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Abstract The greatest challenges of rigorously modeling coupled hydro-mechanical processes in fractured rocks at different scales are associated with computational geometry. In addition, selections of continuous or discontinuous models, physical laws, and coupling priorities at different scales based on different geometric features determine the applicability of a numerical model for a certain type of problem. In this study, we present our multi-scale modeling capabilities that have been developed based on the numerical manifold method for analyzing coupled hydro-mechanical processes in fractured rocks. Based on their geometric features, the fractures are modeled as continua—finite-thickness porous zones, and discontinua—discontinuous interfaces and microscale asperities and granular systems. Different governing equations, physical laws, coupling priorities, and approaches for addressing fracture intersections and shearing are then applied to describe these. We applied these models to simulate coupled processes in fractured rocks using realistic geometry obtained from rock images at different scales. We first calculated shearing of a single fracture with different models and demonstrated the impacts of asperities on shearing. We then applied the continuous and discontinuous models to simulate a network of rough fractures, demonstrating that contact dynamics contribute significantly to the geometric, multi-physical evolution of systems where rough fractures are not mineral filled. For a discrete fracture network, our coupled processes modeling demonstrates that shearing of the discrete fractures can have a major impact on stress and pore pressure distribution. Lastly, we applied the discontinuous granular model to simulate evolution of a complex granular system with a deformation band, demonstrating that the deformation band can dominate contact dynamics, the structural and the stress evolution of the granular system.
Tritium processing facilities may release tritium oxide (HTO) to the atmosphere which poses potential health risks to exposed co-located workers and to offsite individuals. Most radiological consequence analyses determine HTO dose by applying Gaussian plume models to simulate the transport of HTO. Within these models, deposition velocity is used to assess the sum of all deposition processes acting on the plume. While this may account for vegetative and soil uptake or respiration processes, it may currently lack inclusion of the complex interactions within heterogeneous forested environments. In this complex morphology, dispersion patterns are significantly altered by changing flow regimes above and below the forest canopy and by the transfer of plume material across the canopy boundary. To determine the effects of a heterogeneous forest canopy on an airborne HTO plume, a Gaussian plume model coupled with an advection-diffusion plume model was applied to estimate transport in the free atmosphere above the forest and within the forest canopy and understory. During 2012, wind speed and wind direction measurements taken at 5 heights, ranging from 2-m to 28-m, on an instrumented meteorological tower located in a loblolly pine forest at the Department of Energy (DOE) Savannah River Site (SRS), near Aiken, SC. From these measurements, model predictions were made over a full spectrum of meteorological conditions. Deposition and resuspension velocities were calculated based on the model-predicted flux of plume material across the top of the forest canopy. Additionally, net deposition velocity of the plume material was calculated as the difference between the deposition and resuspension velocities. The 1st and 5th percentile net deposition velocities were estimated to be 0.7 cm s-1 and 1.2 cm s-1, respectively.
Abstract Tidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO 2 ) and methane (CH 4 ) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO 2 and CH 4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO 2 and CH 4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (R eco ) modules explained, on average, 73% of the variation in CO 2 exchange with low model error (normalized root mean square error (nRMSE) <1). The CH 4 module also explained the majority of variance in CH 4 emissions in validation sites ( R 2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on R eco , (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH 4 emissions, and (d) CH 4 responses to non‐periodic changes in salinity.
Abstract. The unprecedented use of Earth's resources by humans, in combination with increasing natural variability in natural processes over the past century, is affecting the evolution of the Earth system. To better understand natural processes and their potential future trajectories requires improved integration with and quantification of human processes. Similarly, to mitigate risk and facilitate socio-economic development requires a better understanding of how the natural system (e.g. climate variability and change, extreme weather events, and processes affecting soil fertility) affects human processes. Our understanding of these interactions and feedback between human and natural systems has been formalized through a variety of modelling approaches. However, a common conceptual framework or set of guidelines to model human–natural-system feedbacks is lacking. The presented research lays out a conceptual framework that includes representing model coupling configuration in combination with the frequency of interaction and coordination of communication between coupled models. Four different approaches used to couple representations of the human and natural system are presented in relation to this framework, which vary in the processes represented and in the scale of their application. From the development and experience associated with the four models of coupled human–natural systems, the following eight lessons were identified that if taken into account by future coupled human–natural-systems model developments may increase their success: (1) leverage the power of sensitivity analysis with models, (2) remember modelling is an iterative process, (3) create a common language, (4) make code open-access, (5) ensure consistency, (6) reconcile spatio-temporal mismatch, (7) construct homogeneous units, and (8) incorporating feedback increases non-linearity and variability. Following a discussion of feedbacks, a way forward to expedite model coupling and increase the longevity and interoperability of models is given, which suggests the use of a wrapper container software, a standardized applications programming interface (API), the incorporation of standard names, the mitigation of sunk costs by creating interfaces to multiple coupling frameworks, and the adoption of reproducible workflow environments to wire the pieces together.
Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUE Canopy ). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUE Canopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUE Canopy . The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.
A coupled physical/biological/optical model is developed for studies of phytoplankton variability in the spring 1979 Mid-Atlantic Bight, as observed by CZCS imagery. The model incorporates advection, mixing, sinking, growth as a function of light, temperature, nutrient availability, and death as a function of ingestion. It produced chlorophyll concentrations within the first attenuated depth within 1 standard deviation of CZCS imagery on large scale. The primary production estimates obtained using this model were within reasonable agreement with those measured in situ.
For the deep geological disposal of high-level radioactive waste in argillaceous rocks, the heat production of the waste is an important driver for thermal–hydraulic-mechanical (THM)-coupled processes. These THM processes influence the properties and conditions of the near field that in many repository designs contains bentonite as a clay buffer. One task in the DECOVALEX-2015 (DEvelopment of COupled models and their VALidation against Experiments) project was the modelling of a heated bentonite column (Villar et al. in Long-term THM tests reports: THM cells for the HE-E test: update of results until February 2014. Deliverable-no: D2.2-7.3. CIEMAT Technical Report IEMAT/DMA/2G210/03/2014, 2014) in preparation for the in situ heater experiment HE-E at the underground rock laboratory Mont Terri. DECOVALEX is an international cooperative project that focuses on the development and validation of mathematical models for simulating such coupled processes associated with disposal in deep geological repositories. Eight modelling teams developed their own THM-coupled models for the bentonite column experiment, using six different simulation codes. Each of the teams individually calibrated the THM parameters for the bentonite material. The eight resulting parameter sets agree well and allow a satisfactory reproduction of the TH measurements by all models. The modelling results for the evolution of temperature and relative humidity over time at three sensors in the bentonite column are in good agreement between the teams and with the measured data. Also, changes of the temperature due to modifications of the insulation and the adjustment of the heating power during the course of the experiment are well reproduced. The models were thus able to reproduce the main physical processes of the experiment, both for vapour-dominated diffusion during the heating phase and combined liquid and vapour transport during a subsequent heating and hydration phase. Lastly, based on the parameter sets, the teams predict a penetration of the water infiltration front in the 48-cm column filled with bentonite pellets to a depth between 25 and 35 cm over the 15,000 h (i.e. over 20 months) of the hydration phase of the experiment.
A SAM model for a 200 MWth generic pebble bed high-temperature gas-cooled reactor (PBHTGR) is presented in this work. As an extension to the modeling effort from FY-22 (Ooi et al. (2022)), an improved core channel approach is used to model the pebble bed of the reactor. The improvement greatly simplifies the model and reduces the run time of the model. Furthermore, the SAM model is coupled to the Griffin reactor physics model from Stewart et al. (2021) using the MOOSE MultiApp system where neutronics calculations are performed by the Griffin model and thermal hydraulics calculations by the SAM model. Favorable predictions are produced by the SAM/Griffin coupled model for steady-state normal operating condition. A load-following transient is also simulated with the coupled model where the neutronics and thermal hydraulics responses of the core are predicted correctly by the model. A protected Pressurized Loss of Forced Cooling (PLOFC) accident is simulated with the SAM model, in which the reactor power is determined using the decay heat curve, and therefore the Griffin model is not needed and thus decoupled. A simplified RCCS (Reactor Cavity Cooling System) loop is added to the model to remove decay heat from the core during the transient. Qualitatively, the prediction by the SAM model matches the expected behavior of a PB-HTGR during a PLOFC accident.
The Global Modeling and Assimilation Office (GMAO) plans to use our GEOS-5 atmosphere-ocean general circulation model (AOGCM) to explore issues associated with predictability on decadal time scales and to contribute to the decadal prediction project that is part ofCMIP5. The GEOS-5 AOGCM is comprised of the GEOS-5 AGCM with the Catchment Land Surface Model, coupled to GFDL's MOM, version 4. We have assimilation systems for both the atmosphere and ocean. For our climate prediction efforts, the atmosphere will be initialized from the GEOS-5 Modem Era Retrospective-analysis for Research and Applications (MERRA), available from 1979 to present at 112 resolution, and from 1948 to present at 2 resolution. The ocean assimilation is conducted within the coupled model framework, using the MERRA as a constraint for both the atmosphere and the ocean. The decadal prediction experiments will be conducted with a 1 atmosphere and a 112 ocean. Some initial results will be presented, focusing on initialization aspects of the GEOS-5 system.
Coupled climate models have a large number of input parameters that can affect output uncertainty. We conducted a sensitivity analysis of sea ice proper:es and Arc:c related climate variables to 5 parameters in the HiLAT climate model: air-ocean turbulent exchange parameter (C), conversion of water vapor to clouds (cldfrc_rhminl) and of ice crystals to snow (micro_mg_dcs), snow thermal conduc:vity (ksno), and maximum snow grain size (rsnw_mlt). We used an elementary effect (EE) approach to rank their importance for output uncertainty. EE is an extension of one-at-a-time sensitivity analyses, but it is more efficient in sampling multi-dimensional parameter spaces. We looked for emerging relationships among climate variables across the model ensemble, and used causal discovery algorithms to establish potential pathways for those relationships.
Models from Coupled Model Intercomparison Project Phase 6 produce too much precipitation over the Andes but too little over the Amazon or the Wet Andes-Dry Amazon (WADA) bias pattern. Unlike the conventional view that convection parameterization and land model deficiencies can contribute to Amazonian rainfall biases, we approach this long-standing biased model behavior through the lens of Andean convection. Using Community Earth System Model v1.1 and focusing on the wet season, our mechanism-denial experiments demonstrate that Andean convection notably reduces precipitation over the Amazon during austral summer. The Andean forced Amazonian response operates on weather timescale. Furthermore, the reduction of Amazonian rainfall is detectable within a few hours after initial Andean forcing. The precipitation response is primarily driven by variations in the moisture budget and is moderated by changes in convective available potential energy over the Amazon. Changes in the total advection of moisture over the Amazon are dominated by the vertical advection term and can be attributed to discrepancies in the dynamic omega field. In the experiments, the Andean east flank region is scrutinized where the vertical velocity and moisture fields play an intermediary role for the Andean driven WADA connection. The Andean forcing induces descending anomalies on the Andean east flank. The disturbances of wind and geopotential fields over the Andean east flank propagate eastward via Kelvin waves. Over the Amazon, descending anomalies and advective drying lead to reduction of mid-to-high level cloud, increase of shortwave cloud forcing and surface net radiation, and enhancement of themodynamic stability and rainfall reduction.
Sachdev-Ye-Kitaev (SYK) model, which describes N randomly interacting Majorana fermions in 0+1 dimension, is found to be an solvable UV-complete toy model for holographic duality in nearly AdS 2 dilaton gravity. Ref. [1] proposed a modified model by coupling two identical SYK models, which at low-energy limit is dual to a global AdS 2 geometry. This geometry is an “eternal wormhole" because the two boundaries are causally connected. Increasing the temperature drives a Hawking-Page like transition from the eternal wormhole geometry to two disconnected black holes with coupled matter field. To gain more understanding of the coupled SYK model, in this work, we study the finite temperature spectral function of this system by numerical solving the Schwinger-Dyson equation in real-time. We find in the low-temperature phase the system is well described by weakly interacting fermions with renormalized single-particle gap, while in the high temperature phase the system is strongly interacting and the single-particle peaks merge. We also study the q dependence of the spectral function.
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Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference datasets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2-12% and 57-75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.
Double-ITCZ has puzzled climate model community for more than two decades. Here, we found that over the eastern Pacific, precipitation and sea surface temperature (SST) biases in the NCAR CESM1 are seasonally-dependent, with positive (negative) precipitation/SST biases during boreal summer/fall (winter/spring), although the easterly wind bias persists around the year. This seasonally-dependent bias of precipitation/SST is found to be caused by the failure of the model to reproduce the climatological seasonal wind reversal of North American monsoon. During winter/spring, the climatological easterly wind dominates, so the stronger wind speed in the model enhances the evaporation and lowers the SST. It is opposite when the climatological wind turns to westerly during summer/fall. The easterly wind bias also occurs in the atmospheric model when the observation SST is prescribed, suggesting it is of atmospheric origin. Further, the easterly wind bias is mainly evident in the lower troposphere and quite independent of time, implying the effect of complex and narrow-ranged Central American topography, which is not depicted well in the coarse model resolution. When the model resolution is doubled, both SST and precipitation are improved with the reduced easterly wind bias. During boreal spring, when ITCZ bias is most significant, the northern and southern ITCZ can be improved by 29% and 18.8% respectively in the higher resolution. Finally, we suggest that these seasonally-dependent biases over the eastern Pacific are quite universal among 37 CMIP5 coupled models, and the ongoing CMIP6 project will provide a valuable opportunity to examine whether the bias can be reduced by increasing model resolution.