Modeling and forecasting atmospheric Carbon Dioxide concentrations at Bengaluru city in India
by Iranna Gogeri, K. C. Gouda & T. Sumathy
Atmospheric carbon dioxide (CO2) is considered as most significant greenhouse gas (GHG) in terms of its global warming potential and human-caused emissions. In Indian context, CSIR Fourth Paradigm Institute has established continuous monitoring of GHG stations at Hosakote nearer to Bengaluru, India. In the present study, seasonal autoregressive integrated moving average (SARIMA) model has been developed based on evaluation criteria, considering its statistical performance and ability to capture the trend and patterns in CO2 concentration at station scale which will be useful in regional studies. The present work addresses to find an optimized SARIMA configuration for the accurate prediction of the CO2 concentration and to provide a projection of the same in the near future i.e., till 2027. The proposed model is being trained with data from October 2016 to December 2020 and the performance is validated by comparing simulated results with the observed data for recent 2 years (2021 and 2022). The statistical measures for the model’s performance has been evaluated by computing the important error parameters and correlation analysis.The study demonstrated that the SARIMA model is very much suitable for forecasting the CO2 concentrations, as it provided accurate predictions by showing good agreement with the observed values.The predicted data align well with observations and capture the seasonal variations, suggesting that model developed in this study has captured the patterns and trends in the CO2 concentration data effectively and can be considered a reliable tool for predicting future CO2 concentrations. Also, the projection made till 2027 using the optimised model and it is predicted that there is slight increase in the CO2 concentrations over the Bengaluru region in the near future time i.e., 2023–2027. These inferences will contribute for understanding the CO2 concentration trends in at station scale and provide valuable insights for policymakers, researchers, and stakeholders involved in climate change mitigation and environmental planning.
Precipitation Scaling in Extreme Rainfall Events and the Implications for Future Indian Monsoon: Analysis of High-Resolution Global Climate Model Simulations
by Stella Jes Varghese, Sajani Surendran, Kavirajan Rajendran, Subimal Ghosh, Akio Kitoh, Karumuri Ashok
The increase in water holding capacity of the atmosphere with temperature, given by the Clausius-Clapeyron (CC) relationship, describes the changes in extreme rainfall intensities at warmer atmospheric states. We study the characteristics of extreme rainfall events (EREs) during the Indian summer monsoon season with respect to thermodynamic changes and precipitation-scaling over the Indian subcontinent and its homogeneous rainfall zones. We utilize outputs from a present-day climate simulation and a time-slice future climate change projection experiments of a high-resolution global climate model. Large changes are seen for very EREs (vEREs) which suggests their sensitivity to warmer temperatures. In future, the altered radiative forcing will heat up the upper atmosphere, stabilize it and offset the effect of increasing humidity on precipitation intensity. Our analysis also suggests that more convective clouds and the interplay of increased moisture content and circulation will result in future changes in EREs.
Projected changes in heat wave characteristics over India
by Neethu C & K V Ramesh
Heat waves (HW) are increasing in intensity, frequency, and duration. The projected changes in the characteristics of HW at the regional level are essential input to develop mitigation strategies to minimize social risks in densely populated regions. In this study, we examine the projected spatiotemporal changes in heat wave characteristics under different climate change scenarios using simulations of Coupled Model Intercomparison Project phase 6 (CMIP6) in seven temperature homogeneous zones of India, i.e., North West (NW), North Central (NC), West Coast (WC), East Coast (EC), Interior Peninsula (IP), Western Himalaya (WH) and North East (NE). The results show that the area of occurrence of a daily maximum temperature above 43∘C is projected to increase about 16-fold over WC, 10-fold over EC, and in other zones in the range of 1-3 fold. The warm days are projected to increase fivefold over WC and threefold over NW, EC, IP, and WH. In India, HW days are projected to increase by 7-8 days in the near future (2025-2050) and by 10-17 days in the far future (2076-2100), while under SSP585 over WH (24 days), NW (19 days), and other zones 12-15 days in the far future. EC and WC are plausible to be more vulnerable under SSP370 and SSP585, with an increase in HW intensity (>1.5∘C). The area of occurrence of long-lasting heat waves over WC is expected to have a drastic increase of more than 20-fold under all scenarios, while increasing 12-fold over IP and 8-fold over NC, EC, and WH under SSP585. The projected HW days will be more intense in the coastal zones and more frequent over WH and NW.
Optimizing LSTM and Bi-LSTM models for crop yield prediction and comparison of their performance with traditional machine learning techniques
by V. Kiran Kumar, K. V. Ramesh & V. Rakesh
Advance prediction of crop yield is very critical in the context of ensuring food security as the region specific challenges in social and environmental conditions often infringe plan of policy makers. This study presents a generic methodology to configure and fine tune the state-of-the-art Long Short-Term Memory (LSTM) based Deep Learning (DL) model through hyperparameter optimization for prediction of yield (annual crop production) in Wheat, Groundnut and Barely over India based on multiple independent input variables identified using multicollinearity test. The Monte Carlo cross-validation method is used to validate the optimized LSTM models. Results from the LSTM model tuning showed that among the 4 optimizers tested, Adam was found to perform better irrespective of the crop and Bi-LSTM outperformed sLSTM in terms of prediction accuracy. The percentage reduction in error with Bi-LSTM compared to sLSTM in predicting wheat and groundnut crop yield was 39% and 13% respectively while in case of barley crop, error reduction was marginal (0.34%). The performance of optimized Bi-LSTM model is compared with the performance of traditional machine learning (ML) models such as support vector regression (SVR) and SVR polynomial {2nd and 3rd order}, Auto Regressive Integrated Moving Average (ARIMA) and ARIMAX (ARIMA with exogenous variables) and Vector Auto-regression (VAR). The Bi-LSTM model is found to be superior to ML models; the percentage reduction in mean absolute scaled error with the Bi-LSTM compared to the best performing ML model was 94%, 72%, and 71% in predicting wheat, groundnut and barley yield respectively. This study showed that by choosing proper explanatory (independent) variable and hyperparameter optimization, a simple (single layer) structure of deep neural network (LSTM) outperformed traditional ML models in terms of accuracy for crop yield prediction application.
Page 5 of 33
