Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Past versions of the vulnerability indices have shown the ability to detect susceptible regions by assessing climatic and socioeconomic parameters at local scales. These parameters significantly vary over geographic regions, therefore such an index may not be suitable to identify and predict susceptibility over a large domain. The present endeavour aims to develop a new vulnerablity index that identifies and predicts the spatiotemporal imprint of extreme drought and flood cases at various scales in India by analyzing monthly observed and Coupled Model Intercomparison Phase 5 (CMIP5) rainfall data at a spatial scale of 1° × 1° from 1901 to 2100. It is proposed by consolidating the outcomes of the Standard Precipitation Index (SPI) at different time scales, such as 3 and 12 months, along with the weights of individual grids. The weights of individual grids are calculated through the occurrence of extreme drought and flood years in the recent past to include a climate change factor in the proposed index. Based on the spatial distribution of high index values, the vulnerable regions concerning extreme droughts are expected to be in the Northeast, Northeast-central, East-coast, West, Northwest, North-central and some grids in South India. Similarly, vulnerable regions concerning extreme flood cases are likely to be in the Northeast, West-coast, East-coast and some grids in the Peninsular region. Furthermore, a conceptual model is presented to quantify the severity of extreme cases. The analyses reveal that on the CMIP5 model data, 2024, 2026–2027, 2035, 2036–2037, 2043–2044, 2059–2060 and 2094 are likely to be the most prominent extreme drought years in all India monsoon rainfall and their impacts will persist for a longer time than others. Similarly, the most prominent extreme flood cases are likely to occur in the year 2076, 2079–2080, 2085, 2090, 2092 and 2099.

Department of Database Systems

School of Computer Science and Engineering

Vellore Campus

World University root node

Observed and projected changes in extreme drought and flood-prone regions over India under CMIP5 RCP8.5 using a new vulnerability index

The proposition of a new algorithm facilitates the predictability of weak/strong monsoons that lead to drought/flood events, respectively, in the Indian summer monsoon rainfall (ISMR). The proposed method estimates skewed Gaussian kernel distribution in the extreme values extracted from the rainfall series and confidence levels of drought and flood years are obtained using bootstrap. Using the selected Coupled Model Intercomparison Phase 5 (CMIP5) simulations under representative concentration pathways (RCP) 8.5 scenario, the proposed method detects that extreme droughts (at 99{\%} confidence level) in India are likely to occur in 2024 and 2027 in the early 21st century. Similarly, models project that 2031, 2032 and 2033 will be the most prominent flood years. It is projected that the probability of drought occurrence is likely to increase by 16{\%}. In contrast, it is expected to diminish flood events by 11{\%} in the future under projected global warming. Notably, our analysis reveals that 23.4{\%} of grids covering {\~{}}30{\%} of the Indian region are likely to experience increased frequency and intensity of droughts during 2020–2029, mainly covering the Northeast, Central and Southern India. Furthermore, during this period, the Northeast and some parts in the North would experience floods over 29.6{\%} (which covers {\~{}} 39{\%}) of the total grids. The proposed algorithm may be used for drought and flood monitoring over any geographical terrain.

Theoretical and Applied Climatology

A novel method to detect drought and flood years in Indian rainfall associated with weak and strong monsoon

Drought is a function of time as well as climate variables such as temperature and precipitation. The process of drought forming is slow and it manifests at different time scales, which adversely affects the economy of a country. The identification and characterization of droughts at various spatiotemporal scales are of great importance. It helps in the planning and management of water resources, policymaking and agribusiness industries. In the present paper, the Cauvery River basin is chosen as a study area to analyze the changes in the frequency distribution of extreme droughts and duration, with the combined effect of evapotranspiration and rainfall. The drought indices such as Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) are implemented on monthly rainfall data and potential evapotranspiration of resolution 0.25° × 0.25° long./lat. for the period 1931–2010. The results reveal that the frequency of the extreme droughts over the basin has significantly increased over the post-era of global warming. The increased rate of extreme droughts is particularly evident in downstream of the basin, mainly due to the increase in temperature and deficit rainfall. Further, the implementation of continuous wavelet transform reveals that SPI at 3-(SPI-3) and 12-(SPI-12) month scale are associated with extended reconstruction of sea surface temperature (ERSST) in anti-phase and in-phase, respectively. It is concluded that the in-phase association of SPI-12 and ERSST enhances the drought situation compared to the anti-phase link of SPI-3 and ERSST.

Natural Hazards

Spatiotemporal characteristics of extreme droughts and their association with sea surface temperature over the Cauvery River basin, India

The presence of a sparse rain gauge network in complex terrain like the Himalayas has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-based gridded rainfall data for highly prevalent events like cloudbursts over the northwest Himalayas (NWH). To facilitate the abovementioned task, we intend to evaluate the performance of these observations at 0.25° ☓ 0.25° (latitude–longitude) resolution against a predefined threshold (i.e., 99.99th percentile), thereby initially comprehending the success of IMD data in capturing the cloudburst events reported in media during 2014–16. Further, seven high-resolution satellite products, namely, CMORPH V0.x, PERSIANN-CDR, TMPA 3B42RT V7, IMERG V06B, INSAT-3D multispectral rainfall (IMR), CHIRPS V.2 and PERSIANN-CCS are evaluated against the IMD dataset. The following are our main results. 1) Six out of 18 cloudburst events are detected using IMD gridded data. 2) The contingency statistics at the 99.99th percentile reveal that the probability of detection (POD) of TMPA varies from 19.4{\%} to 53.9{\%} over the geographical stretch of NWH, followed by PERSIANN-CDR (18.6{\%}–48.4{\%}) and IMERG (4.9{\%}–17.8{\%}). 3) A new metric proposed as improved POD (IPOD) has been developed in this work, which takes into account the temporal lag that exists between observed and satellite estimates during an event period. Results show that for an event analysis IPOD provides a better comparison. The IPOD for TMPA is 32.8{\%}–74.4{\%}, followed by PERSIANN-CDR (34.4{\%}–69.11{\%}) and IMERG (15.3{\%}–39.0{\%}). 4) The conclusion stands as precipitation estimates obtained from CHIRPS are most suitable for monitoring cloudburst events over NWH with IPOD of 60.5{\%}–78.6{\%}.

Fulltext

Journal of Hydrometeorology

Performance analysis of imd high-resolution gridded rainfall (0.25° ☓ 0.25°) and satellite estimates for detecting cloudburst events over the Northwest Himalayas

India's agriculture is a significant component of the Gross National Product and is largely dependent on the Indian summer monsoon. This makes an accurate prediction of rainfall a key factor in improving agricultural production. A statistical cycle, widely known to have a strong 2.85-year period and will therefore be called a triennial oscillation here, plays a vital role in such predictions. In the present work, we examine the spatial distribution of this short-period oscillation at 1° × 1° (lat./long.) resolution using 1,260-month data (1901–2005). A power spectral analysis of the observed data set shows that the statistically significant triennial oscillation (at 95{\%} confidence level) is present over 54{\%} of the total number of 354 grids across India, which covers 69.03{\%} area. Projections of selected data from models used in Coupled-Model Inter-comparison Project Phase 5 predict weakening of this oscillation (in amplitude) by 2100. The projections indicate reduced confidence levels of this oscillation at 80{\%}, 85{\%} and 90{\%}, respectively, in 3{\%}, 20{\%} and 62{\%} of the selected 54{\%} grids, which covers 41.3{\%} area. A weakened triennial monsoon cycle will have a severe impact on agriculture and water resource management, particularly over the southwest-coastal, northern and northeast-central parts of India.

Earth and Space Science

Weakening of Triennial Oscillation of the Indian Summer Monsoon Rainfall (at 1° × 1° Gridded Scale) Under Future Global Warming

The Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 precipitation data has been extensively used for inter-comparison with observations and model validation. The rain distribution over the Northwest Himalaya (NWH) were found to be accurate with a strong positive correlation of 0.88 between TRMM and India Meteorological Department (IMD) station data, supporting the use of 3B42 V7 for the study of extreme rainfall events (ERE's) over the region. However, many high resolution satellite data sets were made available in the recent past and their potential have not been evaluated for ERE's like cloudbursts in the NWH. The present endeavor aims to provide guidance to the choice of global precipitation data sets (GPDs). In particular, this study is conducted to evaluate three recent satellite-based rainfall products, i.e. Global Precipitation Measurements (GPM), Indian National Satellite System (INSAT 3D) and CPC Morphing Technique (CMORPH), against the highly used TRMM-RT 3B42 V7 precipitation data for the estimation of rainfall episodes in the recent years (2014-2016). Our results reveal that the magnitude of precipitation and location of peak rainfall are biased in INSAT 3D, whereas CMORPH and the high resolution GPM product capture it with relatively higher values of the employed statistical metrics. Also, the rainfall estimates from GPM and CMORPH are in good agreement with TRMM for cloudbursts events. Particularly, high resolution GPM is useful for monitoring the extreme rainfall event in the region.

Himalayan Weather and Climate and their Impact on the Environment

Inter-comparison of high resolution satellite estimates for cloudburst events in the northwest himalaya

Mathematical Science International Research Journal

MEAN CHANGE YEARS DETECTION IN AVERAGE TEMPERATURE USING FUNCTIONAL DATA ANALYSIS: A CMIP5 MODEL ANALYSIS

The annual cycle of Indian monsoon rainfall plays a critical role in the agricultural as well as the industrial sector. Thus, it is necessary to evaluate the behaviour of the monsoon annual cycle in a warming climate. There are several studies on the variability and uncertainty of the Indian monsoon. This study, examines the impact of climate change on the annual cycle of monsoon rainfall in India from 1871-2100 by applying 20 model simulations designed by theWorld Climate Research Programme (WCRP) coupled with the model inter-comparison Project 5 (CMIP5). It is found that the models MPI-ESM-LR, INM-CM4 and MRI-CGCM3 best capture the spatial patterns of the monsoon rainfall peak month (MRPM) of the winter monsoon compared to observations, whereas HadGEM2-AO and MIROC-ESM-CHEM best capture the MRPM of the summer monsoon. The MIROC, MIROC-ESM and MIROC-ESM-CHEM models best capture the average rainfall intensity as well as the MRPM of all-India rainfall. This paper examines the future spatial distribution of the MRPM for meteorological sub-divisions of India, that can have crucial implications for water resources and management. Although the future projections as per the CMIP5 models indicate no changes in the MRPM of the all-India rainfall, a reduction in average intensity can be expected. The projections indicate a shift in the MRPM in some meteorological sub-divisions, particularly with regard to the summer monsoon but no significant change has been projected for the winter monsoon. For example, the summer monsoon MRPM is projected to move from July to August in northern and central India.

Climate

CMIP5 projected changes in the annual cycle of Indian monsoon rainfall

Monsoons are the life and soul of India's financial aspects, especially that of agribusiness in deciding cropping patterns. Around 80{\%} of the yearly precipitation occurs from June to September amid monsoon season across India. Thus, its seasonal mean precipitation is crucial for agriculture and the national water supply. From the start of the 19th century, several studies have been conducted on the possible increments in Indian summer monsoon precipitation in the future. Unfortunately, none of them has endeavoured to discover the models whose yield give the best fit to the observed data. Here some statistical tests are performed to quantify the models of Coupled Model Inter-comparison Project 5 (CMIP5). Then, after, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to select optimum models. It shows that four models, CCSM4, CESM1-CAM5, GFDL-CM3 and GFDL-ESM2G, best capture the pattern in Indian summer monsoon rainfall over the historical period (1871-2005). Further, Student's t-test is utilized to estimate the significant changes in meteorological subdivisions of selected optimum models. Also, our results reveal the Indian meteorological subdivisions which are liable to encounter significant changes in mean at confidence levels that differ from 80{\%} to 99{\%}.