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Understanding soil organic carbon (SOC) response to global change has been hindered by an inability to map SOC at horizon scales relevant to coupled hydrologic and biogeochemical processes. Standard SOC measurements rely on homogenized samples taken from distinct depth intervals. Such sampling prevents an examination of fine‐scale SOC distribution within a soil horizon. Visible near‐infrared hyperspectral imaging (HSI) has been applied to intact monoliths and split cores surfaces to overcome this limitation. However, the roughness of these surfaces can influence HSI spectra by scattering reflected light in different directions posing challenges to fine‐scale SOC mapping. Here, we examine the influence of prescribed surface orientation on reflected spectra, develop a method for correcting topographic effects, and calibrate a partial least squares regression (PLSR) model for SOC prediction. Two empirical models that account for surface slope, aspect, and wavelength and two theoretical models that account for the geometry of the spectrometer were compared using 681 homogenized soil samples from across the United States that were packed into sample wells and presented to the spectrometer at 91 orientations. The empirical approach outperformed the more complex geometric models in correcting spectra taken at non‐flat configurations. Topographically corrected spectra reduced bias and error in SOC predicted by PLSR, particularly at slope angles greater than 30°. Our approach clears the way for investigating the spatial distributions of multiple soil properties on rough intact soil samples.

 

Soil biota generate CO2 that can vertically export to the atmosphere, and dissolved organic and inorganic carbon (DOC and DIC) that can laterally export to streams and accelerate weathering. These processes are regulated by external hydroclimate forcing and internal structures (permeability distribution), the relative influences of which are rarely studied. Understanding these interactions is essential a hydrological extremes intensify in the future. Here we explore the question: How and to what extent do hydrological and permeability distribution conditions regulate soil carbon transformations and chemical weathering? We address the questions using a hillslope reactive transport model constrained by data from the Fitch Forest (Kansas, United States). Numerical experiments were used to mimic hydrological extremes and variable shallow-versus-deep permeability contrasts. Results demonstrate that under dry conditions (0.08 mm/day), long water transit times led to more mineralization of organic carbon (OC) into inorganic carbon (IC) form (>98\%). Of the IC produced, ~ 75\% was emitted upward as CO2 gas and ~ 25\% was exported laterally as DIC into the stream. Wet conditions (8.0 mm/day) resulted in less mineralization (~88\%), more DOC production (~12\%), and more lateral fluxes of IC (~50\% of produced IC). Carbonate precipitated under dry conditions and dissolved under wet conditions as the fast flow rapidly droves the reaction to disequilibrium. The results depict a conceptual hillslope model that prompts four hypotheses for our community to test. H1: Droughts enhance carbon mineralization and vertical upward carbon fluxes, whereas large hydrological events such as storms and flooding enhance subsurface vertical connectivity, reduce transit times, and promote lateral export. H2: The role of weathering as a net carbon sink or source to the atmosphere depends on the interaction between hydrologic flows and lithology: transition from droughts to storms can shift carbonate from a carbon sink (mineral precipitation) to carbon source (dissolution). H3: Permeability contrasts regulate the lateral flow partitioning via shallow flow paths versus deeper groundwater though this alter reaction rates negligibly. H4: Stream chemistry reflect flow paths and can potentially quantify water transit times: solutes enriched in shallow soils have a younger water signature; solutes abundant at depth carry older water signature. 

Rooting depth is an ecosystem trait that determines the extent of soil development and carbon (C) and water cycling. Recent hypotheses propose that human‐induced changes to Earth's biogeochemical cycles propagate deeply into Earth's subsurface due to rooting depth changes from agricultural and climate‐induced land cover changes. Yet, the lack of a global‐scale quantification of rooting depth responses to human activity limits knowledge of hydrosphere‐atmosphere‐lithosphere feedbacks in the Anthropocene. Here we use land cover data sets to demonstrate that root depth distributions are changing globally as a consequence of agricultural expansion truncating depths above which 99% of root biomass occurs (D99) by ∼60 cm, and woody encroachment linked to anthropogenic climate change extending D99 in other regions by ∼38 cm. The net result of these two opposing drivers is a global reduction of D99 by 5%, or ∼8 cm, representing a loss of ∼11,600 km³of rooted volume. Projected land cover scenarios in 2100 suggest additional future D99 shallowing of up to 30 cm, generating further losses of rooted volume of ∼43,500 km³, values exceeding root losses experienced to date and suggesting that the pace of root shallowing will quicken in the coming century. Losses of Earth's deepest roots—soil‐forming agents—suggest unanticipated changes in fluxes of water, solutes, and C. Two important messages emerge from our analyses: dynamic, human‐modified root distributions should be incorporated into earth systems models, and a significant gap in deep root research inhibits accurate projections of future root distributions and their biogeochemical consequences.

 

The self‐powered and autonomous sensors are incredibly important in advanced engineering, especially defence science. The increasing necessity of simple and smart electronics requires to be sustainably flexible, wearable, and waterproof. Triboelectricity has been a widely used mechanism for motion sensing nowadays. Almost all devices based on triboelectricity require contact between two surfaces. Herein, a touchless triboelectric motion sensor for human motion sensing and movement monitoring is developed. The device was primarily fabricated using simple latex (cis‐1,4‐polyisoprene) structures and copper (electrode materials), which make it a very cost‐effective device for sensory applications. The device is tested with specimens of different areas and heights in motion. The maximum output of the device is noted as 12 V at a specimen height of 5 cm. Further different types of human motions are applied in front of the device to ensure low energy sensitivity using triboelectric phenomena. The lightweight smart device precisely provides significant output signals for each movement of the human body which makes the device a prospective medium for motion sensing and movement monitoring which can be applied in the fields of security, energy, and medicine.

 

Abstract. Advancing our understanding of Earth system dynamics (ESD) depends on thedevelopment of models and other analytical tools that apply physical,biological, and chemical data. This ambition to increase understanding anddevelop models of ESD based on site observations was the stimulus forcreating the networks of Long-Term Ecological Research (LTER), Critical ZoneObservatories (CZOs), and others. We organized a survey, the results of whichidentified pressing gaps in data availability from these networks, inparticular for the future development and evaluation of models that representESD processes, and provide insights for improvement in both data collectionand model integration.

From this survey overview of data applications in the context of LTER andCZO research, we identified three challenges: (1) widen application ofterrestrial observation network data in Earth system modelling,(2) develop integrated Earth system models that incorporate processrepresentation and data of multiple disciplines, and (3) identifycomplementarity in measured variables and spatial extent, and promotingsynergies in the existing observational networks. These challenges lead toperspectives and recommendations for an improved dialogue between theobservation networks and the ESD modelling community, including co-locationof sites in the existing networks and further formalizing theserecommendations among these communities. Developing these synergies willenable cross-site and cross-network comparison and synthesis studies, whichwill help produce insights around organizing principles, classifications,and general rules of coupling processes with environmental conditions.

 

Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.