution network of rain gauges and lack of precise precipitation data is one of the most important problems involved in many Indian watersheds. This study investigates the potential of using an alternate source of data for hydrologic modelling. The Climate Forecast System Reanalysis (CFSR) data is a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system. Ithas been reported as an alternative option for solving the data deficiency of certain watersheds. The suitability of the CFSR to model the stream flow of Kunthipuzha river, flowing through the famous Silent Valley National Park in Kerala was assessed. The Soil and Water Assessment Tool (SWAT) model was made use of for the simulation of hydrologic process. The model was simulated using calibrated parameters in which CN2, ALPHA_BF and ESCO are the major factors affecting runoff.The developed model was run with observed and predicted meteorological data (CFSR) and the simulated results of stream flow were compared using Nash Sutcliffe Efficiency (NSE), Coefficient of determination (R2) and Root mean Square Error (RMSE). The NSE, R2 and RMSE obtained when observed data was usedfor modelling were 0.82, 0.85 and 29.25 respectively, whereas with CFSR data, the values were 0.70, 0.72 and 37.18 respectively. The streamflow modelled with SWAT using observed meteorological data wascloser to the measured streamflow as compared with that using CFSR data. The NSE and R2 obtained with CFSR data (0.7 & 0.72) indicates that gridded data (CFSR data) can perhaps be utilized in data scare regions with reasonable accuracy.
{"title":"Assessing the suitability of CFSR data for SWAT model hydrologic simulation of Kunthipuzha river basin, Kerala, India","authors":"N. SAIRAM N., Anu Varughese","doi":"10.54302/mausam.v75i2.6003","DOIUrl":"https://doi.org/10.54302/mausam.v75i2.6003","url":null,"abstract":"Among the different inputs for the hydrological model, well distributed and precise precipitation datahas a crucial role in accurately simulating the various processes in a watershed. Poor distribution network of rain gauges and lack of precise precipitation data is one of the most important problems involved in many Indian watersheds. This study investigates the potential of using an alternate source of data for hydrologic modelling. The Climate Forecast System Reanalysis (CFSR) data is a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system. Ithas been reported as an alternative option for solving the data deficiency of certain watersheds. The suitability of the CFSR to model the stream flow of Kunthipuzha river, flowing through the famous Silent Valley National Park in Kerala was assessed. The Soil and Water Assessment Tool (SWAT) model was made use of for the simulation of hydrologic process. The model was simulated using calibrated parameters in which CN2, ALPHA_BF and ESCO are the major factors affecting runoff.The developed model was run with observed and predicted meteorological data (CFSR) and the simulated results of stream flow were compared using Nash Sutcliffe Efficiency (NSE), Coefficient of determination (R2) and Root mean Square Error (RMSE). The NSE, R2 and RMSE obtained when observed data was usedfor modelling were 0.82, 0.85 and 29.25 respectively, whereas with CFSR data, the values were 0.70, 0.72 and 37.18 respectively. The streamflow modelled with SWAT using observed meteorological data wascloser to the measured streamflow as compared with that using CFSR data. The NSE and R2 obtained with CFSR data (0.7 & 0.72) indicates that gridded data (CFSR data) can perhaps be utilized in data scare regions with reasonable accuracy.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385897","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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