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All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

The Global Mapping of Electron Precipitation and Ionospheric Conductance From Whistler-Mode Chorus Waves

Auroral precipitation is the second major energy source after solar irradiation that ionizes the Earth’s upper atmosphere. Diffuse electron aurora caused by wave-particle interaction in the inner magnetosphere (L < 8) takes over 60% of total auroral energy flux, strongly contributing to the ionospheric conductance and thus to the ionosphere-thermosphere dynamics. This paper quantifies the impact of chorus waves on the diffuse aurora and the ionospheric conductance during quiet, medium, and strong geomagnetic activities, parameterized by AE <100, 100 < AE < 300, and AE > 300, respectively. Using chorus wave statistics and inner-magnetosphere plasma conditions from Timed History Events and Macroscale Interactions during Substorms (THEMIS) observations, we directly derive the energy spectrum of diffuse electron precipitation under quasi-linear theory. We then calculate the height-integrated conductance from the wave-driven aurora spectrum using the electron impact ionization model of Fang et al. (Geophys. Res. Lett., 2010, 37) and the MSIS atmosphere model. By utilizing Fang’s ionization model, the US Naval Research Laboratory Mass Spectrometer and Incoherent Scattar Radar (NRLMSISE-00) model from 2000s for the neutral atmosphere components, and the University of California, Los Angeles (UCLA) Full Diffusion Code, we improve upon the standard generalization of Maxwellian diffuse electron precipitation patterns and their resulting ionosphere conductance. Our study of global auroral precipitation and ionospheric conductance from chorus wave statistics is the first statistical model of its kind. We show that the total electron flux and conductance pattern from our results agree with those of Ovation Prime model over the pre-midnight to post-dawn sector as geomagnetic activity increases. Our study examines the relative contributions of upper band chorus (UBC) and lower band chorus wave (LBC) driven conductance in the ionosphere. We found LBC waves drove diffuse electron precipitation significantly more than UBC waves, however it is possible that THEMIS data may have underestimated the upper chorus band wave observations for magnetic latitudes below 65 degrees. ° .

diffuse auroral precipitation↗
NASA NTRS1997

Whiting events: biogenic origin due to the photosynthetic activity of cyanobacterial picoplankton

An annual whiting event occurs each year in late May to early June in Fayetteville Green Lake, New York. The initiation of this event correlates with exponential growth of the Synechococcus population within the lake. Synechococcus is the dominant (by approximately 4 orders of magnitude) autotrophic organism owing to the oligotrophic condition of the lake. The delta 13C values of the dissolved inorganic C range seasonally from -9.5% in winter to -6.2% in summer due to photosynthetic activity. Calcite precipitates principally in the microenvironment surrounding Synechococcus because of a photosynthetically driven alkalization process and the availability of the cells as nucleation sites. This calcite has a heavier delta 13C value (>4%) than does the dissolved inorganic C of the lake water owing to the cells' preferential uptake of 12C. A conceptual model suggests that photosynthetic activity and cell surface chemistry, together with the substantial surface area that arises from the great abundance of micron-sized cells, allow Synechococcus to dominate the annual whiting events in Fayetteville Green Lake.

NASA Center ARC↗
DOE OSTI2023

Assessing the methane mitigation potential of innovative management in US rice production

Abstract Rice is an important global crop while also contributing significant anthropogenic methane (CH 4 ) emissions. To support the future of rice production, more information is needed on the impacts of sustainability-driven management used to grow rice with lower associated methane emissions. Recent support for the impacts of different growing practices in the US has prompted the application of a regional methodology (Tier 2) to estimate methane emissions in different rice growing regions. The methodology estimates rice methane emissions from the US Mid-South (MdS) and California (Cal) using region-specific scaling factors applied to a region-specific baseline flux. In our study, we leverage land cover data and soil clay content to estimate methane emissions using this approach, while also examining how changes in common production practices can affect overall emissions in the US. Our results indicated US rice cultivation produced between 0.32 and 0.45 Tg CH 4 annually, which were approximately 7% and 42% lower on average compared to Food and Agriculture Organization of the UN (FAO) and US Environmental Protection Agency (EPA) inventories, respectively. Our estimates were 63% greater on average compared to similar methods that lack regional context. Introducing aeration events into irrigation resulted in the greatest methane reductions across both regions. When accounting for differences between baseline and reduction scenarios, the US MdS typically had higher mitigation potential compared to Cal. The differences in cumulative mitigation potential across the 2008–2020 period were likely driven by lower production area clay content for the US MdS compared to Cal. The added spatial representation in the Tier 2 approach is useful in surveying how impactful methane-reducing practices might be within and across regions.

Environmental Sciences & Ecology↗
NASA NTRSDate not supplied

Role of Antecedent Soil Moisture and Vegetation Stress in Lightning-Initiated Wildfires

Lightning-caused wildfires are a small percentage of all wildfire events within the Conterminous U.S. (CONUS), but they account for over 56% of the acreage burned. The atmospheric conditions favoring wildfire and rapid growth are well understood: large dewpoint depressions, unstable planetary boundary layer, strong winds, etc. However, antecedent land surface conditions affecting dead and live fuel moisture is more difficult to quantify. This study examines over 20 years of antecedent land surface, vegetation stress, and wildfire characteristic data associated with nearly 77,000 lightning-initiated wildfires from the U.S. Forest Service Wildfire Database. We will invoke two in-house databases generated by the NASA Short-term Prediction Research and Transition (SPoRT) Center: an observations-driven, climatological run of the Noah land surface model within the NASA Land Information System (i.e., SPoRT-LIS) to depict soil moisture deficits / anomalies, and a satellite-constrained Evaporative Stress Index (ESI) product to denote areas of stressed vegetation. We will mine these datasets associated with lightning-caused (and null) events to determine important relationships, distributions, and delineators that correspond to elevated threat areas for lightning-initiated wildfires.

Wildfire↗
DOE OSTI2017

Aseismic Motions Drive a Sparse Seismicity During Fluid Injections Into a Fractured Zone in a Carbonate Reservoir

An increase in fluid pressure in faults can trigger seismicity and large aseismic motions. Understanding how fluid and faults interact is an essential goal for seismic hazard and reservoir monitoring, but this key relation remains unclear. We developed an in situ experiment of fluid injections at a 10 meter scale. Water was injected at high pressure in different geological structures inside a fault damaged zone, in limestone at 280 m depth in the Low Noise Underground Laboratory (France). Induced seismicity, as well as strains, pressure, and flow rate, was continuously monitored during the injections. Although nonreversible deformations related to fracture reactivations were observed for all injections, only a few tests generated seismicity. Events are characterized by a 0.5-to-4 kHz content and a small magnitude (approximately -3.5). They are located within 1.5 m accuracy between 1 and 12 m from the injections. Comparing strain measurements and seismicity shows that more than 96% of the deformation is aseismic. The seismic moment is also small compared to the one expected from the injected volume. Moreover, a dual seismic behavior is observed as (1) the spatiotemporal distribution of some cluster of events is clearly independent from the fluid diffusion (2) while a diffusion-type pattern can be observed for some others clusters. Furthermore, the seismicity might therefore appear as an indirect effect to the fluid pressure, driven by aseismic motion and related stress perturbation transferred through failure.

58 GEOSCIENCES↗
DOE OSTI2018

Valuing catastrophe bonds involving correlation and CIR interest rate model

Natural catastrophes lead to problems of insurance and reinsurance industry. Classic insurance mechanisms are often inadequate for dealing with consequences of catastrophic events. Therefore, new financial instruments, including catastrophe bonds (cat bonds), were developed. In this paper we price the catastrophe bonds with a generalized payoff structure, assuming that the bondholder’s payoff depends on an underlying asset driven by a stochastic jump-diffusion process. Simultaneously, the risk-free spot interest rate has also a stochastic form and is described by the multi-factor Cox–Ingersoll–Ross model. We assume the possibility of correlation between the Brownian part of the underlying asset and the components of the interest rate model. Using stochastic methods, we prove the valuation formula, which can be applied to the cat bonds with various payoff functions. We use adaptive Monte Carlo simulations to analyze the numerical properties of the obtained pricing formula for various settings, including some similar to the practical cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

FlameNEST: explicit profile likelihoods with the Noble Element Simulation Technique

We present FlameNEST, a framework providing explicit likelihood evaluations in noble element particle detectors using data-driven models from the Noble Element Simulation Technique. FlameNEST provides a way to perform statistical analyses on real data with no dependence on large, computationally expensive Monte Carlo simulations by evaluating the likelihood on an event-by-event basis using analytic probability elements convolved together in a single TensorFlow multiplication. Furthermore, this robust framework creates opportunities for simple inter-collaboration analyses which will be fundamental for the future of experimental dark matter physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Search for electroweak-scale dijet resonances using trigger-level analysis with the ATLAS detector in 132 fb −1 of 𝑝⁢𝑝 collisions at $\sqrt{𝑠}$ = 13 TeV

This article reports on a search for dijet resonances using 132 fb −1 of 𝑝⁢𝑝 collision data recorded at $\sqrt{𝑠}$ = 13 TeV by the ATLAS detector at the Large Hadron Collider. The search is performed solely on jets reconstructed within the ATLAS trigger to overcome bandwidth limitations imposed on conventional single-jet triggers, which would otherwise reject data from decays of sub-TeV dijet resonances. Collision events with two jets satisfying transverse momentum thresholds of 𝑝 T ≥ 85 GeV and jet rapidity separation of |𝑦*| <0.6 are analysed for dijet resonances with invariant masses from 375 to 1800 GeV. A data-driven background estimate is used to model the dijet mass distribution from multijet processes. No significant excess above the expected background is observed. Upper limits are set at 95% confidence level on coupling values for a benchmark leptophobic axial-vector 𝑍′ model and on the production cross section for a new resonance contributing a Gaussian-distributed line-shape to the dijet mass distribution.

hadron colliders↗
DOE OSTI2018

Zeptocalorimetry

Here we present the development of a Zeptocalorimeter. The motivation for designing and implementing such a device is driven, ultimately, by its anticipated exceptional sensitivity (10-21 J/K, at 2K). Such a device would be highly valuable in detecting minute quantities of mass for threat detection, studying fundamental phonon physics, and detecting energetic dissipation events at the attojoule level. To date, the most sensitive calorimeter demonstrated in the literature at 2K has been developed by the Roukes group at Caltech, where they achieved an addendum heat capacity of 10 -15 J/K with a 1/1000 sensitivity to external stimuli. To obtain such a low value of heat capacity requires a very small thermal mass, and thus, one of the greatest challenges in this project is the fabrication of this device, which requires numerous precision nanofabrication techniques. Furthermore, the heat capacity measurement of this device, as performed from room temperature to cryogenic temperatures, is equally challenging, as the transient signals used to determine the platform's thermal time constant require careful attention to the mitigation of feedthrough capacitance and delicate amplifier offsets. In this report we describe in detail the fabrication process flow for developing the calorimeter, including the layout and device design for obtaining a single lumped RC thermal resistance and capacitance, so that the device can be used for quantitative measurements of nanoscale materials with a suitable thermal link. The measurement method and experimental setup are also given, where we explain the heater and thermometer calibration methods, the thermal resistance measurements, the transient measurements, and lastly the cryogenic setup with intermediate frequency cabling and the thermal sinking of those lines.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Quantifying aerosol size distributions and their temporal variability in the Southern Great Plains, USA

A quality-controlled, 5-year dataset of aerosol number size distributions (particles with diameters (Dp) from 7 nm through 14 µm) was developed using observations from a scanning mobility particle sizer, aerodynamic particle sizer, and a condensation particle counter at the Department of Energy's Southern Great Plains (SGP) site. This data set was used for two purposes. First, typical characteristics of the aerosol size distribution (number, surface area, and volume) were calculated for the SGP site, both for the entire dataset and on a seasonal basis, and size distribution lognormal fit parameters are provided. While the median size distributions generally had similar shapes (four lognormal modes) in all the seasons, there were some significant differences between seasons. These differences were most significant in the smallest particles(Dp<30 nm) and largest particles (Dp>800 nm).Second, power spectral analysis was conducted on this long-term dataset to determine key temporal cycles of total aerosol concentrations, as well as aerosol concentrations in specified size ranges. The strongest cyclic signal was associated with a diurnal cycle in total aerosol number concentrations that was driven by the number concentrations of the smallest particles(Dp<30 nm). This diurnal cycle in the smallest particles occurred in all seasons in ~50% of the observations,suggesting a persistent influence of new particle formation events on the number concentrations observed at the SGP site. This finding is in contrast with earlier studies that suggest new particle formation is observed primarily in the springtime at this site. The timing of peak concentrations associated with this diurnal cycle was shifted by several hours depending on the season,which was consistent with seasonal differences in insolation and boundary layer processes. Significant diurnal cycles in number concentrations were also found for particles with Dp between 140 and 800 nm, with peak concentrations occurring in the overnight hours, which were primarily associated with both nitrate and organic aerosol cycles. Weaker cyclic signals were observed for longer timescales (days to weeks) and are hypothesized to be related to the timescales of synoptic weather variability. The strongest periodic signals (3.5–5 and 7 d cycles) for these longer timescales varied depending on the season, with no cyclic signals and the lowest variability in the summer.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Magnetic Roots and the Driving of Extended Coronal Heating

We report results from a continuation of a previous study, in which we found large bright coronal loops within active regions and extending from active regions that have one end rooted near an island of included magnetic polarity that is a site of enhanced coronal heating and microflares. This suggested that magnetic activity such as microflaring results in enhanced heating in both the compact core field around the island and in the large loops extending from it. We might expect that the intensity variations due to enhanced heating in the compact and extended structures would be correlated. However, although some ex- tended loops do respond to the largest events taking place in the core fields near their feet, they do not show a clear response to most smaller individual events nor to the overall envelope of coronal heating activity in the core fields at their feet as determined from longer-term observations. Thus, while it is clear that the extended loops' heating is being driven from their ends at the magnetic islands, much of this heating is apparently by some form of footpoint activity that is not strongly coupled to the heating in the footpoint core fields. One possibility is that the remote heating in the extended loops is driven by reconnection at the magnetic null over the island, and that this reconnection is driven mainly by core-field activity that produces little coronal heating within the core field itself, perhaps in the manner of the numerical simulations by Karpen, Antiochos, and DeVore.

Porter, Jason G.↗
NASA NTRS2004

Solar Magnetic Explosions, Spicules, and the Heliosphere

We present an example of each of the following observed characteristics of the magnetic origins of quiet-region coronal heating, spicules, macrospicules, and coronal mass ejections (CMEs). (1) In quiet regions, the luminosity of the corona is roughly proportional to the edge length of the underlying photospheric magnetic network. (2) Spicules and EUV explosive events are concentrated at the edges of the magnetic network. (3) Many macrospicules have the magnetic structure of a surge rooted around an inclusion of opposite-polarity magnetic flux. (4) CMEs and eruptive flares are driven by explosions of sheared magnetic fields rooted along polarity dividing lines (neutral lines) in the photospheric magnetic flux. These characteristics together suggest that the mainstay of the heliosphere, the corona/solar wind rooted in quiet regions and coronal holes, may be driven by myriads of tiny magnetic explosions at the network edges, explosions like those that drive CMEs but of vastly smaller scale. If so, the steady solar wind and the CMEs that disrupt it both have the same root cause: explosions of initially-closed, strongly-sheared, bipolar magnetic fields. The photospheric vector magnetograms, chromospheric filtergrams, EUV spectra, and coronal images from Solar-B are expected to have sufficient sensitivity, spatial resolution, and cadence to test this scenario for coronal heating in quiet regions and coronal holes. This work was supported by NASA/OSS through its Solar and Heliospheric Physics SR and T Program and Sun-Earth Connection GI Program.

Moore, Ronald L.↗
NASA NTRS2018

Sun-to-Earth Propagation of the 2015 June 21 Coronal Mass Ejection Revealed by Optical, EUV, and Radio Observations

We investigate the propagation of the 2015 June 21 CME-driven shock as revealed by the type II bursts at metricand longer wavelengths and coronagraph observations. The CME was associated with the second largest geomagneticstorm of solar cycle 24 and a large solar energetic particle (SEP) event. The eruption consisted of twoM-class flares, with the first one being confined, with no metric or interplanetary radio bursts. However, therewas intense microwave burst, indicating accelerated particles injected toward the Sun. The second flare was eruptive that resulted in a halo CME. The CME was deflected primarily by an equatorial coronal hole that resulted in the modification of the intensity profile of the associated SEP event and the duration of the CME at Earth. The interplanetary type II burst was particularly intense and was visible from the corona all the way to the vicinity of the Wind spacecraft with fundamental-harmonic structure. We computed the shock speed using the type II drift rates at various heliocentric distances and obtained information on the evolution of the shock that matched coronagraph observations near the Sun and in-situ observations near Earth. The depth of the geomagnetic storm is consistent with the 1-AU speed of the CME and the magnitude of the southward component.

Gopalswamy, N.↗
DOE OSTI2025

Origin of Stabilization of Ligand-Centered Mixed Valence Ruthenium Azopyridine Complexes: DFT Insights for Neuromorphic Applications

Redox-driven conductance changes are critical processes in molecular- and coordination-complex-based memristive thin films and devices that are envisioned for neuromorphic technologies, but fundamental mechanisms of conductance switching are not fully understood. Here, we explore charge disproportionation (CD) processes in [Ru II L 2 ](PF 6 ) 2 molecular systems that intrinsically involve interfragment charge transfer (IFCT). Using a combination of ab initio molecular dynamics simulation (AIMD), time-dependent density functional theory (TD-DFT), and density functional theory (DFT) calculations, we investigate the electron transfer mechanisms and the roles of temperature and cell volumetric expansion in facilitating the counterion movements and electronic transitions required for low-cost IFCT and charge redistribution. A detailed analysis of the density of states and TD-DFT calculations highlights that unpaired electrons play a crucial role in low-energy transitions, with the azo (N=N) groups of the ligand serving as the primary sites for electronic transport between molecular fragments, further stabilizing the asymmetric state. Localization of added electrons on azo ligands occurs with negligible change at the Ru centers, supported by atomic volume expansions up to +4.74 bohr 3 , and goes along with a progressive reduction of the HOMO−LUMO gap across redox states, suggesting enhanced conductivity. The TD-DFT analysis reveals a dominant IFCT excitation at 2082.76 nm in the doubly reduced (22) state, while a stabilization energy of 1.20 eV of the asymmetric (13) state relative to the symmetric (22) state is predicted by constrained DFT. Periodic DFT and AIMD simulations emulating a molecular film show that the stabilization of the asymmetric state, relative to a symmetric one, translates in net charge separation values (order of ∼0.33 e) that are strongly linked to increased counterion mobility (average counterion displacements exceeding 0.7 Å per atom during CD events) and the involvement of azo groups in electron redistribution. These findings, which align with previously reported experimental and computational data, provide key insights into the IFCT mechanisms and electronic transport facilitated by azo groups, with important implications for redox-driven memristive and neuromorphic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Advancing Open Science in Atmospheric Research: Integrating Data Usability and Machine Learning

In the dynamic realm of atmospheric sciences, the convergence of data science methodologies and open data marks a transformative era, driving research advancements and nurturing aspiring scientists. This abstract highlights two pivotal projects that epitomize open science principles, aligning seamlessly with the session's objective of interdisciplinary synergy and the cultivation of emerging talent. As a NASA-certified data center, our foremost endeavor focuses on enhancing the visibility and traceability of NASA datasets within atmospheric science research. This initiative not only elevates these datasets' prominence but also establishes a robust framework ensuring their credibility in scholarly discourse. By bridging the gap between data sources and research publications, this project serves as an educational catalyst, nurturing a new generation of scholars in open collaboration and dataset authenticity. Concurrently, our second project pioneers an early warning system for flooding events, utilizing machine learning algorithms to predict flooded fractions. Through multi-source data fusion and predictive modeling, this initiative goes beyond forecasting; it embodies the core of open science by enabling proactive risk mitigation strategies. This project not only advances atmospheric sciences but also fosters an environment where young scholars engage in practical, data-driven solutions. These intertwined projects exemplify the fusion of data science with open data solutions, ensuring both the usability of quality datasets and the cultivation of scientific knowledge among emerging scholars. By spotlighting these impactful use cases, our aim is to foster discussions emphasizing the importance of open collaboration, data integrity, and the nurturing of scientific talent in atmospheric sciences." "In the dynamic realm of atmospheric sciences, the convergence of data science methodologies and open data marks a transformative era, driving research advancements and nurturing aspiring scientists. This abstract highlights two pivotal projects that epitomize open science principles, aligning seamlessly with the session's objective of interdisciplinary synergy and the cultivation of emerging talent. As a NASA-certified data center, our foremost endeavor focuses on enhancing the visibility and traceability of NASA datasets within atmospheric science research. This initiative not only elevates these datasets' prominence but also establishes a robust framework ensuring their credibility in scholarly discourse. By bridging the gap between data sources and research publications, this project serves as an educational catalyst, nurturing a new generation of scholars in open collaboration and dataset authenticity. Concurrently, our second project pioneers an early warning system for flooding events, utilizing machine learning algorithms to predict flooded fractions. Through multi-source data fusion and predictive modeling, this initiative goes beyond forecasting; it embodies the core of open science by enabling proactive risk mitigation strategies. This project not only advances atmospheric sciences but also fosters an environment where young scholars engage in practical, data-driven solutions. These intertwined projects exemplify the fusion of data science with open data solutions, ensuring both the usability of quality datasets and the cultivation of scientific knowledge among emerging scholars. By spotlighting these impactful use cases, our aim is to foster discussions emphasizing the importance of open collaboration, data integrity, and the nurturing of scientific talent in atmospheric sciences.

Jennifer Wei↗
DOE OSTI2019

Multivariate Pointwise Information-Driven Data Sampling and Visualization

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Hurricanes and turbulent floods threaten arsenic-contaminated coastal soils and vulnerable communities

Coastal environments, particularly those adjacent to Superfund sites, are at increased risk of contaminant release during natural disasters, posing serious threats to nearby communities. To investigate this issue, we employed an advanced laboratory flood simulator to impose arsenic-contaminated sediments to turbulent flooding events. We further monitored changes in arsenic collocation and solid-phase speciation using advanced synchrotron radiation-based techniques to understand the impacts of flooding on arsenic mobility. Our results demonstrate that turbulent conditions significantly enhance the resuspension of arsenic-rich sediments, resulting in increased arsenic release into the water. This mobilization is driven by the erosion of the reduced sediments and the redox-mediated transformation and dissolution of Fe and Mn (oxyhydr) oxides, which promote the release of As(III). We found that arsenic speciation on resuspended particles is closely tied to shear stress, with As(V) prevailing at low stress and the more toxic As(III) dominating at higher stress levels. In the post-erosion phase, solid-phase As(III) decreased while dissolved As(III) increased, indicating ongoing desorption. The persistence of multiple arsenic species on resuspended particles marks them as potential long-range transport vectors. Thus, the environmental impact of flooding and sediment resuspension extends beyond the event itself, raising longer-term concerns for arsenic mobility. Our comprehensive geospatial analysis revealed substantial overlap between arsenic-contaminated soils and regions at high risk of flooding and hurricanes across the conterminous United States. This overlap disproportionately impacts economically disadvantaged and marginalized communities. Approximately 40 million Americans reside within 10 kilometers of these high-risk contaminated zones, with nearly 28 million exposed to hurricane threats and around 18 million vulnerable to flooding risks. Alarmingly, over 40% of those affected by hurricanes and 33% of those impacted by flooding belong to underrepresented minority and low-income populations. These findings highlight the urgent need for targeted mitigation strategies to protect public health and address environmental justice concerns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Role of underlying surface, rainstorm and antecedent wetness condition on flood responses in small and medium sized watersheds in the Yangtze River Delta region, China

Floods are becoming more frequent and are hard to control due to the shortage of water conservancy projects in small and medium sized basins, especially in developing regions. Understanding the hydrologic responses and estimated flood characteristics to storm events can help to predict flood disasters and form better effective mitigation and adaptation strategies. Here, using observations of several representative watersheds and Artificial Neural Networks (ANN) and Principal Component Regression (PCR) models, we conducted data-driven analyses to examine the flood responding characteristics. We quantified the relative contributions of the influencing factors to the variation of each flood characteristic. Statistical analysis of the observations shows that the drainage area plays a key role in determining the distribution of lag time and peak discharge. Rainstorm variability has direct influence on floods, and typhoon-induced rainstorms with high total rainfall and rainfall intensity generate higher lag time, flood peak, unit discharge and runoff depth, but lower runoff coefficient. The ANN and PCR models accurately predicted the variations of flood features using the driving factors including physical geographical characteristics, rainstorm features, and antecedent condition. Physical geographic characteristics are key influential factors of lag time, flood peak and runoff coefficient, while the rainstorm features control the magnitude of unit discharge and runoff depth. These results indicate that floods are mainly affected by rainstorm features and physical geographic characteristics in the Yangtze River delta, and it might become more damaging with the increasing rainfall extremes and sprawling impervious surfaces in a changing environment.

54 ENVIRONMENTAL SCIENCES↗