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1. Interaction of wind and cold‐season hydrologic processes on erosion from complex topography following wildfire in sagebrush steppe

   https://doi.org/10.1002/esp.4778  

   Vega, Samantha P. ; Williams, C. Jason ; Brooks, Erin S. ; Pierson, Frederick B. ; Strand, Eva K. ; Robichaud, Peter R. ; Brown, Robert E. ; Seyfried, Mark S. ; Lohse, Kathleen A. ; Glossner, Kayla ; et al ( February 2020 , Earth Surface Processes and Landforms)

   Wildfire is a natural component of sagebrush (Artemisiaspp.) steppe rangelands that induces temporal shifts in plant community physiognomy, ground surface conditions, and erosion rates. Fire alteration of the vegetation structure and ground cover in these ecosystems commonly amplifies soil losses by wind‐ and water‐driven erosion. Much of the fire‐related erosion research for sagebrush steppe has focused on either erosion by wind over gentle terrain or water‐driven erosion under high‐intensity rainfall on complex topography. However, many sagebrush rangelands are geographically positioned in snow‐dominated uplands with complex terrain in which runoff and sediment delivery occur primarily in winter months associated with cold‐season hydrology. Current understanding is limited regarding fire effects on the interaction of wind‐ and cold‐season hydrologic‐driven erosion processes for these ecosystems. In this study, we evaluated fire impacts on vegetation, ground cover, soils, and erosion across spatial scales at a snow‐dominated mountainous sagebrush site over a 2‐year period post‐fire. Vegetation, ground cover, and soil conditions were assessed at various plot scales (8 m²to 3.42 ha) through standard field measures. Erosion was quantified through a network of silt fences (n= 24) spanning hillslope and side channel or swale areas, ranging from 0.003 to 3.42 ha in size. Sediment delivery at the watershed scale (129 ha) was assessed by suspended sediment samples of streamflow through a drop‐box v‐notch weir. Wildfire consumed nearly all above‐ground live vegetation at the site and resulted in more than 60% bare ground (bare soil, ash, and rock) in the immediate post‐fire period. Widespread wind‐driven sediment loading of swales was observed over the first month post‐fire and extensive snow drifts were formed in these swales each winter season during the study. In the first year, sediment yields from north‐ and south‐facing aspects averaged 0.99–8.62 t ha⁻¹at the short‐hillslope scale (~0.004 ha), 0.02–1.65 t ha⁻¹at the long‐hillslope scale (0.02–0.46 ha), and 0.24–0.71 t ha⁻¹at the swale scale (0.65–3.42 ha), and watershed scale sediment yield was 2.47 t ha⁻¹. By the second year post fire, foliar cover exceeded 120% across the site, but bare ground remained more than 60%. Sediment yield in the second year was greatly reduced across short‐ to long‐hillslope scales (0.02–0.04 t ha⁻¹), but was similar to first‐year measures for swale plots (0.24–0.61 t ha⁻¹) and at the watershed scale (3.05 t ha⁻¹). Nearly all the sediment collected across all spatial scales was delivered during runoff events associated with cold‐season hydrologic processes, including rain‐on‐snow, rain‐on‐frozen soils, and snowmelt runoff. Approximately 85–99% of annual sediment collected across all silt fence plots each year was from swales. The high levels of sediment delivered across hillslope to watershed scales in this study are attributed to observed preferential loading of fine sediments into swale channels by aeolian processes in the immediate post‐fire period and subsequent flushing of these sediments by runoff from cold‐season hydrologic processes. Our results suggest that the interaction of aeolian and cold‐season hydrologic‐driven erosion processes is an important component for consideration in post‐fire erosion assessment and prediction and can have profound implications for soil loss from these ecosystems. © 2019 John Wiley & Sons, Ltd.

    

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2. Determination of Vapor and Momentum Roughness Lengths Above an Undulating Soil Surface Based on PIV‐Measured Velocity Profiles

   https://doi.org/10.1029/2021WR029578  

   Gao, Bo ; Coltman, Edward ; Farnsworth, John ; Helmig, Rainer ; Smits, Kathleen M. ( July 2021 , Water Resources Research)

   Accurately predicting bare‐soil evaporation requires the proper characterization of the near‐surface atmospheric conditions. These conditions, dependent on factors such as surface microtopography and wind velocity, vary greatly and therefore require high‐resolution datasets to be fully incorporated into evaporation models. These factors are oftentimes parameterized in models through the aerodynamic resistance (r_a), in which the vapor roughness length (z_0v) and the momentum roughness length (z_0m) are two crucial parameters that describe the transport near the soil‐atmosphere interface. Typically, when evaluating bare‐soil evaporation, these two characteristic lengths are assumed equal, although differences are likely to occur especially in turbulent flows over undulating surfaces. Thus, this study aims to investigate the relationship betweenz_0vandz_0mabove undulating surfaces to ultimately improve accuracy in estimating evaporation rate. To achieve this goal, four uniquely designed wind tunnel—soil tank experiments were conducted considering different wind speeds and undulation spacings. Particle image velocimetry (PIV) was used to measure the velocity field above the undulating surface in high resolution. Using the high‐fidelity data set, the logarithmic ratio ofz_0vtoz_0mis determined and used to estimater_a. Results confirm that these lengths differ significantly, with the logarithmic ratio roughly ranging from −15 to −5 under the conditions tested. PIV‐measured results demonstrate this ratio is closely tied to the mass and momentum transport behaviors influenced by surface undulations. Using the data‐integrated formulation ofr_a, predictions of evaporation rate were prepared for both the laboratory and lysimeter experiments, demonstrating the efficacy of the proposed approach in this study.

    

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3. A new process-based and scale-aware desert dust emission scheme for global climate models – Part II: Evaluation in the Community Earth System Model version 2 (CESM2)

   https://doi.org/10.5194/acp-24-2287-2024  

   Leung, Danny M. ; Kok, Jasper F. ; Li, Longlei ; Mahowald, Natalie M. ; Lawrence, David M. ; Tilmes, Simone ; Kluzek, Erik ; Klose, Martina ; Pérez García-Pando, Carlos ( February 2024 , Atmospheric Chemistry and Physics)

   Abstract. Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes.

    

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4. A Quantitative Model‐Based Assessment of Stony Desert Landscape Evolution in the Hami Basin, China: Implications for Plio‐Pleistocene Dust Production in Eastern Asia

   https://doi.org/10.1029/2020GL090064  

   Abell, Jordan T. ; Rahimi, Stefan R. ; Pullen, Alex ; Lebo, Zachary J. ; Zhang, Dehai ; Kapp, Paul ; Gloege, Lucas ; Ridge, Sean ; Nie, Junsheng ; Winckler, Gisela ( October 2020 , Geophysical Research Letters)

   Dust plays an important role in climate, and while our current representation of dust production includes shifts in vegetation, soil moisture, and ice cover, it does not account for the role of landscape evolution. Here, we use the Weather Research and Forecasting model coupled to an aerosol chemistry model to quantify the effects of arid landscape evolution on boundary layer conditions, dust production, and radiative properties in the Hami Basin, China, a dynamic stony desert in eastern Asia. Relative to today, altered surface roughness, sediment erodibility, and albedo combine to produce up to a ~44% increase in wind speeds (mean ≈ 15%), up to a ~59% increase in dust loading (mean ≈ 30%), and up to a ~4.4 W m⁻²increase in downwelling radiation (mean ≈ 2.4 W m⁻²) over the Hami Basin. Our modeling results, along with geomorphological data for the western Gobi Desert, provide evidence that stony deserts acted as important Plio‐Pleistocene dust sources.

    

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5. Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning

   https://doi.org/10.1088/1748-9326/acbbe0  

   ElGhawi, Reda ; Kraft, Basil ; Reimers, Christian ; Reichstein, Markus ; Körner, Marco ; Gentine, Pierre ; Winkler, Alexander J ( March 2023 , Environmental Research Letters)

   Abstract The process of evapotranspiration transfers liquid water from vegetation and soil surfaces to the atmosphere, the so-called latent heat flux ( Q LE ), and modulates the Earth’s energy, water, and carbon cycle. Vegetation controls Q LE by regulating leaf stomata opening (surface resistance r s in the Big Leaf approach) and by altering surface roughness (aerodynamic resistance r a ). Estimating r s and r a across different vegetation types is a key challenge in predicting Q LE . We propose a hybrid approach that combines mechanistic modeling and machine learning for modeling Q LE . The hybrid model combines a feed-forward neural network which estimates the resistances from observations as intermediate variables and a mechanistic model in an end-to-end setting. In the hybrid modeling setup, we make use of the Penman–Monteith equation in conjunction with multi-year flux measurements across different forest and grassland sites from the FLUXNET database. This hybrid model setup is successful in predicting Q LE , however, this approach leads to equifinal solutions in terms of estimated physical parameters. We follow two different strategies to constrain the hybrid model and therefore control for the equifinality that arises when the two resistances are estimated simultaneously. One strategy is to impose an a priori constraint on r a based on mechanistic assumptions (theory-driven strategy), while the other strategy makes use of more observational data and adds a constraint in predicting r a through multi-task learning of both latent and sensible heat flux ( Q H ; data-driven strategy) together. Our results show that all hybrid models predict the target variables with a high degree of success, with R 2 = 0.82–0.89 for grasslands and R 2 = 0.70–0.80 for forest sites at the mean diurnal scale. The predicted r s and r a show strong physical consistency across the two regularized hybrid models, but are physically implausible in the under-constrained hybrid model. The hybrid models are robust in reproducing consistent results for energy fluxes and resistances across different scales (diurnal, seasonal, and interannual), reflecting their ability to learn the physical dependence of the target variables on the meteorological inputs. As a next step, we propose to test these heavily observation-informed parameterizations derived through hybrid modeling as a substitute for ad hoc formulations in Earth system models. 

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