Accurate simulation of terrestrial water storage (TWS), the integrated volume of water in soil, groundwater, snow, surface water and vegetation per unit area, is challenging. TWS is a critical but often overlooked model diagnostic that can  improve understanding of hydrological processes, water resource management, and  climate change impacts assessment. Observations of TWS anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellite and its follow-on (GRACE-FO) missions provide observational constraints on model-simulated TWS. The similarity between large spatial scales of GRACE TWS retrievals and global land models like the Community Land Model (CLM) facilitates  comparison of model-simulated and observed TWS anomalies as a function of input parameter values.  We demonstrate an approach to optimize CLM parameters for improved TWS simulation, using history matching with Evidential Deep Neural Networks (EDNN). History matching is a constraining technique that identifies plausible parameter sets by comparing model outputs to observations. To reduce the  computational expense of generating large perturbed parameter ensembles (PPEs) we use an emulator  EDNN that provides a probabilistic framework for representing uncertainty in the relationship between input parameters and TWS.  A key advantage of the EDNN approach is estimation of both epistemic and aleatoric uncertainty with the use of a single model, reducing the need to sample or train multiple ensembles. We applied the proposed methodology that utilizes history matching with EDNN to a real-world case study in which we  compared CLM simulations to observed TWS data (e.g., from the GRACE satellite mission) over the Contiguous United States (CONUS). The optimization process focuses on key parameters (e.g., dry surface layer, decay factor for fractional saturated area, and medlyn slope of conductance-photosynthesis relationship) within the CLM that govern water storage dynamics. The resulting  parameter sets  yield model simulations of TWS that agree with observations. Moreover, uncertainty estimates could improve TWS simulations, enabling more robust assessments of global water availability and hydrological changes.

Rooting depth is an ecosystem trait that determines the extent of soil development and carbon cycling. Recent hypotheses propose that human-induced changes to Earth’s biogeochemical cycles propagate deeply 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 datasets to demonstrate that global rooting depths have become shallower in the Anthropocene, and are likely to become yet shallower this century. Specifically, globally averaged depths above which 99% of root biomass occurs (D99) are 8.7%, or 16 cm, shallower relative to those for potential vegetation. This net shallowing results from agricultural expansion truncating D99 by 82 cm, and woody encroachment linked to anthropogenic climate change extending D99 by 65 cm. Projected land cover scenarios in 2100 suggest further D99 shallowing of 63 to 72 cm, exceeding that 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 carbon. Our work constrains rooting depth distributions for global models, allowing the land modeling community to explore cascading effects of rooting depth changes on water, carbon, and energy dynamics, and can guide design of field-based efforts to quantify deep anthropogenic influences. Understanding human influence on biota’s reach into Earth’s subsurface will improve predictions of interactive functioning of the biosphere, lithosphere, and hydrosphere.

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 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 datasets 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 km3 of 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.

Stabilization of microbial-derived products such as extracellular enzymes (EE) has gained attention as a possibly important mechanism leading to the persistence of soil organic carbon (SOC). While the controls on EE activities and their stabilization in the surface soil are reasonably well-understood, how these activities change with soil depth and possibly diverge from those at the soil surface due to distinct physical, chemical, and biotic conditions remains unclear. We assessed EE activity to a depth of 1 m (10 cm increments) in 19 soil profiles across the Critical Zone Observatory Network, which represents a wide range of climates, soil orders, and vegetation types. Activities of four carbon (C)-acquiring enzymes (α-glucosidase, β-glucosidase, β-xylosidase, and cellobiohydrolase), two nitrogen (N)-acquiring enzymes (N-acetylglucosaminidase and leucine aminopeptidase), and one phosphorus (P)-acquiring enzyme (acid phosphatase) were measured fluorometrically along with SOC, total N, Olsen P, pH, clay concentration, and phospholipid fatty acids, which we used to characterize the microbial community composition and biomass (MB). For all EEs, activities per gram soil correlated positively with MB and SOC; all of which decreased logarithmically with depth (p < 0.05). Across all sites, over half of the potential soil EE activities per gram soil consistently occurred below 20 cm for all measured EEs. Activities per unit MB or SOC were substantially higher at depth (soils below 20 cm accounted for 80% of whole-profile EE activity), suggesting an accumulation of stabilized (i.e., mineral sorbed) EEs in subsoil horizons. The pronounced enzyme stabilization in subsurface horizons was corroborated by mixed-effects models that showed a significant, positive relationship between clay concentration and MB-normalized EE activities in the subsoil. Furthermore, the negative relationships between soil C, N, and P and C-, N-, and P-acquiring EEs found in the surface soil decoupled at 20 cm, which could have also been caused by EE stabilization. This suggesting that EEs do not reflect soil nutrient availabilities at depth. Taken together, our results suggest that deeper soil horizons hold a significant reservoir of EEs, and that the controls of subsoil EEs differ from their surface soil counterparts.