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Woody encroachment is a widespread phenomenon in grassland ecosystems, driven by overgrazing, fire suppression, nitrogen deposition and climate change, among other environmental changes. The influence of woody encroachment on processes such as chemical weathering however is poorly understood. In particular, for fast reactions such as carbonate weathering, root traits associated with woody encroachment (e.g., coarser, deeper, and longer residence times) can potentially change fluxes of inorganic carbon into streams and back to the atmosphere, providing CO2-climate feedbacks. Here we examine the influence of deepening roots arising from woody encroachment on catchment water balance and carbonate weathering rates at Konza a tallgrass prairie within a carbonate terrain where woody encroachment is suspected to drive the groundwater alkalinity upwards. We use a watershed reactive transport model BioRT-Flux-PIHM to understand the ramifications of deepening roots. Stream discharge and evapotranspiration (ET) measurements were used to calibrate the hydrology model. The subsurface CO2 concentration, water quality data for groundwater, stream, soil water and precipitation were used to constrain the soil respiration and carbonate dissolution reaction rates. The hydrology model has a Nash-Sutcliffe Efficiency value of 0.88. Modelling results from numerical experiments indicate that woody encroachment results in overall lower stream flow due to higher ET, yet the groundwater recharge is higher due to deep macropore development from deepening roots. The deeper macropores enhance carbonate weathering rate as more acidic, CO2-rich water recharges the deeper calcite bedrock. Accounting for the change in inorganic carbon fluxes caused by such land use changes gives a better estimate of carbon fluxes in the biosphere. Such knowledge is essential for effective planning of climate change mitigation strategies. 

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.