•A non-stationary meteorological drought index is developed using large-scale climate indices.•A copula-based bivariate analysis of drought properties is carried ...out.•Reliability-Resilience-Vulnerability of the non-stationary drought index is evaluated.
The most of the available drought indices do not incorporate the environmental changes in the present scenario of climate change. In an attempt to encompass the climate variability in the computation of meteorological drought, a non-stationary gamma distribution with climate indices in its location parameter as a covariate is proposed. The performance of the non-stationary drought is evaluated based on the statistical performance as compared to the stationary drought. Focusing on two Himalayan states in India, the meteorological drought events are described and assessed based on the stationary and non-stationary drought index. Moreover, the bivariate analysis of different drought properties is carried out and compared with the univariate analysis. The management indices such as reliability, resilience, and vulnerability are also computed based on the developed drought index. The results in the study indicate that in most of the cases the non-stationary drought index is capable of capturing the drought characteristics over the study areas. The variability in the probability density of different drought properties is observed under 12-month drought scale in most of the cases. During bivariate analysis, a compare difference is noticed between secondary and primary return periods. Moreover, higher reliability and resilience is noticed during 12-month scale drought period. The newly developed drought index and the copula-based analysis of drought properties provide a new concept for robust and effective management practices in the changing environment.
•Nonstationary COVID 19 risk analysis combining climatic and socioeconomic factors.•Strong climate influence on COVID 19 cases was observed in 474 (76.08%) out of 623 districts.•The total population ...in 50% of districts in 19 out of 35 states were lying under high risk.
This study investigates the influence of climate variables (pressure, relative humidity, temperature and wind speed) in inducing risk due to COVID 19 at rural, urban and total (rural and urban) population scale in 623 pandemic affected districts of India incorporating the socioeconomic vulnerability factors. We employed nonstationary extreme value analysis to model the different quantiles of cumulative COVID 19 cases in the districts by using climatic factors as covariates. Wind speed was the most dominating climatic factor followed by relative humidity, pressure, and temperature in the evolution of the cases. The results reveal that stationarity, i.e., the COVID 19 cases which are independent of pressure, relative humidity, temperature and wind speed, existed only in 148 (23.7%) out of 623 districts. Whereas, strong nonstationarity, i.e., climate dependence, was detected in the cases of 474 (76.08%) districts. 334 (53.6%), 200 (32.1%) and 336 (53.9%) districts out of 623 districts were at high risk (or above) at rural, urban and total population scales respectively. 19 out of 35 states were observed to be under high (or above) Kerala, Maharashtra, Goa and Delhi being the most risked ones. The study provides high-risk maps of COVID 19 pandemic at the district level and is aimed at supporting the decision-makers to identify climatic and socioeconomic factors in augmenting the risks.
Abstract
The Narmada basin is one of the major river basins of Central India. The basin frequently experiences droughts and floods due to its geography and uneven topography. Therefore, it is ...important to understand the spatiotemporal variability of hydroclimatic extremes over the basin. Large-scale climate oscillations (LSCOs) have been observed significantly affecting the patterns of hydroclimatic extremes at the basin and continental scale. In this study, we have analysed the relative influence of LSCOs (EL Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Atlantic multidecadal oscillation (AMO)) over hydroclimatic extremes of the Narmada basin. Precipitation, temperature, and streamflow extremes were analysed in stationary and nonstationary frameworks of generalized extreme value distribution. The precipitation extremes, PRCPTOT and R95p were observed significantly influenced by ENSO, IOD, and AMO individually whereas extreme Rx5day was relatively more influenced by ENSO and AMO individually and collectively. Temperature extremes, TXx was significantly more influenced by ENSO alone (26.47% of the region), while TNx was observed to be substantially more influenced by ENSO and AMO. The upper Narmada basin was found vulnerable to flooding and whereas the basin was projected to experience more frequent and intense heatwave-associated disasters in long term.
A comprehensive assessment of compound hot and dry extremes based on different drought conditions (low precipitation, runoff, or soil moisture) and associated uncertainties is necessary to fully ...understand the possible risks. Here, we analyze changes in the likelihood of compound hot and dry conditions associated with low precipitation, runoff, and soil moisture using Coupled Model Intercomparison Project Phase6 (CMIP6) simulations for present‐day climate (+1°C) and additional global warming levels (+1.5°C, +2°C, +3°C). Further, we investigate the contributions of different components (e.g., global warming levels, climate models, copula types) to the total spread in their future projections. Results show the significance of global warming levels in governing risks of rising compound hot and dry extremes. The hotspot regions include the Mediterranean, South Central America, Amazonia, and Sahara. The rising risks are also accompanied by rising uncertainty as the spread in changing likelihood is significantly contributed by Earth System Models (ESMs), global warming levels, their interactions, and the statistical estimation error. The uncertainty due to ESMs spread was observed to be most significant in the case of compound hot and low soil moisture extremes, which also corresponds to some of the most impactful conditions. It was observed that the estimation error dominates the uncertainty in compound hot and low precipitation extremes as compared to the two other combinations. Our findings indicate that the regional likelihood and associated uncertainties of compound hot‐dry events in CMIP6 projections are functions of both the selection of drought types and the methodology of deriving the joint extremes.
Plain Language Summary
Concurrent hot and dry events can have devastating impacts on human health and ecosystems. Recently, it has been realized that compound hot and dry events have become more frequent under increasing global warming. But the projections of compound hot and dry extremes are accompanied by uncertainties at different levels. Therefore, assessment of both the occurrence under different global warming levels as well as the understanding of uncertainty is equally important for deciding effective risk management strategies. The investigation of the effects of human‐induced climate for compound hot and dry extremes has been mainly focused on the effects associated with meteorological droughts. Since, the projected changes in the compound occurrences of hot and different drought conditions could lead to varied impacts for different sectors, a more comprehensive approach is required. Here, we estimate the changes in the occurrence of compound hot and dry extremes using the climate model simulations under four global warming levels and identify the major sources of uncertainty in projections. We find that a significant increase in likelihood is expected at increasing warming levels with considerable drought dependent uncertainty which is contributed by the climate models, warming magnitude and the effect of sample size.
Key Points
Analysis of future changes in compound hot and different dry extremes in Coupled Model Intercomparison Project Phase6
Likelihood and uncertainty of compound hot and dry extremes increase at different global warming levels
Considerable dry condition dependent uncertainty in the projections is contributed by the warming levels, Earth System Models and the estimation error
Duration and severity are the two most important parameters used for drought characterization. In this study, we used a bivariate copula‐based approach to understand the joint dependence of drought ...duration and severity of three different drought types. Three types of bivariate copulas (Gumbel, Frank and Plackett) are estimated for modelling and the best fit copula is selected over 1,162 grid points (at a resolution of 0.5° × 0.5°) of India. Further, the joint dependence of drought duration and severity are analysed to infer important properties in terms of exceedance probability and return periods. Finally, conditional probability and conditional return periods of drought characteristics are also derived, which could be useful for proper planning and management of the water resource system. From the investigation, it is observed that drought events in the Western and Central India are longer and more severe whereas the ones in the south Indian river basins are more frequent but less severe. Moreover, similar results were also obtained for the conditional probability and conditional return periods. This study provides information regarding the severe and longer drought event hotspots all over the study area and thus helpful for the policymakers in developing effective drought prevention and mitigation strategies comprehensively at a national scale.
The copula‐based approach is used to understand the joint dependence of drought duration and severity. The study is carried out over major river basins in India. Investigates the exceedance probability and joint return period of different drought events. Additionally, the conditional approach is also applied to probability and return period. We used a bivariate copula‐based approach to understand the joint dependence of drought duration and severity of three different drought types. From the investigation, it is observed that the river basins in the Southern part of India have a higher exceedance probability and smaller joint return period compared to the Western river basins of India. Further, investigation suggests that drought events in the Western and Central India are more severe and longer whereas the ones in the south Indian river basins are more frequent but less severe.
The hydro-climatic variables are greatly influenced by the large-scale phenomena at global and regional scales. The present study attempts to characterise the influence of large-scale climatic ...oscillations on the monthly precipitation over meteorologically homogeneous regions in India. To accomplish the study, the monthly precipitation over selected six different regions are obtained during 1951–2015 and correlations with the eight large-scale climatic oscillations namely, Indian Ocean Dipole (IOD), Sea Surface Temperature (SST), Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Indian Summer Monsoon Index (ISMI) are examined using wavelet and global coherence. The outcomes from the analysis suggest that though other climatic indices have noticeable effects on the monthly precipitation over India, the ISMI is the most effective climatic teleconnection. The predominant and effective period of ISMI is at intra-annual scale influencing Central Northeast India (CNI), Peninsular India (PI), and West Central India (WCI), while the major effective period of IOD is in between 8 and 16 months. For the El Niño–Southern Oscillation (ENSO) indices like SST, SOI, and MEI the most prominent period is noticed during 20 to 54 months time scale over different parts of India. The phase difference is not uniform between the studied climatic oscillations and monthly precipitation across the country. For long terms of ISMI, an in-phase situation is observed over all the meteorologically homogenous regions in India. The present study advocates that the wavelet and global coherence approaches are very powerful tools to analysing the relationship between multiple time-series in a time-frequency space and its application in hydrology enables the water resources managers in developing better understanding of meteorological connections with the large-scale low frequency climatic oscillations.
•Influence of large-scale climatic oscillations is analysed over the precipitation homogeneous regions in India.•Wavelet and global coherence approaches are used to perform the analysis.•At annual scale, the Indian Summer Monsoon Index (ISMI) has significant correlation/effects over all the regions in India.•The present study will enable water resources managers in developing sustainable water resources management practices.
Analysing the link between terrestrial ecosystem productivity (i.e., Net Primary Productivity: NPP) and extreme climate conditions is vital in the context of increasing threats due to climate change. ...To reveal the impact of changing extreme conditions on NPP, a copula-based probabilistic model was developed, and the study was carried out over 25 river basins and 10 vegetation types of India. Further, the resiliency of the terrestrial ecosystems to sustain the extreme disturbances was evaluated at annual scale, monsoon, and non-monsoon seasons. The results showed, 15 out of 25 river basins were at high risks, and terrestrial ecosystems in only 5 river basins were resilient to extreme climatic conditions. Moreover, at least 50% area under 4 out of 10 vegetation cover types was found to be facing high chances of a drastic reduction in NPP, and 8 out of 10 vegetation cover types were non-resilient with the changing extreme climate conditions.
The variability in the extreme rainfall events is of growing concern in the context of climate change. Several high rainfall events have occurred in India in recent years and simulations from the ...Intergovernmental Panel on Climate Change suggest a rise in extremes. The low‐frequency global‐scale modes/oscillations are widely considered as the significant drivers of inter‐annual variability of the Indian rainfall pattern and extreme rainfall events. To account for climate external forcings, we assessed the influence of El Nino Southern Oscillation, Indian Ocean Dipole and Atlantic Multidecadal Oscillation on extreme precipitation over 24 major river basins of India using the nonstationary extreme value analysis. Moreover, the uncertainty in the parameters of the fitted nonstationary extreme value distribution is assessed using Bayesian inference. It was found that extreme precipitation events in the country are dominated by these oscillations, especially in central India. Moreover, the return levels of high rainfall were found to be intensifying with increasing return period. We also observed that uncertainty in return levels was significant in almost every river basin. The results presented here contribute to a better understanding of the large‐scale climate variability and its impact on high rainfall pattern, which would provide an essential understanding of the rainfall‐induced hazard prevention and enhance the risk management strategy.
Nonstationary extreme value theory and Bayesian analysis was used to detect the influence of El Nino Southern Oscillation, Indian Ocean Dipole and Atlantic Multidecadal Oscillation in governing extreme precipitation. Analysis was carried out for calculating the return levels of extreme precipitation over 24 major river basins of India. Results show that the association of the oscillations makes extreme precipitation more prolong and intense.
•Study reveals high-risk coastal wetlands for extreme rainfall events in India.•Inundation maps highlight the susceptibility of coastal wetlands to flooding.•Urbanization and sedimentation contribute ...to flood risk in Thane Creek.•The study emphasizes the need for managing and protecting infrastructure in wetland.
Wetlands are often found in areas that undergo periodic flooding, such as coastal seas, lakes, and rivers. Coastal wetlands are particularly vulnerable to climate change effects, such as changes in precipitation patterns, risk of extreme rainfall, and cyclones/storms. This study assessed the uncertainties associated with extreme rainfalls in terms of return levels (RLs; 20 and 50 years) and quantified the potential risk level of these events in the future for coastal wetlands in India. The extreme precipitation indices (EPIs) were evaluated using a non-stationary approach, and the results showed that Thane Creek had the highest RLs, followed by Kolleru Lake. The risk level for each wetland was assessed using the fuzzy logic approach, which considered parameters such as exposure, vulnerability, and threat. The overall risk assessment showed that Thane Creek, Kolleru Lake, Pallikaranai Marsh Reserve Forest, and Tampara Lake are at a “High” risk level for both RLs of EPIs. Furthermore, the automated Shortwave Infrared (SWIR) thresholding technique was employed in Google Earth Engine to create inundation maps of wetlands. This study also indicated that Thane Creek is at risk of flooding based on the analysis of spatiotemporal changes. The impact evaluation of Thane Creek showed that rapid urbanization has encroached upon the creek's boundaries. Therefore, the variability of EPIs may be affected by climatic oscillations, leading to an upsurge in extreme rainfalls, causing the coastal wetlands to flood. Policymakers can use these findings to develop effective strategies for the proper management of coastal wetlands.
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•Present models performed well in estimating watershed parameter (ω) in India.•Machine learning techniques assessed ω better than regression methods.•Regional Bio-geographical ...attributes explained precipitation partitioning better.•Vegetation explained higher variance in ω in the low vegetation months.
Evaluation of the engrossment of watershed surface characteristics on partitioning of precipitation to runoff and evapotranspiration is key to inspect the availability of water at watershed scale. It is more evident in the cases of ungauged watersheds. The present study develops models using multiple linear regression method and machine learning techniques (ANN: Artificial Neural Network and RVM: Relevance Vector Machine) over 793 (25 major river basins and 768 watersheds across India) to estimate the watershed parameter ‘ω’ (in Fu’s Budyko based equation) that represents intrinsic watershed attributes. In addition, seasonality factor is incorporated in the model due to intra-annual variability in vegetation across India. The models attempt to explain the intricate relationship between vegetation alterations and regional water balance. It is seen that the ANN and RVM models have performed better in estimating ω, than the MLR (Multiple Linear Regression) models. In addition, NDVI has shown more engagement in explaining the partitioning process of water in intra-annual low NDVI period compared to high NDVI period. We have also found the present models to be more accurate than the previously developed Budyko based methods in predicting ω. The newly improved models have closely imitated the intrinsic basin attributes and enhanced the functionality of Budyko framework in estimation of water availability, which would play a crucial role in assessment of hydrology of ungauged watersheds of India.
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