H. Vittal

Do dynamic regional models add value to the global model projections of Indian monsoon

Dynamic Regional Climate Models (RCMs) work at fine resolution for a limited region and hence they are presumed to simulate regional climate better than General Circulation Models (GCMs). Simulations by RCMs are used for impacts assessment, often without any evaluation. There is a growing debate on the added value made by the regional models to the projections of GCMs specifically for the regions like, United States and Europe. Evaluation of RCMs for Indian Summer Monsoon Rainfall (ISMR) has been overlooked in literature, though there are few disjoint studies on Indian monsoon extremes and biases. Here we present a comprehensive study on the evaluations of RCMs for the ISMR with all its important characteristics such as northward and eastward propagation, onset, seasonal rainfall patterns, intra-seasonal oscillations, spatial variability and patterns of extremes. We evaluate nine regional simulations from Coordinated Regional Climate Downscaling Experiment and compare them with their host Coupled Model Intercomparison Project-5 GCM projections. We do not find any consistent improvement in the RCM simulations with respect to their host GCMs for any of the characteristics of Indian monsoon except the spatial variation. We also find that the simulations of the ISMR characteristics by a good number of RCMs, are worse than those of their host GCMs. No consistent added value is observed in the RCM simulations of changes in ISMR characteristics over recent periods, compared to past; though there are few exceptions. These results highlight the need for proper evaluation before utilizing regional models for impacts assessment and subsequent policy making for sustainable climate change adaptation.

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.

India’s agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

Lack of Dependence of Indian Summer Monsoon Rainfall Extremes on Temperature: An Observational Evidence

The intensification of precipitation extremes in a warming world has been reported on a global scale and is traditionally explained with the Clausius-Clapeyron (C-C) relation. The relationship is observed to be valid in mid-latitudes; however, the debate persists in tropical monsoon regions, with the extremes of the Indian Summer Monsoon Rainfall (ISMR) being a prime example. Here, we present a comprehensive study on the dependence of ISMR extremes on both the 2 m surface air temperature over India and on the sea surface temperature over the tropical Indian Ocean. Remarkably, the ISMR extremes exhibit no significant association with temperature at either spatial scale: neither aggregated over the entire India/Tropical Indian Ocean area nor at the grid levels. We find that the theoretical C-C relation overestimates the positive changes in precipitation extremes, which is also reflected in the Coupled Model Intercomparison Project 5 (CMIP5) simulations. We emphasize that the changing patterns of extremes over the Indian subcontinent need a scientific re-evaluation, which is possible due to availability of the unique long-term in-situ data. This can aid bias correction of model projections of extremes whose value for climate adaptation can hardly be overemphasized, especially for the developing tropical countries.

Urbanization causes Nonstationarity in Indian Summer Monsoon Rainfall Extremes

Global and local environmental changes are likely to introduce nonstationarity in the characteristics of Indian Summer Monsoon Rainfall (ISMR) extremes. Here, we perform a nonstationary frequency analysis on ISMR extremes in a Generalized Additive Model for Location, Scale and Shape (GAMLSS) framework with a cluster of 74 models, considering nonstationarity in different possible combinations. Interestingly, we observe significant nonstationarity in ISMR extremes in urbanizing/developing-urban areas (transitioning from rural to urban), compare to completely urbanized or rural areas. This presents a postulation that the extent of urbanization plays a significant role in introducing nonstationarity in ISMR extremes. We emphasize the effect of urbanization in changing the character of ISMR extremes, which further needs a scientific re-evaluation by implementing physics-based modeling. The impact of these observational studies will be critical in correcting the bias of model projections of ISMR.

A Framework for Investigating the Diagnostic Trend in Stationary and Nonstationary Flood Frequency Analyses Under Changing Climate

The Nonstationary analysis drew formidable attention to the flood frequency analysis (FFA) research community due to analytically perceivable impacts of climate change, urbanisation and concomitant land use pattern on the flood event series. Albeit, the inclusion of nonstationarity in FFA significantly enhanced the accurate estimation of the return period, however, its application is questionable when the flood variables (FV) are not having persisting significant nonstationarity. In such cases, the assumption of stationarity is still valid and will direct to accurate estimation of the flood quantiles. Hence, prior to conducting the comprehensive FFA, it is vital to inspect the existence of stationarity/nonstationarity in the FV. This can be accomplished by a comprehensive trend analysis. The aim of present study is to emphasize the importance of a comprehensive trend analysis during FFA by proposing a framework to conduct the same. Further, the proposed framework has been demonstrated on unregulated daily streamflow series of two gauging stations, at the Kanawha Fall of Kanawha River, West Virginia, USA, and at the Baltara gauging station of Kosi River, Bihar, India. The results show that the annual maxima (AM) delineated flood peak series has a significant trend in both the gauging stations, providing sufficient evidence of nonstationarity, which is modelled by first- and second-order nonstationary analyses. A comparison between first-order and second-order nonstationarity analyses has also been performed, which suggests higher order nonstationary analysis might give more accurate information on the occurrence of flood extremes. Overall, our study highlights that the proposed framework is an important initial step before initiating FFA to avoid the ambiguity between the selection of stationary and nonstationary analysis.

Future projections of Indian summer monsoon rainfall extremes over India with statistical downscaling and its consistency with observed characteristics

Indian summer monsoon rainfall extremes and their changing characteristics under global warming have remained a potential area of research and a topic of scientific debate over the last decade. This partially attributes to multiple definitions of extremes reported in the past studies and poor understanding of the changing processes associated with extremes. The later one results into poor simulation of extremes by coarse resolution General Circulation Models under increased greenhouse gas emission which further deteriorates due to inadequate representation of monsoon processes in the models. Here we use transfer function based statistical downscaling model with non-parametric kernel regression for the projection of extremes and find such conventional regional modeling fails to simulate rainfall extremes over India. In this conjuncture, we modify the downscaling algorithm by applying a robust regression to the gridded extreme rainfall events. We observe, inclusion of robust regression to the downscaling algorithm improves the historical simulation of rainfall extremes at a 0.25° spatial resolution, as evaluated based on classical extreme value theory methods, viz., block maxima and peak over threshold. The future projections of extremes during 2081–2100, obtained with the developed algorithm show no change to slight increase in the spatial mean of extremes with dominance of spatial heterogeneity. These changing characteristics in future are consistent with the observed recent changes in extremes over India. The proposed methodology will be useful for assessing the impacts of climate change on extremes; specifically while spatially mapping the risk to rainfall extremes over India.

A Comprehensive Social Vulnerability Analysis at a National Scale

Extreme hydro-climatic events, such as extreme rainfall, droughts, and heat waves, are increasing both in its intensity and frequency over India, leading to corresponding rise in economic losses and human casualties. Moreover, these extreme events continue to accelerate in the future climatic scenarios. Thus, proper understanding of the physical and socioeconomic drivers of these extreme events is essential and eventually improves the adaptation strategies. While the early warning systems of these extreme events have significantly improved, evidences on the evaluation of dynamicity of social vulnerability throughout India are still significantly lacking. With regard to this, there is an urgent need to develop a set of comprehensive vulnerability maps for India, which may be utilized by the national disaster management and policy-making agencies, particularly to manage and recover from disaster events by spatially prioritizing the disaster management. Here, we provide a comprehensive review on the current status of the national-scale vulnerability mapping along with the associated challenges, with the primary focus on the Indian scenario. We also emphasize on the recent advancement in the methodologies in mapping the nationwide social vulnerability. Readers must note that the present chapter serves as a guide for optimal data gathering efforts for future improvements and fine-tuning of social vulnerability maps to develop policy tools to reduce vulnerability, mitigate risks, and increase resilience to hazards.

Role of vertical velocity in improving finer scale statistical downscaling for projection of extreme precipitation

Increase in human-induced climate warming is unequivocal and is subsequently causing an increase in the magnitude of precipitation extremes around the globe, inducing substantial damages to the socioeconomic sectors. Thus, a reliable projection of extreme precipitation scenarios is crucial for designing suitable adaptation strategies. At present, this is being attempted with the use of general circulation models (GCMs) projections. However, the GCMs simulate the extreme precipitation events rather poorly, especially over the tropical regions, and also suffer from frequent lack of reliability at local/regional scales. Therefore, it is of paramount significance to identify the relevant physical parameters for the extreme precipitation scenarios, and further implement these parameters in downscaling approaches to significantly improve the impact assessment at a regional scale. Previous studies have reported that the dynamic component (mainly vertical wind velocity) has a significant influence on the precipitation extremes over South Asian regions, along with the thermodynamic component. This indicates that the consideration of vertical wind velocity for projecting the precipitation extremes may significantly increase the efficacy of the downscaling approach, a contemplation that provoked its inclusion in statistical downscaling in this study. The methodology was demonstrated over the Mahanadi river basin (India), which often experiences heavy rainfall during the monsoon and post-monsoon disturbances originating from the Bay of Bengal (BoB). We observed that the dynamic component plays a crucial role in changing the pattern of precipitation extremes over the basin. Further, we noticed that inclusion of this dynamic component in statistical downscaling significantly improved the extreme precipitation projections. Based on these observations, we projected (2026–2055) the mean and extreme precipitation events, considering six most efficient CMIP5 models for the Indian subcontinent under RCP 4.5 and RCP 8.5 scenarios. The outcomes of this study can be utilized in deriving reliable water resource management practices.