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At least 181 records · Page 10

Robust Predictive Control for Modular Solid-State Transformer With Reduced DC Link and Parameter Mismatch

This paper presents the analysis and implementation of a predictive control method for dc-link regulation and voltage balance in a cascaded modular reduced dc-link solid-state transformer (SST). Passive components like bulky dc links limit the power density of power converters, especially medium-voltage (MV) SST. Reduced dc-link or low-inertia converters can dramatically reduce the size, cost, and weight by tolerating larger dc-link ripples and improve the reliability with electrolytic capacitor-less dc link. However, a small dc link leads to tight coupling between the input and the output stages, which is a challenge for control design. In stacked low-inertia converters (SLIC), the low-inertia converter modules are stacked for MV applications, resulting in coupling between the modules and making the control more challenging. A new model predictive control method which can achieve deadbeat regulation on the dc link without weighting factors has been proposed to address this novel problem. This paper focuses on analyzing the condition of the low-inertia dc link up to 80% ripple, the robustness of the control under parameter mismatches, high-order terms, and important implementation issues such as model-based sampling and computation delay compensation. Significantly, the high-order terms are introduced because of the large dc-link ripple. These high-order terms are unique to the SLIC and negligible in conventional high-inertia converters. A discrete-time large-signal model is built to capture the dc-link’s nonlinear dynamics, and the eigenvalues of a small-signal Jacobian matrix are analyzed with Floquet theory to evaluate stability, using the modular soft-switching solid-state transformer (M-S4T) as an example of the SLIC. Simulation and experimental results of an MVDC M-S4T verify the analysis and the predictive control method. Finally, the general application of the predictive control to low-inertia converters is compared against a conventional PI controller using a reduced dc-link active-front-end (AFE) rectifier as an example.

14 SOLAR ENERGY↗

High-Penetration PV Deployment in the Arizona Public Service System, Phase 1 Update: Preprint

In an effort to better understand the impacts of high penetrations of photovoltaic generators on distribution systems, Arizona Public Service and its partners have begun work on a multi-year project to develop the tools and knowledge base needed to safely and reliably integrate high penetrations of utility- and residential-scale photovoltaics (PV). Building upon the APS Community Power Project --Flagstaff Pilot, this project will analyze the impact of PV on a representative feeder in northeast Flagstaff. To quantify and catalog the effects of the estimated 1.3 MW of PV that will be installed on the feeder (both smaller units at homes as well as large, centrally located systems), high-speed weather and electrical data acquisition systems and digital 'smart' meters are being designed andinstalled to facilitate monitoring and to build and validate comprehensive, high-resolution models of the distribution system. These models will be used to analyze the impacts of the PV on distribution circuit protection systems (including anti-islanding), predict voltage regulation and phase balance issues, and develop volt/var control schemes. This paper continues from a paper presented at the2011 IEEE PVSC conference that introduces the project and describes some of the preliminary consideration, as well as project plans and early results. This paper gives a status update of the project and presents selected results from Phase 2 of the project. It discusses baseline feeder modeling, load allocation, data acquisition, utility-scale PV integration, preliminary model validation, andplans for future phases.

14 SOLAR ENERGY↗

Stacked Low-Inertia Converter or Solid-State Transformer: Modeling and Model Predictive Priority-Shifting Control for Voltage Balance

This paper presents control challenges of stacked low-inertia converter (SLIC) or cascaded reduced dc-link solid-state transformer (SST) and proposes a novel model predictive priority-shifting (MPPS) control with implicit modulator and a discrete-time large-signal model for voltage balancing and dc-link regulation. Low-inertia converters, featuring small electrolytic capacitor-less dc links, dramatically reduce cost, size, and weight compared to conventional solutions. However, without a large dc-link buffer, the input and output are tightly coupled, leading to significant control challenges. The control becomes even more challenging with these converters stacked input-series output-parallel (ISOP) for medium-voltage (MV) grid, which causes coupling between the modules besides the coupling within each module. This paper analyzes the multi-objective, multi-degree of freedom control problem, using the modular soft-switching solid-state transformer (M-S4T) as an example of the SLIC. First, distribution of control efforts under controller saturation is critical because multiple control objectives can be conflicting, especially when the module voltages are unbalanced and are being restored. The MPPS can shift the priorities to address this issue. Second, due to the low inertia and high dc-link ripple, classic space vector pulse-width modulation (SVPWM), average model with small-ripple assumption, and control design based on small-signal model cannot accurately modulate, model, and control the nonlinear reduced dc link. Therefore, a discrete-time large-signal model of the M-S4T is established to derive the predictive control in the MPPS. The MPPS and the PI control are compared in MV simulations to show the issue of applying the PI to the SLIC and the effectiveness of the MPPS for voltage balancing and dc-link regulation in a deadbeat manner. Finally, the proposed control is tested on a 5 kV ISOP SiC SST prototype to verify priority shifting to address controller saturation issue and fast and robust voltage balancing.

14 SOLAR ENERGY↗

Criticality calculations and shielding analysis of the Missouri S and T research reactor using SCALE-6.2.2 - 25408

A 3D model of the Missouri S and T Research Reactor (MSTR) was developed using SCALE-6.2.2 in this study. The criticality calculation of MSTR was modeled with the CSAS6/KENO-VI sequence within the SCALE-6.2.2 package. The critical control rods height was predicted, and the criticality was achieved when the control rod withdrawn 22.25 in with the k{sub eff} value of 1.0001±0.00083. Then, the neutron source distribution of the critical core was used as a mesh source for the shielding analysis. The shielding analysis was performed using a flexible MAVRIC/FW-CADIS model of the MSTR that could perform and analyze deep penetration shielding problems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Dynamics of Electric Polarization and Relaxation of Ions at Humid Calcite Surfaces

Mobile ions at mineral surfaces can respond to an applied electric field, adopting a new distribution that effectively represents polarization of the electrical double layer. When the field is released, the ions relax to their equilibrium distribution. In both cases, the dynamics are characteristic of the interface. However, current models of electrokinetic phenomena are not sufficiently robust to accurately predict collective ion dynamics at structurally and chemically specific mineral–water interfaces. Here, in this study, we use electrostatic force microscopy (EFM) to investigate the dynamics of ion relaxation at hydrated calcite (104) surfaces at controlled relative humidity (RH). Electrically biased probes are used to polarize the distributions of calcium and carbonate ions that are intrinsic to this interface across a range of RH values. Polarization kinetics are tracked by monitoring the tip–sample force gradient during charging, and EFM imaging is used to characterize the spatial relaxation dynamics after the applied field is released. Electrostatic finite element modeling of the sample/probe system across length-scales from nanometers to millimeters reproduces the observed stretched exponential charging response. Together, these results allow us to estimate the ion diffusivities at the interface across a wide range of RH values. These diffusivities increase by roughly 5 orders of magnitude as the RH is increased from 5 to 90%, highlighting the critical role of adsorbed water for surface ion solvation that enables ion mobility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Control algorithms for dynamic windows for residential buildings

This study analyzes the influence of control algorithms for dynamic windows on energy consumption, number of hours of retracted shades during daylight and shade operations. Five different control algorithms - heating/cooling, simple rules, perfect citizen, heat flow and predictive weather were developed and compared. The performance of a typical residential building was modeled with EnergyPlus. The program Widow was used to generate a Bi-Directional Distribution Function (BSDF) for two window configurations. The BSDF was exported to EnergyPlus using the IDF file format. The EMS feature in EnergyPlus was used to develop custom control algorithms. The calculations were made for four locations with diverse climate. The results showed that: (a) use of automated shading with proposed control algorithms can reduce the site energy in the range of 11.6-13.0%; in regard to source (primary) energy in the range of 20.1-21.6%, (b) the differences between algorithms in regard to energy savings are not high, (c) the differences between algorithms in regard to number of hours of retracted shades are visible, (e) the control algorithms have a strong influence on shade operation and oscillation of shade can occur, (d) additional energy consumption caused by motor, sensors and a small microprocessor in the analyzed case is very small.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A dynamic model for plant growth: validation study under changing temperatures

A dynamic simulation model to describe vegetative growth of plants, for which some functions and parameter values have been estimated previously by optimization search techniques and numerical experimentation based on data from constant temperature experiments, is validated under conditions of changing temperatures. To test the predictive capacity of the model, dry matter accumulation in the leaves, stems, and roots of tobacco plants (Nicotiana tabacum L.) was measured at 2- or 3-day intervals during a 5-week period when temperatures in controlled-environment rooms were programmed for changes at weekly and daily intervals and in ascending or descending sequences within a range of 14 to 34 degrees C. Simulations of dry matter accumulation and distribution were carried out using the programmed changes for experimental temperatures and compared with the measured values. The agreement between measured and predicted values was close and indicates that the temperature-dependent functional forms derived from constant-temperature experiments are adequate for modelling plant growth responses to conditions of changing temperatures with switching intervals as short as 1 day.

NASA Discipline Number 60-40↗

Feature Profile Evolution of SiO2 Trenches In Fluorocarbon Plasmas

Etching of silicon microstructures for semiconductor manufacturing in chlorine plasmas has been well characterized. The etching proceeds in a two-part process, where the chlorine neutrals passivate the Si surface and then the ions etch away SiClx. However, etching in more complicated gas mixtures and materials, such as etching of SiO2 in Ar/C4F8, requires knowledge of the ion and neutral distribution functions as a function of angle and velocity, in addition to modeling the gas surface reactions. In order to address these needs, we have developed and integrated a suite of models to simulate the etching process from the plasma reactor level to the feature profile evolution level. This arrangement allows for a better understanding, control, and prediction of the influence of equipment level process parameters on feature profile evolution. We are currently using the HPEM (Hybrid Plasma Equipment Model) and PCMCM (Plasma Chemistry Monte Carlo Model) to generate plasma properties and ion and neutral distribution functions for argon/fluorocarbon discharges in a GEC Reference Cell. These quantities are then input to the feature scale model, Simulation of Profile Evolution by Level Sets (SPELS). A surface chemistry model is used to determine the interaction of the incoming species with the substrate material and simulate the evolution of the trench profile. The impact of change of gas pressure and inductive power on the relative flux of CFx and F to the wafer, the etch and polymerization rates, and feature profiles will be examined. Comparisons to experimental profiles will also be presented.

Hwang, Helen↗

The Earth System Prediction Suite: Toward a Coordinated U.S. Modeling Capability

The Earth System Prediction Suite (ESPS) is a collection of flagship U.S. weather and climate models and model components that are being instrumented to conform to interoperability conventions, documented to follow metadata standards, and made available either under open-source terms or to credentialed users. Furthermore, the ESPS represents a culmination of efforts to create a common Earth system model architecture, and the advent of increasingly coordinated model development activities in the United States. ESPS component interfaces are based on the Earth System Modeling Framework (ESMF), community-developed software for building and coupling models, and the National Unified Operational Prediction Capability (NUOPC) Layer, a set of ESMF-based component templates and interoperability conventions. Our shared infrastructure simplifies the process of model coupling by guaranteeing that components conform to a set of technical and semantic behaviors. The ESPS encourages distributed, multiagency development of coupled modeling systems; controlled experimentation and testing; and exploration of novel model configurations, such as those motivated by research involving managed and interactive ensembles. ESPS codes include the Navy Global Environmental Model (NAVGEM), the Hybrid Coordinate Ocean Model (HYCOM), and the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS); the NOAA Environmental Modeling System (NEMS) and the Modular Ocean Model (MOM); the Community Earth System Model (CESM); and the NASA ModelE climate model and the Goddard Earth Observing System Model, version 5 (GEOS-5), atmospheric general circulation model.

54 ENVIRONMENTAL SCIENCES↗

Phase Distribution Phenomena for Simulated Microgravity Conditions: Experimental Work

This report summarizes the work accomplished at Rensselaer to study phase distribution phenomenon under simulated microgravity conditions. Our group at Rensselaer has been able to develop sophisticated analytical models to predict phase distribution in two-phase flows under a variety of conditions. These models are based on physics and data obtained from carefully controlled experiments that are being conducted here. These experiments also serve to verify the models developed.

Singhal, Maneesh↗

Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization

We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operational cost and conditional value-at-risk (CVaR) of device and network constraint violations. These decisions include scheduled power output adjustments and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming the uncertainties across the networks follow prescribed probability distributions, we assume the distributions are only observable through a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real data-generating distribution, we formulate a distributionally robust optimization OPF problem to search for power schedules and reserve policies that are robust to sampling errors inherent in the dataset. A multi-stage closed-loop control strategy based on model predictive control (MPC) is also discussed. A simpIe numerical example illustrates inherent tradeoffs between operational cost and risk of constraint violation, and we show how our proposed method offers a data-driven framework to balance these objectives.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Challenges Facing Design and Analysis Tools

The design and analysis of future aerospace systems will strongly rely on advanced engineering analysis tools used in combination with risk mitigation procedures. The implications of such a trend place increased demands on these tools to assess off-nominal conditions, residual strength, damage propagation, and extreme loading conditions in order to understand and quantify these effects as they affect mission success. Advances in computer hardware such as CPU processing speed, memory, secondary storage, and visualization provide significant resources for the engineer to exploit in engineering design. The challenges facing design and analysis tools fall into three primary areas. The first area involves mechanics needs such as constitutive modeling, contact and penetration simulation, crack growth prediction, damage initiation and progression prediction, transient dynamics and deployment simulations, and solution algorithms. The second area involves computational needs such as fast, robust solvers, adaptivity for model and solution strategies, control processes for concurrent, distributed computing for uncertainty assessments, and immersive technology. Traditional finite element codes still require fast direct solvers which when coupled to current CPU power enables new insight as a result of high-fidelity modeling. The third area involves decision making by the analyst. This area involves the integration and interrogation of vast amounts of information - some global in character while local details are critical and often drive the design. The proposed presentation will describe and illustrate these areas using composite structures, energy-absorbing structures, and inflatable space structures. While certain engineering approximations within the finite element model may be adequate for global response prediction, they generally are inadequate in a design setting or when local response prediction is critical. Pitfalls to be avoided and trends for emerging analysis tools will be described.

Knight, Norman F., Jr.↗

Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm

This paper presents performance evaluation of a new distributed energy resource management system (DERMS) algorithm via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of sub-transmission and distribution networks, the DERMS software controller, and 84 power hardware solar photovoltaic (PV) inverters, standard communication protocols, and a capacitor bank controller. The DERMS algorithm is also called, Grid-Optimization of Solar (GO-Solar) platform which includes predictive state estimation (PSE) and online multiple objective optimization (OMOO) to dispatch the legacy devices and distributed energy resources (e.g., PV). The voltage regulation performance is evaluated under three scenarios, volt-var smart inverter (baseline), and DERMS control for 100% and 30% of PV. The results show that controlling 30% of PV systems with the GO-Solar platform may provide the best balance of control performance and implementation cost.

distributed energy resource management system (DER↗

Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control

This article proposes a deep learning (DL)-based control algorithm—DL velocity-based model predictive control (VMPC)—for reducing traffic congestion with slowly time-varying traffic signal controls. This control algorithm consists of system identification using DL and traffic signal control using VMPC. For the training process of DL, we established a modeling error entropy loss as the criteria inspired by the theory of stochastic distribution control (SDC) originated by the fourth author. Herein, simulation results show that the proposed algorithm can reduce traffic congestion with a slowly varying traffic signal control input. Results of an ablation study demonstrate that this algorithm compares favorably to other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.

97 MATHEMATICS AND COMPUTING↗

Transition Core Modeling for Extended Enrichment & Accident-Tolerant Fuels Using Polaris/PARCS

Commercial light water reactor (LWR) operators and fuel vendors are currently interested in increasing the low-enriched uranium (LEU) fuel enrichments from the current limit of 5.0 $^w/_o$ $^{235}U$ up to 10 $^ w/_o$ $^{235}U$ (referred to as "LEU+") in their current fleets; they are also interested in using accident-tolerant fuel (ATF) with both LEU and LEU+ fuel. This report aims to identify modeling challenges and accuracy concerns in transition core analysis using the SCALE Polaris lattice physics code and U.S. Nuclear Regulatory Commission core simulator PARCS. At the time this study was started, no publicly available LEU+ core designs existed for boiling water reactor (BWR) or pressurized water reactor (PWR) systems. Therefore, fuel lattices were shuffled within a multi-assembly model to mimic neutronically challenging lattice combinations seen in transition cores, such as a fresh LEU+ lattice next to depleted LEU lattices. In addition to multi-assembly models, whole-core BWR transition core calculations were performed for ATF and LEU+ fuel using an existing Hatch-1 Cycle 3 core model. A whole-core BWR model was chosen due to the more heterogeneous core designs compared to those for a PWR core. Since the original core is an old checkerboard core design and no core or fuel design optimization was performed for the modeled fuel types, these core calculations were intended only to provide: (1) Comparisons of core characteristics of interest, such as the pin power distributions and peaking factors, Doppler temperature coefficients (DTCs), and control blade worths (CBWs) under challenging core designs, (2) Identification of reactor physics challenges in modeling LEU+ and ATF cores, and (3) A stress test for the Polaris/PARCS two-step modeling approach, including characterization of the relative accuracy for predicting characteristics of interest such as pin power distributions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Catalysis Science Initiative: Catalyst Design by Discovery Informatics

Catalysts selectively enhance the rates of chemical reactions toward desired products. Such reactions provide great benefit to society in major commercial sectors such as energy production, protecting the environment, and polymer products and thereby contribute heavily to the country’s gross national product. Our premise is that the level of fundamental understanding of catalytic events at the atomic and molecular scale has reached the point that more predictive methods can be developed to shorten the cycle time to new processes. The field of catalysis can be divided into two regimes: heterogeneous and homogeneous. For the heterogeneous catalysis regime, we have used the water-gas shift (WGS) reaction (CO + H2O + CO2 + H2O) over supported metals as a test bed. Detailed analysis and strong coupling of theory with experiment have led to the following conclusions: • The sequence of elementary steps goes through a COOH intermediate • The CO binding energy is a strong function of coverage of CO adsorbed on the surface in many systems • In the case of Au catalysts, the CO adsorption is generally too weak on surface with close atomic packing, but the enhanced binding at corner atoms (which are missing bonding partners) of cubo-octahedral nanoparticles increases the energy to a near optimal value and produces very active catalysts. • Reaction on the metal alone cannot account for the experimental results. The reaction is dual functional with water activation occurring at the metal-support interface. It is clear from our work that the theory component is essential, not only for prediction of new systems, but also for reconciling data and testing hypotheses regarding potential descriptors. Particularly important is the finding that the interface between nano-sized metal particles and the oxides that are used to support them represent a new state of matter in the sense that the interfacial bonding perturbs the chemical state of both metals atoms and the support atoms in the interfacial region. Some of the first theoretical descriptions of this important chemistry and potential new source of control of catalyst properties are be in preparation for submission. On the homogeneous catalysis side, we have used single site olefin polymerization as the testbed. This system is important because changes in a single ligand bonded to the catalytically active metal site can alter the rates of individual steps in the polymerization sequence and thereby change the properties of the resulting polymer, potentially improving its value in a hundred million pound per year industry. We have made a major advance in understanding such systems by developing a population balance kinetic model that allows us to predict the molecular weight distribution (MWD) of the product. That, in turn, allows use of MWD data to fit kinetic parameters. By combining monomer loss data, MWD, measurement of the number of working active sites, and polymer end group analysis, we have a rich data set that is highly discriminating of kinetic mechanism. Thus, we have a robust tool for producing high quality, detailed kinetic parameters, which we have used to refine mechanisms presented in the literature and discover relationships between steric and electronic properties of group IV catalysts and individual rate constants in a number of systems. Our recent work on six-coordinate Zr, Ti, and Hf amine bis(phenolate) systems, we have shown that: • The sterics (bulkiness) of the ligands specifically affect the chain termination reaction • The electron density on the metal controls misinsertion (flipped orientation) of the olefin into the growing polymer • Steric effects related to the size of the ortho ligand on the catalyst have been shown to strongly affect its the degree of dormancy, i.e. tendency to stop reacting • Changes in the size of the amine pendent group on the catalyst can have such a strong effect on chain termination as to change the catalyst from one that produces only oligomers to one that incorporates oligomers in polymer chains to introduce chain branching. These effects control the length of molecular chains, the number of errors in the regularity of the chains, and the variation of chain lengths in the final product distribution. These characteristics, in turn, determine the properties of the resulting polymer material. Predictive modeling of polymerization process cannot be done without the quantitative rate constant determination that our unique and comprehensive approach has produced. This tool, combined with insights on how catalyst chemistry affects the rate constants, is an important step toward establishing the quantitative relationships between catalyst chemistry and polymer properties that will allow more efficient searches for new catalysts and speed the discovery process. Thus, in both the heterogeneous and homogeneous catalysis areas, this work has brought important new understanding to the field. The technical effectiveness of our approach is demonstrated by its success. For heterogeneous catalysis, experimental work alone showed the important of the metal-support interface, but the integration with theory has allowed deeper understanding of the fundamental source of the effects and shown the path for how to use this understanding for discovery. In the case of homogeneous catalysis, methodology for determination of which reactions are needed to account for the data and the extraction of quantitative values for rate constants for those reactions allows accurate modeling of reaction behavior. This allowed correlation of trends in catalyst properties with alterations of specific rates, thus contributing to a data base that will guide catalyst discovery. While not yet realized, the economic benefits of this approach will come in significantly shortened cycle times for discovery and ability to fill product demand at lower price.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Spatiotemporal Patterns of Evapotranspiration in Response to Multiple Environmental Factors Simulated by the Community Land Model

Spatiotemporal patterns of evapotranspiration (ET) over the period from 1982 to 2008 are investigated and attributed to multiple environmental factors using the Community Land Model version 4 (CLM4). Our results show that CLM4 captures the spatial distribution and interannual variability of ET well when compared to observation-based estimates. We find that climate dominates the predicted variability in ET. Elevated atmospheric CO2 concentration also plays an important role in modulating the trend of predicted ET over most land areas, and replaces climate to function as the dominant factor controlling ET changes over the North America, South America and Asia regions. Compared to the effect of climate and CO2 concentration, the roles of other factors such as nitrogen deposition, land use change and aerosol deposition are less pronounced and regionally dependent. The aerosol deposition contribution is the third most important factor for trends of ET over Europe, while it has the smallest impact over other regions. As ET is a dominant component of the terrestrial water cycle, our results suggest that environmental factors like elevated CO2, nitrogen and aerosol depositions, and land use change, in addition to climate, could have significant impact on future projections of water resources and water cycle dynamics at global and regional scales.

community↗

Sensitivity evaluation of the Kudryavtsev permafrost model

Modeling is an important way to assess current and future permafrost spatial distribution and dynamics, especially in data poor areas like the Arctic region. Here, we evaluate a physics-based analytical model, Kudryavtsev's active layer model, which is widely used because it has relatively few data requirements. This model was recently incorporated into a component modeling toolbox, allowing for coupled modeling of permafrost and geomorphic processes over geological timescales. However, systematic quantitative assessment of the influence of its controlling parameters on permafrost temperature and active layer thickness predictions has not been undertaken before. In this work, we investigate the sensitivity of the Kudryavtsev's active layer model by Monte Carlo simulations to generate probability distributions for input parameters and compare predictions with a comprehensive benchmark dataset of in-situ permafrost observations over entire Alaska. Predicted permafrost surface temperature is highly dependent on mean annual air temperature (r = 0.78 on average), annual temperature amplitude (–0.41), and winter-averaged snow thickness (0.30). Uncertainty of predicted permafrost temperature is relatively small (RMSE = 1 °C), when air temperature and snow depth are well constrained. Similarly, RMSE between simulated and observed ALT at stations is ~0.08 m. However, under given air temperature and snow conditions, soil water content bias can significantly affect modeled active layer thickness (RMSE = 0.1 m or 40% of the observed active layer thickness). If soil water content has a large bias, improvements in other parameters may not significantly improve the active layer predictions of the Kudryavtsev's model.

54 ENVIRONMENTAL SCIENCES↗