Browsing by Author "Nitesh Awasthi"

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Appraisal of Climate Response to Vegetation Indices over Tropical Climate Region in India
(MDPI, 2023) Nitesh Awasthi; Jayant Nath Tripathi; George P. Petropoulos; Dileep Kumar Gupta; Abhay Kumar Singh; Amar Kumar Kathwas; Prashant K. Srivastava
Extreme climate events are becoming increasingly frequent and intense due to the global climate change. The present investigation aims to ascertain the nature of the climatic variables association with the vegetation variables such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). In this study, the impact of climate change with respect to vegetation dynamics has been investigated over the Indian state of Haryana based on the monthly and yearly time-scale during the time period of 2010 to 2020. A time-series analysis of the climatic variables was carried out using the MODIS-derived NDVI and LAI datasets. The spatial mean for all the climatic variables except rainfall (taken sum for rainfall data to compute the accumulated rainfall) and vegetation parameters has been analyzed over the study area on monthly and yearly basis. The liaison of NDVI and LAI with the climatic variables were assessed at multi-temporal scale on the basis of Pearson correlation coefficients. The results obtained from the present investigation reveals that NDVI and LAI has strong significant relationship with climatic variables during the cropping months over study area. In contrast, during the non-cropping months, the relationship weakens but remains significant at the 0.05 significance level. Furthermore, the rainfall and relative humidity depict strong positive relationship with NDVI and LAI. On the other, negative trends were observed in case of other climatic variables due to the limitations of NDVI viz. saturation of values and lower sensitivity at higher LAI. The influence of aerosol optical depth was observed to be much higher on LAI as compared to NDVI. The present findings confirmed that the satellite-derived vegetation indices are significantly useful towards the advancement of knowledge about the association between climate variables and vegetation dynamics. © 2023 by the authors.
Assessment of climatic impact on growth and production of rice (Kharif) and wheat (Rabi) using geospatial technology over Haryana
(India Meteorological Department, 2023) Nitesh Awasthi; Jayant Nath Tripathi; K.K. Dakhore; Dileep Kumar Gupta; Y.E. Kadam
Global climate change could have a substantial negative influence on Indian agriculture and becoming more common and intense growing as a result of food security. Indeed, the examination of weather variability on agricultural growth and production is always complex. The weather variability impact on agricultural growth and production has been evaluated by Pearson correlation analysis among various weather variables (minimum temperature, maximum temperature, relative humidity, wind speed and rainfall), vegetation indices (NDVI and LAI) and crop yield (wheat and rice) on yearly and monthly basis for the time period from the year 1991 to 2020 in the present study. Initially, the temporal behavior of weather variables and vegetation indices have been explored on the monthly and yearly time scale for the long term (1991-2020) along with crop yield over Indian state of Haryana. After that a Pearson correlation analysis have been carried out among the weather variables, vegetation indices and crop yield on monthly and yearly time scale, individually to understand the relationship of NDVI-weather and LAI-weather along with the long-term weather impact on agricultural production. A significant correlation is found between NDVI-weather and LAI-weather on monthly and yearly basis. The positive impact of the temperature, relative humidity and rainfall is found on the rice crop production, while the wind speed showed the negative impact on the rice crop production during the Kharif season in Haryana state of India during the years 1998-2018. In case of wheat crop (Rabi season), the minimum temperature, rainfall and relative humidity supports the wheat crop production, while the maximum temperature and wind speed showed the negative impact on the wheat yield in Haryana during the years 1998-2018. Overall, this study has found the annual increase in wheat crop yield approximately 0.044 tons per hectare and rice crop yield 0.029 tons per hectare. © 2023, India Meteorological Department. All rights reserved.
Linkage between the vegetation indices and climate factors over Haryana
(Association of Agrometeorologists, 2022) Nitesh Awasthi; Jayant Nath Tripathi; K.K. Dakhore; Dileep Kumar Gupta; Y.E. Kadam
Present study was an attempt to study the relationship of Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) with climatic parameters (maximum temperature, minimum temperature, relative humidity, rainfall, wind speed and aerosol optical depth) over the Indian state of Haryana using MODIS derived vegetation indices on monthly and yearly values for the time period from 2010 to 2020. The values of correlations coefficients of NDVI and LAI with climatic variables varied with the months, the nature of their variation was similar for two indices. During summer season the correlation values were maximum while these were minimum during rainy season. The overall correlation analysis revealed that the rainfall and relative humidity were positively correlated with NDVI and LAI, while the remaining climate variables had negative impact on the NDVI and LAI. © 2022, Association of Agrometeorologists. All rights reserved.
Long-Term Spatiotemporal Investigation of Various Rainfall Intensities over Central India Using EO Datasets
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Nitesh Awasthi; Jayant Nath Tripathi; George P. Petropoulos; Pradeep Kumar; Abhay Kumar Singh; Kailas Kamaji Dakhore; Kripan Ghosh; Dileep Kumar Gupta; Prashant K. Srivastava; Kleomenis Kalogeropoulos; Sartajvir Singh; Dhiraj Kumar Singh
This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region. © 2024 by the authors.
Performance Assessment of Global-EO-Based Precipitation Products against Gridded Rainfall from the Indian Meteorological Department
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Nitesh Awasthi; Jayant Nath Tripathi; George P. Petropoulos; Dileep Kumar Gupta; Abhay Kumar Singh; Amar Kumar Kathwas
Monitoring water resources globally is crucial for forecasting future geo-hydro disasters across the Earth. In the present study, an attempt was made to assess the functional dimensionality of multi-satellite precipitation products, retrieved from CHIRPS, NASA POWER, ERA-5, and PERSIANN-CDR with respect to the gridded India Meteorological Department (IMD) precipitation dataset over a period of 30+ years (1990–2021) on monthly and yearly time scales at regional, sub regional, and pixel levels. The study findings showed that the performance of the PERSIANN-CDR dataset was significantly better in Central India, Northeast India, and Northwest India, whereas the NASA-POWER precipitation product performed better in Central India and South Peninsular of India. The other two precipitation products (CHIRPS and ERA-5) showed the intermediate performance over various sub regions of India. The CHIRPS and NASA POWER precipitation products underperformed from the mean value (3.05 mm/day) of the IMD gridded precipitation product, while the other two products ERA-5 and PERSIANN-CDR are over performed across all India. In addition, PERSIANN-CDR performed better in Central India, Northeast India, Northwest India, and the South Peninsula, when the yearly mean rainfall was between 0 and 7 mm/day, while ERA-5 performed better in Central India and the South Peninsula region for a yearly mean rainfall above 0–7 mm/day. Moreover, a peculiar observation was made from the investigation that the respective datasets were able to characterize the precipitation amount during the monsoon in Western Ghats. However, those products needed a regular calibration with the gauge-based datasets in order to improve the future applications and predictions of upcoming hydro-disasters for longer time periods with the very dense rain gauge data. The present study findings are expected to offer a valuable contribution toward assisting in the selection of an appropriate and significant datasets for various studies at regional and zonal scales. © 2023 by the authors.