CSIR - National Institute for Data Science and AI

Study of rainfall forecasting and crop yield prediction under varying climatic conditions: a review of research gaps and advanced solutions

by Divyashree HS, R. Girisha & Krushna Chandra Gouda 

Because rainfall forecasting has a direct impact on crop yield, resource management, and overall farm productivity, it is essential to agricultural planning. Rainfall patterns are influenced by a number of internal and external factors, such as variations in temperature, humidity, atmospheric pressure, and monsoon variability. Rainfall forecasting accuracy is crucial for maximizing irrigation schedules, preserving soil moisture, and guaranteeing sustainable farming methods. Rainfall patterns that are unpredictable result in crop failures, inefficient use of resources, and financial losses for farmers. Using past weather data and climate parameters, this review combines cutting-edge machine learning and deep learning models to increase the accuracy of rainfall predictions. These models enhance forecasting accuracy by analyzing historical weather patterns and environmental variables, thereby increasing readiness for severe weather events. For farmers, policymakers, and agricultural researchers to develop data-driven strategies for mitigating climate-related risks, accurate rainfall forecasts provide vital insights. Using Karnataka’s main crops as a case study, this study expands on forecasted rainfall data by estimating crop yield. Accurate yield forecasts are crucial for both food security and economic stability, as climate variations have a significant impact on agricultural productivity. The effectiveness of DL models in predicting agricultural output is analyzed through a review of published literature and forecasting analyses presented between 2020 and 2025, focusing on trends in methodologies, results, and research gaps rather than providing new empirical rainfall or agricultural yield estimates. This review helps improve food security, optimize resource use, and make informed decisions by leveraging advancements in climate modeling and agricultural analytics. The results provide a thorough framework for flexible farming practices, guaranteeing sustainable crop yields in the face of changing environmental.

Artificial Rain Through Cloud Seeding: An Overview of Processes and Principles

by Aditya H Iyer, Krushna Chandra Gouda, Aruna S T

Freshwater is a vital commodity that humans and communi­ties require constant access to for basic activities related to agriculture, survival, and bodily upkeep. While rivers, lakes, and underground aquifers serve as long-term water sources, a significant fraction of the current population resides in re­gions that do not receive a reliable, steady supply of water. In these regions, rainfall is crucial for residents to survive, but it is often seasonal or irregular. Furthermore, accord­ing to UNESCO reports, billions of people worldwide lack access to safe and sanitised water, while natural and man­made disasters such as droughts and floods, resulting from improper land and resource use, exacerbate this issue. To mitigate these and several other problems, cloud seeding is a promising technique that induces precipitation when rain is needed or suspends it during periods of high-intensity rain­fall to reduce flood risk. It is crucial for addressing the afore­mentioned issues, as it can control the flow of rainwater in both quantity and region. This article explores the funda­mentals of cloud seeding, including the scientific principles behind the process, the chemical aspects of seeding materials and processes, the practical methods used to carry them out, and developments in cloud seeding technology since its con­ception in the 1940s, through a thorough review of scientific literature and patents.

Source: https://www.ias.ac.in/article/fulltext/reso/031/03/0411-0434 

Assessing the impact of land use change in the Simulation of extreme temperature events over Odisha during May 2015

by S K Sahoo, Krushna Chandra Gouda, S Himesh and R K Sahu

An unprecedented heat wave lasting approximately two weeks occurred in Odisha during the third and fourth weeks of May 2015, resulting in meteorological hazards. In this study, an extreme temperature event (ETE) occurred on 25-27th May 2015, with about 15 meteorological observation stations in the state of Odisha recording maximum temperatures exceeding 45 °C, resulting in an intense heat wave. The mesoscale modeling framework (WRF4.0) is configured and optimized to simulate ETE at the regional scale in this study using different land-use scenarios. The maximum temperature from a time-ensemble simulation using the current land-use scenario based on Indian Space Research Organization (ISRO) data is found to be more accurate than simulations based on US Geological Survey (USGS) data at India Meteorological Department (IMD) meteorological stations. The mean percentage errors of simulated maximum temperatures over Odisha with respect to IMD station-scale observations are 1.6% (ISRO) and 3% (USGS) on 25th May 2015, and 4.2% (ISRO) and 4.7% (USGS) on 26th May 2015, respectively. Compared with the simulation based on ISRO data (more urbanized), the simulated horizontal surface wind at the different locations in Odisha is generally higher in the simulation based on USGS data. Changes in land use increase the roughness length, reducing surface wind speed. The dynamical aspects are also explored by analyzing humidity, outgoing longwave radiation (OLR), Convective Available Potential Energy (CAPE), and Convective Inhibition (CIN), etc., from the model and validated with the reanalysis products, which support the model performance in capturing the conducive environment resulting in a regional heat wave. The land use analysis reveals that the state-wide increase in urbanization between 1992 and 2015 was about 0.4%, with the highest percentage increase occurring in cities like Bhubaneswar, Sambalpur, Jharsuguda, and Rourkela in the Sundergarh district, which regularly experience the heat wave in the month of May, and the same are accurately simulated by the optimized and calibrated model configuration. Land-use change, including urban expansion and shifting cropping patterns, along with increased anthropogenic activities, is directly linked to the rise in maximum temperatures.
Source: https://doi.org/10.54302/mausam.v77i3.6819