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DOE OSTI2020

Changes in Physical Properties of Everglades Peat Soils Induced by Increased Salinity at the Laboratory Scale: Implications for Changes in Biogenic Gas Dynamics

Abstract Saltwater intrusion due to sea level rise is a major concern for the Florida Everglades because it may induce shifts in ecosystem productivity and physical soil properties. However, the effects of saline water intrusion into the current carbon gas dynamics of the Everglades (particularly in terms of biogenic gas production and emissions, i.e., CH 4 and CO 2 ) are still uncertain. In this work, we present a laboratory‐based study to simulate how sea level rise may alter the physical properties (i.e., hydraulic conductivity) of peat soils from the Everglades and consequently affect the accumulation and release of biogenic gases within the peat matrix. Peat monoliths collected from the Everglades were subjected to progressive increases in salinity from a NaCl solution, and changes to the biogenic gas dynamics regime were simultaneously monitored using a combination of time‐lapse ground‐penetrating radar measurements, manometers, time‐lapse photography, and gas traps. Physical changes to the peat matrix at each salinity interval were assessed using constant head permeameter tests. Consistent with previous research, results show that a progressive increase in salinity (from fresh to saltwater) results in (1) a progressive increase in hydraulic conductivity and (2) a progressive decrease in gas content within the peat matrix (i.e., production) and gas releases. This work has implications for better understanding the potential effects of saltwater intrusion into freshwater peatland systems in the Everglades, particularly in terms of carbon gas dynamics.

Sirianni, Matthew J.↗

DOE OSTI2021

Water balance response of permafrost-affected watersheds to changes in air temperatures

Abstract Observations show increases in river discharge to the Arctic Ocean especially in winter over the last decades but the physical mechanisms driving these changes are not yet fully understood. We hypothesize that even in the absence of a precipitation increase, permafrost degradation alone can lead to increased annual river runoff. To test this hypothesis we perform 12 millennium-long simulations over an idealized hypothetical watershed (1 km 2 ) using a distributed, physically based water balance model (Water flow and Balance Simulation Model, WaSiM). The model is forced by both a hypothetical warming defined by an air temperature increase of 7.5 ∘ C over 100 years, and a corresponding cooling scenario. To assess model sensitivity we vary soil saturated hydraulic conductivity and lateral subsurface flow configuration. Under the warming scenario, changes in subsurface water transport due to ground temperature changes result in a 7%–14% increase in annual runoff accompanied by a 6%–20% decrease in evapotranspiration. The increase in runoff is most pronounced in winter. Hence, the simulations demonstrate that changes in permafrost characteristics due to climate warming and associated changes in evapotranspiration provide a plausible mechanism for the observed runoff increases in Arctic watersheds. In addition, our experiments show that when lateral subsurface moisture transport is not included, as commonly done in global-scale Earth System Models, the equilibrium water balance in response to the warming or cooling is similar to the water balance in simulations where lateral subsurface transport is included. However, the transient changes in water balance components prior to reaching equilibrium differ greatly between the two. For example, for high saturated hydraulic conductivity only when lateral subsurface transport is considered, a period of decreased runoff occurs immediately after the warming. This period is characterized by a positive change in soil moisture storage caused by the soil moisture deficit developed during prior cooling.

Debolskiy, Matvey V. (ORCID:0000000296343627)↗

DOE OSTI2020

High-Performance Simulation of Dynamic Hydrologic Exchange and Implications for Surrogate Flow and Reactive Transport Modeling in a Large River Corridor

Hydrologic exchange flows (HEFs) have environmental significance in riverine ecosystems. Key river channel factors that influence the spatial and temporal variations of HEFs include river stage, riverbed morphology, and riverbed hydraulic conductivity. However, their impacts on HEFs were often evaluated independently or on small scales. In this study, we numerically evaluated the combined interactions of these factors on HEFs using a high-performance simulator, PFLOTRAN, for subsurface flow and transport. The model covers 51 square kilometers of a selected river corridor with large sinuosity along the Hanford Reach of the Columbia River in Washington, US. Three years of spatially distributed hourly river stages were applied to the riverbed. Compared to the simulation when riverbed heterogeneity is not ignored, the simulation using homogeneous riverbed conductivity underestimated HEFs, especially upwelling from lateral features, and overestimated the mean residence times derived from particle tracking. To derive a surrogate model for the river corridor, we amended the widely used transient storage model (TSM) for riverine solute study at reach scale with reactions. By treating the whole river corridor as a batch reactor, the temporal changes in the exchange rate coefficient for the TSM were derived from the dynamic residence time estimated from the hourly PFLOTRAN results. The TSM results were evaluated against the effective concentrations in the hyporheic zone calculated from the PFLOTRAN simulations. Our results show that there is potential to parameterize surrogate models such as TSM amended with biogeochemical reactions while incorporating small-scale process understandings and the signature of time-varying streamflow to advance the mechanistic understanding of river corridor processes at reach to watershed scales. However, the assumption of a well-mixed storage zone for TSM should be revisited when redox-sensitive reactions in the storage zones play important roles in river corridor functioning.

Fang, Yilin↗

DOE OSTI2020

Probabilistic Groundwater Modeling of the RDX Plume at Los Alamos National Laboratory to Support Risk Assessment - 20359

A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Abstract Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model‐data integration workflow to address uncertainty in groundwater response to beaver‐induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning‐based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down‐valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down‐valley underflow, from 2 during dry pond periods to 20 during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down‐valley underflow, minimizes the effect of beaver‐induced inundation on water quality. We emphasize the need for field‐scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response. Plain Language Summary Beavers change the flow of water in river corridors by creating ponds, expanding wetlands, and flooding floodplains. This increases surface water area, promotes plant growth, and enhances biodiversity. However, the impact of this flooding on groundwater flow is not well understood, especially in mountainous areas with gravel layers where water moves easily beneath soil. In this study, we used numerical modeling to investigate how beaver ponds influence groundwater in a mountainous floodplain of the Upper Colorado River Basin. We adapted a machine learning method to validate our numerical models using multiple field data sets. Our findings show that beaver ponds significantly increase vertical water flow from the soil to the gravel during wet periods, compared to when the ponds are fully drained. The study also highlights the importance of floodplain structure in controlling both water flow in gravel layers along the river direction and vertical flow from the soil to the gravel with the presence of beavers. To reduce uncertainty in groundwater response, we emphasize the need for more field‐scale measurements of floodplain structure, hydraulic properties, and evapotranspiration changes. Key Points Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters

Wang, Lijing↗

DOE OSTI2022

Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics

Abstract Considering the global intensification of aridity in tropical biomes due to climate change, we need to understand what shapes the distribution of drought sensitivity in tropical plants. We conducted a pantropical data synthesis representing 1117 species to test whether xylem‐specific hydraulic conductivity (K S ), water potential at leaf turgor loss (Ψ TLP ) and water potential at 50% loss of K S (Ψ P50 ) varied along climate gradients. The Ψ TLP and Ψ P50 increased with climatic moisture only for evergreen species, but K S did not. Species with high Ψ TLP and Ψ P50 values were associated with both dry and wet environments. However, drought‐deciduous species showed high Ψ TLP and Ψ P50 values regardless of water availability, whereas evergreen species only in wet environments. All three traits showed a weak phylogenetic signal and a short half‐life. These results suggest strong environmental controls on trait variance, which in turn is modulated by leaf habit along climatic moisture gradients in the tropics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Post-Calibration Uncertainty Analysis for Travel Times at a Naval Weapons Industrial Reserve Plant

The Naval Weapons Industrial Reserve Plant (NWIRP) in McGregor, Texas began manufacturing explosives in 1980 and several hazardous chemicals were discovered in lakes and streams surrounding the site in 1998. Contaminants traveled to local lakes and streams much faster than initially predicted. This research estimated contaminant travel times and identified locations where monitoring wells should be installed to yield the greatest reductions in uncertainties in travel-time predictions. To this end, groundwater and particle-tracking models for NWIRP site were built to predict hydraulic heads and contaminant travel times. Next, parameter (hydraulic conductivities) uncertainties, parameter identifiabilities, observation (hydraulic heads) worth, and predictive (contaminant travel times) uncertainties were quantified. Parameter uncertainties were reduced by up to 92%; a total of 19 of 158 parameters were at least moderately identifiable; travel-time uncertainties were reduced up to 92%. Additionally, travel-time predictions and post-calibration parameter distributions were generated using the null-space Monte Carlo (NSMC) technique. NSMC predicted that conservative tracers exited the flow system within a year, which matches with field data. Finally, an observations-worth analysis found that additional 11 more measurements would reduce travel-time uncertainties by factors from 1.04 to 4.3 over existing data if monitoring wells were installed at the suggested locations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets

This repository contains simulated outputs from superwell – a hydro-economic tool for long-term assessment of groundwater cost and supply – providing globally gridded groundwater extractable volumes and associated unit costs ($/km³) for accessible groundwater production, based on a variety of user-defined depletion and ponded depth scenarios. Key model documentation: Niazi, H., Ferencz, S. B., Graham, N. T., Yoon, J., Wild, T. B., Hejazi, M., Watson, D. J., & Vernon, C. R. (2025). Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1. Geoscientific Model Development, 18(5), 1737-1767. https://doi.org/10.5194/gmd-18-1737-2025 Find the source code of the superwell model on GitHub: https://github.com/JGCRI/superwell Repository Overview Main output: superwell_outputs.7z contains 6 files (4.5 GB) named as superwell_py_deep_all_0.*PD_0.*DL.csv. These files present superwell outputs of global groundwater extraction volumes and cost estimates on a 0.5° scale for six scenarios with different Ponded Depth (PD; 0.3 and 0.6 m) and Depletion Limit (DL; 5%, 25%, and 40% of available volume) targets over the entire pumping lifetime of a grid cell superwell_py_deep_all_0.3PD_0.25DL_sample_100.csv contains superwell outputs for 100 data points sampled to match the global inputs' distribution superwell_py_deep_all_0.3PD_0.25DL_Grid_72548.csv contains superwell output for a single grid cell concept_v5.png provides an overview of the superwell workflow Outputs Description year_number: year of pumping depletion_limit: set depletion limit (DL) as a volume fraction of total available groundwater Mappings: continent, country, gcam_basin_id, Basin_long_name, grid_id: geographic identifiers and basin information Inputs: grid_area (km²): area of the grid cell whyclass: hydrogeological classification of the aquifer permeability (m/day), porosity (%), total_thickness (m), depth_to_water (m): aquifer properties. The geo-processed input data has been published separately: https://doi.org/10.57931/2307831 Model outputs: orig_aqfr_sat_thickness (m), aqfr_sat_thickness (m): original and remaining/instantaneous saturated thickness of the aquifer hydraulic_conductivity (m/day), transmissivity (m²/day): hydraulic properties of the aquifer radius_of_influence (m), areal_extent (km²): well radius and area of influence from the center of the well number_of_wells (-): number of wells in a grid cell determined by a ratio of well area and grid area max_drawdown (m), drawdown (m), drawdown_interference (m): well and aquifer drawdown during extraction total_head (m): total lift for the groundwater (depth to water plus drawdown) total_well_length (m): total depth of wells drilled well_yield (m³/day): pumping rate or well yield power (kW), energy (kWh): power and energy required for pumping groundwater Volume Outputs: volume_produced_perwell (m³), cumulative_vol_produced_perwell (m³): production volume metrics per well volume_produced_allwells (m³), cumulative_vol_produced_allwells (m³): aggregate extraction volumes for all wells in a grid cell available_volume (m³): available groundwater in storage for the grid cell as determined by aquifer properties depleted_vol_fraction: fraction of total volume pumped over available volumes in a grid cell (same as depletion limit) Cost Outputs: well_installation_cost ($): well installation cost based on the hydrogeological complexity of the aquifer annual_capital_cost, maintenance_cost, nonenergy_cost ($): nonenergy costs energy_cost_rate ($/kWh): electricity rate energy_cost ($): energy cost of pumping groundwater total_cost_perwell ($), total_cost_allwells ($): total annual energy and non-energy cost for each and all wells in a grid cell a unit_cost ($/m³), unit_cost_per_km3 ($/km³), unit_cost_per_acreft ($/acre-ft): total cost of pumping a unit of groundwater, indicated for different spatial units Key Resources Model documentation: Niazi, H., Ferencz, S., Graham, N., Yoon, J., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024; In-prep). Long-term Hydro-economic Assessment Tool for Evaluating Global Groundwater Cost and Supply: Superwell v1. Geoscientific Model Development. Input data: Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MSD-LIVE Data repository. https://doi.org/10.57931/2307831 superwell source code: https://github.com/JGCRI/superwell Cite as Niazi, H., Ferencz, S., Yoon, J., Graham, N., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024). Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets. MSD-LIVE Data repository. https://doi.org/10.57931/2307832 Contact Reach out to Hassan Niazi or Stephen Ferencz or open an issue in the superwell repository for questions or suggestions.

Earth Systems↗

NASA NTRS1980

Agricultural soil moisture experiment, Colby, Kansas 1978: Measured and predicted hydrological properties of the soil

Predictive procedures for developing soil hydrologic properties (i.e., relationships of soil water pressure and hydraulic conductivity to soil water content) are presented. Three models of the soil water pressure-water content relationship and one model of the hydraulic conductivity-water content relationship are discussed. Input requirements for the models are indicated, and computational procedures are outlined. Computed hydrologic properties for Keith silt loam, a soil typer near Colby, Kansas, on which the 1978 Agricultural Soil Moisture Experiment was conducted, are presented. A comparison of computed results with experimental data in the dry range shows that analytical models utilizing a few basic hydrophysical parameters can produce satisfactory data for large-scale applications.

Arya, L. M.↗

DOE OSTI2023

Hydraulic safety and growth rather than climate of origin influence survival in desert shrubs and trees

The species-poor woodlands and forests in arid regions are particularly vulnerable to climate change induced plant die-off. Searching for species suitable to replant in these areas is an effective way to prevent further expansion of land desertification. Good knowledge of drought resistance of species is critical for current replanting efforts, however, the related plant survival mechanisms in the process of restoration remain limited. Here, we tested the dominant drivers of plant survival in relation to climate factors, growth characteristics and functional traits including hydraulics and gas exchange across 35 multiple shrub and tree woody species in a common garden at the Tengger Desert, northwest China. We found the desert plants had a wide range of hydraulic thresholds among these desert species, with relative high hydraulic safety, and low xylem hydraulic conductivity. Pant survival was largely determined by hydraulic safety and growth status, but was not affected by the climate of the species’ site of origin. Shrubs and trees diverged in hydraulic and growth characteristics, while there was no difference between native and introduced species. In this study, our results highlight the critical roles of hydraulic safety and growth on determining the survival and drought adaptation during desert vegetation development, which provide a new insight into ecological restoration in arid regions under changing climate conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Seawater exposure causes hydraulic damage in dying Sitka-spruce trees

Sea-level rise is one of the most critical challenges facing coastal ecosystems under climate change. Observations of elevated tree mortality in global coastal forests are increasing, but the mechanism of salinity-stress induced tree mortality is currently unknown. We monitored progressive mortality and associated gas exchange and hydraulic shifts in Sitka-spruce (Picea sitchensis) trees located within a salinity gradient under an ecosystem-scale change of seawater exposure in Washington State, USA. Percentage of live foliated crown (PLFC) decreased and tree mortality increased with increasing soil salinity during the study period. A strong reduction in gas exchange and xylem hydraulic conductivity (Ks) occurred during tree death, with an increase of the percentage loss of xylem conductivity (PLC) and turgor loss point (ptlp). Hydraulic and osmotic shifts reflected that hydraulic function declined from seawater exposure and dying trees were unable to support osmotic adjustment. Constrained gas exchange was strongly related to hydraulic damage at both stem and leaf levels. Significant correlations between foliar sodium (Na+) concentration and gas exchange and key hydraulic parameters (Ks, PLC and ptlp ) suggest that cellular injury related to the toxic effects of ion accumulation impacted the physiology of these dying trees. This study provides evidence of toxic effects on cellular function that become manifest in all aspects of plant functioning, leading to unfavourable osmotic and hydraulic conditions.

Zhang, Hongxia↗

DOE OSTI2024

Groundwater and Surface Water Flow (GSFLOW) model files to explore bedrock circulation depth and porosity in Copper Creek, Colorado

This data package contains integrated hydrological model input and output files for Copper Creek, Colorado (24 km2), a tributary of the East River located in the headwaters of the Upper Colorado River Basin. The model code is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution and a daily timestep. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model contains 12 model layers and extends 400 m below land surface. The original Copper Creek model was modified to contain geologic layers representing saprolite, shallow bedrock, and deep bedrock. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. For the shallow case, median flow depths occur in the shallow saprolite at depths <8 m, while the deep case promotes a median groundwater flow depth of 100 m. With this modeling framework we compare streamflow response to a plausible worst-case drought lasting up to five years. Streamflow metrics of analysis include average streamflow, fraction of stream network that is dry, no-flow duration, average groundwater flow to streams and time to recovery following the drought. Results and implications are presented in a paper submitted to Geophysical Research Letters titled, "The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought" by Rosemary WH. Carroll, Andrew H. Manning and Kenneth H Williams. A Readme.txt file provides instructions on how to download all model files and execute each model scenario. In addition to the GSFLOW output/prms/copper_drought.csv file containing daily basin water stores and fluxes (refer to GSFLOW manual) and the output/prms/copper_drought_statvar.dat file with output defined in the gsflow3.control file (refer to GSFLOW Manual), output files also include spatially distributed daily values of total evapotranspiration, canopy evaporation, precipitation, snowfall, infiltration, snow water equivalent, potential evapotranspiration, recharge, sublimation, soil moisture, contributing interflow, water table elevations, changes in groundwater storage, groundwater evapotranspiration, interbasin groundwater flow (limited to the alluvium below the stream outlet), and surface-groundwater exchanges within the river system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Temporal persistence of postfire flood hazards under present and future climate conditions in southern Arizona, USA

Changes to soil hydraulic properties that reduce infiltration capacity following fire can increase flash flood risks. These risks are exacerbated by rainfall intensification associated with a warming climate. However, the potential effects of climate-change-driven rainfall intensification on postfire floods remain largely unexplored. Using rainfall and runoff observations from a 49.4 km 2 watershed in southern Arizona, USA, and a hydrologic model (KINEROS2), we examined the temporal evolution following a historic fire of three crucial hydrologic parameters: soil saturated hydraulic conductivity (K sp ), net capillary drive (G p ), and hydraulic roughness (n c ). We explored how the effect of fire on these parameters may influence peak flow under future climate scenarios derived from CMIP6, specifically the medium emissions scenario (SSP2-4.5) and high emissions scenario (SSP5-8.5). Results demonstrate an increase in K sp from 11 mm h −1 in the first postfire year to 60 mm h −1 in postfire year 3. G p similarly increased from 19 mm in the first postfire year to 30 mm in the third, while nc was relatively constant. The highest simulated Q p occurred in the first postfire year. Under the SSP2-4.5 scenario, the likelihood of a 100-year flood is projected to be twice as large by the middle of the century relative to its historical magnitude. Simulations further indicate that the maximum expected discharge associated with a postfire flood, as derived from historical data, could be triggered by a 10-year rainstorm under the SSP5-8.5 scenario by the late century. Simulations also demonstrate that rainfall intensification will lead to greater persistence of elevated flood hazards following fire by the late century under both the SSP2-4.5 and the SSP5-8.5 scenarios.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Hydraulic constraints to stomatal conductance in flooded trees

Stomatal closure is a pervasive response among trees exposed to flooded soil. We tested whether this response is caused by reduced hydraulic conductance in the soil-to-leaf hydraulic continuum (k total ), and particularly by reduced root hydraulic conductance (k root ), which has been widely hypothesized. We tracked stomatal conductance at the leaf level (g s ) and canopy scale (G s ) along with physiological conditions in two temperate tree species, Magnolia grandiflora and Quercus virginiana, that were subjected to flood and control conditions in a greenhouse experiment. Flooding reduced g s , G s , k root and k total . Path analysis showed strong support for direct effects of k total on g s and for flood duration on k total , but not k root on k total . A process-based model that accounted for the k total reduction predicted the timeseries of G s in flood and control treatment trees reasonably well (predicted versus observed G s R 2 = 0.80 and 0.51 for M. grandiflora and Q. virginiana, respectively). However, accounting only for k root reduction in flooded trees was insufficient for predicting observed G s reduction. Together, these results suggest that hydraulic constraints were not limited to roots and highlight the need to account for flooding effects on k total when projecting forest ecosystem function using process-based models.

Plant stress↗

DOE OSTI2022

Processes and mechanisms of coastal woody-plant mortality

Observations of woody plant mortality in coastal ecosystems are globally widespread, but the overarching processes and underlying mechanisms are poorly understood. This knowledge deficiency, combined with rapidly changing water levels, storm surges, atmospheric CO 2 , and vapor pressure deficit, creates large predictive uncertainty regarding how coastal ecosystems will respond to global change. Here we synthesize the literature on the mechanisms that underlie coastal woody-plant mortality, with the goal of producing a testable hypothesis framework. The key emergent mechanisms underlying mortality include hypoxic, osmotic, and ionic-driven reductions in whole-plant hydraulic conductance and photosynthesis that ultimately drive the coupled processes of hydraulic failure and carbon starvation. The relative importance of these processes in driving mortality, their order of progression, and their degree of coupling depends on the characteristics of the anomalous water exposure, on topographic effects, and on taxa-specific variation in traits and trait acclimation. Greater inundation exposure could accelerate mortality globally; however, the interaction of changing inundation exposure with elevated CO 2 , drought, and rising vapor pressure deficit could influence mortality likelihood. Models of coastal forests that incorporate the frequency and duration of inundation, the role of climatic drivers, and the processes of hydraulic failure and carbon starvation can yield improved estimates of inundation-induced woody-plant mortality.

salinity↗

DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Modeling the mechanisms of conifer mortality under seawater exposure

Summary Relative sea level rise (SLR) increasingly impacts coastal ecosystems through the formation of ghost forests. To predict the future of coastal ecosystems under SLR and changing climate, it is important to understand the physiological mechanisms underlying coastal tree mortality and to integrate this knowledge into dynamic vegetation models. We incorporate the physiological effect of salinity and hypoxia in a dynamic vegetation model in the Earth system land model, and used the model to investigate the mechanisms of mortality of conifer forests on the west and east coast sites of USA, where trees experience different form of sea water exposure. Simulations suggest similar physiological mechanisms can result in different mortality patterns. At the east coast site that experienced severe increases in seawater exposure, trees loose photosynthetic capacity and roots rapidly, and both storage carbon and hydraulic conductance decrease significantly within a year. Over time, further consumption of storage carbon that leads to carbon starvation dominates mortality. At the west coast site that gradually exposed to seawater through SLR, hydraulic failure dominates mortality because root loss impacts on conductance are greater than the degree of storage carbon depletion. Measurements and modeling focused on understanding the physiological mechanisms of mortality is critical to reducing predictive uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗