Due to the low-cost and lightweight units, single-wavelength LiDAR bathymetric systems are an ideal option for shallow-water (<12 m) bathymetry. however, one disadvantage of such systems is the lack of near-infrared and raman channels, which results in difficulties in extracting the water surface. therefore, the choice of a suitable waveform processing method is extremely important to guarantee the accuracy of the bathymetric retrieval. in this paper, we test six algorithms for single-wavelength bathymetric waveform processing, i.e. peak detection (pd), the average square difference function (asdf), gaussian decomposition (gd), quadrilateral fitting (qf), richardson–lucy deconvolution (rld), and wiener filter deconvolution (wd). to date, most of these algorithms have previously only been applied in topographic lidar waveforms captured over land. a simulated dataset and an optech aquarius dataset were used to assess the algorithms, with the focus being on their capability of extracting the depth and the bottom response. the influences of a number of water and equipment parameters were also investigated by the use of a monte carlo method. the results showed that the rld method had a superior performance in terms of a high detection rate and low errors in the retrieved depth and magnitude. the attenuation coefficient, noise level, water depth, and bottom reflectance had significant influences on the measurement error of the retrieved depth, while the effects of scan angle and water surface roughness were not so obvious.