ALBERT

Description: Capturing the spatial distribution of high-intensity rainfall over short-time intervals is critical for accurately assessing the efficacy of urban stormwater drainage systems. In a stochastic simulation framework, one method of generating realistic rainfall fields is by multiplicative random cascade (MRC) models. Estimation of MRC model parameters has typically relied on radar imagery or, less frequently, rainfall fields interpolated from dense rain gauge networks. However, such data are not always available. Furthermore, the literature is lacking estimation procedures for spatially incomplete datasets. Therefore, we proposed a simple method of calibrating an MRC model when only data from a moderately dense network of rain gauges is available, rather than from the full rainfall field. The number of gauges needs only be sufficient to adequately estimate the variance in the ratio of the rain rate at the rain gauges to the areal average rain rate across the entire spatial domain. In our example for Warsaw, Poland, we used 25 gauges over an area of approximately 1600 km2. MRC models calibrated using the proposed method were used to downscale 15-min rainfall rates from a 20 by 20 km area to the scale of the rain gauge capture area. Frequency distributions of observed and simulated 15-min rainfall at the gauge scale were very similar. Moreover, the spatial covariance structure of rainfall rates, as characterized by the semivariogram, was reproduced after allowing the probability density function of the random cascade generator to vary with spatial scale.

Description: Capturing the spatial distribution of high-intensity rainfall over short-time intervals is critical for accurately assessing the efficacy of urban stormwater drainage systems. In a stochastic simulation framework, one method of generating realistic rainfall fields is by multiplicative random cascade (MRC) models. Estimation of MRC model parameters has typically relied on radar imagery or, less frequently, rainfall fields interpolated from dense rain gauge networks. However, such data are not always available. Furthermore, the literature is lacking estimation procedures for spatially incomplete datasets. Therefore, we proposed a simple method of calibrating an MRC model when only data from a moderately dense network of rain gauges are available, rather than from the full rainfall field. The number of gauges need only be sufficient to adequately estimate the variance in the ratio of the rain rate at the rain gauges to the areal average rain rate across the entire spatial domain. In our example for Warsaw, Poland, we used 25 gauges over an area of approximately 1600 km2. MRC models calibrated using the proposed method were used to downscale 15-min rainfall rates from a 20 by 20 km area to the scale of the rain gauge capture area. Frequency distributions of observed and simulated 15-min rainfall at the gauge scale were very similar. Moreover, the spatial covariance structure of rainfall rates, as characterized by the semivariogram, was reproduced after allowing the probability density of the random cascade generator to vary with spatial scale.

Description: Accurate measurement of the amount and timing of surface runoff at multiple scales is needed to understand fundamental hydrological processes. At the plot-scale (i.e., length scales on the order of 1 to 10 m) current methods for direct measurement of runoff either store the water in a collection vessel, which is unconducive to long-term monitoring studies, or utilize expensive installations such as large-scale tipping buckets or flume/weir systems. We developed an alternative low-cost, robust and reliable instrument to measure runoff that we call the "Upwelling Bernoulli Tube" (UBeTube). The UBeTube instrument is a pipe with a slot machined in its side that is installed vertically at the base of a runoff collection system. The flow rate through the slot is inferred by measuring the water height within the pipe. The geometry of the slot can be modified to suit the range of flow rates expected for a given site; we demonstrate a slot geometry which is capable of measuring flow rates across more than three orders of magnitude (up to 300 L min−1) while requiring only 30 cm of hydraulic head. System accuracy is dependent on both the geometry of the slot and the accuracy of the water level measurements. With an off-the-shelf pressure transducer sensor, the mean theoretical error for the demonstrated slot geometry was ~17% (ranging from errors of more than 50% at low flow rates to less than 2% at high flow rates), while the observed error during validation was 1–25%. A simple correction factor reduced this mean error to −14%, and further reductions in error could be achieved through the use of taller, narrower slot dimensions (which requires greater head gradients to drive flow) or through more accurate water level measurements. The UBeTube device has been successfully employed in a long-term rainfall-runoff study, demonstrating the ability of the instrument to measure surface runoff across a range of flows and conditions.