No description available.

No description available.

A new stochastic rainfall field model is described which employs the Turning Bands Method (TBM) to transform a unidimensional Gaussian process, generated by the fractional differencing process along a line, into a multidimensional space-time Gaussian process with a specified space-time correlation structure. Transformations are applied to give the rainfall process a non-Gaussian and non-stationary structure. A correction factor is introduced into the model to take account of the effect of topography on rainfall. The model has been applied to the small upland Wye catchment in mid Wales (area 10.55 km2) and shown to reproduce satisfactorily the statistics and correlation structure of observed hourly point rainfall. As an extension to the rainfall field model, a new technique of conditional simulation has been used to generate the rainfall fields. The conditionally simulated rainfall fields reproduce exactly the observed point rainfalls at measurement points and likely realizations of rainfall fields between points. Rainfall fields generated by the above mentioned rainfall field model and the conditional simulation technique are fed directly into the Systeme Hydrologique European (SHE) model and the sensitivity of runoff prediction errors to (i) level of space-time correlation (ii) sampling of rainfall with different schemes in space and (iii) antecedent conditions are explored. It is found that in case of Wye catchment the errors deriving from sampling procedure used are generally small when rainfall fields were based on observed correlation structure. Sensitivity of errors to different correlation levels give the impression that errors increase with a decrease in correlation level. Further it is noticed that this trend of errors is more pronounced in `dry' catchment conditions as compared to `wet' catchment conditions. Overall the results for the small Wye catchment illustrate that the catchment acts as a smoother of the spatially distributed rainfall input at this spatial scale and for the rainfall regime in question. However, the results imply that for the typical raingauge densities encountered for larger catchments, significant errors may occur.

No description available.

Thesis. 1979. M.Arch--Massachusetts Institute of Technology. Dept. of Architecture. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Includes bibliographical references. / M.Arch

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.

An improved physically-based rainfall algorithm was developed using AMSR-E data based on a radiative transfer model. In addition, error models were designed and embedded in the algorithm to assess retrieval errors quantitatively and to reduce net retrieval uncertainties. The algorithm uses six channels (dual polarizations at 36.5, 18.7 and 10.65GHz) and retrieves rain rates on a pixel-by-pixel basis. Monthly rain totals are estimated by summing average rain rates computed by merging six rain rates based on proper weights that are estimated from error models. Error models were constructed based upon the principal error sources of rainfall retrieval such as beam filling error, drop size distribution uncertainty and instrument calibration errors. Several improved schemes that minimize uncertainties of the rainfall retrieval were developed in this study. In particular, improved offset correction that corrects the biases near zero rain plays a very important role for reducing uncertainties which are mainly driven by calibration uncertainty including the modeling errors. AMSR-E's larger calibration uncertainty was substantially absorbed by this offset correction as well as by the weighted average scheme to combine all six channels optimally. As a framework for inter-comparison with the experimental algorithm, the current operational algorithm (NASA, level 3 algorithm) was also updated with respect to AMSR-E data. The experimental algorithm was compared with the operational algorithm for both AMSR-E and TMI data and rainfall retrieval uncertainties were analyzed using error models. When the experimental algorithm was used, many limitations of the operational algorithm were overcome and uncertainties of rainfall retrieval were considerably eliminated.

<p>The overall aim of this project was to establish whether there is a long-term decline of rainfall collected in rainfall gauges within the Jonkershoek Valley that have the longest and best quality records.</p>

The effect on urban rainfall of pollution aerosols is studied both by data analysis and computational simulation. Our study examines data for urban areas undergoing decadal clean-up. We compare the annual precipitation between polluted sites and relatively clean sites through the time range before and during their clean-up periods to see how the air quality may affect the precipitation amount. By comparing the annual precipitation amount between two polluted sites with different elevations we demonstrate the role that elevation may play in rainfall suppression. Based on the data we collected, we built a model to analyze the relationship between air pollution aerosols and precipitation. Finally, we used a model of time dependent condensational aerosol growth to numerically study the relationship of air pollution aerosols and precipitation amount. Based on these results, we found a negative relationship of precipitation amount and air pollution amount; also, the simulation results clearly demonstrated that too many air pollution particles will deplete the water vapor and suppress further growth of condensation nuclei (CN) toward cloud condensation nuclei (CNN). This study supported the theoretical explanation on why air pollution could suppress urban rainfall.

An improved physically-based rainfall algorithm was developed using AMSR-E data based on a radiative transfer model. In addition, error models were designed and embedded in the algorithm to assess retrieval errors quantitatively and to reduce net retrieval uncertainties. The algorithm uses six channels (dual polarizations at 36.5, 18.7 and 10.65GHz) and retrieves rain rates on a pixel-by-pixel basis. Monthly rain totals are estimated by summing average rain rates computed by merging six rain rates based on proper weights that are estimated from error models. Error models were constructed based upon the principal error sources of rainfall retrieval such as beam filling error, drop size distribution uncertainty and instrument calibration errors. Several improved schemes that minimize uncertainties of the rainfall retrieval were developed in this study. In particular, improved offset correction that corrects the biases near zero rain plays a very important role for reducing uncertainties which are mainly driven by calibration uncertainty including the modeling errors. AMSR-E's larger calibration uncertainty was substantially absorbed by this offset correction as well as by the weighted average scheme to combine all six channels optimally. As a framework for inter-comparison with the experimental algorithm, the current operational algorithm (NASA, level 3 algorithm) was also updated with respect to AMSR-E data. The experimental algorithm was compared with the operational algorithm for both AMSR-E and TMI data and rainfall retrieval uncertainties were analyzed using error models. When the experimental algorithm was used, many limitations of the operational algorithm were overcome and uncertainties of rainfall retrieval were considerably eliminated.