Interannual variation of Indian summer (June-September: JJAS) monsoon rainfall (ISMR) depends on its relative intensity during early (June-July: JJ; contribution 52%) and late (August-September: AS; contribution 49%) phases. Apart from variations in sea surface temperature (SST), the primary reasons behind the variability during JJ and AS can be very different due to change in climatic conditions on account of post-onset processes. Here, using a high resolution general circulation model with seasonally varying climatological SST, mechanisms those govern the intensity of rainfall during JJ and AS are investigated. There is no significant relation-ship between intensity of precipitation over Indian region in JJ and AS. Moreover, the factors determining early monsoon (JJ) precipitation are different than that for late monsoon (AS). In absence of interannual SST variation, pre-monsoon soil moisture do not play a significant role for the interannual variation of monsoon precipitation over India. A large scale oscillation of the ITCZ is noticed on interannual time scale spanning from around 60◦E to 150◦E that brings spatially coherent flood and drought over this region. Early monsoon precipitation has a larger dependency on spring snow depth over Eurasia and phase of the upper tropospheric Rossby wave in May. However, late monsoon precipitation over India is mainly governed by the intensity and time scale of the intraseasonally varying convective cloud bands. This study suggests that early monsoon (JJ) precipitation over Indian region is more correlated with pre-monsoon signatures of land-atmosphere parameters. However, in later parts after the onset (AS), the monsoon intensity is primarily driven by its internal dynamics and characteristics of intraseasonal oscillation.

Using a 50-year (1951-2000) gridded (1-degree) daily rainfall data set over the Indian land region, we study two main aspects of the Indian monsoon. The first aspect deals with the changing nature of the rainfall annual cycle. This, to our knowledge, is the first attempt at studying the changing behaviour of the Indian monsoon rainfall annual cycle in a systematic way. The annual cycle is defined as a combination of the first few Fourier harmonics of daily rainfall. We then identify five attributes of the annual cycle for each year and location (grid): (a) the day of maximum intensity (peak day); (b) maximum intensity (peak value); (c) beginning; (d) end; and (e) duration of the annual cycle. An extensive statistical analysis of these five attributes over the central Indian region (16.5 – 26.5N; 74.5 – 86.5E) shows that the probability distributions of all attributes, barring the peak value, show a significant change in the last 25 years (1976-2000) compared to the first 25 years (1951-1975). The second issue addressed in this thesis deals with the behaviour of the intraseasonal oscillations in flood and drought years. Previous studies on this issue have been limited to only specific flood or drought years. Our analysis confirms earlier findings such as the northwestward propagation of the 10-20 day ISO. However, we also find, for the first time, based on 9 flood and 9 drought years, that the 20-60 day has an eastward propagation during drought years and remains stationary in flood years. The analysis is primarily statistical in nature, and providing a physical explanation for some of our findings is beyond the scope of our work. Finally, it is worth noting here that without the long-term gridded data, it would have been difficult to assess coherent changes over a large region and long time-period.

Several previous studies have found that various characteristics of the Indian monsoon rainfall have shown secular changes over the past century. In this study, using a gridded (1degree) daily rainfall dataset, we analyse the spatio-temporal characteristics of the intensity and duration of monsoon (June through September) rainfall for secular changes over the last 50 years. The characteristics of the duration of rain events are described by wet and dry spells. A wet/dry spell is defined as a period of consecutive days with rainfall above/below a particular threshold. We choose to use a threshold that is a function of the local climatological mean, given the spatial heterogeneity of mean monsoon rainfall. The wet and dry spells are then divided into three categories: short [1 to 7 days], moderate [8 to 10 days], long [11 and more days] and analysed for changes over the past 50 years [19512000]. We find that while the number of short duration wet spells show a significant increase over the last 50 years (~15% change), the number of long duration wet spells show a significant decrease (~25%). Furthermore, while the numbers of short duration dry periods have shown a significant increase, the moderate and long duration dry spells do not shown an appreciable change. This increase and decrease in the short and long duration wet spells offset each other and consequently the total number of rainy days during the season has not shown any significant change over the past 50 years. In addition to the duration of wet and dry spells, we also analysed for changes in the accumulated rainfall of the short, medium and long duration wet spells. Our analysis suggests that while the depth of accumulated rainfall in short duration wet spells has shown a significant increase (~20%), the depth of rain in the long duration spells has shown a significant decrease (~30%) in the past fifty years.

In the current study, we have presented a systematic analysis of the diurnal cycle of rainfall over the Indian region using satellite observations, and evaluated the ability of the Weather Research and Forecasting Model (WRF) to simulate some of the salient features of the observed diurnal characteristics of rainfall. Using high resolution simulations, we also investigate the underlying mechanisms of some of the observed diurnal signatures of rainfall. Using the Tropical Rain-fall Measuring Mission (TRMM) 3-hourly, 0.25 ×0.25 degree 3B42 rainfall product for nine years (1999-2007), we extract the finer spatial structure of the diurnal scale signature of Indian summer monsoon rainfall. Using harmonic analysis, we construct a signal corresponding to diurnal and sub-diurnal variability. Subsequently, the 3-hourly time-period or the octet of rain-fall peak for this filtered signal, referred to as the “peak octet,” is estimated with care taken to eliminate spurious peaks arising out of Gibbs oscillations. Our analysis suggests that over the Bay of Bengal, there are three distinct modes of the peak octet of diurnal rainfall corresponding to 1130, 1430 and 1730 IST, from north central to south Bay. This finding could be seen to be consistent with southward propagation of the diurnal rainfall pattern reported by earlier studies. Over the Arabian sea, there is a spatially coherent pattern in the mode of the peak octet (1430 IST), in a region where it rains for more than 30% of the time. In the equatorial Indian Ocean, while most of the western part shows a late night/early morning peak, the eastern part does not show a spatially coherent pattern in the mode of the peak octet, owing to the occurrence of a dual maxima (early morning and early/late afternoon). The Himalayan foothills were found to have a mode of peak octet corresponding to 0230 IST, whereas over the Burmese mountains and the Western Ghats (west coast of India) the rainfall peaks during late afternoon/early evening (1430-1730 IST). This implies that the phase of the diurnal cycle over inland orography (e.g., Himalayas) is significantly different from coastal orography (e.g., Western Ghats). We also find that over the Gangetic plains, the peak octet is around 1430 IST, a few hours earlier compared to the typical early evening maxima over land. The second part of our study involves evaluating the ability of the Weather Research and Fore-casting Model (WRF) to simulate the observed diurnal rainfall characteristics. It also includes conducting high resolution simulations to explore the underlying physical mechanisms of the observed diurnal signatures of rainfall. The model (at 54km resolution) is integrated for the month of July 2006 since this period was particularly favourable for the study of diurnal cycle. We first evaluate the sensitivity of the model to the prescribed sea surface temperature (SST) by using two different SST datasets, namely Final Analyses (FNL) and Real-time Global (RTG). The overall performance of RTG SST was found to be better than FNL, and hence it was used for further model simulations. Next, we investigated the impact of different parameterisations (convective, microphysical, boundary layer, radiation and land surface) on the simulation of diurnal cycle of rainfall. Following this sensitivity study, we identified the suite of physical parameterisations in the model that “best” reproduces the observed diurnal characteristics of Indian monsoon rainfall. The “best” model configuration was used to conduct two nested simulations with one-way, three-level nesting (54-18-6km) over central India and Bay of Bengal. While the 54km and 18km simulations were conducted for July 2006, the 6km simulation was carried out for the period 18-24 July 2006. This period was chosen for our study since it is composed of an active period (19-21 July 2006), followed by a break period (22-24 July 2006). At 6km grid-spacing the model is able to realistically simulate the active and break phases in rainfall. During the chosen active phase, we find that the observed rainfall over central India tends to reach a maximum in the late night/early morning hours. This is in contrast to the observed climatological diurnal maxima of late evening hours. Interestingly, the 6km simulation for the active phase is able to reproduce this late night/early morning maxima. Upon further analysis, we find that this is because of the strong moisture convergence at the mid-troposphere during 2030-2330 IST, leading to the rainfall peak seen during 2330-0230 IST. Based on our analysis, we conclude that during both active and break phases of summer monsoon, mid-level moisture convergence seems to be one of the primary factors governing the phase of the diurnal cycle of rainfall. Over the Bay of Bengal, the 6km model simulation is in very good agreement with observations, particularly during the active phase. The southward propagation observed during 19-20 July 2006, which was not captured by the coarse resolution simulation (54km), is exceedingly well captured by the 6km simulation. The positive anomalies in specific humidity attain a maxima during 2030-0230 IST in the north and during 0830-1430 IST in the south. This confirms the role of moisture convergence in the southward propagation of rainfall. Equally importantly we find that while low level moisture convergence is dominant in the north Bay, it is the mid-level moisture convergence that is predominant in the south Bay.

No description available.

The land–atmosphere interactions and the coupling between climate and land surface hydrological processes are gaining interest in the recent past. The increased knowledge in hydro climatology and the global hydrological cycle, with terrestrial and atmospheric feedbacks, led to the utilization of the climate variables and atmospheric tele-connections in modeling the hydrological processes like rainfall and runoff. Numerous statistical and dynamical models employing different combinations of predictor variables and mathematical equations have been developed on this aspect. The relevance of predictor variables is usually measured through the observed linear correlation between the predictor and the predictand. However, many predictor climatic variables are found to have been switching the relationships over time, which demands a replacement of these variables. The unsatisfactory performance of both the statistical and dynamical models demands a more authentic method for assessing the dependency between the climatic variables and hydrologic processes by taking into account the nonlinear causal relationships and the instability due to these nonlinear interactions. The most obvious cause for limited predictability in even a perfect model with high resolution observations is the nonlinearity of the hydrological systems [Bloschl and Zehe, 2005]. This is mainly due to the chaotic nature of the weather and its sensitiveness to initial conditions [Lorenz, 1963], which restricts the predictability of day-to-day weather to only a few days or weeks. The present thesis deals with developing association rules to extract the causal relationships between the climatic variables and rainfall and to unearth the frequent predictor patterns that precede the extreme episodes of rainfall using a time series data mining algorithm. The inherent nonlinearity and uncertainty due to the chaotic nature of hydrologic processes (rainfall and runoff) is modeled through a nonlinear prediction method. Methodologies are developed to increase the predictability and reduce the predictive uncertainty of chaotic hydrologic series. A data mining algorithm making use of the concepts of minimal occurrences with constraints and time lags is used to discover association rules between extreme rainfall events and climatic indices. The algorithm considers only the extreme events as the target episodes (consequents) by separating these from the normal episodes, which are quite frequent and finds the time-lagged relationships with the climatic indices, which are treated as the antecedents. Association rules are generated for all the five homogenous regions of India (as defined by Indian Institute of Tropical Meteorology) and also for All India by making use of the data from 1960-1982. The analysis of the rules shows that strong relationships exist between the extreme rainfall events and the climatic indices chosen, i.e., Darwin Sea Level Pressure (DSLP), North Atlantic Oscillation (NAO), Nino 3.4 and Sea Surface Temperature (SST) values. Validation of the rules using data for the period 1983-2005, clearly shows that most of the rules are repeating and for some rules, even if they are not exactly the same, the combinations of the indices mentioned in these rules are the same during validation period with slight variations in the representative classes taken by the indices. The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. In the present study, an attempt is made to identify chaos using various techniques and the behaviour of daily rainfall series in different regions. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha river basin, Mahanadi river basin and All India for the period 1955 to 2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series considered. Correlation dimension method is repeated on the phase randomized and first derivative of the data series to check the existence of any pseudo low-dimensional chaos [Osborne and Provenzale, 1989]. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A limit in predictability in chaotic system arises mainly due to its sensitivity to the infinitesimal changes in its initial conditions and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a nonlinear ensemble prediction. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996 to 2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are made from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature. The predictability of the chaotic daily rainfall series is improved by utilizing information from various climatic indices and adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha river basin, India for the period 1955 to 2000 is used for the study. A multivariate phase space is generated, considering a climate data set of 16 variables. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to 8 principal components (PCs). This multivariate series (rainfall along with 8 PCs) are found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is reduced or in other words, the predictability is improved by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be reduced by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. Even though, the sensitivity to initial conditions limit the predictability in chaotic systems, a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All the traditional chaotic prediction methods are based on local models since these methods model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor [Sivakumar et al., 2002a]. This study focuses on combining a local learning wavelet analysis (decomposition) model with a global feedforward neural network model and its implementation on phase space prediction of chaotic streamflow series. The daily streamflow series at Basantpur station in Mahanadi basin, India is found to exhibit a chaotic nature with dimension varying from 5-7. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimension and delay time. Compared with traditional local approximation approach, the total predictive uncertainty in the streamflow is reduced when modeled with wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor is clearly demonstrated.

The thesis is concerned with development of efficient regional frequency analysis (RFA) approaches to estimate quantiles of hydrometeorological events. The estimates are necessary for various applications in water resources engineering. The classical approach to estimate quantiles involves fitting frequency distribution to at-site data. However, this approach cannot be used when data at target site are inadequate or unavailable to compute parameters of the frequency distribution. This impediment can be overcome through RFA, in which sites having similar attributes are identified to form a region, and information is pooled from all the sites in the region to estimate the quantiles at target site. The thesis proposes new approaches to RFA of precipitation, meteorological droughts and floods, and demonstrates their effectiveness. The approach proposed for RFA of precipitation overcomes shortcomings of conventional approaches with regard to delineation and validation of homogeneous precipitation regions, and estimation of precipitation quantiles in ungauged and data sparse areas. For the first time in literature, distinction is made between attributes/variables useful to form homogeneous rainfall regions and to validate the regions. Another important issue is that some of the attributes considered for regionalization vary dynamically with time. In conventional approaches, there is no provision to consider dynamic aspects of time varying attributes. This may lead to delineation of ineffective regions. To address this issue, a dynamic fuzzy clustering model (DFCM) is developed. The results obtained from application to Indian summer monsoon and annual rainfall indicated that RFA based on DFCM is more effective than that based on hard and fuzzy clustering models in arriving at rainfall quantile estimates. Errors in quantile estimates for the hard, fuzzy and dynamic fuzzy models based on the proposed approach are shown to be significantly less than those computed for Indian summer monsoon rainfall regions delineated in three previous studies. Overall, RFA based on DFCM and large scale atmospheric variables appeared promising. The performance of DFCM is followed by that of fuzzy and hard clustering models. Next, a new approach is proposed for RFA of meteorological droughts. It is suggested that homogeneous precipitation regions have to be delineated before proceeding to develop drought severity - areal extent - frequency (SAF) curves. Drought SAF curves are constructed at annual and summer monsoon time scales for each of the homogeneous rainfall regions that are newly delineated in India based on the proposed approach. They find use in assessing spatial characteristics and frequency of meteorological droughts. It overcomes shortcomings associated with classical approaches that construct SAF curves for political (e.g., state, country) and physiographic regions (e.g., river basin), based on spatial patterns of at-site values of drought indices in the study area, without testing homogeneity in rainfall. Advantage of the new approach can be noted especially in areas that have significant variations in temporal and spatial distribution of precipitation (possibly due to variations in topography, landscape and climate). The DFCM is extended to RFA of floods, and its effectiveness in prediction of flood quantiles is demonstrated by application to Godavari basin in India, considering precipitation as time varying attribute. Six new homogeneous regions are formed in Godavari basin and errors in quantile estimates based on those regions are shown to be significantly less than those computed based on sub-zones delineated in Godavari basin by Central Water Commission in a previous study.

Reconstruction studies of seasonal rainfall utilizing stable isotope based proxy approach suffer from the limitations of time resolutions. Conventional methods and archives limit the achievable resolution to annual scales. However, high resolution reconstruction (seasonal to sub-weekly scale) can be achieved in proxy records where growth rates are high enough to leave spatial signatures in an organically or inorganically deposited layer such as growth bands. In this study, aragonitic skeleton of the gastropod Lissachatina fulica (Bowdich, Giant African Land Snails) is investigated with an aim to achieve sub-weekly scale reconstruction of the Indian monsoon rainfall. These terrestrial gastropods are native of Africa and highly invasive. Their evolution in the geological time period dates back to the Pliocene and is presently distributed across the tropical belt. They exhibit a high growth rate in the presence of water and high relative humidity in the environment. As a result, they are ideally suited for the task of palaeo seasonality reconstruction. The isotopic patterns recorded in their growth bands reveal composition of environmental water at seasonal time scales. In vitro studies were carried out on L. fulica to estimate their growth rates and growth responses to changes in the physical conditions within the culture chamber. The Indian monsoon rainfall exhibits characteristic dry spells that are generally sandwiched between periods of active phases of high rainfall during the South West monsoon season. These dry spells are typically characterized by rainfall with low intensity. Isotope fingerprinting of the rain water at daily time resolution, covering the years of 2007-10 exhibited distinct isotopic ratios for the dry and wet spells. Dry spells were clearly demarcated in the record with isotopically enriched signature. In addition, the study indentified the role of three distinct moisture sources on δ18O of rain water at Bangalore, India. The variability in the oxygen isotopic composition of the Indian monsoon rainfall is predominantly controlled by this source moisture variability at inter annual time scales, while temperature and amount of rainfall tend to dominate the variability in the precipitation isotopes at seasonal and weekly scales. Simultaneous isotopic analyses of both rainwater and shell carbonates growth bands were undertaken to understand their relationship to aid in high resolution reconstruction. Carbonate found in the growth bands of the gastropods, which is precipitated under equilibrium condition from rainwater, preserves the signature of rainfall. This provides an opportunity to reconstruct rainfall parameters (i.e. amount and moisture sources) knowing the variability in shell carbonates. Stable isotopic ratios measured across the growth bands of live shell specimens collected from the southern and eastern Indian regions (Bangalore and Kolkata, respectively) were compared with the rainfall isotope ratios at these two locations; signature of dry spells were clearly identified from the study of isotopic composition in the growth bands of the gastropod specimens. The approach was also extended to older samples from historical archives from eastern Indian region (Kolkata, East India). Individual specimens belonging to the same species of gastropod, which were collected during the monsoon season of the year 1918 were used for reconstructing the seasonal pattern in monsoon rainfall over the region. The record of variation in the isotopic composition seen in the shell was compared with the rainfall data from Indian Metrological Division observatory at Kolkata station. The year 1918 was characterized as a major drought year and the signature of dry period was seen preserved in the specimen. The work under taken in this thesis will widen the scope of seasonality reconstruction using terrestrial shell fossils from palaeo records, which have been rarely investigated in paleoclimate studies from the perspective of understanding the seasonal precipitation variability.

Reconstruction studies of seasonal rainfall utilizing stable isotope based proxy approach suffer from the limitations of time resolutions. Conventional methods and archives limit the achievable resolution to annual scales. However, high resolution reconstruction (seasonal to sub-weekly scale) can be achieved in proxy records where growth rates are high enough to leave spatial signatures in an organically or inorganically deposited layer such as growth bands. In this study, aragonitic skeleton of the gastropod Lissachatina fulica (Bowdich, Giant African Land Snails) is investigated with an aim to achieve sub-weekly scale reconstruction of the Indian monsoon rainfall. These terrestrial gastropods are native of Africa and highly invasive. Their evolution in the geological time period dates back to the Pliocene and is presently distributed across the tropical belt. They exhibit a high growth rate in the presence of water and high relative humidity in the environment. As a result, they are ideally suited for the task of palaeo seasonality reconstruction. The isotopic patterns recorded in their growth bands reveal composition of environmental water at seasonal time scales. In vitro studies were carried out on L. fulica to estimate their growth rates and growth responses to changes in the physical conditions within the culture chamber. The Indian monsoon rainfall exhibits characteristic dry spells that are generally sandwiched between periods of active phases of high rainfall during the South West monsoon season. These dry spells are typically characterized by rainfall with low intensity. Isotope fingerprinting of the rain water at daily time resolution, covering the years of 2007-10 exhibited distinct isotopic ratios for the dry and wet spells. Dry spells were clearly demarcated in the record with isotopically enriched signature. In addition, the study indentified the role of three distinct moisture sources on δ18O of rain water at Bangalore, India. The variability in the oxygen isotopic composition of the Indian monsoon rainfall is predominantly controlled by this source moisture variability at inter annual time scales, while temperature and amount of rainfall tend to dominate the variability in the precipitation isotopes at seasonal and weekly scales. Simultaneous isotopic analyses of both rainwater and shell carbonates growth bands were undertaken to understand their relationship to aid in high resolution reconstruction. Carbonate found in the growth bands of the gastropods, which is precipitated under equilibrium condition from rainwater, preserves the signature of rainfall. This provides an opportunity to reconstruct rainfall parameters (i.e. amount and moisture sources) knowing the variability in shell carbonates. Stable isotopic ratios measured across the growth bands of live shell specimens collected from the southern and eastern Indian regions (Bangalore and Kolkata, respectively) were compared with the rainfall isotope ratios at these two locations; signature of dry spells were clearly identified from the study of isotopic composition in the growth bands of the gastropod specimens. The approach was also extended to older samples from historical archives from eastern Indian region (Kolkata, East India). Individual specimens belonging to the same species of gastropod, which were collected during the monsoon season of the year 1918 were used for reconstructing the seasonal pattern in monsoon rainfall over the region. The record of variation in the isotopic composition seen in the shell was compared with the rainfall data from Indian Metrological Division observatory at Kolkata station. The year 1918 was characterized as a major drought year and the signature of dry period was seen preserved in the specimen. The work under taken in this thesis will widen the scope of seasonality reconstruction using terrestrial shell fossils from palaeo records, which have been rarely investigated in paleoclimate studies from the perspective of understanding the seasonal precipitation variability.

In the recent scenario of climate change, the natural variability and uncertainty associated with the hydrologic variables is of great concern to the community. This thesis opens up a new area of multi-disciplinary research. It is a promising field of research in hydrology and water resources that uses the information from the field of atmospheric science. A new way to identify and capture the variability and uncertainty associated with the hydrologic variables is established through this thesis. Assessment of hydroclimatic teleconnection for Indian subcontinent and its use in basin-scale hydrologic time series analysis and forecasting is the broad aim of this PhD thesis. The initial part of the thesis is devoted to investigate and establish the dependence of Indian summer monsoon rainfall (ISMR) on large-scale Oceanic-atmospheric circulation phenomena from tropical Pacific Ocean and Indian Ocean regions. El Niño-Southern Oscillation (ENSO) is the well established coupled Ocean-atmosphere mode of tropical Pacific Ocean whereas Indian Ocean Dipole (IOD) mode is the recently identified coupled Ocean-atmosphere mode of tropical Indian Ocean. Equatorial Indian Ocean Oscillation (EQUINOO) is known as the atmospheric component of IOD mode. The potential of ENSO and EQUINOO for predicting ISMR is investigated by Bayesian dynamic linear model (BDLM). A major advantage of this method is that, it is able to capture the dynamic nature of the cause-effect relationship between large-scale circulation information and hydrologic variables, which is quite expected in the climate change scenario. Another new method, proposed to capture the dependence between the teleconnected hydroclimatic variables is based on the theory of copula, which itself is quite new to the field of hydrology. The dependence of ISMR on ENSO and EQUINOO is captured and investigated for its potential use to predict the monthly variation of ISMR using the proposed method. The association of monthly variation of ISMR with the combined information of ENSO and EQUINOO, denoted by monthly composite index (MCI), is also investigated and established. The spatial variability of such association is also investigated. It is observed that MCI is significantly associated with monthly rainfall variation all over India, except over North-East (NE) India, where it is poor. Having established the hydroclimatic teleconnection at a comparatively larger scale, the hydroclimatic teleconnection for basin-scale hydrologic variables is then investigated and established. The association of large-scale atmospheric circulation with inflow during monsoon season into Hirakud reservoir, located in the state of Orissa in India, has been investigated. The strong predictive potential of the composite index of ENSO and EQUINOO is established for extreme inflow conditions. So the methodology of inflow prediction using the information of hydroclimatic teleconnection would be very suitable even for ungauged or poorly gauged watersheds as this approach does not use any information about the rainfall in the catchment. Recognizing the basin-scale hydroclimatic association with both ENSO and EQUINOO at seasonal scale, the information of hydroclimatic teleconnection is used for streamflow forecasting for the Mahanadi River basin in the state of Orissa, India, both at seasonal and monthly scale. It is established that the basin-scale streamflow is influenced by the large-scale atmospheric circulation phenomena. Information of streamflow from previous month(s) alone, as used in most of the traditional modeling approaches, is shown to be inadequate. It is successfully established that incorporation of large-scale atmospheric circulation information significantly improves the performance of prediction at monthly scale. Again, the prevailing conditions/characteristics of watershed are also important. Thus, consideration of both the information of previous streamflow and large-scale atmospheric circulations are important for basin-scale streamflow prediction at monthly time-scale. Adopting the developed approach of using the information of hydroclimatic teleconnection, hydrologic variables can be predicted with better accuracy which will be a very useful input for better management of water resources.