General Circulation Models (GCMs) are tools designed to simulate time series of climate variables globally, accounting for effects of greenhouse gases in the atmosphere. They attempt to represent the physical processes in the atmosphere, ocean, cryosphere and land surface. They are currently the most credible tools available for simulating the response of the global climate system to increasing greenhouse gas concentrations, and to provide estimates of climate variables (e.g. air temperature, precipitation, wind speed, pressure etc.) on a global scale. GCMs demonstrate a significant skill at the continental and hemispheric spatial scales and incorporate a large proportion of the complexity of the global system; they are, however, inherently unable to represent local subgrid-scale features and dynamics. The spatial scale on which a GCM can operate (e.g., 3.75° longitude x 3.75° latitude for Coupled Global Climate Model, CGCM2) is very coarse compared to that of a hydrologic process (e.g., precipitation in a region, streamflow in a river etc.) of interest in the climate change impact assessment studies. Moreover, accuracy of GCMs, in general, decreases from climate related variables, such as wind, temperature, humidity and air pressure to hydrologic variables such as precipitation, evapotranspiration, runoff and soil moisture, which are also simulated by GCMs. These limitations of the GCMs restrict the direct use of their output in hydrology. This thesis deals with developing statistical downscaling models to assess climate change impacts and methodologies to address GCM and scenario uncertainties in assessing climate change impacts on hydrology. Downscaling, in the context of hydrology, is a method to project the hydrologic variables (e.g., rainfall and streamflow) at a smaller scale based on large scale climatological variables (e.g., mean sea level pressure) simulated by a GCM. A statistical downscaling model is first developed in the thesis to predict the rainfall over Orissa meteorological subdivision from GCM output of large scale Mean Sea Level Pressure (MSLP). Gridded monthly MSLP data for the period 1948 to 2002, are obtained from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project for a region spanning 150 N -250 N in latitude and 800 E -900 E in longitude that encapsulates the study region. The downscaling model comprises of Principal Component Analysis (PCA), Fuzzy Clustering and Linear Regression. PCA is carried out to reduce the dimensionality of the larger scale MSLP and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is performed to derive the membership of the principal components in each of the clusters and the memberships obtained are used in regression to statistically relate MSLP and rainfall. The statistical relationship thus obtained is used to predict the rainfall from GCM output. The rainfall predicted with the GCM developed by CCSR/NIES with B2 scenario presents a decreasing trend for non-monsoon period, for the case study. Climate change impact assessment models developed based on downscaled GCM output are subjected to a range of uncertainties due to both ‘incomplete knowledge’ and ‘unknowable future scenario’ (New and Hulme, 2000). ‘Incomplete knowledge’ mainly arises from inadequate information and understanding about the underlying geophysical process of global change, leading to limitations in the accuracy of GCMs. This is also termed as GCM uncertainty. Uncertainty due to ‘unknowable future scenario’ is associated with the unpredictability in the forecast of socio-economic and human behavior resulting in future Green House Gas (GHG) emission scenarios, and can also be termed as scenario uncertainty. Downscaled outputs of a single GCM with a single climate change scenario represent a single trajectory among a number of realizations derived using various GCMs and scenarios. Such a single trajectory alone can not represent a future hydrologic scenario, and will not be useful in assessing hydrologic impacts due to climate change. Nonparametric methods are developed in the thesis to model GCM and scenario uncertainty for prediction of drought scenario with Orissa meteorological subdivision as a case study. Using the downscaling technique described in the previous paragraph, future rainfall scenarios are obtained for all available GCMs and scenarios. After correcting for bias, equiprobability transformation is used to convert the precipitation into Standardized Precipitation Index-12 (SPI-12), an annual drought indicator, based on which a drought may be classified as a severe drought, mild drought etc. Disagreements are observed between different predictions of SPI-12, resulting from different GCMs and scenarios. Assuming SPI-12 to be a random variable at every time step, nonparametric methods based on kernel density estimation and orthonormal series are used to determine the nonparametric probability density function (pdf) of SPI-12. Probabilities for different categories of drought are computed from the estimated pdf. It is observed that there is an increasing trend in the probability of extreme drought and a decreasing trend in the probability of near normal conditions, in the Orissa meteorological subdivision. The single valued Cumulative Distribution Functions (CDFs) obtained from nonparametric methods suffer from limitations due to the following: (a) simulations for all scenarios are not available for all the GCMs, thus leading to a possibility that incorporation of these missing climate experiments may result in a different CDF, (b) the method may simply overfit to a multimodal distribution from a relatively small sample of GCMs with a limited number of scenarios, and (c) the set of all scenarios may not fully compose the universal sample space, and thus, the precise single valued probability distribution may not be representative enough for applications. To overcome these limitations, an interval regression is performed to fit an imprecise normal distribution to the SPI-12 to provide a band of CDFs instead of a single valued CDF. Such a band of CDFs represents the incomplete nature of knowledge, thus reflecting the extent of what is ignored in the climate change impact assessment. From imprecise CDFs, the imprecise probabilities of different categories of drought are computed. These results also show an increasing trend of the bounds of the probability of extreme drought and decreasing trend of the bounds of the probability of near normal conditions, in the Orissa meteorological subdivision. Water resources planning requires the information about future streamflow scenarios in a river basin to combat hydrologic extremes resulting from climate change. It is therefore necessary to downscale GCM projections for streamflow prediction at river basin scales. A statistical downscaling model based on PCA, fuzzy clustering and Relevance Vector Machine (RVM) is developed to predict the monsoon streamflow of Mahanadi river at Hirakud reservoir, from GCM projections of large scale climatological data. Surface air temperature at 2m, Mean Sea Level Pressure (MSLP), geopotential height at a pressure level of 500 hecto Pascal (hPa) and surface specific humidity are considered as the predictors for modeling Mahanadi streamflow in monsoon season. PCA is used to reduce the dimensionality of the predictor dataset and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is carried out to derive the membership of the principal components in each of the clusters and the memberships thus obtained are used in RVM regression model. RVM involves fewer number of relevant vectors and the chance of overfitting is less than that of Support Vector Machine (SVM). Different kernel functions are used for comparison purpose and it is concluded that heavy tailed Radial Basis Function (RBF) performs best for streamflow prediction with GCM output for the case considered. The GCM CCSR/NIES with B2 scenario projects a decreasing trend in future monsoon streamflow of Mahanadi which is likely to be due to high surface warming. A possibilistic approach is developed next, for modeling GCM and scenario uncertainty in projection of monsoon streamflow of Mahanadi river. Three GCMs, Center for Climate System Research/ National Institute for Environmental Studies (CCSR/NIES), Hadley Climate Model 3 (HadCM3) and Coupled Global Climate Model 2 (CGCM2) with two scenarios A2 and B2 are used for the purpose. Possibilities are assigned to GCMs and scenarios based on their system performance measure in predicting the streamflow during years 1991-2005, when signals of climate forcing are visible. The possibilities are used as weights for deriving the possibilistic mean CDF for the three standard time slices, 2020s, 2050s and 2080s. It is observed that the value of streamflow at which the possibilistic mean CDF reaches the value of 1 reduces with time, which shows reduction in probability of occurrence of extreme high flow events in future and therefore there is likely to be a decreasing trend in the monthly peak flow. One possible reason for such a decreasing trend may be the significant increase in temperature due to climate warming. Simultaneous occurrence of reduction in Mahandai streamflow and increase in extreme drought in Orissa meteorological subdivision is likely to pose a challenge for water resources engineers in meeting water demands in future.

The most important component of the climate system over the Indian Ocean region is the southwest monsoon, which dictates the life and economy of billions of people in the tropics. Being a phenomena that involves interaction between atmosphere, ocean and land, the southwest monsoon is strongly influenced by upper ocean, primarily through warm sea surface temperature (SST). This is particularly true about the southeastern Arabian Sea (SEAS) and the onset of southwest monsoon over the peninsular India. A localized patch of warm water, known as the Arabian Sea mini warm pool (ASMWP), forms in the SEAS during February–March. It remain as the warmest spot in the northern Indian Ocean till early April. A large region, surrounding the SEAS, attains SST exceeding 30°C during April–May, with often the ASMWP as its core. The ASMWP is believed to have a critical impact on the air-sea interaction during the onset phase of southwest monsoon and on the formation of the onset vortex, during late May or early June. This thesis addresses the formation mechanisms of ASMWP, using a high-resolution Ocean General Circulation Model (OGCM) of the Indian Ocean. In addition to the formation of ASMWP, the SEAS is characterized by several features in its hydrography and circulation, which have been invoked in the past to explain the preferential warming of this oceanic region. During November–January, the prevailing surface currents transport low-salinity water from the Bay of Bengal into the SEAS and leads to strong haline stratification in the upper layer and formation of barrier layer (layer between mixed layer and isothermal layer). The vertical distribution of temperature in the SEAS exhibit inversions (higher subsurface temperature than that at surface) during December–February. A high in sea level and anticyclonic eddies develop in the SEAS during December and they propagate westward. These eddies modify the hydrography through downwelling and play an important role in the redistribution of advected low-salinity water within the SEAS. The seasonally reversing coastal and equatorial currents present in and around SEAS also have a major contribution in setting up the hydrography, through the advection and redistribution of cooler low-salinity water. These features make the SEAS a unique oceanographic region. The first hypothesis on the formation of ASMWP, which has been suggested by diagnostic studies, is based on the barrier layer mechanism. The barrier layer, caused by the influx of low-salinity water at surface, is argued to maintain a shallow mixed layer which can warm more efficiently. In addition, presence of barrier layer can prevent mixed layer cooling, by cutting off the interaction of mixed layer with cooler thermocline water below. However, a coupled model study have shown that there is no significant impact on the ASMWP formation from barrier layer, but only a weak warming effect during it mature phase during April. The second hypothesis, which is based on an OGCM study, has suggested that the temperature inversions present within the barrier layer can heat the mixed layer through turbulent entrainment and in turn lead to the formation of ASMWP during February–March. Both hypotheses rule out the possibility of air-sea heat fluxes being the primary reason in its formation. The strong salinity stratification in the SEAS during December–March is central to the hypotheses about formation of the ASMWP. Observational studies have only limited success in assessing the contribution from barrier layer and temperature inversions, as the ASMWP always form in their presence. OGCMs offer a better alternative. However, modelling processes in the northern Indian Ocean, especially that in the SEAS, is a challenging problem. Previous Indian Ocean models have had serious difficulties in simulating the low-salinity water in the Bay of Bengal and its intrusion into the SEAS. The northward advection of low-salinity water in the SEAS, along the west coast of India, is used to be absent in model simulations. Moreover, the coarse resolution inhibited those models from simulating faster surface currents and vigorous eddies as seen in the observations. In this thesis, we use an OGCM of the Indian Ocean, based on the recent version of Modular Ocean Model (MOM4p0), to study the ASMWP. The model has high resolutions in the horizontal (1/4o x 1/4o) and vertical (40 levels, with 5 m spacing in upper 60 m), and has been forced with daily values momentum, heat and freshwater fluxes. The turbulent (latent and sensible) and long wave heat fluxes have been calculated as a function of model SST. The freshwater forcing consists of precipitation, evaporation and river runoff, and there are no surface restoring or flux adjustments. The river runoff has been distributed over several grid points about the river mouth instead of discharging into a singe grid point, which has resulted in remarkable improvements in salinity simulation. The model simulates the Indian Ocean temperature, salinity and circulation remarkably well. The pattern of model temperature distribution and evolution matches very well with that in the observations. Significant improvements have been made in the salinity simulation, including the Bay of Bengal freshwater plume and intrusion of low-salinity water from the bay into the SEAS. The salinity distribution within the SEAS is also well represented in the model. The use of appropriate horizontal friction parameters has resulted in the simulation of realistic currents. The observed features in the SEAS, including the life cycle of the ASMWP, low-salinity water, barrier layer, temperature inversions, eddies and currents are well represented in the model. Present study has unraveled the processes involved in the life cycle of barrier layer and temperature inversions in the SEAS. Presence of low-salinity water is necessary for their formation. Barrier layer develops in the SEAS during November, after the intrusion of low-salinity water from the Bay of Bengal. The barrier layer is thickest during January–February, and it dissipates during March–April. The variations and peak of barrier layer thickness is controlled by variations in isothermal layer depth, which in turn is dominated by the downwelling effects of anticyclonic eddies. The intense solar heating during March–April leads to the formation of shallow isothermal layer and results in the dissipation of barrier layer. Temperature inversions starts developing in the SEAS during December, reaches its peak during January–February and dissipates in the following months. Advection of cooler low-salinity water over warmer salty water and penetrating shortwave radiation is found to cause temperature inversions within the SEAS, whereas winter cooling is also important to the north and south of the SEAS. There is significant variation in the magnitude, depth of occurrence and formation mechanisms of temperature inversions within the SEAS. Analysis of model mixed layer heat budget has shown that the SEAS SST is mainly controlled by atmospheric forcing, including the life cycle of ASMWP. It has also shown that the heating from temperature inversions do not contribute to the formation of ASMWP. In an experiment in which a constant salinity of 35 psu was maintained over the entire model domain, the ASMWP evolved very similar to that in the standard run, suggesting that the salinity effects are not necessary for the formation of ASMWP. Examination of wind field show that the winds over the SEAS during November–February are low due to the blocking of northeasterly winds by Western Ghats. Several process experiments by modifying the wind and turbulent heat fluxforcing fields have shown that these low winds lead to the formation of ASMWP in the SEAS during February–March. The low winds reduce latent heat loss, resulting in net heat gain by the ocean. This helps the SEAS to keep warmer SST while the surrounding region experience intense cooling under the strong dry northeasterly winds. As the winds are weak over the SEAS, the mixed layer is not able to feel the stratification beneath and the mixed layer depth is determined by solar heating, with or without salinity effects. In addition, the weak winds are not able to entrain the temperature inversions present in the barrier layer. The winds are weak during March–April too, and the air-sea heat fluxes dictate the SST evolution during this period. Therefore, during November–April, the SEAS acts as a low wind heat-dominated regime, where the evolution of sea surface temperature is solely determined by atmospheric forcing. We show that, in such regions, the evolution of surface layer temperature is not dependent on the characteristics of subsurface ocean, including the presence of barrier layer and temperature inversions.