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Abstract. Hydraulic redistribution (HR), the movement of water via plant root systems that connect soil compartments with different water potential, should influences soil moisture dynamics particularly in water-limited ecosystems. Realistic representation of HR in ecosystem models is essential to improve the ability of these models to predict ecosystem function in dryland regions. In this study, we integrated HR into the Terrestrial ECOsystem model and employed a Bayesian Markov Chain Monte Carlo technique to optimize soil hydraulic parameters and root conductance using four years of soil moisture observations from a piñon-juniper woodland. We found that (i) integrating HR generally improved model prediction of soil moisture during dry periods, particularly in the top 30 cm of the soil profile, where more than 50 % of root biomass exists, mostly during dry periods; (ii) HR increased surface soil moisture by up to 60 % during dry periods; (iii) HR decreased with increasing precipitation magnitude and frequency, however, the length of dry spells between rainfall events also influenced HR rates; and (iv) upward HR in the top 60 cm soil profile became more pronounced as dry conditions progressed, with rates ranging from 0.10 to 0.50 mm d⁻¹. These findings highlight that HR plays a likely role in sustaining soil moisture during extended dry periods and has a limited effect during precipitation events. Future research should investigate the effect of HR on other ecosystem processes, such as net ecosystem exchange of carbon and evapotranspiration under varying climatic conditions. 

ABSTRACT Cover crops, a promising strategy to increase soil organic carbon (SOC) storage in croplands and mitigate climate change, have typically been shown to benefit soil carbon (C) storage from increased plant C inputs. However, input‐driven C benefits may be augmented by the reduction of C outputs induced by cover crops, a process that has been tested by individual studies but has not yet been synthesized. Here we quantified the impact of cover crops on organic C loss via soil erosion (SOC erosion) and revealed the geographical variability at the global scale. We analyzed the field data from 152 paired control and cover crop treatments from 57 published studies worldwide using meta‐analysis and machine learning. The meta‐analysis results showed that cover crops widely reduced SOC erosion by an average of 68% on an annual basis, while they increased SOC stock by 14% (0–15 cm). The absolute SOC erosion reduction ranged from 0 to 18.0 Mg C⁻¹ ha⁻¹ year⁻¹and showed no correlation with the SOC stock change that varied from −8.07 to 22.6 Mg C⁻¹ ha⁻¹ year⁻¹at 0–15 cm depth, indicating the latter more likely related to plant C inputs. The magnitude of SOC erosion reduction was dominantly determined by topographic slope. The global map generated by machine learning showed the relative effectiveness of SOC erosion reduction mainly occurred in temperate regions, including central Europe, central‐east China, and Southern South America. Our results highlight that cover crop‐induced erosion reduction can augment SOC stock to provide additive C benefits, especially in sloping and temperate croplands, for mitigating climate change. 

Abstract Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha⁻¹) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha⁻¹), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha⁻¹) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha⁻¹) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha⁻¹), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha⁻¹). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha⁻¹) and 75.3% (7.0 ± 0.38 Mg ha⁻¹) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies. 

Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions. 

We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM. 

Extreme droughts generally decrease productivity in grassland ecosystems1,2,3 with negative consequences for nature’s contribution to people4,5,6,7. The extent to which this negative effect varies among grassland types and over time in response to multi-year extreme drought remains unclear. Here, using a coordinated distributed experiment that simulated four years of growing-season drought (around 66% rainfall reduction), we compared drought sensitivity within and among six representative grasslands spanning broad precipitation gradients in each of Eurasia and North America—two of the Northern Hemisphere’s largest grass-dominated regions. Aboveground plant production declined substantially with drought in the Eurasian grasslands and the effects accumulated over time, while the declines were less severe and more muted over time in the North American grasslands. Drought effects on species richness shifted from positive to negative in Eurasia, but from negative to positive in North America over time. The differing responses of plant production in these grasslands were accompanied by less common (subordinate) plant species declining in Eurasian grasslands but increasing in North American grasslands. Our findings demonstrate the high production sensitivity of Eurasian compared with North American grasslands to extreme drought (43.6% versus 25.2% reduction), and the key role of subordinate species in determining impacts of extreme drought on grassland productivity. 

We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And How does micro-environmental variation among plots influence how treatments alter soil moisture profiles over three soil depths? This data package includes sensor data from the Mean x Variance experiment in the Plains grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM, which is dominated by the grass species Bouteloua gracilis (blue grama). 

Key Points More than 70 microbial models have recently been developed to simulate soil carbon dynamics Diversity in model structures and parameters indicates uncertainty in translating current knowledge of microbial processes into models Data‐driven model development and parameterization are highly recommended to improve the prediction of microbial models