Soil organic carbon (SOC) is going through rapid reorganization due to anthropogenic influences. Understanding how biogeochemical transformation and erosion-induced SOC redistribution influence SOC profiles and stocks is critical to our food security and adaptation to climate change. The important roles of erosion and deposition on SOC dynamics have drawn increasing attention in the past decades, but quantifying such dynamics is still challenging. Here we develop a process-based quasi 3-D model that couples surface runoff, soil moisture dynamics, biogeochemical transformation, and landscape evolution. We apply this model to a subcatchment in Iowa to understand how natural forcing and farming practices affect the SOC dynamics in the critical zone. The net soil thickness and SOC stock change rates are −0.336 (mm/yr) and −1.9 (g C/m 2 /year), respectively. Our model shows that in a fast transport landscape, SOC transport is the dominant control on SOC dynamics compared to biogeochemical transformation. The SOC profiles have "noses" below the surface at depositional sites, which are consistent with cores sampled at the same site. Generally, erosional sites are local net atmospheric carbon sinks and vice versa for depositional sites, but exceptions exist as seen in the simulation results. Furthermore, the mechanical soil mixing arising from tillage enhances SOC stock at erosional sites and reduces it at depositional ones. This study not only helps us understand the evolution of SOC stock and profiles in a watershed but can also serve as an instrument to develop practical means for protecting carbon loss due to human activities. Plain Language SummaryUnderstanding how soil organic carbon (SOC) content changes in space and time are critical for our food security and adaptation to climate change. It changes through the belowground transformation-decomposition of litter and release of CO 2 , and surficial transport-lateral physical redistribution. The balance between the two interactions has been strongly shifted by human activities. Quantifying such interactions has remained challenging. Here we developed a 3-D model, which simulates the movement and burial of SOC and compare the impacts of natural and human activities in the critical zone. We apply this model to a watershed in Iowa. Our results show that the net soil thickness and SOC stock change rates are both decreasing. The fast burial of legacy carbon by modern carbon results in a "nose" profile at depositional sites, which is consistent with soil cores sampled in the watershed. The lateral transport rate can be significantly larger than the transformation rate, but this balance is modified by the mechanical mixing from tillage. Generally, erosional sites are net sinks for atmospheric carbon and depositional sites are net sources. The model can serve as an important tool for protecting soil carbon change caused by both human and natural events.

In intensively managed landscapes, interactions between surface (tillage) and subsurface (tile drainage) management with prevailing climate/weather alter landscape characteristics, transport pathways, and transformation rates of surface/ subsurface water, soil/sediment, and particulate/dissolved nutrients. To capture the high spatial and temporal variability of constituent transport and residence times in the critical zone (between the bedrock and canopy) of these altered landscapes, both storm event and continuous measurements are needed. The Intensively Managed Landscapes Critical Zone Observatory (IML-CZO) is comprised of three highly characterized, well instrumented, and representative watersheds (i.e., Clear Creek, Iowa; Upper Sangamon River, Illinois; and Minnesota River, Minnesota). It is organized to quantify the heterogeneity in structure and dynamic response of critical zone processes to human activities in the context of the glacial and management (anthropogenic) legacies. Observations of water, sediment, and nutrients are made at nested points of the landscape in the vertical and lateral directions during and between storm events (i.e., continuously). The measurements and corresponding observational strategy are organized as follows. First, reference measurements from surface soil and deep core extractions, geophysical surveys, lidar, and hyperspectral data, which are common across all Critical Zone Observatories, are available. The reference measurements include continuous quantification of energy, water, solutes, and sediment fluxes. The reference measurements are complemented with event-based measurements unique to IML-CZO. These measurements include water table fluctuations, enrichment ratios, and roughness as well as bank erosion, hysteresis, sediment sources, and lake/floodplain sedimentation. The coupling of reference and event-based measurements support testing of the central hypothesis (i.e., system shifts from transformer to transporter in IML-CZO due to the interplay between management and weather/climate). Data collected since 2014 are available through a data repository and through the Geodashboard interface, which can be used for process-based model simulations.

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Floodplains and terraces in river valleys play important roles in the transport dynamics of water and sediment. While flat areas in river valleys can be identified from LiDAR data, directly characterizing them as either floodplain or terraces is not yet possible. To address this challenge, we hypothesize that, since geomorphic features are strongly coupled to hydrological and hydraulic dynamics and their associated variability, there exists a return frequency, or possibly a narrow band of return frequencies, of flow that is associated with floodplain formation; and this association can provide a distinctive signature for distinguishing them from terraces. Based on this hypothesis we develop a novel approach for distinguishing between floodplains and terraces that involves transforming the transverse cross-sectional geometry of a river valley into a curve, named a river valley hypsometric (RVH) curve, and linking hydraulic inundation frequency with the features of this curve. Our approach establishes that the demarcation between floodplains and terraces can be established from the structure of steps and risers in the RVH curves which can be obtained from the DEM data. Further, it shows that these transitions may themselves be shaped by floods with 10-to 100-year recurrence. We additionally show that, when floodplain width and height (above channel bottom) are normalized by bankfull width and depth, the ratio lies in a narrow range independent of the scale of the river valley.

Evapotranspiration (ET), an amalgam of evaporation from the soil to the atmosphere and plant transpiration in the root zone, is a critical component of the hydrologic cycle. The ability to quantify ET is crucial to science-based predictions of an ecosystem's dynamics and health (Fath, 2018;Fisher et al., 2011); and the impact of droughts, precipitation patterns and snow-melt on groundwater recharge (

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Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two examples of hillslopes (south-facing and north-facing, respectively) in the East River Watershed in Colorado that validates the effectiveness of the model. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modelling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the north-facing hillslope has a deeper soil layer than the south-facing hillslope. By comparing the soil thickness estimated between a machine learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the south-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the north-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the south-facing slopes may influence soil characteristics. With only seven parameters for calibration, this hybrid model can provide a realistic soil thickness map at other study sites by with a relatively small amount of sampling dataset. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction, but integrates the strengths of both statistical approaches and process-based modeling approaches.

Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.