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.

Weaknesses in dust emission modelling hidden by tuning to dust in the atmosphere

Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many of the current dust emission models (TEM) assume that the Earth’s land surface is constantly devoid of vegetation, then adjust the dust emission using a vegetation cover reciprocal, and finally calibrate to dust in the atmosphere. We compare this approach with an albedo-based dust emission model (AEM) which calibrates Earth’s land surface shadow to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. We also compare these dust emission models with estimates of dust in the atmosphere using dust optical depth frequency (DOD). Using existing datasets of satellite observed dust emission from dust point sources (DPS), we show that during the same period, DOD frequency exceeds DPS frequency by up to two orders of magnitude (RMSEDOD = 67 days). Relative to DPS frequency, both models over-estimated dust emission frequency by up to one order of magnitude (RMSETEM = 6 days; RMSEAEM = 4 days) but showed strong relations with DPS frequency suitable for calibrating models to observed dust emission. Theoretically, the TEM is incomplete in its formulation, which despite the pragmatic adjustment using the vegetation cover reciprocal, causes dust emission to be highly dependent on wind speed and over-estimates large (> 0.1 kg m−2 a−1) dust emission over vast vegetated areas. Consequently, the TEM produces considerable falsely positive change in dust emission, relative to the AEM. Since the main difference between the dust emission models is the treatment of aerodynamic roughness we conclude that its crude representation in the TEM has caused large, previously unknown, uncertainty in Earth System Models (ESMs). Our results indicate that tuning dust emission models to dust in the atmosphere has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without tuning. In ESMs the AEM can be driven by available prognostic albedo to represent the fidelity of drag partition physics to reduce uncertainty of aerosol effects on, and responses to, contemporary and future environmental change.

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.

Mapping the aerodynamic roughness of the Greenland Ice Sheet surface using ICESat-2: evaluation over the K-transect

The aerodynamic roughness of heat, moisture, and momentum of a natural surface are important parameters in atmospheric models, as they co-determine the intensity of turbulent transfer between the atmosphere and the surface. Unfortunately this parameter is often poorly known, especially in remote areas where neither high-resolution elevation models nor eddy-covariance measurements are available. In this study we adapt a bulk drag partitioning model to estimate the aerodynamic roughness length (z0m) such that it can be applied to 1D (i.e. unidirectional) elevation profiles, typically measured by laser altimeters. We apply the model to a rough ice surface on the K-transect (west Greenland Ice Sheet) using UAV photogrammetry, and we evaluate the modelled roughness against in situ eddy-covariance observations. We then present a method to estimate the topography at 1 m horizontal resolution using the ICESat-2 satellite laser altimeter, and we demonstrate the high precision of the satellite elevation profiles against UAV photogrammetry. The currently available satellite profiles are used to map the aerodynamic roughness during different time periods along the K-transect, that is compared to an extensive dataset of in situ observations. We find a considerable spatio-temporal variability in z0m, ranging between 10−4 m for a smooth snow surface and 10−1 m for rough crevassed areas, which confirms the need to incorporate a variable aerodynamic roughness in atmospheric models over ice sheets.

Variations in surface roughness of heterogeneous surfaces in the Nagqu area of the Tibetan Plateau

Temporal and spatial variations of the surface aerodynamic roughness lengths (Z0 m) in the Nagqu area of the northern Tibetan Plateau were analysed in 2008, 2010 and 2012 using MODIS satellite data and in situ atmospheric turbulence observations. Surface aerodynamic roughness lengths were calculated from turbulent observations by a single-height ultrasonic anemometer and retrieved by the Massman model. The results showed that Z0 m has an apparent characteristic of seasonal variation. From February to August, Z0 m increased with snow ablation and vegetation growth, and the maximum value reached 4–5 cm at the BJ site. From September to February, Z0 m gradually decreased and reached its minimum values of about 1–2 cm. Snowfall in abnormal years was the main reason for the significantly lower Z0 m compared with that in normal conditions. The underlying surface can be divided into four categories according to the different values of Z0 m: snow and ice, sparse grassland, lush grassland and town. Among them, lush grassland and sparse grassland accounted for 62.49 % and 33.74 %, and they have an annual variation of Z0 m between 1–4 and 2–6 cm, respectively. The two methods were positively correlated, and the retrieved values were lower than the measured results due to the heterogeneity of the underlying surface. These results are substituted into the Noah-MP (multi-parameterisation) model to replace the original parameter design numerical simulation experiment. After replacing the model surface roughness, the sensible heat flux and latent heat flux were simulated with a better diurnal dynamics.

Correcting for Systematic Underestimation of Topographic Glacier Aerodynamic Roughness Values From Hintereisferner, Austria

Spatially-distributed values of glacier aerodynamic roughness (z0) are vital for robust estimates of turbulent energy fluxes and ice and snow melt. Microtopographic data allow rapid estimates of z0 over discrete plot-scale areas, but are sensitive to data scale and resolution. Here, we use an extensive multi-scale dataset from Hintereisferner, Austria, to develop a correction factor to derive z0 values from coarse resolution (up to 30 m) topographic data that are more commonly available over larger areas. Resulting z0 estimates are within an order of magnitude of previously validated, plot-scale estimates and aerodynamic values. The method is developed and tested using plot-scale microtopography data generated by structure from motion photogrammetry combined with glacier-scale data acquired by a permanent in-situ terrestrial laser scanner. Finally, we demonstrate the application of the method to a regional-scale digital elevation model acquired by airborne laser scanning. Our workflow opens up the possibility of including spatio-temporal variations of z0 within glacier surface energy balance models without the need for extensive additional field data collection.

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.