Trend analysis of rainfall is often carried out in water resources management to understand its distribution over a given region. The cumulative seasonal and annual rainfall derived from monthly datasets spanning 102 years (1901–2002) for 11 districts of the semi-arid Karnataka, India, was used for the trend analysis. The two-step homogeneous test approach was carried out on all the time series. Then, lag-1 autocorrelation was conducted only on homogeneous time series. Only 78.18 % of the total time series data were detected as homogeneous, and 95.35% of time series data were found to have insignificant autocorrelation. Then, the Innovative Trend Analysis (ITA) method was applied to 43 homogeneous rainfall time series, as well as to 41 time series using the MK and SR tests, and to two time series using the mMK test. The MK and SR tests detected a significant trend in 14.63% of the time series, while the ITA method was able to detect a trend in 93.02% of the total time series data. The MK and SR tests revealed significant trends in winter and post-monsoon season precipitation for two districts, but only for one district in the case of summer and annual rainfall. No trend was identified for monsoon season precipitation. The mMK test showed a positive trend for the post-monsoon season in a district, while the ITA method revealed significant trends for all seasons in most districts. The sub-trend analysis revealed trends that traditional methods were unable to detect.
by G.K. Patra, Kantha Rao Bhimala, Ashapurna Marndi, Saikat Chowdhury, Jarjish Rahaman, Sutanu Nandi, Ram Rup Sarkar, K.C. Gouda, K.V. Ramesh, Rajesh P. Barnwal, Siddhartha Raj, Anil Saini
Abstract
Deep learning (DL) is a very powerful computational tool for various applications in scientific and industrial research which can be real-time implemented for societal benefits. Several factors impact the development of optimized DL models for better prediction including the amount of quality sample data, domain-specific knowledge, and the architecture of the model for extraction of the useful features/patterns from the data. The present chapter demonstrates the state-of-the-art DL methodologies used by the researchers from different laboratories under the Council of Scientific and Industrial Research (CSIR), India to solve important research activities across several sectors like Medical, Healthcare, Agriculture, Energy, etc. The Convolutional Neural Network (CNN) techniques are utilized for Tumor diagnosis, classifying molecular subtypes of glioma tissues, and predicting driver gene mutations in glioma. Similarly, the Long short-term memory (LSTM) model is applied for the assessment of crop production, and transfer learning is used for the classification of tea leaves. Further, the ensemble LSTM methodology is implemented for short-term prediction of wind speed to enhance the renewable energy sectors. Finally, the multivariate LSTM models were developed by integrating the weather parameters for the prediction of covid-19 spread over different states in India which is an input for policy planning and supply chain management during the pandemic time. All the use cases are being validated and the results are quite satisfying and provide confidence for the real-time application of DL for scientific and industrial research and societal benefit to the common people.
Source: https://doi.org/10.1016/bs.host.2022.12.002
by Priya Singh, Yogendra Bhaskar, Pulkit Verma, Shweta Rana, Prabudh Goel, Sujeet Kumar, Krushna Chandra Gouda, Harpreet Singh
Background: The emergence of coronavirus disease (COVID-19) as a global pandemic has resulted in the loss of many lives and a significant decline in global economic losses. Thus, for a large country like India, there is a need to comprehend the dynamics of COVID-19 in a clustered way.
Objective: To evaluate the clinical characteristics of patients with COVID-19 according to age, gender, and preexisting comorbidity. Patients with COVID-19 were categorized according to comorbidity, and the data over a 2-year period (1 January 2020 to 31 January 2022) were considered to analyze the impact of comorbidity on severe COVID-19 outcomes.
Methods: For different age/gender groups, the distribution of COVID-19 positive, hospitalized, and mortality cases was estimated. The impact of comorbidity was assessed by computing incidence rate (IR), odds ratio (OR), and proportion analysis.
Results: The results indicated that COVID-19 caused an exponential growth in mortality. In patients over the age of 50, the mortality rate was found to be very high, ~80%. Moreover, based on the estimation of OR, it can be inferred that age and various preexisting comorbidities were found to be predictors of severe COVID-19 outcomes. The strongest risk factors for COVID-19 mortality were preexisting comorbidities like diabetes (OR: 2.39; 95% confidence interval (CI): 2.31–2.47; p < 0.0001), hypertension (OR: 2.31; 95% CI: 2.23–2.39; p < 0.0001), and heart disease (OR: 2.19; 95% CI: 2.08–2.30; p < 0.0001). The proportion of fatal cases among patients positive for COVID-19 increased with the number of comorbidities.
Conclusion: This study concluded that elderly patients with preexisting comorbidities were at an increased risk of COVID-19 mortality. Patients in the elderly age group with underlying medical conditions are recommended for preventive medical care or medical resources and vaccination against COVID-19.
by K. C. Gouda, Sudhansu S. Rath, Nidhi Singh, Sayanta Ghosh & Renu Lata
Extreme rainfall events (EREs) are very localized and intense events witnessing heavy rainfall resulting in flash floods and landslides in the Himalayan region. The trend of EREs in the state of Himachal Pradesh shows an increasing trend, so in this study, an attempt is being made for quantifying the climatological feature of these EREs in the state of Himachal Pradesh in the western Himalayan mountain region. Multi-source observed datasets for the long-term period, i.e., 1901 to 2020, are considered for the analysis. The climatology analysis indicates that northern and southern parts of Himachal Pradesh receive comparatively more rainfall, and the state witnessed a decreasing trend of the rainfall in the current decade. The classifications of these EREs in terms of monsoon and non-monsoon with a different threshold of daily rainfall are being analyzed, and it is found that almost 90% of extreme events are observed in the monsoon season. The zone centered around 32.5° N and 76.25° E seems to be the hotspot for the frequent EREs in the mountainous state. The spatial analysis of the ERE trend also indicated that almost 40% of the state witnesses the heavy rainfall throughout the years. This observational study will surely help in understanding the dynamics of these EREs in the higher altitude regions and can be used for hydro-meteorological disaster management and mitigation.
by Smrati Purwar, V. Rakesh, Ajay Bankar & G. N. Mohapatra
A strong equatorial flow at lower troposphere exists over the Indian Ocean plays a crucial role in modulating the Indian Summer Monsoon Rainfall (ISMR) referred as monsoon low-level jet (MLLJ). The variation in strength and height of MLLJ can provide an important lead in predicting the variations in ISMR. The variability of MLLJ is examined using the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis ERA-5; National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)/Climate Forecast System, version 2 (CFSv2); the Modern-Era Retrospective Analysis for Research and Application, version-2 (MERRA-2); and radiosonde data during 40-year period (1979–2018). The results suggest that there is an increasing trend in MLLJ strength during July and September, but a decreasing trend is observed during August. The MLLJ height showed an increasing trend in all the data sets. Significant interannual oscillations in MLLJ strength and height are observed with a prominent periodicity of 2–3 years. The ISMR over Western Ghats region and central India showed strong positive correlation with the MLLJ strength as well as height except during August. It is noted that the influence of the MLLJ on rainfall over the Indian landmass is stronger during the onset and withdrawal phases of monsoon. The increasing trend in MLLJ height indicated the possibility of increase in rainfall over many regions in India especially Western Ghat and Central India.
Source: https://doi.org/10.1007/s00704-022-04301-3
- Exacerbation of Indian Summer Monsoon Breaks by the Indirect Effect of Regional Dust Aerosols
- Assessing the impact of modified LULC on extreme hydrological event over a complex terrain: A case study for kodagu 2018 flood event
- Association of winter vegetation activity across the indo-gangetic plain with the subsequent Indian summer monsoon rainfall
- Seismic hazard potential of Kachchh paleo-rift basin of Indian craton from seismic and geodetic strain rates