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Streamlining land surface model Initialization: Automated data retrieval for VELMA using HMS REST API and GDAL
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Impacts of Topography‐Driven Water Redistribution on Terrestrial Water Storage Change in California Through Ecosystem Responses
Abstract Lateral subsurface flow plays an essential role in sustaining the terrestrial ecosystem, but it is not explicitly represented in most Earth System Models. In this study, we implemented an explicit lateral saturated flow model into the E3SM land model (ELM). The model explicitly describes lateral flow in the saturated zone by representing, for each model grid, an idealized hillslope consisting of five hydrologically connected soil columns. We conducted three model experiments driven by 0.125° atmospheric forcing data during 1980–2015 over California using models of the default ELM, a modified version of ELM to enhance infiltration, and the model with the lateral saturated flow model. The simulated runoff, evapotranspiration, and terrestrial water storage anomaly (TWSA) from the three simulations were evaluated against available observations, and the model explicitly representing lateral flow performs best. The new model produces greater gridcell‐averaged evapotranspiration especially over the mountainous regions with moderate relief and seasonally dry climates. Most importantly, it improves the modeled seasonal variations, interannual variabilities, and the recent decadal decline of TWSA. Many of these improvements can be attributed to the enhanced ecosystem resilience to droughts as demonstrated by transpiration increases caused by lateral flow. Model sensitivity experiments suggest that subsurface runoff is most sensitive to the ratio between horizontal and vertical saturated hydraulic conductivity, followed by hillslope planforms (convergent, divergent, and uniform), number of columns, and lower boundary conditions. Future work should effectively characterize hillslopes in global models and explore the long‐term influences of lateral water movement on modeled biogeochemical cycle.
Health and climate impacts of future United States land freight modelled with global-to-urban models
We forecast emissions, health and climate impacts of freight truck and rail transportation in the United States under various policies between 2010 and 2050. We use a system-of-systems approach, combining models of downscaled economy, modal choice, network loading, urban development, and emission technology. We predict that overall air pollutant emissions and health impacts from the freight-truck-rail system will be greatly reduced from 2010 to 2030, but climate forcing will continue to increase if petroleum remains the dominant fuel source. A carbon tax could shift freight shipments from trucking to the more energy efficient rail, providing the greatest climate benefits among all policies, whereas a policy enforcing truck fleet maintenance could cause the largest reduction in air pollutant emissions, offering the largest health benefits. Increasing urban compactness could reduce freight activity but increase population exposure per unit emission , offering a slightly positive health benefit over the current trend of urban decentralization.
Four Fundamental Questions to Evaluate Land Change Models with an Illustration of a Cellular Automata–Markov Model
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Aquifer recharge, depletion, and connectivity: Inferences from GRACE, land surface models, and geochemical and geophysical data
We used data from the Gravity Recovery and Climate Experiment (GRACE) and outputs of the CLM4.5 model to estimate recharge and depletion rates for large aquifers, investigate the connectivity of an aquifer's subbasins, and identify barriers and preferred pathways for groundwater flow within an aquifer system. The Nubian Sandstone Aquifer System and its subbasins (Dakhla, Northern Sudan Platform, and Kufra) in northeast Africa were used for demonstration purposes, and findings were tested and verified against geological, geophysical, remote sensing, geochronologic, and geochemical data. There are four major findings. (1) The average annual precipitation data over recharge areas in the southern Kufra section and the Northern Sudan Platform subbasin were estimated at 54.8 km 3 , and 32.8 km 3 , respectively, and knowing the annual extraction rates over these two areas (similar to 0.40 ± 0.20 km 3 ), recharge rates were estimated at 0.78 ± 0.49 km 3 /yr and 1.44 ± 0.42 km 3 /yr, respectively. (2) GRACEderived groundwater depletion rates over the Dakhla subbasin and the Northern Kufra section were estimated at 4.44 ± 0.42 km 3 /yr and 0.48 ± 0.32 km 3 /yr, respectively. (3) The observed depletion in the southern parts of the Dakhla subbasin is apparently caused by the presence of the east- west- trending Uweinat-Aswan basement uplift, which impedes the south-to-north groundwater flow and hence reduces replenishment from recharge areas in the south. (4) A major northeast- southwest- trending shear zone (Pelusium shear system) is apparently providing a preferred groundwater flow pathway from the Kufra to the Dakhla subbasin. Thus, inte-grated approach provides a replicable and cost-effective model for better understanding of the hydrogeologic setting of large aquifers worldwide and for optimum management of these groundwater resources.
Identification of key parameters controlling demographically structured vegetation dynamics in a Land Surface Model
This dataset includes outputs of 5000 simulations of CLM4.5(ED) to quantify the sensitivity of the model outputs to changes in model parameters using the Fourier Amplitude Sensitivity Test (FAST). Each simulation is generated by simultaneously sampling from 15% deviations of the default values of >80 vegetation parameters. This dataset includes 1) model codes of CLM4.5(ED) used for the sensitivity analysis; 2) the parameter samples; and 3) the corresponding model outputs of vegetation status (e.g., Gross Primary Production, Leaf Area Index and Biomass) and demography (e.g., diameter growth and mortality rates). The outputs are organized to the format of the FAST toolbox (https://sites.google.com/site/xuchongang/uasatoolbox).
Evaluating improving and maintaining a performance portable land-ice model using Albany Trilinos and Kokkos.
Abstract not provided.
Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models
Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.
Mapping Mountain Front Recharge Areas in Arid Watersheds Based on a Digital Elevation Model and Land Cover Types
Here, a recent assessment that quantified potential impacts of solar energy development on water resources in the southwestern United States necessitated the development of a methodology to identify locations of mountain front recharge (MFR) in order to guide land development decisions. A spatially explicit, slope-based algorithm was created to delineate MFR zones in 17 arid, mountainous watersheds using elevation and land cover data. Slopes were calculated from elevation data and grouped into 100 classes using iterative self-organizing classification. Candidate MFR zones were identified based on slope classes that were consistent with MFR. Land cover types that were inconsistent with groundwater recharge were excluded from the candidate areas to determine the final MFR zones. No MFR reference maps exist for comparison with the study’s results, so the reliability of the resulting MFR zone maps was evaluated qualitatively using slope, surficial geology, soil, and land cover datasets. MFR zones ranged from 74 km2 to 1,547 km2 and accounted for 40% of the total watershed area studied. Slopes and surficial geologic materials that were present in the MFR zones were consistent with conditions at the mountain front, while soils and land cover that were present would generally promote groundwater recharge. Visual inspection of the MFR zone maps also confirmed the presence of well-recognized alluvial fan features in several study watersheds. While qualitative evaluation suggested that the algorithm reliably delineated MFR zones in most watersheds overall, the algorithm was better suited for application in watersheds that had characteristic Basin and Range topography and relatively flat basin floors than areas without these characteristics. Because the algorithm performed well to reliably delineate the spatial distribution of MFR, it would allow researchers to quantify aspects of the hydrologic processes associated with MFR and help local land resource managers to consider protection of critical groundwater recharge regions in their development decisions.
Observational benchmarks inform representation of soil organic carbon dynamics in land surface models
Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.
SUPECA kinetics for scaling redox reactions in networks of mixed substrates and consumers and an example application to aerobic soil respiration
Abstract. Several land biogeochemical models used for studying carbon–climate feedbacks have begun explicitly representing microbial dynamics. However, to our knowledge, there has been no theoretical work on how to achieve a consistent scaling of the complex biogeochemical reactions from microbial individuals to populations, communities, and interactions with plants and mineral soils. We focus here on developing a mathematical formulation of the substrate–consumer relationships for consumer-mediated redox reactions of the form A + BE→ products, where products could be, e.g., microbial biomass or bioproducts. Under the quasi-steady-state approximation, these substrate–consumer relationships can be formulated as the computationally difficult full equilibrium chemistry problem or approximated analytically with the dual Monod (DM) or synthesizing unit (SU) kinetics. We find that DM kinetics is scaling inconsistently for reaction networks because (1) substrate limitations are not considered, (2) contradictory assumptions are made regarding the substrate processing rate when transitioning from single- to multi-substrate redox reactions, and (3) the product generation rate cannot be scaled from one to multiple substrates. In contrast, SU kinetics consistently scales the product generation rate from one to multiple substrates but predicts unrealistic results as consumer abundances reach large values with respect to their substrates. We attribute this deficit to SU's failure to incorporate substrate limitation in its derivation. To address these issues, we propose SUPECA (SU plus the equilibrium chemistry approximation – ECA) kinetics, which consistently imposes substrate and consumer mass balance constraints. We show that SUPECA kinetics satisfies the partition principle, i.e., scaling invariance across a network of an arbitrary number of reactions (e.g., as in Newton's law of motion and Dalton's law of partial pressures). We tested SUPECA kinetics with the equilibrium chemistry solution for some simple problems and found SUPECA outperformed SU kinetics. As an example application, we show that a steady-state SUPECA-based approach predicted an aerobic soil respiration moisture response function that agreed well with laboratory observations. We conclude that, as an extension to SU and ECA kinetics, SUPECA provides a robust mathematical representation of complex soil substrate–consumer interactions and can be applied to improve Earth system model (ESM) land models.
Future projection of greenhouse gas emissions due to permafrost degradation using a simple numerical scheme with a global land surface model
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Soil moisture background error covariance and data assimilation in a coupled land‐atmosphere model
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Sources of discrepancies between satellite‐derived and land surface model estimates of latent heat fluxes
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Evolution of 2016 drought in the Southeastern United States from a Land surface modeling perspective
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Application of an approach and landing system model to the space shuttle orbiter vehicle
An analytic model and procedure are described which can be used to estimate probability distributions of touchdown conditions, without utilizing a Monte Carlo simulation. Example applications are presented, including one-step and two-step flare strategies for the space shuttle orbiter. The computed probability distributions for the two-step flare are compared with the corresponding histograms from an independent Monte Carlo simulation of the same situation. The computed distributions are very good fits to the Monte-Carlo-generated histograms for pertinent longitudinal and lateral variables at three points (including touchdown) along the vehicle's final approach trajectory. The model, thus verified, has more inherent power for assessing the effects of system element changes than conventional Monte Carlo techniques.
The Ohio land allocation model: Report on phase 1
There are no author-identified significant results in this report.