No abstract

Advances in topobathymetric LiDARs could enable rapid surveys at sub‐meter resolution over entire stream networks. This is the first step to improving our knowledge of riverine systems, both their morphology and role in ecosystems. The Experimental Advanced Airborne Research LiDAR B (EAARL‐B) system is one such topobathymetric sensor, capable of mapping both terrestrial and aquatic systems. Whereas the original EAARL was developed to survey littoral areas, the new version, EAARL‐B, was also designed for riverine systems but has yet to be tested. Thus, we evaluated the ability of EAARL‐B to map bathymetry and floodplain topography at sub‐meter resolution in a mid‐size gravel‐bed river. We coupled the EAARL‐B survey with highly accurate field surveys (0.03 m vertical accuracy and approximately 0.6 by 0.6 m resolution) of three morphologically distinct reaches, approximately 200 m long 15 m wide, of the Lemhi River (Idaho, USA). Both point‐to‐point and raster‐to‐raster comparisons between ground and EAARL‐B surveyed elevations show that differences (ground minus EAARL‐B surveyed elevations) over the entire submerged topography are small (root mean square error, RMSE, and median absolute error, M, of 0.11 m), and large differences (RMSE, between 0.15 and 0.38 m and similar M) are mainly present in areas with abrupt elevation changes and covered by dense overhanging vegetation. RMSEs are as low as 0.03 m over paved smooth surfaces, 0.07 m in submerged, gradually varying topography, and as large as 0.24 m along banks with and without dense, tall vegetation. EAARL‐B performance is chiefly limited by point density in areas with strong elevation gradients and by LiDAR footprint size (0.2 m) in areas with topographic features of similar size as the LiDAR footprint. © 2018 John Wiley & Sons, Ltd.

Studies of the effects of hydrodynamic model dimensionality on simulated flow properties and derived quantities such as aquatic habitat quality are limited. It is important to close this knowledge gap especially now that entire river networks can be mapped at the microhabitat scale due to the advent of point‐cloud techniques. This study compares flow properties, such as depth and velocity, and aquatic habitat quality predicted from pseudo‐2D and fully 2D hydrodynamic modeling. The models are supported by high‐resolution, point‐cloud derived bathymetries, from which close‐spaced cross‐sections were extracted for the 1D modeling, of three morphologically and hydraulically different river systems. These systems range from small low‐gradient meandering pool–riffle to large steep confined plane‐bed rivers. We test the effects of 1D and 2D models on predicted hydraulic variables at cross‐sections and over the full bathymetry to quantify the differences due to model dimensionality and those from interpolation. Results show that streambed features, whose size is smaller than cross‐sectional spacing, chiefly determine the different results of 1D and 2D modeling whereas flow discharge, stream size, morphological complexity and model grid sizes have secondary effects on flow properties and habitat quality for a given species and life stage predicted from 1D and 2D modeling. In general, the differences in hydraulic variables are larger in the bathymetric than in the cross‐sectional analysis, which suggests that some errors are introduced from interpolation of spatially disaggregated simulated variables with a 1D model, instead of model dimensionality 1D or 2D. Flow property differences are larger for velocity than for water surface elevation and depth. Differences in weighted usable area (WUA) derived from 1D and 2D modeling are relatively small for low‐gradient meandering pool–riffle systems, but the differences in the spatial distribution of microhabitats can be considerable although clusters of same habitat quality are spatially comparable. Copyright © 2014 John Wiley & Sons, Ltd.

Stream water temperature plays a significant role in aquatic ecosystems where it controls many important biological and physical processes. Reliable estimates of water temperature at the daily time step are critical in managing water resources. We developed a parsimonious piecewise Bayesian model for estimating daily stream water temperatures that account for temporal autocorrelation and both linear and nonlinear relationships with air temperature and discharge. The model was tested at 8 climatically different basins of the USA and at 34 sites within the mountainous Boise River Basin (Idaho, USA). The results show that the proposed model is robust with an average root mean square error of 1.25 °C and Nash–Sutcliffe coefficient of 0.92 over a 2‐year period. Our approach can be used to predict historic daily stream water temperatures in any location using observed daily stream temperature and regional air temperature data.