Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “event driven”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantifying Distribution System Resilience From Utility Data: Large Event Risk and Benefits of Investments

We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large-cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Comment on “How will induced seismicity in Oklahoma respond to decreased saltwater injection rates?” by C. Langenbruch and M. D. Zoback

The state of Oklahoma has experienced an unprecedented increase in earthquake activity since 2009, likely driven by large-scale wastewater injection operations. Statewide injection rates peaked in early 2015 and steadily decreased thereafter, approximately coinciding with collapsing oil prices and regulatory action. If seismic activity is primarily driven by fluid injection, a noticeable seismogenic response to the decrease in injection rates is expected. Langenbruch and Zoback suggest that “the probability of potentially damaging larger events, should significantly decrease by the end of 2016 and approach historic levels within a few years.” We agree that the rate of small earthquakes has decreased toward the second half of 2016. However, their specific predictions about seismic hazard require reexamination. We test the influence of the model parameters of Langenbruch and Zoback based on fits to observed seismicity distributions. The results suggest that a range of realistic aftershock decay rates and b values can lead to an increase in moderate earthquake probabilities from 37 to 80% in 2017 without any further alteration to the model. In addition, the observation that all four M ≥ 5 earthquakes to date occurred when injection rates were below the triggering threshold of Langenbruch and Zoback challenges the applicability of the model for the most societally significant events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Mars global atmospheric oscillations - Annually synchronized, transient normal-mode oscillations and the triggering of global dust storms

Transient events of an unusual character have been discovered in the daily pressure variations of the Mars atmosphere's pressure at the planetary surface which last only a few Martian days, appear to repeat on an annual basis, cover a large part of the given day's hemisphere, occur in pairs separated by 20-days in some cases, and coincide with the annual pressure minimum. They also consist of spectral components nearly identical in frequency with diurnal and semidiurnal harmonics. It is presently suggested that these events are Kelvin, normal-mode, transient, global oscillations. An almost-diurnal and an almost-semidiurnal high-frequency global oscillation distinct from solar-driven tides may be common on Mars.

Tillman, James E.↗
NASA NTRS1998

Magnetotail Structure and its Internal Particle Dynamics During Northward IMF

This study uses Global magnetohydrodynamic (MHD) simulations driven by solar wind data along with Geotail observations of the magnetotail to investigate the magnetotail's response to changes in the interplanetary magnetic field (IMF); observed events used in the study occurred on March 29, 1993 and February 9, 1995. For events from February 9, 1995, we also use the time-dependent MHD magnetic and electric fields and the large-scale kinetic (LSK) technique to examine changes in the Geotail ion velocity distributions. Our MHD simulation shows that on March 29, 1993, during a long period of steady northward IMF, the tail was strongly squeezed and twisted around the Sun-Earth axis in response to variations in the IMF B(sub y) component. The mixed (magnetotail and magnetosheath) plasma observed by Geotail results from the spacecraft's close proximity to the magnetopause and its frequent crossings of this boundary. In our second example (February 9, 1995) the IMF was also steady and northward, and in addition had a significant B(sub y) component. Again the magnetotail was twisted, but not as strongly as on March 29, 1993. The Geotail spacecraft, located approximately 30 R(sub E) downtail, observed highly structured ion distribution functions. Using the time-dependent LSK technique, we investigate the ion sources and acceleration mechanisms affecting the Geotail distribution functions during this interval. At 1325 UT most ions are found to enter the magnetosphere on the dusk side earthward of Geotail with a secondary source on the dawn side in the low latitude boundary layer (LLBL). A small percentage come from the ionosphere. By 1347 UT the majority of the ions come from the dawn side LLBL. The distribution functions measured during the later time interval are much warmer, mainly because particles reaching the spacecraft from the dawnside are affected by nonadiabatic scattering and acceleration in the neutral sheet.

Ashour-Abdalia, M.↗
NASA NTRS1998

Magnetotail Structure and its Internal Particle Dynamics During Northward IMF

This study uses Global magnetohydrodynamic (MHD) simulations driven by solar wind data along with Geotail observations of the magnetotail to investigate the magnetotail's response to changes in the interplanetary magnetic field (IMF); observed events used in the study occurred on March 29, 1993 and February 9, 1995. For events from February 9, 1995, we also use the time-dependent MHD magnetic and electric fields and the large-scale kinetic (LSK) technique to examine changes in the Geotail ion velocity distributions. Our MHD simulation shows that on March 29, 1993, during a long period of steady northward IMF, the tail was strongly squeezed and twisted around the Sun-Earth axis in response to variations in the IMF B(sub y) component. The mixed (magnetotail and magnetosheath) plasma observed by Geotail results from the spacecraft's close proximity to the magnetopause and its frequent crossings of this boundary. In our second example (February 9, 1995) the IMF was also steady and northward, and in addition had a significant B(sub y) component. Again the magnetotail was twisted, but not as strongly as on March 29, 1993. The Geotail spacecraft, located approximately 30 R(sub E) downtail, observed highly structured ion distribution functions. Using the time-dependent LSK technique, we investigate the ion sources and acceleration mechanisms affecting the Geotail distribution functions during this interval. At 1325 UT most ions are found to enter the magnetosphere on the dusk side earthward of Geotail with a secondary source on the dawn side in the low latitude boundary layer (LLBL). A small percentage come from the ionosphere. By 1347 UT the majority of the ions come from the dawn side LLBL. The distribution functions measured during the later time interval are much warmer, mainly because particles reaching the spacecraft from the dawn side are affected by nonadiabatic scattering and acceleration in the neutral sheet.

Ashour-Abdalla, M.↗
NASA NTRS2001

Evolution of safety-critical requirements post-launch

This paper reports the results of a small study of requirements changes to the onboard software of three spacecraft subsequent to launch. Only those requirement changes that resulted from post-launch anoma-lies (i.e., during operations) were of interest here, since the goal was to better understand the relation-ship between critical anomalies during operations and how safety-critical requirements evolve. The results of the study were surprising in that anomaly-driven, post-launch requirements changes were rarely due to previous requirements having been incorrect. Instead, changes involved new requirements (1) for the software to handle rare events or (2) for the software to compensate for hardware failures or limitations. The prevalence of new requirements as a result of post-launch anomalies suggests a need for increased requirements-engineering support of maintenance activities in these systems. The results also confirm both the difficulty and the benefits of pursuing requirements completeness, especially in terms of fault tolerance, during development of critical systems.

Software requirements↗
NASA NTRS2016

Solar Coronal Jets: Observations, Theory, and Modeling

Coronal jets represent important manifestations of ubiquitous solar transients, which may be the source of significant mass and energy input to the upper solar atmosphere and the solar wind. While the energy involved in a jet-like event is smaller than that of "nominal" solar flares and coronal mass ejections (CMEs), jets share many common properties with these phenomena, in particular, the explosive magnetically driven dynamics. Studies of jets could, therefore, provide critical insight for understanding the larger, more complex drivers of the solar activity. On the other side of the size-spectrum, the study of jets could also supply important clues on the physics of transients close or at the limit of the current spatial resolution such as spicules. Furthermore, jet phenomena may hint to basic process for heating the corona and accelerating the solar wind; consequently their study gives us the opportunity to attack a broad range of solar-heliospheric problems.

Raouafi, N. E.↗
NASA NTRS1979

Transversely accelerated ions - An ionospheric source of hot magnetospheric ions

The paper considers observational data on transversely accelerated ion (TAI) events observed from the Isis 1 and 2 satellites. A source of hot magnetospheric ions was discovered in the auroral topside ionosphere, and a part of the cold ionospheric ion distribution is transversely accelerated within a source region as low as 1000 km. Subsequent to transverse acceleration, the ions are driven upward into the magnetosphere by the gradient B mirror force; they are observed by the soft particle spectrometers on the Isis 1 and 2 satellites, with sharply defined pitch angle distributions centered between 90 and 120 deg, and with energy distributions up to several hundred electron volts. Observations at 1400 km indicate that the transversely accelerated ion (TAI) events are predominantly a winter nightside auroral zone phenomenon, but in the summertime TAI events were observed at altitudes above 2750 km on the day-side within the cleft. Typical intensities exceeding 10 to the 8th power ions/sq cm s sr during TAI events suggest that they may be a significant source of hot ions for the magnetosphere.

Klumpar, D. M.↗
NASA NTRS2024

Data-Driven Method for Nonlinear Modal Interaction Identification in Fighter Aircraft

The purpose of this paper is to report a new data-driven analysis methodology for identifying nonlinear modal interactions and its application to aeroelastic wind-tunnel data from a generic fighter configuration, which reveals global modal interactions in this system caused by multiple resonance capture events initiated by local nonlinearities. It is shown that during supersonic testing at the Transonic Dynamics Tunnel, vibro-impacts at the embedded external fuel tank shakers on the inboard underwing stations of the generic fighter introduce strong nonlinearity that gives rise to nonlinear modal interactions. Specifically, time–frequency analysis of the acceleration data demonstrates that 6∶5 and 7∶6 transient resonance captures occur across the aircraft exclusively during this vibro-impact forcing window. Further analysis suggests that these resonance captures are dependent on Mach number. These results show experimentally that local vibro-impacts can cause global nonlinear effects in an aeroelastic system and that these effects can be identified using the new methodology herein. The data-driven analysis scheme employed shows applicability to full-aircraft flight test data for identifying nonlinear modal interactions without knowledge of the precise nonlinear contributors.

nonlinear dynamics↗
DOE OSTI2021

Ripple-Type Control for Enhancing Resilience of Networked Physical Systems

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events. Purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a “hotline” communication network. According to this protocol, agents act on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources and it is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests corroborate the efficacy of the novel scheme.

distributed control↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗
DOE OSTI2022

DINGO: Digital assistant to grid operators for resilience management of power distribution system

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. Here in this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Release history and transport parameters of relativistic solar electrons inferred from near-the-sun in situ observations

We study four consecutive 300–800 keV electron events observed on 1980 May 28 by Helios-1, when the spacecraft was located at 0.31 au from the Sun. We use two different techniques to extract the release time history of electrons at the Sun: (1) a data-driven method based on the assumption that particles conserve their magnetic moment as they propagate between the Sun and the spacecraft and (2) an inversion method that utilizes particle transport simulation results. Both methods make use of the particle angular distributions measured relative to the local direction of the magnetic field. The general characteristics of the release time profiles obtained by these two techniques are similar, especially during their rising phases. We find indications that the strength of the interplanetary scattering varies with the size of the solar parent event, suggesting that scattering processes are not necessarily an inherent property of the medium but are related to the amount of released particles at the Sun. We use the inferred release profiles to compute the expected intensities at 1 au. In contrast to simultaneous near-Earth observations by the Interplanetary Monitoring Platform (IMP-8), our simulations predict the observation of four separate events at 1 au. Processes that could contribute to the observation of one single time-extended event at 1 au include (1) distinct magnetic connections of the spacecraft to the particle sources, (2) the spatio-temporal evolution of the particle sources, and (3) different particle transport conditions, including a variation of λ{sub r} with radial distance and/or heliolongitude, as well as the possibility that electrons reached IMP-8 by diffusion perpendicular to the interplanetary magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2022

Limitations in Representation of Physical Processes Prevents Successful Simulation of PM2.5 During KORUS-AQ

High levels of fine particulate matter (PM2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea United States-Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM2.5. Atmospheric models often have difficulty simulating PM2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by -64% but overestimates nitrate by +36%. The largest underestimate in sulfate occurs during the pollution event, where models typically struggle to generate elevated sulfate concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100% against aircraft observations. We hypothesize that this is due to a large missing sink, which we implement here as a factor of five increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH3 could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO2 hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N2O5 hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model’s inability to simulate the buildup of PM2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO2 is added to the model which previously only considered aqueous production of sulfate from SO2 in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO2 simulation implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO2 decreases the fraction of PM2.5 attributable to long-range transport from 66% to 54%. Locally-produced sulfate increased from 1% to 25% of locally-produced PM2.5, implying that local emissions controls would have a larger effect than previously thought. However, this additional uptake of SO2 is coupled to the model nitrate prediction which affects the aerosol liquid water abundance and chemistry driving sulfate-nitrate-ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO2 to aerosol and simple improvements to the model nitrate simulation results in 30% less sulfate due to 40% less nitrate and aerosol water, and results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and nighttime boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate+nitrate+ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM2.5 in South Korea to ensure continued air quality improvements.

Katherine R. Travis↗
NASA NTRS2016

A Satellite Data-Driven, Client-Server Decision Support Application for Agricultural Water Resources Management

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in a typical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Agricultural↗
DOE OSTI2021

Confinement of relativistic electrons in a magnetic mirror en route to a magnetized relativistic pair plasma

Creating a magnetized relativistic pair plasma in the laboratory would enable the exploration of unique plasma physics relevant to some of the most energetic events in the universe. As a step toward a laboratory pair plasma, we have demonstrated an effective confinement of multi-MeV electrons inside a pulsed-power-driven 13 T magnetic mirror field with a mirror ratio of 2.6. The confinement is diagnosed by measuring the axial and radial losses with magnetic spectrometers. The loss spectra are consistent with ≤2.5 MeV electrons confined in the mirror for ∼1 ns. With a source of 1012 electron-positron pairs at comparable energies, this magnetic mirror would confine a relativistic pair plasma with Lorentz factor γ∼6 and magnetization σ∼40.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Improving Short Term Predictability of Hydrologic Models with Deep Learning

Focal Area: Focal Area 2. 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: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?

54 ENVIRONMENTAL SCIENCES↗