No description available.

No description available.

No description available.

The fine-scale structure of global (30N-30S) tropical rainfall is characterised using 13 years (1998-2010) of 3-hourly and daily, 0.25-degree Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall product. At the outset, the dominant timescales present in rainfall are identified. Specifically, the Fourier spectrum (in time) is estimated in two ways (a) spectrum of spatially averaged (SoSA) rainfall; and (b) spatial average of the spectrum (SAoS) of rainfall at each grid point. This procedure is applied on rainfall at the 3-hourly and daily temporal resolutions. Both estimates of the spectrum show the presence of a very strong seasonal cycle. But, at subseasonal timescales, the two methods of estimating spectrum show a marked difference in daily rainfall. Specifically, with SoSA the variability peaks at a subseasonal timescale of around 5 days, with a possible secondary peak around 30-40 days (mostly in the southern tropics). With SAoS, the variability is distributed across a range of timescales, from 2 days to 90 days. However, with finer resolution (3-hourly) observations, it is seen that (besides the seasonal cycle) both methods agree and yield a dominant diurnal scale. Along with other subseasonal scales, the contribution and geographical distribution of diurnal scale variability is estimated and shown to be highly significant. Given its large contribution to the variability of tropical rainfall, the diurnal cycle is extracted by means of a Fourier-based filtering and analysed. The diurnal rainfall anomaly is constructed by eliminating all timescales larger than 1 day. Following this, taking care to avoid spurious peaks associated with Gibbs oscillations, the time of day (called the peak octet) when the diurnal anomaly is largest is identified. The peak octet is estimated for each location in the global tropics. This is repeated for 13 years, and the resulting mode of the time of maximum rainfall is established. It is seen that (i) most land regions receive rainfall during the late afternoon/early evening hours; (ii) rainfall over open oceans lack a dominant diurnal signature with a possible combination of early morning and afternoon showers; (iii) coastal regions show a clear south/southwest propagation in the mode of the peak octet of rainfall. In addition to being a comprehensive documentation of the diurnal cycle at very fine scales, the results serve as a critical test for the validation of theoretical and numerical models of global tropical rainfall.

No description available.

No description available.

Spatial variability of rainfall is a significant factor in hydrologic and water quality modeling. In recent years, characterizing and analyzing the effect of spatial variability of rainfall in hydrologic applications has become vital with the advent of remotely sensed precipitation estimates that have high spatial resolution. In this study, the effect of spatial variability of rainfall in hourly runoff generation was analyzed using the Soil and Water Assessment Tool (SWAT) for Big Sandy Creek and Walnut Creek Watersheds in North Central Texas. The area of the study catchments was 808 km2 and 196 km2 for Big Sandy Creek and Walnut Creek Watersheds respectively. Hourly rainfall measurements obtained from raingauges and weather radars were used to estimate runoff for the years 1999 to 2003. Results from the study indicated that generated runoff from SWAT showed enormous volume bias when compared against observed runoff. The magnitude of bias increased as the area of the watershed increased and the spatial variability of rainfall diminished. Regardless of high spatial variability, rainfall estimates from weather radars resulted in increased volume of simulated runoff. Therefore, weather radar estimates were corrected for various systematic, range-dependent biases using three different interpolation methods: Inverse Distance Weighting (IDW), Spline, and Thiessen polygon. Runoff simulated using these bias adjusted radar rainfall estimates showed less volume bias compared to simulations using uncorrected radar rainfall. In addition to spatial variability of rainfall, SWAT model structures, such as overland flow, groundwater flow routing, and hourly evapotranspiration distribution, played vital roles in the accuracy of simulated runoff.

Thesis (M. Phil.)--University of Hong Kong, 1979.

published_or_final_version / Statistics / Master / Master of Social Sciences

Extreme rainfall events may have catastrophic impacts on the population and infrastructures, therefore it is essential to have accurate knowledge of extreme rainfall characteristics. Moreover, both the scientific community and policymakers have recently shown a growing interest in the potential impacts of climate change on water resources management. Indeed, changes in the intensity and frequency of occurrence of extreme rainfall events may have serious impacts. As such, it is important to understand not only the current patterns of extreme rainfalls but also how they are likely to change in the future. / The objective of the present research is therefore to find the best method for estimating accurately extreme rainfalls for the current time period and future periods in the context of climate change. The analysis of extreme rainfall data from the province of Quebec (Canada) revealed that, according to L-moment ratio diagrams, the data may be well described by the Generalized-Extreme-Value (GEV) distribution. Results also showed that a simple scaling relationship between non-central moments (NCM) and duration can be established and that a scaling method based on NCMs and scaling exponents can be used to generate accurate estimates of extreme rainfalls at Dorval station (Quebec, Canada). Other results demonstrated that the method of NCMs can accurately estimate distribution parameters and can be used to construct accurate Intensity-Duration-Frequency (IDF) curves. / Furthermore, a regional analysis was performed and homogenous regions of weather stations within Quebec were identified. A method for the estimation of missing data at ungauged sites based on regional NCMs was found to yield good estimates. / In addition, the potential impacts of climate change on extreme rainfalls were assessed. Changes in the distribution of annual maximum (AM) precipitations were evaluated using simulations from two Global Climate Models (GCMs) under the A2 greenhouse gas emission scenario: the Coupled Global Climate Model version 2 (CGCM2A2) of the Canadian Centre for Climate Modelling and Analysis, and the Hadley Centre's Model version 3 (HadCM3A2). Simulations from these two models were downscaled spatially using the Statistical DownScaling Model (SDSM). A bias-correction method to adjust the downscaled AM daily precipitations for Dorval station was tested and results showed that after adjustments, the values fit the observed AM daily precipitations well. The analysis of future AM precipitations revealed that, after adjustments, AM precipitations downscaled from CGCM2A2 increase from current to future periods, while AM precipitations downscaled from HadCM3A2 show a mild decrease from current to future periods, for daily and sub-daily scales.