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DOE OSTI2024

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling

Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The data is published via PNNL DataHub.

Li, Lingcheng↗

DOE OSTI2026

VIC-Global Parameter Dataset Sensitivity with the Variable Infiltration Capacity Model: Evaluating the importance of dynamic land surface parameters when using the VIC-Global parameter dataset

Accurate prediction of runoff is essential to water resources management, flood risk assessment, and ecosystem protection. However, many hydrological models still have relatively substantial limitations when representing the influence of land use and land cover (LULC) on runoff generation and routing. Changes in LULC, such as deforestation, urban expansion, agricultural intensification, and wetland loss, have been shown to alter the water balance at the land surface through fundamental hydrologic processes (e.g., interception, infiltration, evapotranspiration, and soil storage). However, it remains an open question what the exact magnitude and timing of these impacts are for the spatial and temporal scales commonly used in engineering applications. In this analysis we focus on one aspect of recent LULC change for assessing human impacts, which is urbanization. Specifically we seek to determine the impacts of urbanization on the magnitude and timing of surface runoff and baseflow in HUC-12 basins in Clark County, Nevada which has experienced rapid urbanization. We use the Variable Infiltration Capacity (VIC) hydrology model with a widely used off-the-shelf dataset of land surface parameters, VIC-Global, both of which have been commonly used in the past for water and energy balance modeling for large scale hydrologic studies. We examine two scenarios where the first scenario removes all urbanized land cover and parameterizes those areas of the basins as barren or open shrubland. The second scenario tests the opposite case where all areas of the basins are classified as urban regardless of their present classification. The results from the VIC model show there is a low sensitivity for daily surface runoff between scenarios. The daily baseflow values indicate similar low sensitivity to the classification change during specific periods, but then have substantial differences during other period when large precipitation events are occurring. This is likely due to the assumed parameter values for the urban land cover classification made by the VIC-Global dataset. Using a static land cover parameterization is reasonable for large domain hydrology models that are being used for near-term planning horizons (<30 years). However, longer planning horizons where feedbacks between the atmosphere and land surface are important, especially in transient climate situations, considerations for how to update land surface parameters should be incorporated.

42 ENGINEERING↗

DOE OSTI2019

Identification of Critical Surface Parameters Driving Lectin-Mediated Capture of Bacteria from Solution

Lectin-functional interfaces are useful for isolation of bacteria from solution because they are low-cost and allow nondestructive, reversible capture. This study provides a systematic investigation of physical and chemical surface parameters that influence bacteria capture over lectin-functionalized polymer interfaces and then applies these findings to construct surfaces with significantly enhanced bacteria capture. The designer block copolymer poly(glycidyl methacrylate)-block-poly(vinyldimethyl azlactone) was used as a lectin attachment layer, and lectin coupling into the polymer film through azlactone–lectin coupling reactions was first characterized. Here, experimental parameters including polymer areal chain density, lectin molecular weight, and lectin coupling buffer were systematically varied to identify parameters driving highest azlactone conversions and corresponding lectin surface densities. To introduce physical nanostructures into the attachment layer, nanopillar arrays (NPAs) of varied heights (300 and 2100 nm) were then used to provide an underlying surface template for the functional polymer layer. Capture of Escherichia coli on lectin–polymer surfaces coated over both flat and NPA surfaces was then investigated. For flat polymer interfaces, bacteria were detected on the surface after incubation at a solution concentration of 10 3 cfu/mL, and a corresponding detection limit of 1.7 × 10 3 cfu/mL was quantified. This detection limit was 1 order of magnitude lower than control lectin surfaces functionalized with standard, carbodiimide coupling chemistry. NPA surfaces containing 300 nm tall pillars further improved the detection limit to 2.1 × 10 2 cfu/mL, but also reduced the viability of captured cells. Finally, to investigate the impact of cell surface parameters on capture, we used Agrobacterium tumefaciens cells genetically modified to allow manipulation of exopolysaccharide adhesin production levels. Statistical analysis of surface capture levels revealed that lectin surface density was the primary factor driving capture, as opposed to exopolysaccharide adhesin expression. These findings emphasize the critical importance of the synthetic interface and the development of surfaces that combine high lectin densities with tailored physical features to drive high levels of capture. These insights will aid in design of biofunctional interfaces with physicochemical surface properties favorable for capture and isolation of bacteria cells from solutions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

On the fine structure of spectra of the inelastic-electron-scattering cross section and the Si surface parameter

Reflection electron-energy loss spectra are obtained for a series of Si samples with different crystallographic orientations, prepared under different technological conditions. Using the experimental spectra, the electron energy loss dependences of the product of the mean inelastic free path and differential inelastic electron scattering cross section are calculated. A new technique is suggested for analyzing the spectra of inelastic electron scattering cross section by simulating experimental spectra with the use of the three-parameter Tougaard universal cross section functions. The results of the simulation are used to determine the nature of loss peaks and to calculate the surface parameter.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate–carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model–data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. Furthermore, the new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

58 GEOSCIENCES↗

DOE OSTI2022

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

58 GEOSCIENCES↗

DOE OSTI2019

Effects of Ultrasonic Surface Rolling Parameters on Surface Integrity of TC17 Alloy

The main purpose of this paper was to investigate the effects of different static forces and feed speeds of ultrasonic surface rolling (USR) on the surface integrity of TC17 alloy. Surface roughness, in-depth residual stress, in-depth microhardness, and cross-sectional microstructure of the processed materials were measured and analyzed to get a comprehensive knowledge of the surface characteristics under different conditions. The results indicate that the specimens treated by USR achieve a considerable improvement in surface roughness (R{sub a} 0.04-0.10 μm); meanwhile, the surface morphology is smooth and uniform. Moreover, the surface residual stress and microhardness are greatly improved compared to those of untreated specimen. The maximum compressive residual stress is approximately − 966 MPa at the depth of 300 μm, and the thickness of strengthened layer is about 1000 μm. The material of affected layer experiences plastic deformation in a certain extent. Moreover, the gradient change in microstructure and white layer are also observed on the subsurface layer. It is also concluded from the study that static force has a more significant impact on surface integrity characteristics compared with feed speed.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Large-eddy simulations of surface roughness parameter sensitivity to canopy-structure characteristics

Surface roughness parameters, namely the roughness length and displacement height, are an integral input used to model surface fluxes. However, most models assume these parameters to be a fixed property of plant functional type and disregard the governing structural heterogeneity and dynamics. In this study, we use large-eddy simulations to explore, in silico, the effects of canopy-structure characteristics on surface roughness parameters. We performed a virtual experiment to test the sensitivity of resolved surface roughness to four axes of canopy structure: (1) leaf area index, (2) the vertical profile of leaf density, (3) canopy height, and (4) canopy gap fraction. We found roughness parameters to be highly variable, but uncovered positive relationships between displacement height and maximum canopy height, aerodynamic canopy height and maximum canopy height and leaf area index, and eddy-penetration depth and gap fraction. We also found negative relationships between aerodynamic canopy height and gap fraction, as well as between eddy-penetration depth and maximum canopy height and leaf area index. We generalized our model results into a virtual "biometric" parameterization that relates roughness length and displacement height to canopy height, leaf area index, and gap fraction. Using a decade of wind and canopy-structure observations in a site in Michigan, we tested the effectiveness of our model-driven biometric parameterization approach in predicting the friction velocity over heterogeneous and disturbed canopies. We compared the accuracy of these predictions with the friction-velocity predictions obtained from the common simple approximation related to canopy height, the values calculated with large-eddy simulations of the explicit canopy structure as measured by airborne and ground-based lidar, two other parameterization approaches that utilize varying canopy-structure inputs, and the annual and decadal means of the surface roughness parameters at the site from meteorological observations. We found that the classical representation of constant roughness parameters (in space and time) as a fraction of canopy height performed relatively well. Nonetheless, of the approaches we tested, most of the empirical approaches that incorporate seasonal and interannual variation of roughness length and displacement height as a function of the dynamics of canopy structure produced more precise and less biased estimates for friction velocity than models with temporally invariable parameters.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

The systematic study of N/Z dependence on surface diffuseness parameter in the fusion of heavy neutron-rich colliding nuclei

Surface diffuseness parameter used in Woods-Saxon form of potential have been extracted from a large number of experimentally studied neutron-rich fusion cross sections at near barrier energies. The results of our systematic study reveals that the extracted diffuseness parameter depend linearly on the N/Z ratio of the fusing nuclei. Further, we demonstrated that the extracted values of surface diffuseness parameter lies within the range a = 0.40 to 0.77 fm as compared to commonly accepted value form scattering i.e. 0.63 fm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2013

Inverse Modeling of Hydrologic Parameters Using Surface Flux and Runoff Observations in the Community Land Model

This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Two inversion strategies, the deterministic least-square fitting and stochastic Markov-Chain Monte-Carlo (MCMC) - Bayesian inversion approaches, are evaluated by applying them to CLM4 at selected sites. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the least-square fitting provides little improvements in the model simulations but the sampling-based stochastic inversion approaches are consistent - as more information comes in, the predictive intervals of the calibrated parameters become narrower and the misfits between the calculated and observed responses decrease. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to the different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

inverse, modeling, hydrologic, parameters, flux, r↗

DOE OSTI2020

Mechanical and chemical properties of PVD and cold spray Cr-coatings on Zircaloy-4

In this paper, accident-tolerant fuel (ATF) cladding materials aim to improve fuel reliability and safety during accident scenarios in water-cooled reactors. These ATF cladding should also perform comparable or better than the current zirconium alloy cladding under reactor normal operating conditions. Surface-modified Zircaloy-4 was produced by depositing a protective coating of chromium by two different coating techniques, Physical Vapor Deposition (PVD) and Cold Spray (CS). The coating thickness, structure, composition, and mechanical properties of the coated Zircaloy were assessed with advanced materials characterization techniques. Results showed a strengthening effect of the Cr-coating, along with ~83% higher hardness compared to that of the Zircaloy-4 substrate. Surface topography and surface chemistry of the Cr-coated Zircaloy-4 were assessed with contact profilometry, atomic force microscopy, contact angle goniometry, and X-ray photoelectron spectroscopy. Deposited Cr-coating significantly improved surface wettability compared to the substrate Zircaloy-4. The formation of surface oxides on the coatings by prolonged exposure to ambient air showed a further increase in the hydrophobic behavior. Surface parameters and surface chemistry after flow boiling critical heat flux tests, conducted at atmospheric pressure, evidenced a slight increase in surface roughness with notable changes in wettability associated with the formation of surface oxides.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Pilgrim Hot Springs: GEOPHIRES Inputs and Outputs for Direct-Use Geothermal District Heating and Cooling

This dataset includes files for a techno-economic analysis conducted using the GEOPHIRES simulator to examine the feasibility of expanding a larger district heating site in a remote location: Pilgrim Hot Springs, Alaska. Files included here are GEOPHIRES inputs and outputs for five different scenarios with varying demand, cycle, and system design characteristics to analyze. Also included is the link to the GEOPHIRES GitHub, as well as a link to the dataset that contains the energy modelling used to determine the heating demand for the district. For a list of the differences between scenarios, see the included "Input Overview.txt" file. Fields included in the input files are: subsurface technical parameters, surface technical parameters, financial parameters, capital and O&M parameters, as well as simulation parameters. The output files are case reports that summarize all equipment, reservoir characteristics, costs, and heating profiles.

15 GEOTHERMAL ENERGY↗

DOE OSTI2016

Modulation of surface meteorological parameters by extratropical planetary-scale Rossby waves

This study examines the link between upper-tropospheric planetary-scale Rossby waves and surface meteorological parameters based on the observations made in association with the Ganges Valley Aerosol Experiment (GVAX) campaign at an extratropical site at Aryabhatta Research Institute of Observational Sciences, Nainital (29.45° N, 79.5° E) during November–December 2011. The spectral analysis of the tropospheric wind field from radiosonde measurements indicates a predominance power of around 8 days in the upper troposphere during the observational period. An analysis of the 200 hPa meridional wind (v200 hPa) anomalies from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis shows distinct Rossby-wave-like structures over a high-altitude site in the central Himalayan region. Furthermore, the spectral analysis of global v200 hPa anomalies indicates the Rossby waves are characterized by zonal wave number 6. The amplification of the Rossby wave packets over the site leads to persistent subtropical jet stream (STJ) patterns, which further affects the surface weather conditions. The propagating Rossby waves in the upper troposphere along with the undulations in the STJ create convergence and divergence regions in the mid-troposphere. Therefore, the surface meteorological parameters such as the relative humidity, wind speeds, and temperature are synchronized with the phase of the propagating Rossby waves. Moreover, the present study finds important implications for medium-range forecasting through the upper-level Rossby waves over the study region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Computing the shape gradient of stellarator coil complexity with respect to the plasma boundary

Coil complexity is a critical consideration in stellarator design. The traditional two-step optimization approach, in which the plasma boundary is optimized for physics properties and the coils are subsequently optimized to be consistent with this boundary, can result in plasma shapes which cannot be produced with sufficiently simple coils. To address this challenge, we propose a method to incorporate considerations of coil complexity in the optimization of the plasma boundary. Coil complexity metrics are computed from the current potential solution obtained with the REGCOIL code (Landreman, Nucl. Fusion , vol. 57, 2017, 046003). While such metrics have previously been included in derivative-free fixed-boundary optimization (Drevlak et al. , Nucl. Fusion , vol. 59, 2018, 016010), we compute the local sensitivity of these metrics with respect to perturbations of the plasma boundary using the shape gradient (Landreman & Paul, Nucl. Fusion , vol. 58, 2018, 076023). We extend REGCOIL to compute derivatives of these metrics with respect to parameters describing the plasma boundary. In keeping with previous research on winding surface optimization (Paul et al. , Nucl. Fusion , vol. 58, 2018, 076015), the shape derivatives are computed with a discrete adjoint method. In contrast with the previous work, derivatives are computed with respect to the plasma surface parameters rather than the winding surface parameters. To further reduce the resolution required to compute the shape gradient, we present a more efficient representation of the plasma surface which uses a single Fourier series to describe the radial distance from a coordinate axis and a spectrally condensed poloidal angle. This representation is advantageous over the standard cylindrical representation used in the VMEC code (Hirshman & Whitson, Phys. Fluids , vol. 26, 1983, pp. 3553–3568), as it provides a uniquely defined poloidal angle, eliminating a null space in the optimization of the plasma surface. In comparison with previous spectral condensation methods (Hirshman & Breslau, Phys. Plasmas , vol. 5, 1998, p. 2664), the modified poloidal angle is obtained algebraically rather than through the solution of a nonlinear optimization problem. The resulting shape gradient highlights features of the plasma boundary that are consistent with simple coils and can be used to couple coil and fixed-boundary optimization.

Physics↗

DOE OSTI2018

Capturing the variable reactivity of goethites in surface complexation modeling by correlating model parameters with specific surface area

The development of predictive tools capable of simulating adsorption behavior on goethite (α-FeOOH) surfaces has evolved over the past several decades. Recent research has focused on capturing the variation of surface reactivity and adsorption behavior for different preparations of synthetic goethites. Here in this research, an approach to estimate modeling parameters based on the specific surface area (SSA) of goethites was established and combined with a Charge Distribution – Multi-site Complexation (CD-MUSIC) surface complexation model. It was assumed that differences in crystal face contributions (CFC), inner-Helmholtz capacitances (C 1 ), and protonation constants of surface oxygens (pK a ’s) among different preparations of goethite contribute to the variation of surface reactivity. Thus, correlations of these modeling parameters with the SSA of a range of goethites were identified by comprehensively combining microscopic, macroscopic, and modeling data reported in literature along with adsorption data collected from macroscopic experiments conducted as part of this study. A CD-MUSIC model shown to be capable of accurately predicting adsorption for several metal ions and oxyanion selenite on a 62.9 m 2 /g SSA goethite served as the basis for the development of the adsorption modeling approach described in this paper. The predictive capability of the newly developed modeling approach was verified by comparing predictions of protons, SeO 3 2- , and Cd 2+ adsorption with experimental data from goethites with SSAs ranging between 21.3 and 105 m 2 /g; in total, 40 adsorption data sets from 13 different goethites were used for model verification. The results from this study indicate that the variation of surface reactivity of goethites can be successfully simulated by changing CFC, C 1 , and pK 2,singly (i.e. second protonation constant of singly-coordinated oxygens on the goethite surface) based on the SSA, which implies that self-consistent adsorption parameters can be applied to predict adsorption behavior over a range of goethite preparations.

58 GEOSCIENCES↗

DOE OSTI2020

Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon

Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns to the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.

Wang, Chen↗

DOE OSTI2018

Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)

Land surface models (LSMs), which form the land component of earth system models, rely on numerous processes for describing carbon, water and energy budgets, often associated with highly uncertain parameters. Data assimilation (DA) is a useful approach for optimising the most critical parameters in order to improve model accuracy and refine future climate predictions. In this study, we compare two different DA methods for optimising the parameters of seven plant functional types (PFTs) of the ORCHIDEE LSM using daily averaged eddy-covariance observations of net ecosystem exchange and latent heat flux at 78 sites across the globe. We perform a technical investigation of two classes of minimisation methods – local gradient-based (the L-BFGS-B algorithm, limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints) and global random search (the genetic algorithm) – by evaluating their relative performance in terms of the model–data fit and the difference in retrieved parameter values. We examine the performance of each method for two cases: when optimising parameters at each site independently (“single-site” approach) and when simultaneously optimising the model at all sites for a given PFT using a common set of parameters (“multi-site” approach). We find that for the single site case the random search algorithm results in lower values of the cost function (i.e. lower model–data root mean square differences) than the gradient-based method; the difference between the two methods is smaller for the multi-site optimisation due to a smoothing of the cost function shape with a greater number of observations. The spread of the cost function, when performing the same tests with 16 random first-guess parameters, is much larger with the gradient-based method, due to the higher likelihood of being trapped in local minima. When using pseudo-observation tests, the genetic algorithm results in a closer approximation of the true posterior parameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challenges of different DA techniques and provide some advice on using it for the LSM parameter optimisation.

58 GEOSCIENCES↗