IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India
“…The purpose was to reveal the anomaly and trend of change of both maximum and minimum temperature across time and space in the period of concern. As literatures underscore, the use of grid data has gained significant attention, particularly in the field of hydro- climatology and disaster risk assessment [ 74 ]. Fig.…”
“…The purpose was to reveal the anomaly and trend of change of both maximum and minimum temperature across time and space in the period of concern. As literatures underscore, the use of grid data has gained significant attention, particularly in the field of hydro- climatology and disaster risk assessment [ 74 ]. Fig.…”
“…It also adds threat to the food security of the nation. Moreover, owing to the poor adaptabilities and inadequate preparedness, the developing countries like India will have to face detrimental effects of climate change (Aadhar et al 2019;Bahita et al 2021aBahita et al , 2021bGosain et al, 2006;Nandi et al 2024). The problems will be further added by anthropogenic activities (Swain et al 2018b(Swain et al , 2022a(Swain et al , 2022c.…”
Precipitation is a very important parameter to estimate climate variability. This paper presents an application of Turning point test to seasonal and annual precipitation data for a period of 30 years over Seonath Basin, a major sub-basin of Mahanadi River Basin. The objective of the application of Turning point test is to catch the randomness of the precipitation time series over the study area. The randomness of precipitation data is checked in general before the trend analysis.The precipitation time series possessing more randomness may give misleading results for trend analysis. Considering this issue, the monthly data was collected for 39 stations, which were categorized into 4 seasons of the year i.e. winter, pre-monsoon, monsoon and post-monsoon. The results reveal for almost all the stations showing insignificant randomness for monsoon, post-monsoon and annual precipitation time series. However, in the winter and pre-monsoon seasons, about half of the stations are showing significant randomness.
“…In India, where over two-thirds of population relies on agriculture directly, rainfall plays a crucial role, particularly due to the Southwest monsoon (Bahita et al 2021a(Bahita et al , 2021bDhal and Swain 2022;Kannan et al 2010;Swain 2014Swain , 2017Swain et al 2019aSwain et al , 2019bSwain et al , 2020aSwain et al , 2021aSwain et al , 2021bSwain et al , 2021cSwain et al , 2022aSharma et al 2020). The majority of annual rainfall is attributed to monsoon rains, making any alterations or fluctuations in precipitation a substantial factor impacting the country's agriculture and food security (Nandi et al 2024;Swain, 2014;Swain et al 2020bSwain et al , 2022bSwain et al , 2022d. Therefore, proactive measures are essential to mitigate potential disasters or inconveniences arising from precipitation anomalies, necessitating the accurate estimation of annual precipitation (Gagiu et al, 2015).…”
Rainfall estimation holds paramount significance for optimizing water resource utilization and effective management, particularly in countries like India, characterized by an agro-based economy and limited irrigation infrastructure. This paper introduces a multiple linear regression (MLR) model tailored for estimating annual rainfall in Khordha district, Odisha, India. The model is designed to predict rainfall for a given year by leveraging data from the preceding three years.Through rigorous testing on a century-long dataset of annual rainfall, the model demonstrated exceptional performance. By setting the intercept of the MLR model to zero, the derived equation yielded highly accurate results. The predicted values exhibited a robust correspondence with observed data, resulting in a R² value of 0.963. This remarkable R² value underscores the model's suitability for application across the study area. The robustness and accuracy of this model offers valuable insights for enhancing hydro-meteorological assessments and informing strategic water resource management decisions in the region.
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