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NASA NTRS2003

The Ionization History of The Intergalactic Medium:

The funded project seeked a unified description of the ionization, physical structure, and evolution of the intergalactic medium (IGM) and quasar intervening absorption systems. We proposed to conduct theoretical studies of the IGM and QSO absorbers in the context of current theories of galaxy formation, developing and using numerical and analytical techniques aimed at a detailed modeling of cosmological radiative transfer, gas dynamics, and thermal and ionization evolution. The ionization history of the IGM has important implications for the metagalactic UV background, intergalactic helium absorption 21-cm tomography, metal absorption systems, fluctuations in the microwave background, and the cosmic rate of structure and star formation. All the original objectives of our program have been achieved, and the results widely used and quoted by the community. Indeed, they remain relevant as the level and complexity of research in this area has increased substantially since our proposal was submitted, due to new discoveries on galaxy formation and evolution, a flood of high-quality data on the distant universe, new theoretical ideas and direct numerical simulations of structure formation in hierarchical clustering theories.

Madau, Piero↗

DOE OSTI2024

Enhancing Extreme Precipitation Predictions With Dynamical Downscaling: A Convection‐Permitting Modeling Study in Texas and Oklahoma

Precipitation in the Southern Plains of the United States is relatively well depicted by the Community Earth System Model (CESM). However, despite its ability to capture seasonal mean precipitation anomalies, CESM consistently underestimates extreme pluvial and drought events, rendering it an insufficient tool for extending simulation lead times for exceptional events, such as the abnormally dry May 2011, which helped drive Texas into its worst period of drought in more than a century, and the abnormally wet May 2015, which led to widespread flooding in that state. Ensemble‐based regional climate experiments are completed for the two extreme years using Weather Research and Forecasting model (WRF) and downscaled from CESM. WRF simulations are at convection‐permitting grid resolution for improved physical representation of simulated precipitation over the Southern Great Plains. By integrating convection‐permitting models (CPMs) into each individual member of a CESM ocean data assimilation ensemble, this study demonstrates that high‐resolution dynamical downscaling can improve model skillfulness at capturing these two events and is thus a potentially useful tool for forecasting extremely high and extremely low precipitation events at subseasonal or even seasonal lead times.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

An Observation-Driven Framework for Modeling Post-Fire Hydrologic Response: Evaluation for Two Central California Case Studies

In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale for post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of fire impacts on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify wildfire impacts on runoff generation. We consider the vegetation and soil disturbances as the primary contributors to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty and consider model sensitivity, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We evaluated the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration. Analysis of two events in central California shows that 1) the LAI assimilation has a greater impact on water balance; and 2) the soil scheme captures a range of outcomes, with substantial impacts on surface runoff for some ensemble members, which could affect flood potential.

Timothy M Lahmers↗

NASA NTRS2021

Comparison of SAR and CYNSS surface water extent metrics over the Yucatan Lake wetland site

Wetlands have a major role in the carbon cycle, outgassing large quantities of carbon dioxide and methane through processes that are directly and strongly influenced by the duration and timing of inundation. Therefore, understanding the seasonal pattern of inundation can be a component for regional to global scale carbon models. Measurement of inundation extent also establishes a benchmark for the current status of wetland areas, useful in assessing the future impacts of climate change. The incorporation of frequent measurements of inundation extent into large-scale hydrological models would permit the evaluation of more detailed seasonal and longer-term floodplain dynamics and their associated management implications.The Cyclone GNSS (CYGNSS) constellation of satellites launched in 2016, and carries receivers capable of receiving data from L-band GNSS reflections. Delay Doppler maps (DDM) are generated on board and telemetered to the ground, along with a small number of raw data takes that can be used for special processing on the ground for evaluation purposes. It has been previously shown that these data can be sensitive to inundation.The NASA ISRO Synthetic Aperture Radar, currently planned for launch in January 2023, has both an L-band and S-band SAR for earth imaging. The L-band SAR, which will image the Earth's land mass twice every 12 days, has a requirement for measuring wetland inundation extent at the 1 ha scale. One of the sites that will be used to validate this requirement is Yucatan Lake, Louisiana. This oxbow lake and surrounding area located adjacent to the Mississippi river experiences periodic and extensive flooding in the surrounding forest areas.In 2019, NASA's UAVSAR fully polarimetric airborne L-band SAR conducted a flight campaign to image a dozen sites in the SE USA at approximately 12-day intervals and both in the morning and evening, to simulate the type of data NISAR will obtain. One site imaged during this campaign was the Yucatan Lake area, spanning water stages from low to high flood conditions.It has been demonstrated previously that GNSS reflectometry such as that measured by CYGNSS may be used to characterize surface inundation. It has also been known for decades that L-band SAR may be used to characterize not only the presence of open water, but also the presence of subcanopy inundation in forested areas. In this paper we will present results comparing data from these two types of instruments.

Lavalle, Marco↗

NASA NTRS2001

Regional Climate Studies with Variable-Resolution Stretched-Grid GCMs

A variable resolution GCM using a global stretched grid with fine resolution over the area(s) of interest, is a viable new approach to regional and subregional climate studies and applications. It is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step in regional climate modeling. The first version of the SG-GCM based on the GEOS (Goddard Earth Observing System) GCM using a finite-difference approximation, has been developed and thoroughly tested during the last few years. Successful simulations have been performed with the SG-GCM for the anomalous regional climate events of the U.S. 1988 summer drought and 1993 summer flood. They have shown the practical feasibility of the SG-approach for regional climate modeling. The GEOS SG-DAS (Data Assimilation System) incorporating the SG-GCM has also been developed and tested. The assimilated regional fields and diagnostics are used for validating the SG-GCM regional simulations. Two new SG-GCMs are being developed. The first is the SG-version of the new NASANCAR FV-GCM (with the finite-volume (FV) dynamics), and the second is the SG-version of the new GCM with spectral-element dynamics. Both GCMs use the WAR CCM4 physics. Using these advanced numerics will provide increased computational efficiency for the new the SG-GCMs, and will allow us to employ more flexible stretching strategies beneficial for the efficient regional down-scaling. The major current developments are focused on: simulating the 1997-1999 (and beyond) ENSO cycle and related monsoonal circulations, with enhanced regional resolution; studying intraseasonal and interannual regional climate variability for the extended multiyear (AMIP-type) SG-GCM simulations; and studying the impact of ensemble integrations.

Fox-Rabinovitz, Michael↗

DOE OSTI2022

Impact of Concurrent Solubilization and Fines Migration on Fracture Aperture Growth in Shales during Acidized Brine Injection

Acidic hydraulic fracturing fluid chemically and physically alters shale rock fabric during injection and shut-in, creating a “reaction-altered zone” along the fracture faces. To better characterize the variable thickness and composition of this reaction-altered zone under advective flow, we take a coupled experimental and modeling approach. A fluidic cell, with six fiducial markers, is first fabricated to keep the rock sample in place during the core floods and to allow image alignment of acquired images. Then, we conduct a series of reactive core floods in a clay-rich siliceous Wolfcamp shale sample with 10 wt % carbonate, using a synthetic fracturing fluid under no confining stress and at room temperature. High-resolution computed tomography (CT) scans are periodically conducted to observe the spatial alteration of the fracture network. We then perform scanning electron microscopy (SEM-EDS) on the two orthogonal surfaces (fracture surface and freshly cut profile face) to generate high-resolution elemental maps that show the change in mineralogy, both with distance along a given flow path along a fracture surface and with depth from the fracture surface into the shale matrix. These results are contrasted against a two-dimensional (2D) advection-diffusion reaction model developed previously for batch reactions between shale and synthetic fracturing fluids. The model simulates the geochemical interaction occurring at the fracture/matrix interface and penetrating into the shale matrix during the reactive core flood. Both model and experimental results show that the acidic brine is neutralized during the core flood, corresponding to an increase in fracture aperture as a function of fluid volume injected with the greatest change near the inlet. SEM-EDS scans reveal significant dissolution of carbonates on the fracture surface without pyrite oxidation. The reactive transport model indicates that carbonate depletion into the shale interior should be observable, yet SEM-EDS shows no discernible loss of carbonate in the orthogonal profile face. The combination of these observations suggests an additional fracture evolution mechanism in the reactive system, i.e., fines migration. We show that fines migration enhances the access of fracturing fluid to the matrix resulting in a more pronounced fracture widening. Finally, we conclude that coupled mineral dissolution and fines migration govern fracture aperture growth during acidized brine injection. In this work, we effectively show the underlying risk of relying solely on models that do not include an important (transport) process that can alter the system significantly and propose a combined chemomechanical mechanism for fracture evolution appropriate for this shale mineralogy.

58 GEOSCIENCES↗

NASA NTRS2011

The Preflight Calibration of the Thermal Infrared Sensor (TIRS) on the Landsat Data Continuity Mission

The preflight calibration testing of TIRS evaluates the performance of the instrument at the component, subsystem and system level, The overall objective is to provide an instrument that is well calibrated and well characterized with specification compliant data that will ensure the data continuity of Landsat from the previous missions to the LDCM, The TIRS flight build unit and the flight instrument were assessed through a series of calibration tests at NASA Goddard Space Flight Center. Instrument-level requirements played a strong role in defining the test equipment and procedures used for the calibration in the thermal/vacuum chamber. The calibration ground support equipment (CGSE), manufactured by MEI and ATK Corporation, was used to measure the optical, radiometric and geometric characteristics of TIRS, The CGSE operates in three test configurations: GeoRad (geometric, radiometric and spatial), flood source and spectral, TIRS was evaluated though the following tests: bright target recovery, radiometry, spectral response, spatial shape, scatter, stray light, focus, and uniformity, Data were obtained for the instrument and various subsystems under conditions simulating those on orbit In the spectral configuration, a monochromator system with a blackbody source is used for in-band and out-of-band relative spectral response characterization, In the flood source configuration the entire focal plane array is illuminated simultaneously to investigate pixel-to-pixel uniformity and dead or inoperable pixels, The remaining tests were executed in the GeoRad configuration and use a NIST calibrated cavity blackbody source, The NIST calibration is transferred to the TIRS sensor and to the blackbody source on-board TIRS, The onboard calibrator will be the primary calibration source for the TIRS sensor on orbit.

Smith, Ramsey↗

NASA NTRS1975

A sensitivity analysis of regional and small watershed hydrologic models

Continuous simulation models of the hydrologic behavior of watersheds are important tools in several practical applications such as hydroelectric power planning, navigation, and flood control. Several recent studies have addressed the feasibility of using remote earth observations as sources of input data for hydrologic models. The objective of the study reported here was to determine how accurately remotely sensed measurements must be to provide inputs to hydrologic models of watersheds, within the tolerances needed for acceptably accurate synthesis of streamflow by the models. The study objective was achieved by performing a series of sensitivity analyses using continuous simulation models of three watersheds. The sensitivity analysis showed quantitatively how variations in each of 46 model inputs and parameters affect simulation accuracy with respect to five different performance indices.

Ambaruch, R.↗

NASA NTRS2020

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: the Case of Rio deJaneiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001-2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12-35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

IMERG↗

DOE OSTI2026

The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning

Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines. This study provides the first assessment of ML performance against actual operational forecasting systems operated by the California Nevada River Forecast Center (CNRFC), which combines the Community Hydrologic Prediction System (CHPS) with forecasters-in-the-loop. Results demonstrate that forecasters-in-the-loop systems consistently outperform ML models in both general forecasts and flood alerting across lead times up to 96 hr, even when ML models use observed forcings, while CNRFC operational process relies on biased weather forecasts. Our analysis reveals that forecaster expertise maintains forecast reliability despite inaccurate precipitation inputs, with human-guided systems showing superior performance degradation characteristics at extended lead times. These findings highlight the irreplaceable value of human expertise in operational forecasting and caution against overstating current ML capabilities in real-world applications.

Tran, Vinh Ngoc [Univ. of Michigan, Ann Arbor, MI ↗

DOE OSTI2022

Investigating coastal backwater effects and flooding in the coastal zone using a global river transport model on an unstructured mesh

Abstract. Coastal backwater effects are caused by the downstream water level increase as a result of elevated sea level, high river discharge and their compounding influence. Such effects have crucial impacts on floods in densely populated regions but have not been well represented in large-scale river models used in Earth system models (ESMs), partly due to model mesh deficiency and oversimplifications of river hydrodynamics. Using two mid-Atlantic river basins as a testbed, we perform the first attempt to simulate the backwater effects comprehensively over a coastal region using the MOSART river transport model under an ESM framework, i.e., Energy Exascale Earth System Model (E3SM) configured on a regionally refined unstructured mesh, with a focus on understanding the backwater drivers and their long-term variations. By including sea level variations at the river downstream boundary, the model performance in capturing backwaters is greatly improved. We also propose a new flood event selection scheme to facilitate the decomposition of backwater drivers into different components. Our results show that while storm surge is a key driver, the influence of extreme discharge cannot be neglected, particularly when the river drains to a narrow river-like estuary. Compound flooding, while not necessarily increasing the flood peaks, exacerbates the flood risk by extending the duration of multiple coastal and fluvial processes. Furthermore, our simulations and analysis highlight the increasing strength of backwater effects due to sea level rise and more frequent storm surge during 1990–2019. Thus, backwaters need to be properly represented in ESMs to improve the predictive understanding of coastal flooding.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Polymer Retention Evaluation in a Heavy Oil Sand for a Polymer Flooding Application on Alaska’s North Slope

For a polymer flooding field trial in a heavy oil reservoir on Alaska’s North Slope, polymer retention is a key parameter. Because of the economic impact of retention, this parameter was extensively studied using field core material and conditions. Several important findings were noted. Polymer retention based on effluent viscosity measurements can be overestimated unless the correct (non-linear) relation between polymer concentration and viscosity is used. Polymer degradation (either mechanical or oxidative) can also lead viscosity-based measurements to overestimate retention. Inaccessible pore volume (IAPV) can be overestimated if insufficient brine is flushed through the sand between polymer banks. Around 100 pore volumes (PV) of brine may be needed to displace mobile polymer to approach a true residual resistance factor and properly measure IAPV. Even for a sand pack with kwsor = 20 mD, IAPV was zero for HPAM with molecular weight (Mw) of 18 million g/mol. Fine-grain particles (<20 μm) strongly impacted polymer retention values. Native NB#1 sand with a significant component of particles <20 μm exhibited 290 μg/g, while the same sand exhibited 28 μg/g after these small particles were removed. Polymer retention did not necessarily correlate with mineral composition. The NB#1, NB#3, and OA sands had similar elemental and clay compositions, but the NB#1 sand exhibited ~10 times higher retention than the NB#3 sand. Polymer retention did not necessarily correlate with permeability. NB#1 sand exhibited much higher retention than OA sand, even though NB#1 sand was twice as permeable as OA sand. No evidence of chromatographic separation of HPAM molecular weights was found in our experiments. Although retention tended to be greater without a residual oil saturation (than at Sor), the effect was not strong. Aging a core (with high oil saturation) at 60°C reduced HPAM retention by a factor of two. As expected, under similar conditions, polymer retention was greater for a higher Mw HPAM (18 million g/mol) than for a lower Mw HPAM (10-12 million g/mol). In many cases with high polymer retention values (e.g., 240 μg/g), polymer arrival at the end of the core was relatively quick, but achieving the injected concentration occurred gradually over many pore volumes. This effect was not due chromatographic separation of polymer molecular weights. Results from modeling of this behavior was consistent with concentrationdependent polymer retention. The form assumed for the retention function in a simulator can have an important impact on the timing and magnitude of the oil response from a polymer flood. Field-based observations can underestimate polymer retention, depending on when the tracer and polymer concentrations were measured and the assumptions made about reservoir heterogeneity.

Wang, Dongmei↗

NASA NTRS1997

Numerical Simulation of the 9-10 June 1972 Black Hills Storm Using CSU RAMS

Strong easterly flow of low-level moist air over the eastern slopes of the Black Hills on 9-10 June 1972 generated a storm system that produced a flash flood, devastating the area. Based on observations from this storm event, and also from the similar Big Thompson 1976 storm event, conceptual models have been developed to explain the unusually high precipitation efficiency. In this study, the Black Hills storm is simulated using the Colorado State University Regional Atmospheric Modeling System. Simulations with homogeneous and inhomogeneous initializations and different grid structures are presented. The conceptual models of storm structure proposed by previous studies are examined in light of the present simulations. Both homogeneous and inhomogeneous initialization results capture the intense nature of the storm, but the inhomogeneous simulation produced a precipitation pattern closer to the observed pattern. The simulations point to stationary tilted updrafts, with precipitation falling out to the rear as the preferred storm structure. Experiments with different grid structures point to the importance of removing the lateral boundaries far from the region of activity. Overall, simulation performance in capturing the observed behavior of the storm system was enhanced by use of inhomogeneous initialization.

Nair, U. S.↗

DOE OSTI2025

Characterizing How Meteorological Forcing Selection and Parameter Uncertainty Influence Community Land Model Version 5 Hydrological Applications in the United States

Despite the increasing use of large-scale Land Surface Models (LSMs) in predicting hydrological responses in extreme conditions, there's a critical gap in understanding the uncertainties in these predictions. This study addresses this gap through a detailed diagnostic evaluation of the uncertainties arising from meteorological forcing selection and model parametrization in hydrological simulations of the Community Land Model version 5 (CLM5). CLM5 is configured at a spatial scale of about 12-km to simulate runoff processes for 464 headwater watersheds, selected from the Catchment Attributes for Large-Sample Studies (CAMELS) dataset to be representative of physiographic and climatic gradients across the conterminous United States. For each watershed, CLM5 is driven by five commonly used gridded forcing datasets in combination with a large ensemble (> 1200) of key CLM5 hydrologic parameters. Our results suggest that uncertainty in CLM5 runoff simulations resulting from both forcing and parametric sources is markedly higher in arid regions, e.g., Great Plains and Midwest regions. Uncertainty in low flow is dominated by parametric uncertainty, while the selection of meteorological forcing contributes more dominantly to high flow and seasonal flows during fall and spring. Our analysis also demonstrates that the selection of forcing datasets and the metrics used to calibrate CLM5 significantly impact the model’s predictive accuracy in extreme event severity for both floods and droughts. Overall, the results from this study highlight the need to understand and account for forcing and parametric uncertainties in CLM5 simulations, particularly for hazard and risk assessments addressing hydrologic extremes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Storm↗

NASA NTRS2025

An Observation-Driven Framework for Modeling Post-Fire Hydrologic Response: Evaluation for Two Central California Case Studies

In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale of post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of the impacts of fire on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify the impacts of wildfire on runoff generation. We consider the disturbances in the vegetation and soil as the two main factors contributing to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We consider the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration for two case studies. These case studies demonstrate the general applicability of hydrophobicity formulation to serve as a guideline for exploring the range of hydrologic responses post-fire.

Fire Hydrology↗

DOE OSTI2022

Typological representation of the offshore oceanographic environment along the Alaskan North Slope

Erosion and flooding impacts to Arctic coastal environments are intensifying with nearshore oceanographic conditions acting as a key environmental driver. Robust and comprehensive assessment of the nearshore oceanographic conditions require knowledge of the following boundary conditions: incident wave energy, water level, incident wind energy, ocean temperature and salinity, bathymetry, and shoreline orientation. The number of offshore oceanographic boundary conditions can be large, requiring a significant computational investment to reproduce nearshore conditions. This present study develops location-independent typologies to reduce the number of boundary conditions needed to assess nearshore oceanographic environments in both a Historical (2007–2019) and Future (2020–2040) timespan along the Alaskan North Slope. We used WAVEWATCH III® and Delft3D Flexible Mesh model output from six oceanographic sites located along a constant ~50 m bathymetric line spanning the Chukchi to Beaufort Seas. K-means clustering was applied to the energy-weighted joint-probability distribution of significant wave height (H s ) and peak period (T p ). Distributions of wave and wind direction, wind speed, and water level associated with location-independent centroids were assigned single values to describe a reduced order, typological rendition of offshore oceanographic conditions. Reanalysis data (e.g., ASRv2, ERA5, and GOFS) grounded the historical simulations while projected conditions were obtained from downscaled GFDL-CM3 forced under RCP8.5 conditions. Location-dependence for each site is established through the occurrence joint-probability distribution in the form of unique scaling factors representing the fraction of time that the typology would occupy over a representative year. As anticipated, these typologies show increasingly energetic ocean conditions in the future. They also enable computationally efficient simulation of the nearshore oceanographic environment along the North Slope of Alaska for better characterization of coastal processes (e.g., erosion, flooding, or sediment transport).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

Zhang, Xiaodong↗