In the stochastic hydrology literature, suitable time series modelling approaches have been developed for modelling daily streamflow. However, problems arise with this approach if changes are occurring to the precipitation regime generating the historic streamflow data, or if land-use changes are occurring within the catchment which may alter the water balance and the streamflow regime. Traditional time series modelling approaches employ historic streamflow data only and will generate synthetic data which are representative only of the historic conditions. It is not possible to predict how the model parameters should be changed to reflect changes in the climate (precipitation) and catchment response regimes. Developing a methodology to deal with the stochastic generation of daily streamflow that reflects changes to the catchment system and climatic inputs (rainfall and potential evapotranspiration) and then applying the corresponding methodology to a study catchment (upper Thames) in England is the focus of this study. To study the water resources impacts of land-use change on the daily streamflow regime of a catchment, a daily rainfall-runoff model is needed which can accommodate various land cover characteristics and provide separate estimates of potential and actual evapotranspiration in its evapotranspiration component for each land cover type. Given a model with this capability, the impacts of various land-use scenarios on daily streamflow can be investigated. In the case of climate change, since GCMs do not provide useable results on a short time scale such as a day and on a spatial scale such as a catchment of about 1000 km2, a methodology is required to predict the changes which may occur in the climate inputs of a catchment, and the resulting impacts on water resources. The approach developed here for water resources impact studies of land-use change and climate change has three main elements: (I) Two stochastic models, one for rainfall (Neyman-Scott Rectangular Pulses, NSRP, model) and the other for potential evapotranspiration (PET), are employed to generate daily rainfall and daily PET sequencesr,e spectively. Thesem odels have been validated using historic records for the study catchment. ABSTRACT ii (II) The ARNO model has been calibrated and validated using daily streamflow data for the study catchment. The evapotranspiration component of the model has been modified to obtain a satisfactory water balance. The model is then extended to include the explicit calculation of interception for different land cover types within the catchment. The runoff from these areas is then routed to the catchment outlet. The rainfall and PET models are used to generate synthetic daily input series to the modified ARNO model for present catchment land-use conditions, and overall procedure is validated using the historic streamflow record. This is then worked out using the extended model and referred to as the constructed` control' scenariow hich is used as a benchmarkf or assessingla nd-usec hange impacts on water resources for two different land-use scenarios. (III) The transient GCM climate scenarios are used as the starting point for assessing climate change impacts. Regression relationships are derived between atmospheric circulation variables and rainfall statistics used in fitting the NSRP model for present climate conditions and then used to predict the rainfall statistics for future conditions using GCM outputs. That is, the scenarios of a climate model are downscaled by a regression technique to a resolution sufficient to represent daily rainfall at the catchment scale. To generate potential evapotranspiration (PET) scenarios, an empirical equation is used to estimate PET daily values as a function of temperature, thus enabling future scenarios to be generated as a function of GCM temperature predictions. Generated rainfall and PET scenarios are used as inputs to the adapted ARNO catchment response model to generate daily streamflow data. Impact assessments using both land-use change and climate change scenarios are then carried out using a range of water resources assessment measures such as flow duration curves, cumulative run sums and storage/yield relationships, and the practical implications discussed.

Observational evidence and future climate change scenarios suggest an amplification of climatic contrasts across the UK. This is seen most prominently in the marked increase in notable flood events and drought episodes and may profoundly affect water resource systems in vulnerable areas, as exemplified by the 1995 Yorkshire drought. The 1995-96 drought resulted in severe stress to the Yorkshire water supply, necessitating the emergency measure of tanking in water from outside the region, and was caused by an unusual pattern of weather and precipitation. This research is an investigation into both natural climatic variability and possible future climate change in Yorkshire aiming to quantify the risk of future occurrence of severe drought events, such as that of 1995. Historical drought characteristics and spatial-temporal precipitation variability in Yorkshire are examined and linked to synoptic weather patterns. A multi-site stochastic rainfall model is then developed using conditioning by synoptic weather types. The model can account for spatial variability and allows the concurrent simulation of precipitation time-series for very different climatological sub-regions within the same water resource area. This model is used to investigate the impact of natural climatic variability and possible future climate change upon water resource reliability, resilience and vulnerability in Yorkshire. The structure of the stochastic rainfall model enables the impact of variations in weather type persistence or frequency to be investigated. In addition, rainfall model statistics can be altered to simulate instances of increased precipitation intensity or proportion dry days for example, for individual weather groups. The UKCIP98 Medium-High climate change scenarios for 2021- 2050 and 205 1-2080 are investigated using modifications to weather type frequency, precipitation and potential evapotranspiration. Results indicate that water resources in Yorkshire are likely to become more reliable on average under the examined climate change scenarios due to increased winter precipitation. However, model simulations also suggest a reduction in resource resilience and increased vulnerability to drought. Severe droughts comparable to that of 1995 show only a slight increase in frequency by 2080. However, there will be a significant increase in both magnitude and duration of severe drought, as a consequence of summer precipitation reductions and increased climatic variability. This methodology of simulating the impacts of potential atmospheric circulation change on precipitation regimes can provide a basis for the future planning and management of water resource systems.

No description available.

Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1979. / Bibliography: leaves 87-93. / Microfiche. / xvi, 218 leaves ill., maps 29 cm

One challenge that faces hydrologists in water resources planning is to predict the catchment???s response to a given rainfall. Estimation of parameter uncertainty (and model uncertainty) allows assessment of the risk in likely applications of hydrological models. Bayesian statistical inference, with computations carried out via Markov Chain Monte Carlo (MCMC) methods, offers an attractive approach to model specification, allowing for the combination of any pre-existing knowledge about individual models and their respective parameters with the available catchment data to assess both parameter and model uncertainty. This thesis develops and applies Bayesian statistical tools for parameter estimation, comparison of model performance and hierarchical model aggregation. The work presented has three main sections. The first area of research compares four MCMC algorithms for simplicity, ease of use, efficiency and speed of implementation in the context of conceptual rainfall-runoff modelling. Included is an adaptive Metropolis algorithm that has characteristics that are well suited to hydrological applications. The utility of the proposed adaptive algorithm is further expanded by the second area of research in which a probabilistic regime for comparing selected models is developed and applied. The final area of research introduces a methodology for hydrologic model aggregation that is flexible and dynamic. Rigidity in the model structure limits representation of the variability in the flow generation mechanism, which becomes a limitation when the flow processes are not clearly understood. The proposed Hierarchical Mixtures of Experts (HME) model architecture is designed to do away with this limitation by selecting individual models probabilistically based on predefined catchment indicators. In addition, the approach allows a more flexible specification of the model error to better assess the risk of likely outcomes based on the model simulations. Application of the approach to lumped and distributed rainfall runoff models for a variety of catchments shows that by assessing different catchment predictors the method can be a useful tool for prediction of catchment response.

Title page, abstract and table of contents only. The complete thesis in print form is available from the University of Adelaide Library. / This thesis describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique is demonstrated using a high-resolution point rainfall model based on wet-dry alternating storm events. A model has been produced that is well defined and can be vigorously compared against numerous observed statistics in a quantative manner. The rainfall model presented in this thesis can be used to provide accurate synthetic rainfall data at sites with minimal historical rainfall data providing a powerful tool for application in hydrological risk analysis across Australia. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1282259 / Thesis (Ph.D.) -- University of Adelaide, Dept. of Civil and Environmental Engineering, 2007

Title page, abstract and table of contents only. The complete thesis in print form is available from the University of Adelaide Library. / This thesis describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique is demonstrated using a high-resolution point rainfall model based on wet-dry alternating storm events. A model has been produced that is well defined and can be vigorously compared against numerous observed statistics in a quantative manner. The rainfall model presented in this thesis can be used to provide accurate synthetic rainfall data at sites with minimal historical rainfall data providing a powerful tool for application in hydrological risk analysis across Australia. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1282259 / Thesis (Ph.D.) -- University of Adelaide, Dept. of Civil and Environmental Engineering, 2007

Thesis (M.S. - Renewable Natural Resources)--University of Arizona, 1985. / Includes bibliographical references (leaves 119-122).

Thesis (M.S. - Hydrology)--University of Arizona. / Includes bibliographical references (leave 57).

Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, June 2008. / Thesis Advisor(s): Murphree,Tom. "June 2008." Description based on title screen as viewed on August 26, 2008. Includes bibliographical references (p.83-87). Also available in print.