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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework

Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

High Values of the Arctic Amplification in Early Decades of the 21st Century: Causes of Discrepancy by CMIP6 Models Between Observation and Simulation

Arctic Amplification (AA) in the first decade of the 21st century has reached values between 4 and 5, with a subsequent decrease to current values of about 3.6, while the value was from 2 to 3 during the twentieth century. The ensemble mean of the CMIP6 models has difficulty in reproducing the recently observed high values of the AA. In this report, we identify the main reason for this difficulty to be the CMIP6 models overestimate of the mean global temperature trend since about 1990. The largest values of the AA are observed in winter and spring. A sharp AA peak in 1987 spring was caused by a peak in the Arctic temperature trend occurring at the same time as a dip in the trend of mean global temperature. The winter AA has increased almost monotonically since 1990. Dividing the AA between the Arctic land and ocean areas shows that the ocean area makes a larger contribution to the AA. Our future projection of the AA suggests an increasing AA for about the next decade, followed by a slow decrease to about 3.5 in the 2050s.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A new method for inferring city emissions and lifetimes of nitrogen oxides from high-resolution nitrogen dioxide observations: a model study

We present a new method to infer emissions and lifetimes of nitrogen oxides (NO x ) based on tropospheric nitrogen dioxide (NO 2 ) observations together with reanalysis wind fields for cities located in polluted backgrounds. Since the accuracy of the method is difficult to assess due to lack of “true values” that can be used as a benchmark, we apply the method to synthetic NO 2 observations derived from the NASA-Unified Weather Research and Forecasting (NU-WRF) model at a high horizontal spatial resolution of 4 km × 4 km for cities over the continental United States. We compare the inferred emissions and lifetimes with the values given by the NU-WRF model to evaluate the method. The method is applicable to 26 US cities. The derived results are generally in good agreement with the values given by the model, with the relative differences of 2 % ± 17 % (mean ± standard deviation) and 15 % ± 25 % for lifetimes and emissions, respectively. Our investigation suggests that the use of wind data prior to the satellite overpass time improves the performance of the method. The correlation coefficients between inferred and NU-WRF lifetimes increase from 0.56 to 0.79 and for emissions increase from 0.88 to 0.96 when comparing results based on wind fields sampled simultaneously with satellite observations and averaged over 9 h data prior to satellite observations, respectively. We estimate that uncertainties in NO x lifetime and emissions arising from the method are approximately 15 % and 20 %, respectively, for typical (US) cities. The total uncertainties reach up to 43% (lifetimes) and 45% (emissions) by considering the additional uncertainties associated with satellite NO 2 observations and wind data. We expect this new method to be applicable to NO 2 observations from the TROPOspheric Monitoring Instrument (TROPOMI) and geostationary satellites, such as Geostationary Environment Monitoring Spectrometer (GEMS) or the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument, to estimate urban NO x emissions and lifetimes globally.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Offshore low-level jet observations and model representation using lidar buoy data off the California coast

Abstract. Low-level jets (LLJs) occur under a variety of atmospheric conditions and influence the available wind resource for wind energy projects. In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas administered by the Bureau of Ocean Energy Management: Humboldt and Morro Bay. The wind profile observations from the lidars and collocated near-surface meteorological stations (4–240 m) provide valuable year-long analyses of offshore LLJ characteristics at heights relevant to wind turbines. At Humboldt, LLJs were associated with flow reversals and north-northeasterly winds, directions that are more aligned with terrain influences than the predominant northerly flow. At Morro Bay, coastal LLJs were observed primarily during northerly flow as opposed to the predominant north-northwesterly flow. LLJs were observed more frequently in colder seasons within the lowest 250 m a.s.l. (above sea level), in contrast with the summertime occurrence of the higher-altitude California coastal jet influenced by the North Pacific High, which typically occurs at heights of 300–400 m. The lidar buoy observations also validate LLJ representation in atmospheric models that estimate potential energy yield of offshore wind farms. The European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) was unsuccessful at identifying all observed LLJs at both buoy locations within the lowest 200 m. An extension of the National Renewable Energy Laboratory (NREL) 20-year wind resource dataset for the Outer Continental Shelf off the coast of California (CA20-Ext) yielded marginally greater captures of observed LLJs using the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary layer (PBL) scheme than the 2023 National Offshore Wind dataset (NOW-23), which uses the Yonsei University (YSU) scheme. However, CA20-Ext also produced the most LLJ false alarms, which are instances when a model identified an LLJ but no LLJ was observed. CA20-Ext and NOW-23 exhibited a tendency to overestimate the duration of LLJ events and underestimate LLJ core heights.

17 WIND ENERGY↗
DOE OSTI2025

Education for PV Modeling Professionals: Observations from the 2025 PVPMC Workshop

We surveyed professionals in the photovoltaic industry to understand interests in education and training for performance modeling of solar power systems. We found that most professionals are self-taught and rely on a variety of public sources of technical materials. Available formal education, such as courses or certification programs, either lacks detail or is focused on software user training. Responses indicate several opportunities to create public resources that would benefit professional learning for solar power system modeling: • Create a glossary of terms and common variable names. • Develop guidance on uncertainty analysis in the context of solar power systems modeling. • Assemble a catalog of available educational materials and data sources.

14 SOLAR ENERGY↗
DOE OSTI2020

The effect of atmospheric transmissivity on model and observational estimates of the sea ice albedo feedback

The sea-ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS) – how much the surface albedo in sea-ice regions changes as the planet warms– and the radiative sensitivity (RS) – how much the top of atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite based RS is within the model range of RS that differs by a factor of two across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS which, in conjunction with the satellite-based RS yields an SIAF of 0.16 ± 0.04 W m-2 K-1. This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model specific RS and IS and find a model mean feedback parameter of 0.37 W m-2 K-1 which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.

Arctic, Antarctic, climate change, Climate & Earth↗
DOE OSTI2021

Observations and Modeling of Fiber-Optics Strain on Hydraulic Fracture Height Growth in HFTS-2

Understanding fracture height growth can be of great significance to optimizing field development and improving recovery. The Hydraulic Fracturing Test Site 2 (HFTS-2) has provided a unique opportunity and an advanced dataset to allow us to observe and understand fracture geometries rigorously. Low frequency distributed acoustic sensing (LF-DAS) data from a vertical well in HFTS-2 showed three key observations: (i) excessive upward height growth (>1000 ft) and limited downward growth of the hydraulic fractures during pumping, (ii) considerable additional upward fracture height growth (~300 ft) after well shut in, and (iii) very complex LF-DAS strain rate patterns for a small fiber-to-stage offset. Advanced geomechanical modeling was performed to simulate the hydraulic fracture propagation and the resulting strain responses in the vertical direction. The modeling results demonstrated asymmetric upward and downward fracture height growths as observed in HFTS-2 with a similar upward height growth rate. Simulated waterfall plots of vertical strain rate showed distinct patterns for different fiber-to-fracture distances. The upward-growing fracture tip can be clearly identified by the interfaces between compressing and extending zones. It was also found that the complex strain rate patterns observed in HFTS-2 for small fiber-stage offsets were not caused by the mechanical layering but possibly result from the simultaneous propagation of multiple hydraulic fractures at different rates. Furthermore, the simulation results improved the understating of the HFTS-2 LF-DAS data and the simulated strain rate patterns could also serve as templates for fracture height interpretation from LF-DAS data in future.

58 GEOSCIENCES↗
DOE OSTI2022

Occurrence of Warm Freezing Rain: Observation and Modeling Study

Freezing rain has been normally considered to be composed of supercooled raindrops when the 2 m air temperature (hereafter T as ) is below freezing. However, according to a statistical survey of freezing rain observations in China from 2000 to 2019, we find that there were 656 events that occurred at T as greater than 0°C (hereafter warm freezing rain and denoted by WFR), which account for 7% of the total freezing rain observations. Additionally, nearly 3% (266 observations) of freezing rain events occurred when the near-surface wet-bulb temperature was greater than 0°C. The modeling and sensitivity experiments on the nonequilibrium raindrop temperature (hereafter T r ) how that the temperature difference between raindrops and the atmosphere is the main cause of WFR. The magnitudes of the Δ T ar (difference between raindrop temperature T r and air temperature T a ) and Δ T ur (difference between T r and wet-bulb temperature T w ) are determined by the raindrop diameter D, temperature lapse rate $\Gamma$, and relative humidity RH. Increases of D and $\Gamma$, and a decrease of RH enhance Δ T ar and Δ T ur and thus the occurrence of WFR. Further simulations of 4 idealized and 370 real sounding profiles reveal that either the T a or the T w cannot properly distinguish the WFR events. When considering the temperature difference between raindrops and the atmosphere, the WFR can form by the “melting of solid hydrometeors” or “supercooled warm rain process.” This study can also deepen our understanding of the conditions of WFR and freezing rain formation at different altitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Observations and Modeling of Fiber Optic Strain on Hydraulic Fracture Height Growth in Hydraulic Fracturing Test Site 2 (HFTS-2)

Summary Understanding fracture height growth can be of great significance to optimizing field development and improving recovery. The Hydraulic Fracturing Test Site 2 (HFTS-2) has provided a unique opportunity and an advanced data set to allow us to observe and understand fracture geometries rigorously. Low-frequency distributed acoustic sensing (LF-DAS) data from a vertical well in HFTS-2 showed three key observations: (i) excessive upward height growth (>1,000 ft) and limited downward growth of the hydraulic fractures during pumping, (ii) considerable additional upward fracture height growth (~300 ft) after well shut in, and (iii) very complex LF-DAS strain rate patterns for a small fiber-to-stage offset. Advanced geomechanical modeling was performed to simulate the hydraulic fracture propagation and the resulting strain responses in the vertical direction. The modeling results demonstrated asymmetric upward and downward fracture height growths as observed in HFTS-2 with a similar upward height growth rate. Simulated waterfall plots of vertical strain rate showed distinct patterns for different fiber-to-fracture distances. The upward-growing fracture tip can be clearly identified by the interfaces between compressing and extending zones. It was also found that the complex strain rate patterns observed in HFTS-2 for small fiber-stage offsets were not caused by the mechanical layering but possibly result from the simultaneous propagation of multiple hydraulic fractures at different rates. The simulation results improved the understating of the HFTS-2 LF-DAS data, and the simulated strain rate patterns could also serve as templates for fracture height interpretation from LF-DAS data in future.

Engineering↗
DOE OSTI2026

DESI Strong Lens Foundry. I. HST Observations and Modeling with GIGA-Lens

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼3500 new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). Except for one ambiguous case, we have confirmed 50 of the 51 candidates to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich data set, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754−06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, to a strong lens with HST data, and achieved statistical convergence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Benthic Kinetic Energy in an Earth System Model and Observations

The benthic layer of the ocean is closely correlated with every upper layer above it. Close analysis of benthic layer stress can yield an accurate depiction of the state of the ocean, as well as the atmosphere above it. Through the use of the Department of Energy’s Energy Exascale Earth System Model (E3SM), we took a look at how these features changed over time in a simulation spanning the historical period from 1958 to 2018 in the Arctic Ocean north of 60° N. Making use of E3SM’s regional refinement capability to place higher resolution in regions of interest, the E3SM-Arctic configuration has ~10 km resolution in the ocean and sea ice components in this region, coarsening up to ~60 km outside of the Arctic (Veneziani et al. 2022). In particular, we narrowly focused on the benthic kinetic energy levels, and how trends changed over time. To validate the fidelity of the E3SM model, we compared model current speed outputs over a 369-day period in the Bering Strait to actual recorded observations during this time period. Our analysis of the benthic kinetic energy mean, standard deviation, and trend throughout this time period can be used to characterize the effects of environmental impacts in the Arctic Ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Influence of Tibetan Plateau sensible heat on pre-monsoon dust burdens over South Asia: Observational and modeling evidence

Tibetan Plateau sensible heating (TPSH) effect is recognized as a key driver of the South Asian climate; however its role in regulating the regional dust burdens over South Asia during the pre-monsoon season remains unclear. Using long-term reanalysis and/or observational dust and TPSH data, we show that the dust optical depth at 550 nm (DOD 550 ) over South Asia is significantly positively correlated with TPSH at the interannual scale. DOD 550 over South Asia increases (decreases) by 20 % (12 %) in the strongest (weakest) TPSH years. The phenomenon of TPSH-dust relationships is reinforced by sensitivity experiments by altering TPSH from global climate models. Further mechanism analysis reveals that TPSH perturbation induces regional circulation anomalies over South Asia, which features a low-tropospheric cyclonic response around the Tibetan Plateau. This cyclonic anomaly strengthens the prevailing northwesterly winds, increasing regional dust emissions and transportation over South Asia. This novel mechanism helps understand the South Asian dust change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis

Abstract Solid‐state synthesis from powder precursors is the primary processing route to advanced multicomponent ceramic materials. Designing reaction conditions and precursors for ceramic synthesis can be a laborious, trial‐and‐error process, as heterogeneous mixtures of precursors often evolve through a complicated series of reaction intermediates. Here, ab initio thermodynamics is used to model which pair of precursors has the most reactive interface, enabling the understanding and anticipation of which non‐equilibrium intermediates form in the early stages of a solid‐state reaction. In situ X‐ray diffraction and in situ electron microscopy are then used to observe how these initial intermediates influence phase evolution in the synthesis of the classic high‐temperature superconductor YBa 2 Cu 3 O 6+ x (YBCO). The model developed herein rationalizes how the replacement of the traditional BaCO 3 precursor with BaO 2 redirects phase evolution through a low‐temperature eutectic melt, facilitating the formation of YBCO in 30 min instead of 12+ h. Precursor selection plays an important role in tuning the thermodynamics of interfacial reactions and emerges as an important design parameter in planning kinetically favorable synthesis pathways to complex ceramic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Estimating building occupancy: a machine learning system for day, night, and episodic events

Building occupancy research increasingly emphasizes understanding the social and physical dynamics of how people occupy space. Opportunities in the open source domain including social media, Volunteered Geographic Information, crowdsourcing, and sensor data have proliferated, resulting in the exploration of building occupancy dynamics at varying spatiotemporal scales. At Oak Ridge National Laboratory, research into building occupancies through the development of a global learning framework that accommodates exploitation of open source authoritative sources, including governmental census and surveys, journal articles, real estate databases, and more, to report national and subnational building occupancies across the world continues through the Population Density Tables (PDT) project. This probabilistic learning system accommodates expert knowledge, experience, and open-source data to capture local, socioeconomic, and cultural information about human activity. It does so through a systematic process of data harmonization techniques in the development of observation models for over 50 building types to dynamically update baseline estimates and report probabilistic diurnal and episodic building occupancy estimates. This discussion will explore how PDT is implemented at scale and expanded based on the development of observation model classes and will explain how to interpret and spatially apply the reported probability occupancy estimates and uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Understanding and Predictability of Integrated Mountain Hydroclimate (Workshop Report)

Mountainous systems cover approximately 23% of Earth’s land and are distributed across all continents. They can capture and store atmospheric moisture that is then cycled through the terrestrial surface and subsurface system, released to downstream communities, and cycled back to the atmosphere. Mountain hydroclimate—characterized by steep gradients, geological, ecological, and biogeochemical diversity—is influenced by topographic forcing and elevated warming and susceptible to large subseasonal to multidecadal variability and rapid changes. Terrestrial hydrological and biogeochemical cycles also experience cascading effects from global warming impacts, such as multidecadal declines in mountain snowpack, longer growing seasons, and increased frequency and severity of extreme events like droughts and wildfires. However, little is known about the effects of these impacts and their feedbacks on climate systems and surface-subsurface compartments. Also unknown are the full implications of changing hydroclimate and extreme events on hydro biogeochemical cycles across atmosphere, terrestrial, and human systems in mountain regions and beyond. This knowledge gap is critical, given human reliance on mountain systems for stable water supply and quality. Mountain systems’ increasing vulnerability to climate change and human perturbations motivates the need to improve understanding of integrated mountain hydroclimate (IMHC) systems and their feedbacks and impacts on humans across scales. However, due to large heterogeneity and strong gradients, coupled natural-human processes in mountain regions present significant challenges for observations, modeling, predictions, and projections. Motivated by gaps in mountain hydroclimate understanding, observations, and modeling and the need for credible projections of future changes, the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program organized a virtual workshop on “Understanding and Predictability of Integrated Mountain Hydroclimate.” Sponsored by BER’s Earth and Environmental Systems Sciences Division (EESSD), the workshop aimed to inform and catalyze EESSD’s growing interests in enhancing predictive understanding of IMHC. Organizers structured the workshop to identify (1) knowledge gaps, (2) observational and modeling challenges, (3) short-term (1 to 3 years) and long-term (10 years and beyond) research opportunities, and (4) strategies for fostering collaboration and coordination. To address the outstanding challenges of IMHC, the workshop included two sessions organized by disciplinary, cross-disciplinary, and crosscutting science topics. The disciplinary and cross-disciplinary topics focused on essential IMHC elements: atmosphere, terrestrial, and human systems and their interactions. Breakout sessions on disciplinary and cross-disciplinary topics facilitated identification of crosscutting topics and central emerging themes. Session 1 focused on connecting existing DOE investments to accelerate progress related to scientific challenges in understanding mountain hydroclimate. In Session 2, participants further explored key Session 1 takeaways through the lens of multiagency collaborations and coordination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Intra-annual variations of regional total column ozone, aerosol optical depth, and water vapor from ground-based, satellite-based and model-based observations

The Microtops II ozonometer has been deployed to continuously measure the total column ozone (TCO), pre-cipitable water content (PWC) and aerosol optical thickness (AOT) at Atigre village (16.74° N, 74.37° E, 604 m above sea level, masl) located on the southeastern slope of Indian western Ghats. The Microtops II ozonometer measurements during the period from September 2017 to June 2018 were analyzed along with the retrieved products of Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra satellites and also the ERA-interim reanalysis model. We found that the TCO (or PWC) data from the OMI (or MODIS) and the ERA reanalysis model data products are in good agreement. The mea-surements showed strong seasonal variability such that TCO was lowest in the winter season but increased in the summer season, and both PWC and AOT were lowest in the post-monsoon season and increased in the summer season. In continuous ERA-interim reanalysis TCO observations, we found the three types of periodicities in the daily mean TCO as well as PWC (i) The weekly (7 days), (ii) quasi-biweekly (14 to 16 days), (iii) Madden Julian Oscillations (MJO) (30–60 days) oscillations. These oscillations depend on the periodic weather changes in the troposphere. We also observe that the average day to day variability in daily mean TCO observations in the observational period is about 1.4%, 1.1%, 2.62% in the Microtops II ozonometer, ERA-interim reanalysis, and OMI data respectively. We found the positive correlation between daily mean AOT and PWC in all seasons (except monsoon), which is maximum in the winter season, showing the hygroscopic nature of aerosols. The sources of water vapor and aerosol at our location are also studied using back-trajectory analysis.

Nade, Dada R.↗
DOE OSTI2021

Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics

Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.

54 ENVIRONMENTAL SCIENCES↗