Subhankar Karmakar

An Information System for Risk-Vulnerability Assessment to Flood

An exhaustive knowledge of flood risk in different spatial locations is essential for developing an effective flood mitigation strategy for a watershed. In the present study, a riskvulnerability analysis to flood is performed. Four components of vulnerability to flood: 1) physical, 2) economic, 3) infrastructure and 4) social; are evaluated individually using a Geographic Information System (GIS) environment. The proposed methodology estimates the impact on infrastructure vulnerability due to inundation of critical facilities, emer gency service stations and bridges. The components of vulnerability are combined to determine an overall vulnerability to flood. The exposures of land use/land cover and soil type (permeability) to flood are also considered to include their effects on severity of flood. The values of probability of occurrence of flood, vulnerability to flood, and exposures of land use and soil type to flood are used to finally compute flood risk at different locations in a watershed. The proposed methodology is implemented for six major damage centers in the Upper Thames River watershed, located in the SouthWestern Ontario, Canada to assess the flood risk. An information system is developed for systematic presentation of the flood risk, probability of occurrence of flood, vulnerability to flood, and exposures of land use and soil type to flood by postal code regions or Forward Sortation Areas (FSAs). The flood information system is designed to provide support for different users, i.e., general public, decisionmakers and water management professionals. An interactive analysis tool is developed within the information system to assist in evaluation of the flood risk in response to a change in land use pattern.

Impacts of urbanization on Indian summer monsoon rainfall extremes

Urban areas have different climatology with respect to their rural surroundings. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. Here we take up an observational study to understand the influence of urbanization on the characteristics of precipitation (specifically extremes) in India. We identify 42 urban regions and compare their extreme rainfall characteristics with those of surrounding rural areas. We observe that, on an overall scale, the urban signatures on extreme rainfall are not prominently and consistently visible, but they are spatially nonuniform. Zonal analysis reveals significant impacts of urbanization on extreme rainfall in central and western regions of India. An additional examination, to understand the influences of urbanization on heavy rainfall climatology, is carried with station level data using a statistical method, quantile regression. This is performed for the most populated city of India, Mumbai, in pair with a nearby nonurban area, Alibaug; both having similar geographic location. The derived extreme rainfall regression quantiles reveal the sensitivity of extreme rainfall events to the increased urbanization. Overall the study identifies the climatological zones in India, where increased urbanization affects regional rainfall pattern and extremes, with a detailed case study of Mumbai. This also calls attention to the need of further experimental investigation, for the identification of the key climatological processes, in different regions of India, affected by increased urbanization.

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.

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

AbstractThe establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights • The effectiveness of existing river water quality monitoring network is assessed• Significance of seasonal redesign of the monitoring network is demonstrated• Rationalization of water quality parameters is performed in a statistical framework

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.

Grey Fuzzy Multi-Objective Optimization

This chapter provides a description of grey fuzzy multi-objective optimization. A prerequisite background on grey systems, along with preliminary definitions is provided. Formulation of the grey fuzzy optimization starting with a general fuzzy optimization problem is discussed. Extension of the grey fuzzy optimization with the acceptability index to include multiple objectives is presented. Application of the grey fuzzy multi-objective optimization is demonstrated with the problem of waste load allocation in the field of environmental engineering.

A facility location model for municipal solid waste management system under uncertain environment

In municipal solid waste management system, decision makers have to develop an insight into the processes namely, waste generation, collection, transportation, processing, and disposal methods. Many parameters (e.g., waste generation rate, functioning costs of facilities, transportation cost, and revenues) in this system are associated with uncertainties. Often, these uncertainties of parameters need to be modeled under a situation of data scarcity for generating probability distribution function or membership function for stochastic mathematical programming or fuzzy mathematical programming respectively, with only information of extreme variations. Moreover, if uncertainties are ignored, then the problems like insufficient capacities of waste management facilities or improper utilization of available funds may be raised. To tackle uncertainties of these parameters in a more efficient manner an algorithm, based on interval analysis, has been developed. This algorithm is applied to find optimal solutions for a facility location model, which is formulated to select economically best locations of transfer stations in a hypothetical urban center. Transfer stations are an integral part of contemporary municipal solid waste management systems, and economic siting of transfer stations ensures financial sustainability of this system. The model is written in a mathematical programming language AMPL with KNITRO as a solver. The developed model selects five economically best locations out of ten potential locations with an optimum overall cost of [394,836, 757,440] Rs.1 /day ([5906, 11,331] USD/day) approximately. Further, the requirement of uncertainty modeling is explained based on the results of sensitivity analysis.

Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads

The design of a water quality monitoring network (WQMN) is a complicated decision-making process because each sampling involves high installation, operational, and maintenance costs. Therefore, data with the highest information content should be collected. The effect of seasonal variation in point and diffuse pollution loadings on river water quality may have a significant impact on the optimal selection of sampling locations, but this possible effect has never been addressed in the evaluation and design of monitoring networks. The present study proposes a systematic approach for siting an optimal number and location of river water quality sampling stations based on seasonal or monsoonal variations in both point and diffuse pollution loadings. The proposed approach conceptualizes water quality monitoring as a two-stage process; the first stage of which is to consider all potential water quality sampling sites, selected based on the existing guidelines or frameworks, and the locations of both point and diffuse pollution sources. The monitoring at all sampling sites thus identified should be continued for an adequate period of time to account for the effect of the monsoon season. In the second stage, the monitoring network is then designed separately for monsoon and non-monsoon periods by optimizing the number and locations of sampling sites, using a modified Sanders approach. The impacts of human interventions on the design of the sampling net are quantified geospatially by estimating diffuse pollution loads and verified with land use map. To demonstrate the proposed methodology, the Kali River basin in the western Uttar Pradesh state of India was selected as a study area. The final design suggests consequential pre- and post-monsoonal changes in the location and priority of water quality monitoring stations based on the seasonal variation of point and diffuse pollution loadings.

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.