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High resolution topographic data are necessary to understand benthic habitat, quantify processes at the water-sediment interface, and support computational fluid dynamics models for both surface and hyporheic hydraulics. In riverine systems, these data are typically collected using traditional surveying methods (total station, DGPS, etc.), airborne or terrestrial laser scanning, and photogrammetry. Recently, handheld surveying equipment has been rapidly acquiring popularity in part due to its processing capacity, price, size, and versatility. One such device is the iPhone LiDAR, which could have a good balance between precision and ease of use and is a potential replacement for conventional measuring tools. Here, we evaluated the accuracy of the LiDAR sensor and a Structure from Motion (SfM) method based on photos collected using the iPhone Cameras. We compared the LiDAR and SfM elevations to those from a high-precision laser scanner for an experimental rough water-worked gravelbed channel with boulder-like structures. We observed that both the LiDAR and SfM methods captured the overall streambed morphology and detected large (Hs
15 cm) and macro (5cm  Hs < 15cm) scales of topographic variations (Hs, roughness). The SfM technique also captured small scale (Hs <5cm) roughness whereas the LiDAR consistently simplified it with errors of 3.7 mm. 

Abstract Porous media flows are common in both natural and anthropogenic systems. Mapping these flows in a laboratory setting is challenging and often requires non-intrusive measurement techniques, such as particle image velocimetry (PIV) coupled with refractive index matching (RIM). RIM-coupled PIV allows the mapping of velocity fields around transparent solids by analyzing the movement of neutrally buoyant micron-sized seeding particles. The use of this technique in a porous medium can be problematic because seeding particles adhere to grains, which causes the grain bed to lose transparency and can obstruct pore flows. Another non-intrusive optical technique, planar laser-induced fluorescence (PLIF), can be paired with RIM and does not have this limitation because fluorescent dye is used instead of particles, but it has been chiefly used for qualitative flow visualization. Here, we propose a quantitative PLIF-based methodology to map both porous media flow fields and porous media architecture. Velocity fields are obtained by tracking the advection-dominated movement of the fluorescent dye plume front within a porous medium. We also propose an automatic tracking algorithm that quantifies 2D velocity components as the plume moves through space in both an Eulerian and a Lagrangian framework. We apply this algorithm to three data sets: a synthetic data set and two laboratory experiments. Performance of this algorithm is reported by the mean (bias error,B) and standard deviation (random error,SD) of the residuals between its results and the reference data. For the synthetic data, the algorithm produces maximum errors ofB & SD= 32% & 23% in the Eulerian framework, respectively, andB & SD= −0.04% & 3.9% in the Lagrangian framework. The small-scale laboratory experimental data requires the Eulerian framework and produce errors ofB & SD= −0.5% & 33%. The Lagrangian framework is used on the large-scale laboratory experimental data and produces errors ofB & SD= 5% & 44%. Mapping the porous media architecture shows negligible error for reconstructing calibration grains of known dimensions. 

Abstract The dimensionless critical shear stress (τ*_c) needed for the onset of sediment motion is important for a range of studies from river restoration projects to landscape evolution calculations. Many studies simply assume aτ*_cvalue within the large range of scatter observed in gravel‐bedded rivers because direct field estimates are difficult to obtain. Informed choices of reach‐scaleτ*_cvalues could instead be obtained from force balance calculations that include particle‐scale bed structure and flow conditions. Particle‐scale bed structure is also difficult to measure, precluding wide adoption of such force‐balanceτ*_cvalues. Recent studies have demonstrated that bed grain size distributions (GSD) can be determined from detailed point clouds (e.g. using G3Point open‐source software). We build on these point cloud methods to introduce Pro+, software that estimates particle‐scale protrusion distributions andτ*_cfor each grain size and for the entire bed using a force‐balance model. We validated G3Point and Pro+ using two laboratory flume experiments with different grain size distributions and bed topographies. Commonly used definitions of protrusion may not produce representativeτ*_cdistributions, and Pro+ includes new protrusion definitions to better include flow and bed structure influences on particle mobility. The combined G3Point/Pro+ provided accurate grain size, protrusion andτ*_cdistributions with simple GSD calibration. The largest source of error in protrusion andτ*_cdistributions were from incorrect grain boundaries and grain locations in G3Point, and calibration of grain software beyond comparing GSD is likely needed. Pro+ can be coupled with grain identifying software and relatively easily obtainable data to provide informed estimates ofτ*_c. These could replace arbitrary choices ofτ*_cand potentially improve channel stability and sediment transport estimates. 

Abstract. Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 ppb (parts per billion) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr−1 in both 2020 and 2021. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N2O to the total enhanced effective radiative forcing of greenhouse gases was 6.4 % for 1750–2022. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N2O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N2O emissions. We use bottom-up (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) and top-down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions have increased 40 % (or 1.9 Tg N yr−1) in the past 4 decades (1980–2020). Direct agricultural emissions in 2020 (3.9 Tg N yr−1, best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources, including fossil fuel and industry, waste and wastewater, and biomass burning (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1) . For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.5 (lower–upper bounds: 10.6–27.0) Tg N yr−1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr−1. For the 2010–2019 period, the annual BU decadal-average emissions for both natural and anthropogenic sources were 18.2 (10.6–25.9) Tg N yr−1 and TD emissions were 17.4 (15.8–19.20) Tg N yr−1. The once top emitter Europe has reduced its emissions by 31 % since the 1980s, while those of emerging economies have grown, making China the top emitter since the 2010s. The observed atmospheric N2O concentrations in recent years have exceeded projected levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6), underscoring the importance of reducing anthropogenic N2O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose the establishment of a global network for monitoring and modeling N2O from the surface through to the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al., 2023). 

Abstract Quantification of velocity and pressure fields over streambeds is important for predicting sediment mobility, benthic and hyporheic habitat qualities, and hyporheic exchange. Here, we report the first experimental investigation of reconstructed water surface elevations and three‐dimensional time‐averaged velocity and pressure fields quantified with non‐invasive image techniques for a three‐dimensional free surface flow around a barely submerged vertical cylinder over a plane bed of coarse granular sediment in a full‐scale flume experiment. Stereo particle image velocimetry coupled with a refractive index‐matched fluid measured velocity data at multiple closely‐spaced parallel and aligned planes. The time‐averaged pressure field was reconstructed using the Rotating Parallel Ray Omni‐Directional integration method to integrate the pressure gradient terms obtained by the balance of all the Reynolds‐Averaged Navier‐Stokes equation terms, which were evaluated with stereo particle image velocimetry. The detailed pressure field allows deriving the water surface profile deformed by the cylinder and hyporheic flows induced by the cylinder. 

Abstract Porous media are ubiquitous, a key component of the water cycle and locus of many biogeochemical transformations. Mapping media architecture and interstitial flows have been challenging because of the inherent difficulty of seeing through solids. Previous works used particle image velocimetry (PIV) coupled with refractive index‐matching (RIM) to quantify interstitial flows, but they were limited to specialized and often toxic fluids that precluded investigating biological processes. To address this limitation, we present a low‐cost and scalable method based on RIM coupled PIV (RIM‐PIV) and planar laser induced fluorescence (RIM‐PLIF) to simultaneously map both media architecture and interstitial velocities. Our method uses irregularly shaped grains made of a fluorocarbon plastic with refractive index of 1.36 and specific gravity of 1.93. This allows using a water–glycerin solution for the RIM fluid. By using RIM‐PIV, we mapped media structure with 2% accuracy, which improved to 0.2% with RIM‐PLIF because of improved image contrast.