Increasing global mean temperature or global warming has the potential to affect the hydrologic cycle. In the 21st century, according to the UN Intergovernmental Panel on Climate Change (IPCC), alterations in the frequency and magnitude of high intensity rainfall events are very likely. Increasing trend of urbanization across the globe is also noticeable, simultaneously. These changes will have a great impact on water infrastructure as well as environment in urban areas. One of the impacts may be the increase in frequency and extent of flooding. India, in the recent years, has witnessed a number of urban floods that have resulted in huge economic losses, an instance being the flooding of Mumbai in July, 2005. To prevent catastrophic damages due to floods, it has become increasingly important to understand the likely changes in extreme rainfall in future, its effect on the urban drainage system, and the measures that can be taken to prevent or reduce the damage due to floods. Reliable estimation of future design rainfall intensity accounting for uncertainties due to climate change is an important research issue. In this context, rainfall intensity-duration-frequency (IDF) relationships are one of the most extensively used hydrologic tools in planning, design and operation of various drainage related infrastructures in urban areas. There is, thus, a need for a study that investigates the potential effects of climate change on IDF relationships. The main aim of the research reported in this thesis is to investigate the effect of climate change on Intensity-Duration-Frequency relationship in an urban area. The rainfall in Bangalore City is used as a case study to demonstrate the applications of the methodologies developed in the research Ahead of studying the future changes, it is essential to investigate the signature of changes in the observed hydrological and climatological data series. Initially, the yearly mean temperature records are studied to find out the signature of global warming. It is observed that the temperature of Bangalore City shows an evidence of warming trend at a statistical confidence level of 99.9 %, and that warming effect is visible in terms of increase of minimum temperature at a rate higher than that of maximum temperature. Interdependence studies between temperature and extreme rainfall reveal that up to a certain range, increase in temperature intensifies short term rainfall intensities at a rate more than the average rainfall. From these two findings, it is clear that short duration rainfall intensities may intensify in the future due to global warming and urban heat island effect. The possible urbanization signatures in the extreme rainfall in terms of intensification in the evening and weekends are also inferred, although inconclusively. The IDF relationships are developed with historical data and changes in the long term daily rainfall extreme characteristics are studied. Multidecedal oscillations in the daily rainfall extreme series are also examined. Further, non-parametric trend analyses of various indices of extreme rainfall are carried out to confirm that there is a trend of increase in extreme rainfall amount and frequency, and therefore it is essential to the study the effects of climate change on the IDF relationships of the Bangalore City. Estimation of future changes in rainfall at hydrological scale generally relies on simulations of future climate provided by Global Climate Models (GCMs). Due to spatial and temporal resolution mismatch, GCM results need to be downscaled to get the information at station scale and at time resolutions necessary in the context of urban flooding. The downscaling of extreme rainfall characteristics in an urban station scale pose the following challenges: (1) downscaling methodology should be efficient enough to simulate rainfall at the tail of rainfall distribution (e.g., annual maximum rainfall), (2) downscaling at hourly or up to a few minutes temporal resolution is required, and (3) various uncertainties such as GCM uncertainties, future scenario uncertainties and uncertainties due to various statistical methodologies need to be addressed. For overcoming the first challenge, a stochastic rainfall generator is developed for spatial downscaling of GCM precipitation flux information to station scale to get the daily annual maximum rainfall series (AMRS). Although Regional Climate Models (RCMs) are meant to simulate precipitation at regional scales, they fail to simulate extreme events accurately. Transfer function based methods and weather typing techniques are also generally inefficient in simulating the extreme events. Due to its stochastic nature, rainfall generator is better suited for extreme event generation. An algorithm for stochastic simulation of rainfall, which simulates both the mean and extreme rainfall satisfactorily, is developed in the thesis and used for future projection of rainfall by perturbing the parameters of the rainfall generator for the future time periods. In this study, instead of using the customary two states (rain/dry) Markov chain, a three state hybrid Markov chain is developed. The three states used in the Markov chain are: dry day, moderate rain day and heavy rain day. The model first decides whether a day is dry or rainy, like the traditional weather generator (WGEN) using two transition probabilities, probabilities of a rain day following a dry day (P01), and a rain day following a rain day (P11). Then, the state of a rain day is further classified as a moderate rain day or a heavy rain day. For this purpose, rainfall above 90th percentile value of the non-zero precipitation distribution is termed as a heavy rain day. The state of a day is assigned based on transition probabilities (probabilities of a rain day following a dry day (P01), and a rain day following a rain day (P11)) and a uniform random number. The rainfall amount is generated by Monte Carlo method for the moderate and heavy rain days separately. Two different gamma distributions are fitted for the moderate and heavy rain days. Segregating the rain days into two different classes improves the process of generation of extreme rainfall. For overcoming the second challenge, i.e. requirement of temporal scales, the daily scale IDF ordinates are disaggregated into hourly and sub-hourly durations. Disaggregating continuous rainfall time series at sub-hourly scale requires continuous rainfall data at a fine scale (15 minute), which is not available for most of the Indian rain gauge stations. Hence, scale invariance properties of extreme rainfall time series over various rainfall durations are investigated through scaling behavior of the non-central moments (NCMs) of generalized extreme value (GEV) distribution. The scale invariance properties of extreme rainfall time series are then used to disaggregate the distributional properties of daily rainfall to hourly and sub-hourly scale. Assuming the scaling relationships as stationary, future sub-hourly and hourly IDF relationships are developed. Uncertainties associated with the climate change impacts arise due to existence of several GCMs developed by different institutes across the globe, climate simulations available for different representative concentration pathway (RCP) scenarios, and the diverse statistical techniques available for downscaling. Downscaled output from a single GCM with a single emission scenario represents only a single trajectory of all possible future climate realizations and cannot be representative of the full extent of climate change. Therefore, a comprehensive assessment of future projections should use the collective information from an ensemble of GCM simulations. In this study, 26 different GCMs and 4 RCP scenarios are taken into account to come up with a range of IDF curves at different future time periods. Reliability ensemble averaging (REA) method is used for obtaining weighted average from the ensemble of projections. Scenario uncertainty is not addressed in this study. Two different downscaling techniques (viz., delta change and stochastic rainfall generator) are used to assess the uncertainty due to downscaling techniques. From the results, it can be concluded that the delta change method under-estimated the extreme rainfall compared to the rainfall generator approach. This study also confirms that the delta change method is not suitable for impact studies related to changes in extreme events, similar to some earlier studies. Thus, mean IDF relationships for three different future extreme events, similar to some earlier studies. Thus, mean IDF relationships for three different future periods and four RCP scenarios are simulated using rainfall generator, scaling GEV method, and REA method. The results suggest that the shorter duration rainfall will invigorate more due to climate change. The change is likely to be in the range of 20% to 80%, in the rainfall intensities across all durations. Finally, future projected rainfall intensities are used to investigate the possible impact of climate change in the existing drainage system of the Challaghatta valley in the Bangalore City by running the Storm Water Management Model (SWMM) for historical period, and the best and the worst case scenario for three future time period of 2021–2050, 2051–2080 and 2071–2100. The results indicate that the existing drainage is inadequate for current condition as well as for future scenarios. The number of nodes flooded will increase as the time period increases, and a huge change in runoff volume is projected. The modifications of the drainage system are suggested by providing storage pond for storing the excess high speed runoff in order to restrict the width of the drain The main research contribution of this thesis thus comes from an analysis of trends of extreme rainfall in an urban area followed by projecting changes in the IDF relationships under climate change scenarios and quantifying uncertainties in the projections.

Municipal solid waste (MSW) production has significantly increased in the rapidly urbanizing developing world and also changed composition with increased decomposable organic fraction in MSW (OFMSW) and plastics content. This has stressed the environment in many ways while city managers and citizens have responded with various technological and management solutions leading to a need for scientific, environmental, technological and sustainability assessments of the emerging problems. This sets the research agenda and framework for this study wherein the MSW generation, composition, processing and treatment methods, open dumping practices, environmental liability, natural degradation, sustainability issues etc. have been studied for the city of Bangalore as a model for such an emerging problem. Results show that MSW generation ranged from 0.1-0.4 kg/person/day and the OFMSW content was >80% emerging predominantly from fruit, vegetable and food wastes. About 10-15% of daily MSW generated appeared to be haphazardly dumped around the city in ~700 small to large dumps ranging from 10-6,500 m2 with potential for large GHG emissions. Their spread and characteristics were assessed for 3 consecutive years using a novel rapid survey method developed at IISc involving motorcycle borne student volunteer teams, GPS enabled locating, physical measurements and satellite image interpretations. Results indicated that dump sites were of three types, ephemeral small sized in the core area (303) functioning as transfer stations, medium sized ones in outer areas that were closed rapidly with construction debris and very soon inhabited with dwellings and the larger and longer duration dumps (2-3 years, 393) in the peripheral regions within 10 km from the city administrative boundary. This method was compared with physical measurement and satellite imaging and gave very high level of accuracy and is hence suggested for other cities as well. A smaller fraction of MSW is also dumped in open drains that lead to choking and flooding of 3 locations and this was studied with some detail. The environmental footprint of such dumps were assessed by theoretical and experimental on-site and off-site approaches and experimental results show low GHG (CH4) emissions and emission factors that was largely attributable to the shallow depth of dumps (~0.7 m) and its low pH. The decomposition rates were experimentally determined for open dump sites and drivers for decomposition monitored. By providing differential access to macro-fauna, meso-micro organisms and only soil contact in field scale experiments it was determined that the greatest loss in weight occurred primarily due to the rapid drying process that brings down decomposition within 6 days. During the early stages of decomposition, mostly micro with meso organisms are responsible and after 6 days, the moisture content falls below 60% making microbiological decomposition difficult and enabling other foraging organisms to take over. The weight loss (decay) could be patterned both on exponential decay or a two component fit representing a rapid initial decay followed by a slower long term decay process similar to soil application of organic matter. Monitoring the decentralized MSWM practices in the city suggests that small scale composting and biomethanation is gaining acceptance and is the possible direction for OFMSW in growing cities.

Indian cities rely predominantly on buses for public transport. The issues of performance measurement and efficiency analyses for the bus company have been gaining significance due to severe operating conditions and financial constraints in which these bus companies provide the service. Performance is defined as the levels of success of the service with respect to different parameters such as quality of service, cost effectiveness and safety. Performance is measured in terms of operational efficiency and financial efficiency. Operational Efficiency of an organization is the ability to utilize its available resources to the maximum extent. Financial Efficiency is a measure of the organization’s ability to translate its financial resources into revenue. Public bus transportation plays a pivotal role in India in bringing about greater mobility both within and between urban and rural areas. Through increased mobility, road transport also contributes immensely to social and economic development of different regions of the country. Public transport is provided by surface road transport using buses by the State Road Transport Undertakings (SRTUs) and by private operators. In this thesis, scientific analysis of the performance of SRTUs is carried out at different levels considering physical and financial parameters through multivariate techniques, non-parametric techniques and qualitative techniques. A comprehensive study on all the SRTUs of Karnataka at depot, division level are done and determined which quantitative method is suited for depot level and division level studies. From quantitative and qualitative studies of SRTUs strategies are developed and recommendations are made to improve the performance of SRTUs. Further, in addition to Bangalore metropolitan transport corporation (BMTC) performance analyses, the routes are analyzed to reduce the dead kilometer. Major contributions from this work: 1. Both inter and intra city operations of the public transport corporation in the state of Karnataka have been exhaustively analysed using operational and financial parameters. 2. Large amount of data over a long period has been collated from State road transport units and a standard format has been developed for collecting both operational and financial parameters for SRTU’s. 3. A generic framework and plan for performance evaluation of SRTU’s has been developed using ratio and benchmarking analysis, and, non-parametric and multivariate techniques like DEA (constant return to scale (CRS) and variable return to scale (VRS)), DEA-principal component analysis (PCA), DEA- bootstrapping. These analyses have been carried out at different levels, like transport corporations level (KSRTC NEKRTC, NWKRTC, BMTC), division level (33 divisions), and Depot level (193 depots). 4. Non parametric and multivariate Models have been developed and validated using DEAP and GAMS software before embarking on the above detailed analyses. 5. Analytical hierarchy approach (AHP), which is multi criteria structured technique, has been adopted to evaluate and analyze performance of the SRTU’s, divisions and depots based on qualitative and quantitative data. 6. User and operator perception studies of different SRTU’s of Karnataka have been done to evaluate the performance of these corporations from qualitative techniques. 7. From these comprehensive non parametric techniques, the efficiency of the SRTU’s have been evaluated and found that KSRTC has been the best operating unit among the SRTU’s considered for the study. The same has been observed from the AHP as well as perception surveys carried out as part of this thesis. 8. Operation and financial performance including profitability studies of Mysore urban transportation (Mysore city transport division) has been carried out before and after implementation of intelligent transport system (ITS). 9. The dead kilometer minimization model was formulated, which is a mixed integer programming problem, to get the optimal solution considering the capacity of the depot and time period of operation for the chosen network. An optimization technique has been developed for solving the dead kilometer problem in the operations of BMTC buses for the Volvo division (division operates 794 schedules). The alternative depot locations have been identified to reduce the dead kilometer, leading to large amount of savings for the corporation. 10. From the detailed analyses using non parametric techniques, multivariate and multi-criteria techniques along with perception surveys, strategies and recommendations have been arrived at to improve performance of the public transport corporations. This thesis consists of nine chapters and they are as below; Chapter 1 provides a brief introduction of public bus transport systems in India, their problems and need for performance evaluation of SRTUs. The impacts study of Mysore ITS, dead kilometer minimization problem for BMTC along with evaluating the performance of SRTUs by quantitative and qualitative data. This chapter provides the objective of the work and scope of the work. The main objectives of this research are 1. To develop a generic framework and plan for evaluation by identifying the performance indicators and data sources for evaluation.