No abstract

Digital elevation models (or maps) (DEMs) are increasingly becoming available to consulting engineers and academic researchers for delineating catchment boundaries. There are two important questions to be answered in using DEMs for generating those boundaries: first, how good are the computer-generated boundaries and second, what map resolution should be used from a host of data products on the market? This paper describes some explorations of these two topical issues through a case study for the Brue catchment in the southwest of England. The study concludes that computer-generated boundaries failed to delineate accurately or reliably the catchment border with all the available digital maps tested. This was mainly because the computer cannot pick up man-made features (highways, ditches, etc.), in addition to data quality and algorithm problems. With respect to map resolution, it has been found that although the discrepancy of the delineation increases with the grid size (i.e. poor resolution maps would generate lowerquality boundaries), there is a threshold which defines a zone where the clear relationship between the map resolution and discrepancy becomes very unstable. This is a very important conclusion since it indicates that higher map resolution may produce poorer results than lower map resolution, which is quite serious in practical projects for map users who face increased map costs, longer computing time and potentially poorer results. In the end, the paper proposes an integrated technique that uses the strengths of both manual and computer methods to produce an optimised boundary.

Quantization is a process by where continuous signals are transformed into discrete values. It is an important part of the signal processing involved in using weather radar. Technological advances have made it easier to increase the number of quantization levels, as witnessed by the replacement of a 3 bit system by an 8 bit system by the UK Meteorological Office. Research has been conducted in the past demonstrating the error statistics of quantized rainfall, although these studies have used real radar data. The novelty of this study is in using synthetic rain, generated with a Poisson cluster model to represent hourly rainfall, and subsequently disaggregated using a fractal cascade to a fine 5 min time scale. The advantage of this approach is the length of time series that can be generated far outweighs the limited duration of historical rainfall series, especially at such fine time scales. This provides sufficient rainfall data, especially high intensity rainfall, to say something statistically significant about the error statistics. The models are parameterised for different months and also for a non-seasonal set. Rainfall is then generated for a summer case, a winter case, and for the non-seasonal case. It is discovered that the error distribution varies significantly as the parameters change for 3 bit rainfall. This error distribution is relatively constant for 8 bit data, within its working range (up to 126 mm/h). At a fine time scale, such high intensity events are not uncommon. This knowledge is useful when investigating historical radar data at lower quantization levels, for the purpose of flood frequency analysis, and remains relevant, especially, if as some studies have shown, the occurrence of high intensity storms is likely to increase.