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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,495 records · Page 83
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DOE OSTI2015

Consequences of systematic model drift in DYNAMO MJO hindcasts with SP-CAM and CAM5

Hindcast simulations of MJO events during the dynamics of the MJO (DYNAMO) field campaign are conducted with two models, one with conventional parameterization (CAM5) and a comparable model that utilizes superparameterization (SP–CAM). SP–CAM is shown to produce a qualitatively better reproduction of the fluctuations of precipitation and low–level zonal wind associated with the first two DYNAMO MJO events compared to CAM5. Interestingly, skill metrics using the real–time multivariate MJO index (RMM) suggest the opposite conclusion that CAM5 has more skill than SP–CAM. This inconsistency can be explained by a systematic increase of RMM amplitude with lead time, which results from a drift of the large–scale wind field in SP–CAM that projects strongly onto the RMM index. CAM5 hindcasts exhibit a contraction of the moisture distribution, in which extreme wet and dry conditions become less frequent with lead time. SP–CAM hindcasts better reproduce the observed moisture distribution, but also have stronger drift patterns of moisture budget terms, such as an increase in drying by meridional advection in SP–CAM. This advection tendency in SP–CAM appears to be associated with enhanced off–equatorial synoptic eddy activity with lead time. In conclusion, systematic drift moisture tendencies in SP–CAM are of similar magnitude to intraseasonal moisture tendencies, and therefore are important for understanding MJO prediction skill.

42 ENGINEERING↗
DOE OSTI2015

Explicitly representing soil microbial processes in Earth system models: Soil microbes in earth system models

Microbes influence soil organic matter (SOM) decomposition and the long-term stabilization of carbon (C) in soils. Herein, we contend that by revising the representation of microbial processes and their interactions with the physicochemical soil environment, Earth system models (ESMs) may make more realistic global C cycle projections. Explicit representation of microbial processes presents considerable challenges due to the scale at which these processes occur. Thus, applying microbial theory in ESMs requires a framework to link micro-scale process-level understanding and measurements to macro-scale models used to make decadal- to century-long projections. Here, we review the diversity, advantages, and pitfalls of simulating soil biogeochemical cycles using microbial-explicit modeling approaches. We present a roadmap for how to begin building, applying, and evaluating reliable microbial-explicit model formulations that can be applied in ESMs. Drawing from experience with traditional decomposition models we suggest: (1) guidelines for common model parameters and output that can facilitate future model intercomparisons; (2) development of benchmarking and model-data integration frameworks that can be used to effectively guide, inform, and evaluate model parameterizations with data from well-curated repositories; and (3) the application of scaling methods to integrate microbial-explicit soil biogeochemistry modules within ESMs. With contributions across scientific disciplines, we feel this roadmap can advance our fundamental understanding of soil biogeochemical dynamics and more realistically project likely soil C response to environmental change at global scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

The origin of water vapor rings in tropical oceanic cold pools

Tropical deep convection over the ocean is found to grow preferentially from thermodynamically preconditioned regions of high specific humidity and, thus, high moist static energy. Because of this, rings of enhanced specific humidity at the leading edges of evaporatively driven cold pools have recently received considerable attention. The prevailing theory explains these rings by the water vapor source from the evaporation of rain drops below cloud base. Their origin is reported on in this letter using large-eddy simulations of individual cumulus clouds that rise into a tropical atmosphere over ocean. It is demonstrated that - in contrast to this theory - water vapor rings are primarily explained by surface latent heat fluxes rather than by the evaporation of rain. This result implies that conceptual models used in subgrid-scale parameterizations of deep convection should consider the formation of rings of increased specific humidity by the cold-pool-induced enhancement of surface fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A reduced complexity framework to bridge the gap between AGCMs and cloud‐resolving models

Abstract The role of convective parameterizations at high horizontal resolution and their impacts on clouds, circulation, and precipitation processes represent large uncertainties in atmospheric general circulation models (AGCMs). As the statistical equilibrium in which radiative cooling is balanced by convective heating, radiative‐convective equilibrium (RCE) offers a simplified framework to investigate such uncertainties. The Community Atmosphere Model 5 is configured in a RCE setup that consists of an ocean‐covered planet with diurnally varying, spatially uniform insolation with no rotation effects. A series of simulations are performed in which the planetary radius is incrementally reduced. Because of the homogeneity of the setup, the effect is to reduce grid spacing, mimicking increased resolution without increasing the number of grid points. The results suggest that the reduced planet approach is able to reproduce the behavior of convection from full high‐resolution simulations. At grid spacing less than 20 km, convective motions are predominantly produced by resolved scales.

Reed, Kevin A.↗
DOE OSTI2016

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Roles of Wind Shear at Different Vertical Levels, Part I: Cloud System Organization and Properties

Understanding of critical processes that contribute to the organization of mesoscale convective systems is important for accurate weather forecast and climate prediction. In this study, we investigate the effects of wind shear at different vertical levels on the organization and properties of cloud systems using the Weather Research & Forecasting (WRF) model with a spectral-bin microphysical scheme. The sensitivity experiments are performed by increasing wind shear at the lower (0-5 km), middle (5-10 km), upper (> 10 km) and the entire troposphere, respectively, based on a control run for a mesoscale convective system (MCS) with weak wind shear. We find that increasing wind shear at the both lower and middle vertical levels reduces the domain-accumulated precipitation and the occurrence of heavy rain, while increasing wind shear at the upper levels changes little on precipitation. Although increasing wind shear at the lower-levels is favorable for a more organized quasi-line system which leads to enlarged updraft core area, and enhanced updraft velocities and vertical mass fluxes, the precipitation is still reduced by 18.6% compared with the control run due to stronger rain evaporation induced by the low-level wind shear. Strong wind shear in the middle levels only produces a strong super-cell over a narrow area, leading to 67.3% reduction of precipitation over the domain. By increasing wind shear at the upper levels only, the organization of the convection is not changed much, but the increased cloudiness at the upper-levels leads to stronger surface cooling and then stabilizes the atmosphere and weakens the convection. When strong wind shear exists over the entire vertical profile, a deep dry layer (2-9 km) is produced and convection is severely suppressed. There are fewer very-high (cloud top height (CTH) > 15 km) and very-deep (cloud thickness > 15 km) clouds, and the precipitation is only about 11.8% of the control run. The changes in cloud microphysical properties further explain the reduction of surface rain by strong wind shear especially at the lower- and middle-levels. The insights obtained from this study help us better understand the cloud system organization and provide foundation for better parameterizing organized MCS.

cloud system organization↗
DOE OSTI2015

Low-cloud characteristics over the tropical western Pacific from ARM observations and CAM5 simulations

Here, this study evaluates the ability of the Community Atmospheric Model version 5 (CAM5) to reproduce low clouds observed by the Atmospheric Radiation Measurement (ARM) cloud radar at Manus Island of the tropical western Pacific during the Years of Tropical Convection. Here low clouds are defined as clouds with their tops below the freezing level and bases within the boundary layer. Low-cloud statistics in CAM5 simulations and ARM observations are compared in terms of their general occurrence, mean vertical profiles, fraction of precipitating versus nonprecipitating events, diurnal cycle, and monthly time series. Other types of clouds are included to put the comparison in a broader context. The comparison shows that the model overproduces total clouds and their precipitation fraction but underestimates low clouds in general. The model, however, produces excessive low clouds in a thin layer between 954 and 930 hPa, which coincides with excessive humidity near the top of the mixed layer. This suggests that the erroneously excessive low clouds stem from parameterization of both cloud and turbulence mixing. The model also fails to produce the observed diurnal cycle in low clouds, not exclusively due to the model coarse grid spacing that does not resolve Manus Island. Lastly, this study demonstrates the utility of ARM long-term cloud observations in the tropical western Pacific in verifying low clouds simulated by global climate models, illustrates issues of using ARM observations in model validation, and provides an example of severe model biases in producing observed low clouds in the tropical western Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A new approach to modeling aerosol effects on East Asian climate: Parametric uncertainties associated with emissions, cloud microphysics, and their interactions

In this paper, we adopt a parametric sensitivity analysis framework that integrates the quasi-Monte Carlo parameter sampling approach and a surrogate model to examine aerosol effects on the East Asian Monsoon climate simulated in the Community Atmosphere Model (CAM5). A total number of 256 CAM5 simulations are conducted to quantify the model responses to the uncertain parameters associated with cloud microphysics parameterizations and aerosol (e.g., sulfate, black carbon (BC), and dust) emission factors and their interactions. Results show that the interaction terms among parameters are important for quantifying the sensitivity of fields of interest, especially precipitation, to the parameters. The relative importance of cloud-microphysics parameters and emission factors (strength) depends on evaluation metrics or the model fields we focused on, and the presence of uncertainty in cloud microphysics imposes an additional challenge in quantifying the impact of aerosols on cloud and climate. Due to their different optical and microphysical properties and spatial distributions, sulfate, BC, and dust aerosols have very different impacts on East Asian Monsoon through aerosol-cloud-radiation interactions. The climatic effects of aerosol do not always have a monotonic response to the change of emission factors. The spatial patterns of both sign and magnitude of aerosol-induced changes in radiative fluxes, cloud, and precipitation could be different, depending on the aerosol types, when parameters are sampled in different ranges of values. We also identify the different cloud microphysical parameters that show the most significant impact on climatic effect induced by sulfate, BC and dust, respectively, in East Asia.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Mixed-phase cloud physics and Southern Ocean cloud feedback in climate models

Increasing optical depth poleward of 45° is a robust response to warming in global climate models. Much of this cloud optical depth increase has been hypothesized to be due to transitions from ice-dominated to liquid-dominated mixed-phase cloud. In this study, the importance of liquid-ice partitioning for the optical depth feedback is quantified for 19 Coupled Model Intercomparison Project Phase 5 models. All models show a monotonic partitioning of ice and liquid as a function of temperature, but the temperature at which ice and liquid are equally mixed (the glaciation temperature) varies by as much as 40 K across models. Models that have a higher glaciation temperature are found to have a smaller climatological liquid water path (LWP) and condensed water path and experience a larger increase in LWP as the climate warms. The ice-liquid partitioning curve of each model may be used to calculate the response of LWP to warming. It is found that the repartitioning between ice and liquid in a warming climate contributes at least 20% to 80% of the increase in LWP as the climate warms, depending on model. Intermodel differences in the climatological partitioning between ice and liquid are estimated to contribute at least 20% to the intermodel spread in the high-latitude LWP response in the mixed-phase region poleward of 45°S. As a result, it is hypothesized that a more thorough evaluation and constraint of global climate model mixed-phase cloud parameterizations and validation of the total condensate and ice-liquid apportionment against observations will yield a substantial reduction in model uncertainty in the high-latitude cloud response to warming.

58 GEOSCIENCES↗
DOE OSTI2016

Experimental particle formation rates spanning tropospheric sulfuric acid and ammonia abundances, ion production rates, and temperatures

Abstract Binary nucleation of sulfuric acid and water as well as ternary nucleation involving ammonia are thought to be the dominant processes responsible for new particle formation (NPF) in the cold temperatures of the middle and upper troposphere. Ions are also thought to be important for particle nucleation in these regions. However, global models presently lack experimentally measured NPF rates under controlled laboratory conditions and so at present must rely on theoretical or empirical parameterizations. Here with data obtained in the European Organization for Nuclear Research CLOUD (Cosmics Leaving OUtdoor Droplets) chamber, we present the first experimental survey of NPF rates spanning free tropospheric conditions. The conditions during nucleation cover a temperature range from 208 to 298 K, sulfuric acid concentrations between 5 × 10 5 and 1 × 10 9 cm −3 , and ammonia mixing ratios from zero added ammonia, i.e., nominally pure binary, to a maximum of ~1400 parts per trillion by volume (pptv). We performed nucleation studies under pure neutral conditions with zero ions being present in the chamber and at ionization rates of up to 75 ion pairs cm −3 s −1 to study neutral and ion‐induced nucleation. We found that the contribution from ion‐induced nucleation is small at temperatures between 208 and 248 K when ammonia is present at several pptv or higher. However, the presence of charges significantly enhances the nucleation rates, especially at 248 K with zero added ammonia, and for higher temperatures independent of NH 3 levels. We compare these experimental data with calculated cluster formation rates from the Atmospheric Cluster Dynamics Code with cluster evaporation rates obtained from quantum chemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks

Abstract Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input‐output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m −2 d −1 ). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7–1.41 gC m −2 d −1 ), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8–2.09 gC m −2 d −1 ). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty.

Papale, Dario↗
DOE OSTI2016

Testing a land model in ecosystem functional space via a comparison of observed and modeled ecosystem flux responses to precipitation regimes and associated stresses in a Central U.S. forest: Test Model in Ecosystem Functional Space

Testing complex land surface models has often proceeded by asking the question: does the model prediction agree with the observation? This approach has yet led to high-performance terrestrial models that meet the challenges of climate and ecological studies. Here we test the Community Land Model (CLM) by asking the question: does the model behave like an ecosystem? We pursue its answer by testing CLM in the ecosystem functional space (EFS) at the Missouri Ozark AmeriFlux (MOFLUX) forest site in the Central U.S., focusing on carbon and water flux responses to precipitation regimes and associated stresses. In the observed EFS, precipitation regimes and associated water and heat stresses controlled seasonal and interannual variations of net ecosystem exchange (NEE) of CO 2 and evapotranspiration in this deciduous forest ecosystem. Such controls were exerted more strongly by precipitation variability than by the total precipitation amount per se. A few simply constructed climate variability indices captured these controls, suggesting a high degree of potential predictability. While the interannual fluctuation in NEE was large, a net carbon sink was maintained even during an extreme drought year. Although CLM predicted seasonal and interanual variations in evapotranspiration reasonably well, its predictions of net carbon uptake were too small across the observed range of climate variability. Also, the model systematically underestimated the sensitivities of NEE and evapotranspiration to climate variability and overestimated the coupling strength between carbon and water fluxes. Its suspected that the modeled and observed trajectories of ecosystem fluxes did not overlap in the EFS and the model did not behave like the ecosystem it attempted to simulate. A definitive conclusion will require comprehensive parameter and structural sensitivity tests in a rigorous mathematical framework. We also suggest that future model improvements should focus on better representation and parameterization of process responses to environmental stresses and on more complete and robust representations of carbon-specific processes so that adequate responses to climate variability and a proper degree of coupling between carbon and water exchanges are captured.

58 GEOSCIENCES↗
DOE OSTI2015

Evaluation of high‐level clouds in cloud resolving model simulations with ARM and KWAJEX observations

Abstract In this study, we evaluate high‐level clouds in a cloud resolving model during two convective cases, ARM9707 and KWAJEX. The simulated joint histograms of cloud occurrence and radar reflectivity compare well with cloud radar and satellite observations when using a two‐moment microphysics scheme. However, simulations performed with a single moment microphysical scheme exhibit low biases of approximately 20 dB. During convective events, two‐moment microphysical overestimate the amount of high‐level cloud and one‐moment microphysics precipitate too readily and underestimate the amount and height of high‐level cloud. For ARM9707, persistent large positive biases in high‐level cloud are found, which are not sensitive to changes in ice particle fall velocity and ice nuclei number concentration in the two‐moment microphysics. These biases are caused by biases in large‐scale forcing and maintained by the periodic lateral boundary conditions. The combined effects include significant biases in high‐level cloud amount, radiation, and high sensitivity of cloud amount to nudging time scale in both convective cases. The high sensitivity of high‐level cloud amount to the thermodynamic nudging time scale suggests that thermodynamic nudging can be a powerful “tuning” parameter for the simulated cloud and radiation but should be applied with caution. The role of the periodic lateral boundary conditions in reinforcing the biases in cloud and radiation suggests that reducing the uncertainty in the large‐scale forcing in high levels is important for similar convective cases and has far reaching implications for simulating high‐level clouds in super‐parameterized global climate models such as the multiscale modeling framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Quantifying the impacts of land surface schemes and dynamic vegetation on the model dependency of projected changes in surface energy and water budgets

Assessing and quantifying the uncertainties in projected future changes of energy and water budgets over land surface are important steps toward improving our confidence in climate change projections. In our study, the contribution of land surface models to the inter-GCM variation of projected future changes in land surface energy and water fluxes are assessed based on output from 19 global climate models (GCMs) and offline Community Land Model version 4 (CLM4) simulations driven by meteorological forcing from the 19 GCMs. Similar offline simulations using CLM4 with its dynamic vegetation submodel are also conducted to investigate how dynamic vegetation feedback, a process that is being added to more earth system models, may amplify or moderate the intermodel variations of projected future changes. Projected changes are quantified as the difference between the 2081–2100 period from the Representative Concentration Pathway 8.5 (RCP8.5) future experiment and the 1981–2000 period from the historical simulation. Under RCP8.5, projected changes in surface water and heat fluxes show a high degree of model dependency across the globe. Although precipitation is very likely to increase in the high latitudes of the Northern Hemisphere, a high degree of model-related uncertainty exists for evapotranspiration, soil water content, and surface runoff, suggesting discrepancy among land surface models (LSMs) in simulating the surface hydrological processes and snow-related processes. Large model-related uncertainties for the surface water budget also exist in the Tropics including southeastern South America and Central Africa. Moreover, these uncertainties would be reduced in the hypothetical scenario of a single near-perfect land surface model being used across all GCMs, suggesting the potential to reduce uncertainties through the use of more consistent approaches toward land surface model development. Under such a scenario, the most significant reduction is likely to be seen in the Northern Hemisphere high latitudes. Including representation of vegetation dynamics is expected to further amplify the model-related uncertainties in projected future changes in surface water and heat fluxes as well as soil moisture content. This is especially the case in the high latitudes of the Northern Hemisphere (e.g., northwestern North America and central North Asia) where the projected vegetation changes are uncertain and in the Tropics (e.g., the Amazon and Congo Basins) where dense vegetation exists. Finally, findings from this study highlight the importance of improving land surface model parameterizations related to soil and snow processes, as well as the importance of improving the accuracy of dynamic vegetation models.

58 GEOSCIENCES↗
DOE OSTI2015

The effect of large-scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM

The effect of global climate model (GCM) time step—which also controls how frequently global and embedded cloud resolving scales are coupled—is examined in the Superparameterized Community Atmosphere Model ver 3.0. Systematic bias reductions of time-mean shortwave cloud forcing (~10 W/m 2 ) and longwave cloud forcing (~5 W/m 2 ) occur as scale coupling frequency increases, but with systematically increasing rainfall variance and extremes throughout the tropics. An overarching change in the vertical structure of deep tropical convection, favoring more bottom-heavy deep convection as a global model time step is reduced may help orchestrate these responses. The weak temperature gradient approximation is more faithfully satisfied when a high scale coupling frequency (a short global model time step) is used. These findings are distinct from the global model time step sensitivities of conventionally parameterized GCMs and have implications for understanding emergent behaviors of multiscale deep convective organization in superparameterized GCMs. Lastly, the results may also be useful for helping to tune them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration

The sensitivities of simulated mesoscale convective systems (MCSs) in the central U.S. to microphysics and grid configuration are evaluated here in a global climate model (GCM) that also permits global-scale feedbacks and variability. Since conventional GCMs do not simulate MCSs, studying their sensitivities in a global framework useful for climate change simulations has not previously been possible. To date, MCS sensitivity experiments have relied on controlled cloud resolving model (CRM) studies with limited domains, which avoid internal variability and neglect feedbacks between local convection and larger-scale dynamics. However, recent work with superparameterized (SP) GCMs has shown that eastward propagating MCS-like events are captured when embedded CRMs replace convective parameterizations. This study uses a SP version of the Community Atmosphere Model version 5 (SP-CAM5) to evaluate MCS sensitivities, applying an objective empirical orthogonal function algorithm to identify MCS-like events, and harmonizing composite storms to account for seasonal and spatial heterogeneity. A five-summer control simulation is used to assess the magnitude of internal and interannual variability relative to 10 sensitivity experiments with varied CRM parameters, including ice fall speed, one-moment and two-moment microphysics, and grid spacing. MCS sensitivities were found to be subtle with respect to internal variability, and indicate that ensembles of over 100 storms may be necessary to detect robust differences in SP-GCMs. Furthermore, these results emphasize that the properties of MCSs can vary widely across individual events, and improving their representation in global simulations with significant internal variability may require comparison to long (multidecadal) time series of observed events rather than single season field campaigns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging

In floodplain environments, a naturally reduced zone (NRZ) is considered to be a common biogeochemical hot spot, having distinct microbial and geochemical characteristics. Although important for understanding their role in mediating floodplain biogeochemical processes, mapping the subsurface distribution of NRZs over the dimensions of a floodplain is challenging, as conventional wellbore data are typically spatially limited and the distribution of NRZs is heterogeneous. In this work, we present an innovative methodology for the probabilistic mapping of NRZs within a three-dimensional (3-D) subsurface domain using induced polarization imaging, which is a noninvasive geophysical technique. Measurements consist of surface geophysical surveys and drilling-recovered sediments at the U.S. Department of Energy field site near Rifle, CO (USA). Inversion of surface time domain-induced polarization (TDIP) data yielded 3-D images of the complex electrical resistivity, in terms of magnitude and phase, which are associated with mineral precipitation and other lithological properties. By extracting the TDIP data values colocated with wellbore lithological logs, we found that the NRZs have a different distribution of resistivity and polarization from the other aquifer sediments. To estimate the spatial distribution of NRZs, we developed a Bayesian hierarchical model to integrate the geophysical and wellbore data. In addition, the resistivity images were used to estimate hydrostratigraphic interfaces under the floodplain. Validation results showed that the integration of electrical imaging and wellbore data using a Bayesian hierarchical model was capable of mapping spatially heterogeneous interfaces and NRZ distributions thereby providing a minimally invasive means to parameterize a hydrobiogeochemical model of the floodplain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics

Large-scale land models assume unidirectional land-to-river hydrological interactions, without considering feedbacks between channels and land. Using a tested, physically based model with explicit multiway interactions between overland, channel, wetland, and groundwater flows, we assessed how the representation and properties of channels influence simulated land surface hydrologic, biogeochemical, and ecosystem dynamics. A zone near the channels where various fluxes and states are significantly influenced by the channels, referred to as the fan of influence (FoI) of channels, has been identified. We elucidated two mechanisms inducing the model-derived FoI: the base flow mechanism, in which incised, gaining streams lower the water table and induce more base flow, and the relatively more efficient conveyance of the channel network compared to overland flow. We systematically varied drainage density and grid resolution to quantify the size of the FoI, which is found to span a large fraction of the watershed (25-50%) for hydrologic variables including depth to water table and recharge, etc. The FoI is more pronounced with low-resolution simulations but remains noticeable in hyperresolution (25 m) subbasin simulations. The FoI and the channel influence on basin-average fluxes are also similar in simulations with alternative parameter sets. We found that high-order, entrenched streams cause larger FoI. In addition, removing the simulated channels has disproportionally large influence on modeled wetland areas and inundation duration, which has implications for coupled biogeochemical or ecological modeling. Our results suggest that explicit channel representation provides important feedbacks to land surface dynamics which should be considered in meso or large-scale simulations. Since grid refinement incurs prohibitive computational cost, subgrid channel parameterization has advantages in efficiency over grid-based representations that do not distinguish between overland flow and channel flow.

58 GEOSCIENCES↗