Near-bed wave orbital velocities and shear stresses are important parameters in many sediment-transport and hydrodynamic models of the coastal ocean, estuaries, and lakes. Simple methods for estimating bottom orbital velocities from surface-wave statistics such as significant wave height and peak period often are inaccurate except in very shallow water. This paper briefly reviews approaches for estimating wave-generated bottom orbital velocities from near-bed velocity data, surface-wave spectra, and surface-wave parameters; MATLAB code for each approach is provided. Aspects of this problem have been discussed elsewhere. We add to this work by providing a method for using a general form of the parametric surface-wave spectrum to estimate bottom orbital velocity from significant wave height and peak period, investigating effects of spectral shape on bottom orbital velocity, comparing methods for calculating bottom orbital velocity against values determined from near-bed velocity measurements at two sites on the US east and west coasts, and considering the optimal representation of bottom orbital velocity for calculations of near-bed processes. Bottom orbital velocities calculated using near-bed velocity data, measured wave spectra, and parametric spectra for a site on the northern California shelf and one in the mid-Atlantic Bight compare quite well and are relatively insensitive to spectral shape except when bimodal waves are present with maximum energy at the higher-frequency peak. These conditions, which are most likely to occur at times when bottom orbital velocities are small, can be identified with our method as cases where the measured wave statistics are inconsistent with Donelan's modified form of the Joint North Sea Wave Project (JONSWAP) spectrum. We define the 'effective' forcing for wave-driven, near-bed processes as the product of the magnitude of forcing times its probability of occurrence, and conclude that different bottom orbital velocity statistics may be appropriate for different problems. ?? 2008 Elsevier Ltd.