Aerodynamic roughness length of crevassed tidewater glaciers from UAV mapping

The aerodynamic roughness length (z0) is an important parameter in the bulk approach for calculating turbulent fluxes and their contribution to ice melt. However, z0 estimates for heavily crevassed tidewater glaciers are rare or only generalised. This study used uncrewed aerial vehicles (UAVs) to map inaccessible tidewater glacier front areas. The high-resolution images were utilised in a structure-from-motion photogrammetry approach to build digital elevation models (DEMs). These DEMs were applied to five models (split across transect and raster methods) to estimate z0 values of the mapped area. The results point out that the range of z0 values across a crevassed glacier is large, by up to 3 orders of magnitude. The division of the mapped area into sub-grids (50 m × 50 m), each producing one z0 value, accounts for the high spatial variability in z0 across the glacier. The z0 estimates from the transect method are in general greater (up to 1 order of magnitude) than the raster method estimates. Furthermore, wind direction (values parallel to the ice flow direction are greater than perpendicular values) and the chosen sub-grid size turned out to have a large impact on the z0 values, again presenting a range of up to 1 order of magnitude each. On average, z0 values between 0.08 and 0.88 m for a down-glacier wind direction were found. The UAV approach proved to be an ideal tool to provide distributed z0 estimates of crevassed glaciers, which can be incorporated by models to improve the prediction of turbulent heat fluxes and ice melt rates.

Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR

The aerodynamic roughness length (Z0) and surface geometry at ultra-high resolution in precision agriculture and agroforestry have substantial potential to improve aerodynamic process modeling for sustainable farming practices and recreational activities. We explored the potential of unmanned aerial vehicle (UAV)-borne LiDAR systems to provide Z0 maps with the level of spatiotemporal resolution demanded by precision agriculture by generating the 3D structure of vegetated surfaces and linking the derived geometry with morphometric roughness models. We evaluated the performance of three filtering algorithms to segment the LiDAR-derived point clouds into vegetation and ground points in order to obtain the vegetation height metrics and density at a 0.10 m resolution. The effectiveness of three morphometric models to determine the Z0 maps of Danish cropland and the surrounding evergreen trees was assessed by comparing the results with corresponding Z0 values from a nearby eddy covariance tower (Z0_EC). A morphological filter performed satisfactorily over a homogeneous surface, whereas the progressive triangulated irregular network densification algorithm produced fewer errors with a heterogeneous surface. Z0 from UAV-LiDAR-driven models converged with Z0_EC at the source area scale. The Raupach roughness model appropriately simulated temporal variations in Z0 conditioned by vertical and horizontal vegetation density. The Z0 calculated as a fraction of vegetation height or as a function of vegetation height variability resulted in greater differences with the Z0_EC. Deriving Z0 in this manner could be highly useful in the context of surface energy balance and wind profile estimations for micrometeorological, hydrologic, and ecologic applications in similar sites.

Aerodynamic roughness length of crevassed tidewater glaciers from UAV mapping

The aerodynamic roughness length (z0) is an important parameter in the bulk approach for calculating turbulent fluxes and their contribution to ice melt. However, for heavily crevassed tidewater glaciers z0 estimations are rare or only generalized. This study used unmanned aerial vehicles (UAVs) to map inaccessible tidewater glacier front areas. The high-resolution images were used in a structure-from-motion photogrammetry approach to build digital elevation models (DEMs). These DEMs were applied to five different models (split across transect and raster methods) to estimate z0 values of the mapped area. The results point out that the range of z0 values across a glacier is large, with up to three (locally even four) orders of magnitude. The division of the mapped area into sub-grids (50 m x 50 m), each producing one z0 value, best accounts for the high spatial variability of z0 across the glacier. The z0 estimations from the transect method are in general higher (up to one order of magnitude) than the raster method estimations. Furthermore, wind direction (values parallel to the ice flow direction are larger than perpendicular) and the chosen sub-grid size turned out to have a large impact on the z0 values, again presenting a range of up to one order of magnitude each. On average, z0 values between 0.08 m and 0.88 m for a down-glacier wind direction were found. The UAV approach proved to be an ideal tool to provide distributed z0 estimations of crevassed glaciers, which can be incorporated by models to improve the prediction of turbulent heat fluxes and ice melt rates.

Impact of Effective Roughness Length on Mesoscale Meteorological Simulations over Heterogeneous Land Surfaces in Taiwan

The Weather Research and Forecasting (WRF) modeling system obtains the aerodynamic roughness length (z0) from a land use (LU) lookup table. The effective aerodynamic roughness length (z0eff) was estimated for the island of Taiwan by considering the individual roughness lengths (z0i) of the underlying LU types within a modeling grid box. Two z0eff datasets were prepared: one using the z0i from the default LU lookup table and the other using the observed z0i for three LU types (urban, dry cropland and pasture, and irrigated cropland and pasture). The spatial variability of the z0eff distribution was higher than that of the LU table-based z0 distribution. Three WRF sensitivity experiments were performed: (1) dominant LU table-based z0 (namely, S1), (2) z0eff estimated from the default z0i (namely, S2), and (3) z0eff estimated from the observed z0i (namely, S3). Comparisons of the thermal field, temperature, and surface sensible and latent heat fluxes revealed no significant differences among the three simulations. The wind field overestimation and surface momentum flux underestimation in S1 were reduced in S2 and S3, and these improvements were more prominent over areas with highly heterogeneous land surface conditions.