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NASA NTRS2021

Measurements of Rainfall Rate, Drop Size Distribution, and Variability at Middle and Higher Latitudes: Application to the Combined DPR-GMI Algorithm

The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth’s global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager which provide measurements to retrieve the drop size distribution (DSD) and rain rates using a Combined Radar-Radiometer Algorithm (CORRA). Our objective is to validate key assumptions and parameterizations in CORRA and enable improved estimation of precipitation products, especially in the middle-to-higher latitudes in both hemispheres. The DSD parameters and statistical relationships between DSD parameters and radar measurements are a central part of the rainfall retrieval algorithm, which is complicated by regimes where DSD measurements are abysmally sparse (over the open ocean). In view of this, we have assembled optical disdrometer datasets gathered by research vessels, ground stations, and aircrafts to simulate radar observables and validate the scattering lookup tables used in CORRA. The joint use of all DSD datasets spans a large range of drop concentrations and characteristic drop diameters. The scaling normalization of DSDs defines an intercept parameter N(W), which normalizes the concentrations, and a scaling diameter D(m), which compresses or stretches the diameter coordinate axis. A major finding of this study is that a single relationship between N(W) and D(m), on average, unifies all datasets included, from stratocumulus to heavier rainfall regimes. A comparison with the N(W)–D(m) relation used as a constraint in versions 6 and 7 of CORRA highlights the scope for improvement of rainfall retrievals for small drops (D(m) < 1 mm) and large drops (D(m) > 2 mm). The normalized specific attenuation–reflectivity relationships used in the combined algorithm are also found to match well the equivalent relationships derived using DSDs from the three datasets, suggesting that the currently assumed lookup tables are not a major source of uncertainty in the combined algorithm rainfall estimates.

Viswanathan Bringi↗

DOE OSTI2012

The impact of the El Niño-Southern Oscillation on maximum temperature extremes

The impact of the El Niño-Southern Oscillation (ENSO) on temperature extremes is examined in both observations and coupled climate model simulations. HadEX2, a newly developed observed gridded dataset of climate extremes indices shows marked contrasts in seasonal composites of the monthly maximum value of daily maximum temperature during the cold and warm phases of ENSO. Extreme maximum temperatures are significantly cooler over Australia, southern Asia, Canada and South Africa during strong La Niña events compared to El Niño events and significantly warmer over the contiguous United States and southern South America. Two climate models are contrasted for their ability to capture these relationships given their very different simulations of ENSO. While both models capture some aspects of the observed patterns, the fidelity of the ENSO simulation appears to be crucial for simulating the magnitude and sign of the extreme maximum temperature relationships. The impact of future climate change on these patterns is also investigated.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Characterizing the Vertical Profile of Aerosol Particle Extinction and Linear Depolarization over Southeast Asia and the Maritime Continent: The 2007-2009 View from CALIOP

Vertical profiles of 0.532 μm aerosol particle extinction coefficient and linear volume depolarization ratio are described for Southeast Asia and the Maritime Continent. Quality-screened and cloud-cleared Version 3.01 Level 2 NASA Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) 5-km Aerosol Profile datasets are analyzed from 2007 to 2009. Numerical simulations from the U.S. Naval Aerosol Analysis and Predictive System (NAAPS), featuring two-dimensional variational assimilation of NASA Moderate Resolution Imaging Spectroradiometer and Multi-angle Imaging Spectro- Radiometer quality-assured datasets, combined with regional ground-based lidar measurements, are considered for assessing CALIOP retrieval performance, identifying bias, and evaluating regional representativeness. CALIOP retrievals of aerosol particle extinction coefficient and aerosol optical depth (AOD) are high over land and low over open waters relative to NAAPS (0.412/0.312 over land for all data points inclusive, 0.310/0.235 when the per bin average is used and each is treated as single data points; 0.102/0.151 and 0.086/0.124, respectively, over ocean). Regional means, however, are very similar (0.180/0.193 for all data points and 0.155/0.159 when averaged per normalized bin), as the two factors offset one another. The land/ocean offset is investigated, and discrepancies attributed to interpretation of particle composition and a-priori assignment of the extinction-to-backscatter ratio ("lidar ratio") necessary for retrieving the extinction coefficient from CALIOP signals. Over land, NAAPS indicates more dust present than CALIOP algorithms are identifying, indicating a likely assignment of a higher lidar ratio representative of more absorptive particles. NAAPS resolvesmore smoke overwater than identified with CALIOP, indicating likely usage of a lidar ratio characteristic of less absorptive particles to be applied that biases low AOD there. Over open waters except within the Bay of Bengal, aerosol particle scattering is largely capped below 1.5 km MSL, though ground-based lidar measurements at Singapore differ slightly from this finding. Significant aerosol particle presence over land is similarly capped near 3.0 km MSL over most regions. Particle presence at low levels regionally, except over India, is dominated by relatively non-depolarizing particles. Industrial haze, sea salt droplets and fresh smoke are thus most likely present.

linear volume depolarization ratio↗

DOE OSTI2016

TH-EF-BRA-03: Assessment of Data-Driven Respiratory Motion-Compensation Methods for 4D-CBCT Image Registration and Reconstruction Using Clinical Datasets

Purpose: To evaluate the performance of a 4D-CBCT registration and reconstruction method that corrects for respiratory motion and enhances image quality under clinically relevant conditions. Methods: Building on previous work, which tested feasibility of a motion-compensation workflow using image datasets superior to clinical acquisitions, this study assesses workflow performance under clinical conditions in terms of image quality improvement. Evaluated workflows utilized a combination of groupwise deformable image registration (DIR) and image reconstruction. Four-dimensional cone beam CT (4D-CBCT) FDK reconstructions were registered to either mean or respiratory phase reference frame images to model respiratory motion. The resulting 4D transformation was used to deform projection data during the FDK backprojection operation to create a motion-compensated reconstruction. To simulate clinically realistic conditions, superior quality projection datasets were sampled using a phase-binned striding method. Tissue interface sharpness (TIS) was defined as the slope of a sigmoid curve fit to the lung-diaphragm boundary or to the carina tissue-airway boundary when no diaphragm was discernable. Image quality improvement was assessed in 19 clinical cases by evaluating mitigation of view-aliasing artifacts, tissue interface sharpness recovery, and noise reduction. Results: For clinical datasets, evaluated average TIS recovery relative to base 4D-CBCT reconstructions was observed to be 87% using fixed-frame registration alone; 87% using fixed-frame with motion-compensated reconstruction; 92% using mean-frame registration alone; and 90% using mean-frame with motion-compensated reconstruction. Soft tissue noise was reduced on average by 43% and 44% for the fixed-frame registration and registration with motion-compensation methods, respectively, and by 40% and 42% for the corresponding mean-frame methods. Considerable reductions in view aliasing artifacts were observed for each method. Conclusion: Data-driven groupwise registration and motion-compensated reconstruction have the potential to improve the quality of 4D-CBCT images acquired under clinical conditions. For clinical image datasets, the addition of motion compensation after groupwise registration visibly reduced artifact impact. This work was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA166119. Hugo and Weiss hold a research agreement with Philips Healthcare and license agreement with Varian Medical Systems. Weiss receives royalties from UpToDate. Christensen receives funds from Roger Koch to support research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

SCALE Input and Result Files Supporting SCALE Inventory and Reactivity Analysis of the gFHR

This dataset contains input and result files of computational simulations with the SCALE code system. The simulations cover radionuclide inventory and reactivity analyses of a fluoride salt-cooled high temperature pebble-bed reactor (PB-FHR), specifically the generic FHR benchmark. Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

To explore the integrated effects of ocean and ocean surface wave related physical processes on TC simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data.

17 WIND ENERGY↗

DOE OSTI2026

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

17 WIND ENERGY↗

NASA NTRS2020

Interannual Variability and Trends of Combustion Aerosol and Dust in Major Continental Outflows Revealed by MODIS Retrievals and CAM5 Simulations During 2003-2017

Emissions and long-range transport of mineral dust and combustion-related aerosol from burning fossil fuels and biomass vary from year to year, driven by the evolution of the economy and changes in meteorological conditions and environmental regulations. This study offers both satellite and model perspectives of interannual variability and possible trend of combustion aerosol and dust in major continental outflow regions over the past 15 years (2003-2017). The decade-long record of aerosol optical depth (AOD, denoted as t), separately for combustion aerosol (τ(sub c)) and dust (τ(sub d)), over global oceans is derived from the Collection 6 aerosol products of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard both Terra and Aqua. These MODIS/Aqua datasets, complemented by aerosol source-tagged simulations using the Community Atmospheric Model Version 5 (CAM5), are then analyzed to understand the interannual variability and potential trend of τ(sub c) and τ(sub d) in the major continental outflows. Both MODIS and CAM5 consistently yield a similar decreasing trend of -0.017 to - 0.020 decade(exp -1) for τ(sub c) over the North Atlantic Ocean and the Mediterranean Sea that is attributable to reduced emissions from North America and Europe, respectively. On the contrary, both MODIS and CAM5 display an increasing trend of +0.017 to +0.036 decade(exp -1) for τ(sub c) over the tropical Indian Ocean, the Bay of Bengal, and the Arabian Sea, which reflects the influence of increased anthropogenic emissions from South Asia and Middle East in the last two decades. Over the northwestern Pacific Ocean that is often affected by East Asian emissions of pollution and dust, the MODIS retrievals show a decreasing trend of - 0.021 decade(exp -1) for τ(sub c) and -0.012 decade(exp -1) for τ(sub d), which is however not reproduced by the CAM5 model. In other outflow regions strongly influenced by biomass burning smoke or dust, both MODIS retrievals and CAM5 simulations show no statistically significant trends; and the MODIS observed interannual variability is usually larger than that of the CAM5 simulation.

Hongbin Yu↗

DOE OSTI2020

Dynamic domain kinematic modelling for predicting interflow over leaky impeding layers

Traditional Boussinesq or kinematic simulations of interflow (i.e., lateral subsurface flow) assume no leakage through the impeding layer and require a no-flow boundary condition at the ridge top. However, recent analyses of many interflow-producing landscapes indicate that leaky impeding layers are common, that most interflow percolates well before reaching the toe slope, and therefore, the downslope contributing length is shorter than the hillslope length. In watersheds characterised by perched interflow over a low conductivity layer through permeable topsoil, interflow with percolation may be modelled with a kinematic wave model using a mobile upslope boundary condition defining the hillslope portion contributing interflow to valleys. Here, we developed and applied a dynamic interflow model to simulate interflow using a downslope travel distance concept such that only the active contributing length is modelled at any time. The model defines a variable active area based on the depth of the perched layer, the topographic slope and the ratio of the hydraulic conductivity of topsoil to that of the impeding layer. It incorporates a two-layer soil moisture accounting water balance analysis, a pedo-transfer function, and percolation and evaporation routines to predict interflow rates in continuous and event-based scenarios. We tested the modelling concept on two sets of data (2-year dataset of rainfall observations for the continuous simulation and a multi-day irrigation experiment for the event simulation) from a 121-m-long open interflow collection trench on an experimental hillslope at the Savannah River Site, South Carolina. The continuous model simulation partially represented the observed interflow hydrograph and perched water depth in the experimental hillslope with correlation coefficients of 0.85 and 0.35, respectively. Model performance improved significantly at event-scale analysis. The modelling approach realistically represents interflow dynamics in hillslopes with leaky impeding layers and can be integrated into catchment-scale hydrology models for more detailed hillslope process modelling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

SU-D-207A-07: The Effects of Inter-Cycle Respiratory Motion Variation On Dose Accumulation in Single Fraction MR-Guided SBRT Treatment of Renal Cell Carcinoma

Purpose: To assess the dose deposition in simulated single-fraction MR-Linac treatments of renal cell carcinoma, when inter-cycle respiratory motion variation is taken into account using online MRI. Methods: Three motion characterization methods, with increasing complexity, were compared to evaluate the effect of inter-cycle motion variation and drifts on the accumulated dose for an SBRT kidney MR-Linac treatment: 1) STATIC, in which static anatomy was assumed, 2) AVG-RESP, in which 4D-MRI phase-volumes were time-weighted, based on the respiratory phase and 3) PCA, in which 3D volumes were generated using a PCA-model, enabling the detection of inter-cycle variations and drifts. An experimental ITV-based kidney treatment was simulated in a 1.5T magnetic field on three volunteer datasets. For each volunteer a retrospectively sorted 4D-MRI (ten respiratory phases) and fast 2D cine-MR images (temporal resolution = 476ms) were acquired to simulate MR-imaging during radiation. For each method, the high spatio-temporal resolution 3D volumes were non-rigidly registered to obtain deformation vector fields (DVFs). Using the DVFs, pseudo-CTs (generated from the 4D-MRI) were deformed and the dose was accumulated for the entire treatment. The accuracies of all methods were independently determined using an additional, orthogonal 2D-MRI slice. Results: Motion was most accurately estimated using the PCA method, which correctly estimated drifts and inter-cycle variations (RMSE=3.2, 2.2, 1.1mm on average for STATIC, AVG-RESP and PCA, compared to the 2DMRI slice). Dose-volume parameters on the ITV showed moderate changes (D99=35.2, 32.5, 33.8Gy for STATIC, AVG-RESP and PCA). AVG-RESP showed distinct hot/cold spots outside the ITV margin, which were more distributed for the PCA scenario, since inter-cycle variations were not modeled by the AVG-RESP method. Conclusion: Dose differences were observed when inter-cycle variations were taken into account. The increased inter-cycle randomness in motion as captured by the PCA model mitigates the local (erroneous) hotspots estimated by the AVG-RESP method.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Ultrasound guided fluorescence molecular tomography with improved quantification by an attenuation compensated born-normalization and in vivo preclinical study of cancer

Ultrasound imaging, having the advantages of low-cost and non-invasiveness over MRI and X-ray CT, was reported by several studies as an adequate complement to fluorescence molecular tomography with the perspective of improving localization and quantification of fluorescent molecular targets in vivo. Based on the previous work, an improved dual-modality Fluorescence-Ultrasound imaging system was developed and then validated in imaging study with preclinical tumor model. Ultrasound imaging and a profilometer were used to obtain the anatomical prior information and 3D surface, separately, to precisely extract the tissue boundary on both sides of sample in order to achieve improved fluorescence reconstruction. Furthermore, a pattern-based fluorescence reconstruction on the detection side was incorporated to enable dimensional reduction of the dataset while keeping the useful information for reconstruction. Due to its putative role in the current imaging geometry and the chosen reconstruction technique, we developed an attenuation compensated Born-normalization method to reduce the attenuation effects and cancel off experimental factors when collecting quantitative fluorescence datasets over large area. Results of both simulation and phantom study demonstrated that fluorescent targets could be recovered accurately and quantitatively using this reconstruction mechanism. Finally, in vivo experiment confirms that the imaging system associated with the proposed image reconstruction approach was able to extract both functional and anatomical information, thereby improving quantification and localization of molecular targets.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2013

Consistency of simulated and observed regional changes in temperature, sea level pressure and precipitation

There is increasing pressure from stakeholders for highly localised climate change projections. A comprehensive assessment of climate model performance at the grid box scale in simulating recent change, however, is not available at present. Therefore, we compare observed changes in near-surface temperature, sea level pressure (SLP) and precipitation with simulations available from the Coupled Model Intercomparison Projects 3 and 5 (CMIP3 and CMIP5). In both multi-model datasets we find coherent areas of inconsistency between observed and simulated local trends per degree global warming in both temperature and SLP in the majority of models. Localised projections should thus take into account the possibility of regional biases shared across models. In contrast, simulated changes in precipitation are not significantly different from observations due to low signal-to-noise ratio of local precipitation changes. Therefore, recent regional rainfall change is likely not providing useful constraints for future projections as of yet. Comparing the two most recent sets of internationally coordinated climate model experiments, we find no indication of improvement in the models’ ability to reproduce local trends in temperature, SLP and precipitation.

Environmental Sciences & Ecology↗

DOE OSTI2021

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

Cheng, Yanyan↗

DOE OSTI2021

Large-Scale Forcing Impact on the Development of Shallow Convective Clouds Revealed From LASSO Large-Eddy Simulations

Real-world large-eddy simulations (LES) are driven by time-varying large-scale forcings (LSF) - e.g., temperature advection, moisture advection, and subsidence - derived from large-scale weather models. This study investigates the impact of the uncertainty in LSF on real-world LES in terms of the development of shallow convection at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory for the 11 June 2016 case using LES provided by the U.S. Department of Energy's LES ARM Symbiotic Simulation and Observation (LASSO) activity. The LASSO dataset provides an ensemble of LES for the selected case, which consists of LES runs that were driven by different LSF. The two contrasting LES runs investigated here generate different types of convective clouds, i.e., nonprecipitating shallow clouds and precipitating cumulus congestus, mainly due to the difference of LSF in temperature advection in the free troposphere. The temperature advection modulates the strength of the capping inversion and therefore the buoyancy of the air parcels rising from the atmospheric boundary layer (ABL). The inversion, together with large-scale updrafts, controls the penetration of the ABL thermals into the free troposphere, leading to cumulus congestus in the case of a weaker inversion. In contrast, clouds remain shallow in the case of a strong inversion. Differences between the two simulations are amplified over time, as mixed-phase clouds are formed near the top of the congestus in the weaker inversion case. Furthermore, this high dependency of LES results to LSF stresses the importance of accurate LSF by large-scale models to real-world LES simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Understanding Atom Probe Tomography of Oxide-Supported Metal Nanoparticles by Correlation with Atomic Resolution Electron Microscopy and Field Evaporation Simulation

Metal-dielectric composite materials, specifically metal nanoparticles supported on or embedded in metal oxides, are widely used in catalysis. The accurate optimization of such nanostructures warrants the need for detailed three-dimensional characterization. Atom probe tomography is uniquely capable of generating sub-nanometer structural and compositional data with part-per-million mass sensitivity, but there are reconstruction artifacts for composites containing materials with strongly differing fields of evaporation, as for oxide-supported metal nanoparticles. By correlating atom probe tomography with scanning transmission electron microscopy for Au nanoparticles embedded in an MgO support, deviations from an ideal topography during evaporation are demonstrated directly, and correlated with compositional errors in the reconstructed data. Finite element simulations of the field evaporation process confirm that protruding Au nanoparticles will evolve on the tip surface, and that evaporation field variations lead to an inaccurate assessment of the local composition, effectively lowering the spatial resolution of the final reconstructed dataset. Cross-correlating the experimental data with simulations results in a more detailed understanding of local evaporation aberrations during APT analysis of metal-oxide composites, paving the way towards a more accurate three-dimensional characterization of this technologically important class of materials.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2024

SCALE Input and Result Files Supporting SCALE Inventory and Reactivity Analysis as Part of the Hermes 2021 PSAR Review

This dataset contains input and result files of computational simulations with the SCALE code system. The simulations cover radionuclide inventory and reactivity analyses of a fluoride salt-cooled high temperature pebble-bed reactor (PB-FHR), specifically the Hermes low-power PB-FHR demonstration reactor. Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases

equilibrium core↗

NASA NTRS2015

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

simulation↗

DOE OSTI2026

Xanthos-Lake Dataset

The Xanthos-Lake v1.0 dataset provides the input data, trained machine-learning models, and simulation outputs needed to characterize lake water balance, snow and ice conditions, and mixing-layer temperature within the Xanthos global hydrological modeling framework. The dataset supports lake representation across a wide range of lake sizes and hydroclimatic conditions by combining xLSIM, a basin-specific machine-learning emulator of lake snow, ice, ice-cover fraction, and mixing-layer temperature, with the Xanthos-Lake water-balance model. The archive contains NetCDF datasets used to train and evaluate xLSIM, trained model weights, processed meteorological and lake-property inputs, and basin- and lake-category-specific simulation outputs. These materials are organized into four primary data groups, described below. Snowice_model_inputs: Contains the NetCDF input data used to train xLSIM. The xLSIM machine-learning framework uses three lake-based datasets. The meteorological forcing dataset provides monthly relative humidity, specific humidity, surface wind speed, maximum and minimum air temperature, downward longwave and shortwave radiation, snowfall, surface air pressure, and total precipitation. Lake surface area is included as an additional static predictor. The target-state dataset provides lake ice thickness, snow depth, snow cover, and lake mixing-layer temperature, while a companion lake-surface dataset provides the lake ice-cover fraction. Before training, ice thickness and snow depth are converted from meters to centimeters, mixing-layer temperature is converted from kelvin to degrees Celsius and constrained to nonnegative values, and ice-cover fraction is converted from a fraction to a percentage. The predictor variables are normalized using statistics calculated across the selected lakes and time steps. Snowice_model_outputs: Contains the NetCDF outputs generated by xLSIM. For each basin, xLSIM produces a file containing observed and predicted lake-state variables for the training, validation, and testing periods. The modeled variables include lake ice thickness, snow depth, snow cover, mixing-layer temperature, and lake ice-cover fraction. For basins without a sufficiently persistent snow-and-ice signal, the emulator predicts only mixing-layer temperature. The outputs also include training and validation loss histories, the selected model configuration, identifiers of the lakes used in training, and SHAP-based feature-importance information at the global, lake, and seasonal-regime levels. The trained machine-learning model weights are provided separately within the dataset archive. Together, these files support model evaluation and subsequent coupling with the Xanthos-Lake water-balance framework. XanthosLAKES: Contains the NetCDF input data used by the Xanthos-Lake framework. Monthly meteorological inputs include relative and specific humidity, downward shortwave and longwave radiation, mean, maximum, and minimum air temperature, wind speed, precipitation, snowfall, and surface air pressure. Static lake-property datasets provide lake identifiers, geographic locations, surface area, volume, mean depth, elevation, drainage area, fetch, outlet-routing information, and associated Xanthos grid-cell attributes. Separate bathymetric datasets provide the coefficients of the area–depth and volume–depth relationships for each aggregated lake unit. GLEV-based records provide observed lake surface area and evaporation data used to initialize lake states, define reference conditions, and calibrate and evaluate the model. Xanthos-Lake Outputs: Contains the basin- and lake-category-specific NetCDF outputs generated by Xanthos-Lake. Monthly variables include lake surface area, storage volume, outlet discharge, evaporation rate, evaporation volume, lake–groundwater exchange, lake inflow, ice thickness, snow depth, snow-cover fraction, ice-cover fraction, and mixing-layer temperature. The files also contain lake-specific calibration and validation statistics, including normalized root-mean-square error, mean absolute error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias. Stored calibrated and derived parameters include the weir discharge coefficient, fractional freeboard, groundwater exchange coefficient, reference water level, corresponding reference surface area and storage volume, weir-width adjustment factor, and the fraction of routed inflow entering the lake. Basin identifiers, lake category, simulation period, calibration and validation periods, and parameter-schema information are retained as NetCDF metadata.

Abeshu, Guta [Pacific Northwest National Laborator↗