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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Incorporation of New Convective Ice Microphysics into the NASA GISS GCM and Impacts on Cloud Ice Water Path (IWP) Simulation

The CMIP5 configurations of the GISS Model-E2 GCM simulated a mid- and high latitude ice IWP that decreased by 50 relative to that simulated for CMIP3 (Jiang et al. 2012; JGR). Tropical IWP increased by 15 in CMIP5. While the tropical IWP was still within the published upper-bounds of IWP uncertainty derived using NASA A-Train satellite observations, it was found that the upper troposphere (200 mb) ice water content (IWC) exceeded the published upper-bound by a factor of 2. This was largely driven by IWC in deep-convecting regions of the tropics.Recent advances in the model-E2 convective parameterization have been found to have a substantial impact on tropical IWC. These advances include the development of both a cold pool parameterization (Del Genio et al. 2015) and new convective ice parameterization. In this presentation, we focus on the new parameterization of convective cloud ice that was developed using data from the NASA TC4 Mission. Ice particle terminal velocity formulations now include information from a number of NASA field campaigns. The new parameterization predicts both an ice water mass weighted-average particle diameter and a particle cross sectional area weighted-average size diameter as a function of temperature and ice water content. By assuming a gamma-distribution functional form for the particle size distribution, these two diameter estimates are all that are needed to explicitly predict the distribution of ice particles as a function of particle diameter.GCM simulations with the improved convective parameterization yield a 50 decrease in upper tropospheric IWC, bringing the tropical and global mean IWP climatologies into even closer agreement with the A-Train satellite observation best estimates.

tropical regions↗
NASA NTRS2014

Performance Verification of the Gravity and Extreme Magnetism Small Explorer GEMS X-Ray Polarimeter

olarimetry is a powerful tool for astrophysical observations that has yet to be exploited in the X-ray band. For satellite-borne and sounding rocket experiments, we have developed a photoelectric gas polarimeter to measure X-ray polarization in the 2-10 keV range utilizing a time projection chamber (TPC) and advanced micro-pattern gas electron multiplier (GEM) techniques. We carried out performance verification of a flight equivalent unit (1/4 model) which was planned to be launched on the NASA Gravity and Extreme Magnetism Small Explorer (GEMS) satellite. The test was performed at Brookhaven National Laboratory, National Synchrotron Light Source (NSLS) facility in April 2013. The polarimeter was irradiated with linearly-polarized monochromatic X-rays between 2.3 and 10.0 keV and scanned with a collimated beam at 5 different detector positions. After a systematic investigation of the detector response, a modulation factor greater than or equal to 35% above 4 keV was obtained with the expected polarization angle. At energies below 4 keV where the photoelectron track becomes short, diffusion in the region between the GEM and readout strips leaves an asymmetric photoelectron image. A correction method retrieves an expected modulation angle, and the expected modulation factor, approximately 20% at 2.7 keV. Folding the measured values of modulation through an instrument model gives sensitivity, parameterized by minimum detectable polarization (MDP), nearly identical to that assumed at the preliminary design review (PDR).

Time Projection↗
NASA NTRS2016

Understanding Seasonal Variability in Thin Cirrus Clouds from Continuous MPLNET Observations at GSFC in 2012

Optically thin cirrus cloud (optical depth < 0.3) net radiative forcing represents one of the primary uncertainties in climate feedback, as sub-visible clouds play a fundamental role in atmospheric radiation balance and climate change. A lidar is a very sensitive optical device to detect clouds with an optical depth as low as 10-4. In this paper we assess the daytime net radiative forcing of sub-visible cirrus clouds detected at Goddard Space Flight Center, a permanent observational site of the NASA Micro Pulse Lidar Network in 2012. Depending on their height, season and hour of the day, the solar albedo effect can outweigh the infrared greenhouse effect, cooling the earth atmosphere system rather than warming it exclusively. As result, based on latitude, the net forcing of sub-visible cirrus clouds can be more accurately parameterized in climate models.

Lolli, Simone↗
NASA NTRS2019

Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over the High Mountain Asia

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA)framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted:(1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0-10 cm soil temperature are reduced (on average) by 10 and 7%,respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10-40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.

High Mountain Asia↗
NASA NTRS2020

AWARE: The Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment

The US Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) performed comprehensive meteorological and aerosol measurements, and ground-based atmospheric remote sensing at two Antarctic stations using the most advanced instrumentation available. A suite of cloud research radars, lidars, spectral and broadband radiometers, aerosol chemical and microphysical sampling equipment, and meteorological instrumentation was deployed at McMurdo Station on Ross Island from December 2015 through December 2016. A smaller suite of radiometers and meteorological equipment including radiosondes, optimized for surface energy budget measurement, was deployed on the West Antarctic Ice Sheet between 4 December 2015 and 17 January 2016. AWARE provided Antarctic atmospheric data comparable to several well-instrumented high Arctic sites that have operated for many years and that reveal numerous contrasts with the Arctic in aerosol and cloud microphysical properties. These include persistent differences in liquid cloud occurrence, cloud height and cloud thickness. Antarctic aerosol properties are also quite different from the Arctic in both seasonal cycle and composition, due to the continent’s isolation from lower latitudes by Southern Ocean storm tracks. Antarctic aerosol number and mass concentrations are not only non-negligible but perhaps play a more important role than previously recognized because of the higher sensitivities of clouds at the very low concentrations caused by the large-scale dynamical isolation. Antarctic aerosol chemical composition, particularly organic components, has implications for local cloud microphysics. The AWARE data set, fully available online in the ARM Program data archive, offers numerous case studies for unique and rigorous evaluation of mixed-phase cloud parameterization in climate models.

Atmospheric Radiation Measurement (ARM)↗
NASA NTRS2020

The interactive global fire module pyrE (v1.0)

Fires affect the composition of the atmosphere and Earth’s radiation balance by emitting a suite of reactive gases and particles. An interactive fire module in an Earth system model (ESM) allows us to study the natural and anthropogenic drivers, feedbacks, and interactions of open fires. To do so, we have developed pyrE, the NASA GISS (Goddard Institute for Space Studies) interactive fire emissions *module. The pyrE module is driven by environmental variables like flammability and cloud-to-ground lightning, calculated by the GISS ModelE ESM, and parameterized by anthropogenic impacts based on population density data. Fire emissions are generated from the flaming phase in pyrE (active fires). Using pyrE, we examine fire occurrence, regional fire suppression, burned area, fire emissions, and how it all affects atmospheric composition. To do so, we evaluate pyrE by comparing it to satellite-based datasets of fire count, burned area, fire emissions, and aerosol optical depth (AOD). We demonstrate pyrE’s ability to simulate the daily and seasonal cycles of open fires and resulting emissions. Our results indicate that interactive fire emissions are biased low by 32 %–42 %, depending on emitted species, compared to the GFED4s (Global Fire Emissions Database) inventory. The bias in emissions drives underestimation in column densities, which is diluted by natural and anthropogenic emissions sources and production and loss mechanisms. Regionally, the resulting AOD of a simulation with interactive fire emissions is underestimated mostly over Indonesia compared to a simulation with GFED4s emissions and to MODIS AOD. In other parts of the world pyrE’s performance in terms of AOD is marginal to a simulation with prescribed fire emissions.

Fires↗
NASA NTRSDate not supplied

Improving Drought Monitoring for the Middle East and North Africa Region via Data Assimilation Using the NASA Land Information System (LIS)

Food and water security become an increasingly pressing concern for countries within the Middle East and North Africa (MENA) region under climate change. Droughts are among the most important issues facing this region in both economic and social terms, which have become much more severe in recent decades. An effective drought monitoring and early warning system is, therefore, critical to support drought impact assessment and risk management. This presentation describes the effort to improve the simulation of hydrological conditions for such semi-arid environment by Noah Multi-parameterization Land Surface Model within the NASA Land Information System (LIS) via assimilating remotely sensed leaf area index and soil moisture in the presence of irrigation. Multi-source satellite evapotranspiration products are used to evaluate the model performance under the impact of the choice of met-forcings, the presence of irrigation and data assimilation. The improved model configuration is aimed to deliver a better soil moisture estimation for the composite drought indicator (CDI) for drought monitoring and to provide a better initial condition in support for hydrological drought forecasting.

Wanshu Nie↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRSDate not supplied

Predictions of the Azimuthal Variation of the Noise from Chevron Jets using an Acoustic Analogy

An existing acoustic analogy formulation is used to make predictions of the turbulent mixing noise from a series of chevron jets tested at NASA Glenn Research Center. Reynolds-averaged Navier-Stokes solutions are used as input to provide the mean flow and turbulence quantities needed to compute the Green’s function and parameterize the source model. The Green’s function for the non-axisymmetric mean flows generated by these nozzles is computed using a second-order finite volume method. A slightly modified version of an existing source model originally developed for round jets is used to assess its capability in these non-axisymmetric flows. Prediction results are compared with experimental data for a range of azimuthal orders of the nozzle geometry and flow speeds. The results show that the RANS-based acoustic analogy prediction method is able to capture the observed azimuthal variation of the sound field fairly well at relatively low jet exit velocities. At higher speeds, the prediction method still captures the observed trends in azimuthal directivity but not the absolute levels, particularly at higher frequencies. Areas for potential improvement of the method are suggested.

Jet Noise↗
NASA NTRSDate not supplied

Predictions of the Azimuthal Variation of the Noise from Chevron Jets using an Acoustic Analogy

An existing acoustic analogy formulation is used to make predictions of the turbulent mixing noise from a series of chevron jets tested at NASA Glenn Research Center. Reynolds-averaged Navier-Stokes solutions are used as input to provide the mean flow and turbulence quantities needed to compute the Green’s function and parameterize the source model. The Green’s function for the non-axisymmetric mean flows generated by these nozzles is computed using a second-order finite volume method. A slightly modified version of an existing source model originally developed for round jets is used to assess its capability in these non-axisymmetric flows. Prediction results are compared with experimental data for a range of azimuthal orders of the nozzle geometry and flow speeds. The results show that the RANS-based acoustic analogy prediction method is able to capture the observed azimuthal variation of the sound field fairly well at relatively low jet exit velocities. At higher speeds, the prediction method still captures the observed trends in azimuthal directivity but not the absolute levels, particularly at higher frequencies. Areas for potential improvement of the method are suggested.

Jet Noise↗
NASA NTRSDate not supplied

Updated PSC Climatology Based on Caliop Measurements From 2006-2023

After more than three decades of research, the roles of polar stratospheric clouds (PSCs) in stratospheric ozone depletion are well established. Heterogeneous reactions on PSCs convert the stable chlorine reservoirs HCl and ClONO2 to chlorine radicals that destroy ozone catalytically. PSCs also prolong ozone depletion by delaying chlorine deactivation through the removal of gas-phase HNO3 and H2O by sedimentation of large nitric acid trihydrate (NAT) and ice particles. A substantial recovery of the ozone layer is expected by the middle of this century with reduced global production of ozone depleting substances in accordance with the Montreal Protocol and subsequent amendments. But as climate changes, leading to a colder and perhaps wetter stratosphere and upper troposphere, reliable model predictions of recovery of the Antarctic ozone hole and of potentially more severe ozone depletion in the Arctic are challenging. This is due both to a lack of detailed understanding of the underlying physics and the fact that many global models use simple parameterizations that do not accurately represent PSC processes. A more complete picture of PSC processes on vortex-wide scales has emerged from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument on the CALIPSO satellite that has been observing PSCs at latitudes up to 82 degrees in both hemispheres since June 2006. The CALIOP Version 2.0 (v2) PSC algorithm was recently developed to address known deficiencies in previous algorithms and includes additional refinements to increase the robustness of the inferred PSC composition. In this paper, we present an updated PSC reference data record and comprehensive climatology constructed by applying the v2 algorithm to the more than 17-year CALIOP spaceborne lidar dataset. In addition, we will examine the potential impact of aerosol and water vapor injections into the stratosphere from the January 2022 Hunga Tonga eruption on PSC occurrence in both the Arctic and Antarctic regions.

polar stratospheric clouds↗
DOE OSTI2013

Evidence of Progress - Measurement of Impacts of Australia's S&L Program from 1990-2010

Australia first put categorical energy efficiency labels on residential appliances in the mid-1980s, and the first Minimum Energy Performance Standards (MEPS) for refrigerators was implemented in 1999. Updated in 2005, these MEPS were aligned with US 2001 levels. Considered together, these actions set Australia apart as having one of the most aggressive appliance efficiency programs in the world. For these reasons, together with good data on product sales over time, Australia represents a potentially fruitful case study for understanding the dynamics energy efficiency standards and labeling (EES&L) programs impacts on appliance markets. This analysis attempts to distinguish between the impacts of labeling alone as opposed to MEPS, and to probe the time-dependency of such impacts. Fortunately, in the Australian case, detailed market sales data and a comprehensive registration system provides a solid basis for the empirical evaluation of these questions. This paper analyzes Australian refrigerator efficiency data covering the years 1993-2009. Sales data was purchased from a commercial market research organization (in this case, the GfK Group) and includes sales and average price in each year for each appliance model; this can be used to understand broader trends by product class and star rating category, even where data is aggregated. Statistical regression analysis is used to model market introduction and adoption of high efficiency refrigerators according to logistic adoption model formalism, and parameterizes the way in which the Australian programs accelerated adoption of high-efficiency products and phased out others. Through this analysis, the paper presents a detailed, robust and quantitative picture of the impacts of EES&L in the Australian case, but also demonstrates a methodology of the evaluation of program impacts that could form the basis of an international evaluation framework for similar programs in other countries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Corrosion Behavior of Developmental Iron Phosphate Waste Forms: FY20 Status Report

Corrosion tests were performed to assess the dissolution behaviors of developmental iron phosphate materials that are being evaluated as an alternative to the glass-bonded sodalite ceramic waste form to immobilize salt wastes from electrochemical separation operations. Production of the iron phosphate waste form results in dehalogenation of the waste salt that increases potential waste loading. Ferric oxide is added to the waste form to increase the chemical durability. Tests conducted to optimize the amount of ferric oxide used to make the waste form are summarized in this report. Specimens prepared from materials made with between 17 and 34 mole % ferric oxide were subjected to two test methods to assess the effects of the iron content on the relative durability and retention of immobilized salt cations including cesium, strontium, and neodymium. Modified ASTM C1308 test results show the intrinsic durability was significantly higher for materials made with 27 mol % ferric oxide. Further additions generated iron-rich inclusion phases and did not improve durability. Modified ASTM C1285 test results show that solution feedback attenuates the dissolution rate similar to what is observed for borosilicate glasses within seven days. Analyses of test specimens after 42 days show no evidence that surface layers formed. Combined with previous studies addressing the salt loading, a composition region of the ammonium (di)hydrogen phosphate-waste salt-ferric oxide ternary has been defined for durable waste form compositions. Materials with that composition can be used to determine optimal processing conditions, assess long-term performance, and parameterize a degradation model.

Stariha, S. A.↗
DOE OSTI2013

Evidence of progress. Measurement of impacts of Australia's S and L program from 1990-2010

Australia first put categorical energy efficiency labels on residential appliances in the mid-1980s, and the first Minimum Energy Performance Standards (MEPS) for refrigerators was implemented in 1999. Updated in 2005, these MEPS were aligned with US 2001 levels. Considered together, these actions set Australia apart as having one of the most aggressive appliance efficiency programs in the world. For these reasons, together with good data on product sales over time, Australia represents a potentially fruitful case study for understanding the dynamics energy efficiency standards and labeling (EES and L) programs impacts on appliance markets. This analysis attempts to distinguish between the impacts of labeling alone as opposed to MEPS, and to probe the time-dependency of such impacts. Fortunately, in the Australian case, detailed market sales data and a comprehensive registration system provides a solid basis for the empirical evaluation of these questions. This paper analyzes Australian refrigerator efficiency data covering the years 1993-2009. Sales data was purchased from a commercial market research organization (in this case, the GfK Group) and includes sales and average price in each year for each appliance model – this can be used to understand broader trends by product class and star rating category, even where data is aggregated. Statistical regression analysis is used to model market introduction and adoption of high efficiency refrigerators according to logistic adoption model formalism, and parameterizes the way in which the Australian programs accelerated adoption of high-efficiency products and phased out others. Through this analysis, the paper presents a detailed, robust and quantitative picture of the impacts of EES and L in the Australian case, but also demonstrates a methodology of the evaluation of program impacts that could form the basis of an international evaluation framework for similar programs in other countries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Force Matching Approaches to Extend Density Functional Theory to Large Time and Length Scales

We present methods for the creation of semi-empirical quantum approaches and reactive force fields through force matching to quantum simulation data for materials under reactive conditions. Our methodologies overcome the extreme computational cost of standard Kohn-Sham Density Functional Theory (DFT) by mapping DFT computed simulation data onto functional forms with linear dependence on their parameters. This allows for quick parameterization of our models by avoiding the non-linear fitting bottlenecks associated with most molecular dynamics model development. We illustrate our approach with two different systems: (i) determination of density functional tight binding models for aqueous glycine dimerization, and (ii) determination of the Chebyshev Interactional Model for Efficient Simulation (ChIMES) reactive force field for metallic liquid carbon. In each case, we observe that our approach is easy to parametrize and yields a model that is orders of magnitude faster than DFT while largely retaining its accuracy. Overall, our methods have potential use for studying complex long time and length scale chemical reactivity at extreme conditions, where there is a significant need for computationally efficient atomistic simulations methods to aid in the interpretation and design of experiments.

Lindsey, R.↗
DOE OSTI2024

Geophysical and Environmental Monitoring Data, and Subsurface Flow Modelling Results for Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO

This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗