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.

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.

The subject of this study is rainfall-runoff forecasting and flood warning. Denote by (X(t),Y(t)) a sequence of equally spaced bivariate random variables representing rainfall and runoff, respectively. A flood is said to occur at time period (n + 1) if Y(n + 1) > T where T is a fixed number. The main task of flood warning is that of deciding whether or not to issue a flood alarm for the time period n + 1 on the basis of the past observations of rainfall and runoff up to and including time n. With each decision, warning or no warning, there is a certain probability of an error (false alarm or no alarm). Using notions from classical decision theory, the optimal solution is the decision that minimizes Bayes risk. In Chapter 1 a more precise definition of flood warning will be given. A critical review (Chapter 2) of classical methods for forecasting used in hydrology reveals that these methods are not adequate for flood warning and similar types of decision problems unless certain Gaussian assumptions are satisfied. The purpose of this study is to investigate the application of a nonparametric technique referred to as the k-nearest neighbor (k-NN) methods to flood warning and least squares forecasting. The motivation of this method stems from recent results in statistics which extends nonparametric methods for inferring regression functions in a time series setting. Assuming that the rainfall-runoff process can be cast in the framework of Markov processes then, with some additional assumptions, the k-NN technique will provide estimates that converge with an optimal rate to the correct decision function. With this in mind, and assuming that our assumptions are valid, then we can claim that this method will, as the historical record grows, provide the best possible estimate in the sense that no other method can do better. A detailed description of the k-NN estmator is provided along with a scheme for calibration. In the final chapters, the forecasts of this new method are compared with the forecasts of several other methods commonly used in hydrology, on both real and simulated data.

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<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>

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An examination of research and information needs in urban hydrology suggested the investigation of urban water balances and micro- hydrological processes. This should facilitate more accurate modelling of the rainfall-runoff process from urban impermeable surfaces. Greater London data produced annual water balances for 5 heavily urbanized Thames tributaries and estimates of the annual yield ranging from 12-72%. Mean annual runoff for largely rural basins in South East England in comparison was 15-44% of rainfall. The inadequacy of the data for water balance studies led to the instrumentation of a small urbanized catchment at Redbourn, Hertfordshire. Standard meteorological measures were recorded. New instrumentation was designed to measure runoff from shallow pitched roofs while commercially produced instruments were adapted and installed to monitor runoff from a block of flat asphalt-and-chippings garage roofs, and runoff from asphalt roads and pavements at the highway drain outfall. Runoff from these impermeable surfaces is less than 100% even during winter months when evaporation is low. Percentage runoff is 76% for both the pitched and flat roofs while that from the paved surfaces is only 17%. Despite differences in slope, runoff volumes from the pitched and flat roofs are almost identical suggesting that the flat roof does not afford much greater depression storage and evaporation losses. The flat roof does however attenuate storm runoff producing lower flow rates and longer runoff duration than the pitched roofs. Road runoff is very low because of infiltration. The calculated depression storage is 0.25 mm for both roof types and 1.00 mm for the road surface. An average water balance compiled for the roofs gave evaporation as the residual 19% of rainfall. Using an average roof evaporation rate in the road surface water balance gave infiltration as 36% of rainfall with 17% runoff, 21% evaporation and 26% depression storage. Runoff from metre-square roof samples produced slightly different percentage runoff figures for the same winter period. Average percent runoff from red Redland 49 tiles (set at 30°) was 98%, grey Stonewold tiles (set at 17½°) produced 85% and asphalt roofing felt produced 38% runoff. These results are evaluated in the light of probable errors in measurements.

The aim of the study was to establish the effects of human-induced watershed changes on strearnflows. The research hypothesised that land use change influences base flows. Enjoro river in Kenya was used as the case study. In the 1940s, the watershed was characterised with a sparse population, forestry and large scale conservative agriculture. The river regime was naturally perennial. Between 1960 and 1990 land subdivision, intensive cultivation, urbanisation, and deforestation changed such stable ecosystem Several approaches were used to evaluate the perceived cause-effect relations in the watershed. The time series of the flows, rainfall, and other climatic records were used to infer effects of changed physical characteristics in the watershed. Quantitative evaluation of the changes was accomplished by simple graphs, homogeneity tests, satellite imagery and model simulations of hydrologic variables. Analysis of the data series before and after the presumed changes provided an understanding of the variability masked in the hydrologic system. These comparisons allowed for the determination of the period in which the watershed changes influenced the river regime. The combinede ffect of humana nd natural factors decreasedth e river basef lows. A 30% increase in deforestation, 20% in agriculture and 10.4% in urbanisation was observed- Water availability decreased from a runoff coefficient of 22% in the 1960s, 10% in the 1970s and 8% in the 1980s. This progressive decline in runoff developed into hydrologic drought regime in the 1980s. Normalized difference vegetation index (NDVI) predicted well the flow changes in the watershed. Simulations of rainfall and flow supported the changes observed in the hydrologic variables. The optimised parameters with HYRROM showed 'store' parameters (SS, RDEL, GDEL) to be sensitive to changes in vegetation cover especially during the dry years of 1965,1973 and 1984. The model simulated some parameters in the watershed which could be used to infer changes in strearnflows due changes in land use. It was however, difficult to estimate and to validate long-term model parameters because of limited data and the contrasting geography of the region which induced hydrologic variability. The model did not isolate effects of specific land uses, although it predicted the observed flows. There is evidently, a need for future research on the problem. The investigation demonstrated the difficulty in identifying differences in strearnflows from watersheds undergoing simultaneous physical changes and human interventions. Since a specific effect of a particular land use change could not be isolated independently, continued research on the development of an integrating watershed coefficient is recommended. Remote sensing techniques should be incorporated in the development of integrating watershed coefficients.