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P.D. Bates

Publications and source records attributed to P.D. Bates.

2 recordsLinked to original sources

An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results

Water Resources Research↗

Flood frequency analysis for nonstationary annual peak records in an urban drainage basin

Flood frequency analysis in urban watersheds is complicated by nonstationarities of annual peak records associated with land use change and evolving urban stormwater infrastructure. In this study, a framework for flood frequency analysis is developed based on the Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS), a tool for modeling time series under nonstationary conditions. GAMLSS is applied to annual maximum peak discharge records for Little Sugar Creek, a highly urbanized watershed which drains the urban core of Charlotte, North Carolina. It is shown that GAMLSS is able to describe the variability in the mean and variance of the annual maximum peak discharge by modeling the parameters of the selected parametric distribution as a smooth function of time via cubic splines. Flood frequency analyses for Little Sugar Creek (at a drainage area of 110 km 2 ) show that the maximum flow with a 0.01-annual probability (corresponding to 100-year flood peak under stationary conditions) over the 83-year record has ranged from a minimum unit discharge of 2.1 m 3 s - 1 km - 2 to a maximum of 5.1 m 3 s - 1 km - 2 . An alternative characterization can be made by examining the estimated return interval of the peak discharge that would have an annual exceedance probability of 0.01 under the assumption of stationarity (3.2 m 3 s - 1 km - 2 ). Under nonstationary conditions, alternative definitions of return period should be adapted. Under the GAMLSS model, the return interval of an annual peak discharge of 3.2 m 3 s - 1 km - 2 ranges from a maximum value of more than 5000 years in 1957 to a minimum value of almost 8 years for the present time (2007). The GAMLSS framework is also used to examine the links between population trends and flood frequency, as well as trends in annual maximum rainfall. These analyses are used to examine evolving flood frequency over future decades. ?? 2009 Elsevier Ltd.

Advances in Water Resources↗