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NASA NTRS2012

Calibration of Two-dimensional Floodplain Modeling in the Atchafalaya River Basin Using SAR Interferometry

Two-dimensional (2D) satellite imagery has been increasingly employed to improve prediction of floodplain inundation models. However, most focus has been on validation of inundation extent, with little attention on the 2D spatial variations of water elevation and slope. The availability of high resolution Interferometric Synthetic Aperture Radar (InSAR) imagery offers unprecedented opportunity for quantitative validation of surface water heights and slopes derived from 2D hydrodynamic models. In this study, the LISFLOOD-ACC hydrodynamic model is applied to the central Atchafalaya River Basin, Louisiana, during high flows typical of spring floods in the Mississippi Delta region, for the purpose of demonstrating the utility of InSAR in coupled 1D/2D model calibration. Two calibration schemes focusing on Manning s roughness are compared. First, the model is calibrated in terms of water elevations at a single in situ gage during a 62 day simulation period from 1 April 2008 to 1 June 2008. Second, the model is calibrated in terms of water elevation changes calculated from ALOS PALSAR interferometry during 46 days of the image acquisition interval from 16 April 2008 to 1 June 2009. The best-fit models show that the mean absolute errors are 3.8 cm for a single in situ gage calibration and 5.7 cm/46 days for InSAR water level calibration. The optimum values of Manning's roughness coefficients are 0.024/0.10 for the channel/floodplain, respectively, using a single in situ gage, and 0.028/0.10 for channel/floodplain the using SAR. Based on the calibrated water elevation changes, daily storage changes within the size of approx 230 sq km of the model area are also calculated to be of the order of 107 cubic m/day during high water of the modeled period. This study demonstrates the feasibility of SAR interferometry to support 2D hydrodynamic model calibration and as a tool for improved understanding of complex floodplain hydrodynamics

Hahn Chul Jung↗

NASA NTRS1976

Multilayered models for electromagnetic reflection amplitudes

The remote sensing of snowpack characteristics with surface installations or with an airborne system could have important applications in water resource management and flood prediction. To derive some insight into such applications, the electromagnetic response of multilayer snow models is analyzed. Normally incident plane waves are assumed at frequencies ranging from 10 to the 6th power to 10 to the 10th power Hz, and amplitude reflection coefficients are calculated for models having various snow-layer combinations, including ice sheets. Layers are defined by a thickness, permittivity, and conductivity; the electrical parameters are constant or prescribed functions of frequency. To illustrate the effect of various layering combinations, results are given in the form of curves of amplitude reflection coefficients, versus frequency for a variety of models. Under simplifying assumptions, the snow thickness and effective dielectric constant can be estimated from the reflection coefficient variations as a function of frequency.

Linlor, W. I.↗

NASA NTRS1989

Improvement and extension of a radar forest backscattering model

Radar modeling of mangal forest stands, in the Sundarbans area of Southern Bangladesh, was developed. The modeling employs radar system parameters such as wavelength, polarization, and incidence angle, with forest data on tree height, spacing, biomass, species combinations, and water content (including slightly conductive water) both in leaves and trunks of the mangal. For Sundri and Gewa tropical mangal forests, five model components are proposed, which are required to explain the contributions of various forest species combinations in the attenuation and scattering of mangal vegetated nonflooded or flooded surfaces. Statistical data of simulated images (HH components only) were compared with those of SIR-B images both to refine the modeling procedures and to appropriately characterize the model output. The possibility of delineation of flooded or non-flooded boundaries is discussed.

Simonett, David S.↗

NASA NTRS2014

Simulation of Seasonal Snow Microwave TB Using Coupled Multi-Layered Snow Evolution and Microwave Emission Models

The accurate quantification of SWE has important societal benefits, including improving domestic and agricultural water planning, flood forecasting and electric power generation. However, passive-microwave SWE algorithms suffer from variations in TB due to snow metamorphism, difficult to distinguish from those due to SWE variations. Coupled snow evolution-emission models are able to predict snow metamorphism, allowing us to account for emissivity changes. They can also be used to identify weaknesses in the snow evolution model. Moreover, thoroughly evaluating coupled models is a contribution toward the assimilation of TB, which leads to a significant increase in the accuracy of SWE estimates.

microwave↗

NASA NTRS2014

Expansion of the Real-time Sport-land Information System for NOAA/National Weather Service Situational Awareness and Local Modeling Applications

The NASA Short-term Prediction Research and Transition (SPoRT) Center has been running a real-time version of the Land Information System (LIS) since summer 2010 (hereafter, SPoRTLIS). The real-time SPoRT-LIS runs the Noah land surface model (LSM) in an offline capacity apart from a numerical weather prediction model, using input atmospheric and precipitation analyses (i.e., "forcings") to drive the Noah LSM integration at 3-km resolution. Its objectives are to (1) produce local-scale information about the soil state for NOAA/National Weather Service (NWS) situational awareness applications such as drought monitoring and assessing flood potential, and (2) provide land surface initialization fields for local modeling initiatives. The current domain extent has been limited by the input atmospheric analyses that drive the Noah LSM integration within SPoRT-LIS, specifically the National Centers for Environmental Prediction (NCEP) Stage IV precipitation analyses. Due to the nature of the geographical edges of the Stage IV precipitation grid and its limitations in the western U.S., the SPoRT-LIS was originally confined to a domain fully nested within the Stage IV grid, over the southeastern half of the Conterminous United States (CONUS). In order to expand the real-time SPoRT-LIS to a full CONUS domain, alternative precipitation forcing datasets were explored in year-long, offline comparison runs of the Noah LSM. Based on results of these comparison simulations, we chose to implement the radar/gauge-based precipitation analyses from the National Severe Storms Laboratory as a replacement to the Stage IV product. The Multi-Radar Multi-Sensor (MRMS; formerly known as the National Mosaic and multi-sensor Quantitative precipitation estimate) product has full CONUS coverage at higher-resolution, thereby providing better coverage and greater detail than that of the Stage IV product. This paper will describe the expanded/upgraded SPoRT-LIS, present comparisons between the original and upgraded SPoRT-LIS, and discuss the path forward for future collaboration opportunities with SPoRT partners in the NWS.

Case, Jonathan L.↗

NASA NTRS2001

Numerical Simulations of Precipitation Processes, Microphysics, and Microwave Radiative Properties of flood Producing Storms in Mediterranean & Adriatic Basins

A comprehensive understanding of the meteorological and microphysical nature of Mediterranean storms requires a combination of in situ data analysis, radar data analysis, and satellite data analysis, effectively integrated with numerical modeling studies at various scales. An important aspect of understanding microphysical controls of severe storms, is first understanding the meteorological controls under which a storm has evolved, and then using that information to help characterize the dominant microphysical processes. For hazardous Mediterranean storms, highlighted by the October 5-6, 1998 Friuli flood event in northern Italy, a comprehensive microphysical interpretation requires an understanding of the multiple phases of storm evolution. This involves intense convective development, Sratiform decay, orographic lifting, and sloped frontal lifting processes, as well as the associated vertical motions and thermodynamical instabilities governing physical processes that effect details of the size distributions and fall rates of the various types of hydrometeors found within the storm environment. This talk overviews the microphysical elements of a severe Mediterranean storm in such a context, investigated with the aid of TRMM satellite and other remote sensing measurements, but guided by a nonhydrostatic mesoscale model simulation of the Friuli flood event. The data analysis for this paper was conducted by my research groups at the Global Hydrology and Climate Center in Huntsville, AL and Florida State University in Tallahassee, and in collaboration with Dr. Alberto Mugnai's research group at the Institute of Atmospheric Physics in Rome. The numerical modeling was conducted by Professor Oreg Tripoli and Ms. Giulia Panegrossi at the University of Wisconsin in Madison, using Professor Tripoli's nonhydrostatic modeling system (NMS). This is a scalable, fully nested mesoscale model capable of resolving nonhydrostatic circulations from regional scale down to cloud scale and below.

Smith, Eric A.↗

NASA NTRS1973

Study of ball bearing torque under elastohydrodynamic lubrication

Spinning and rolling torques were measured in an angular-contact ball bearing with and without a cage under several lubrication regimes in a modified NASA spinning torque apparatus. Two lubricants were used, a di-2 ethylhexyl sebacate and a synthetic paraffinic oil, at shaft speeds of 1000, 2000, and 3000 rpm and bearing loads from 10 lbs to 90 lbs. An analytical model was developed from previous spinning friction models to include rolling with spinning under lubrication regimes from thin film to flooded conditions. The bearing torque values have a wide variation, under any condition of speed and load, depending on the amount of lubricant present in the bearing. The analytical model compared favorably with experimental results under several lubrication regimes.

Townsend, D. P.↗

NASA NTRS1973

Study of ball bearing torque under elastohydrodynamic lubrication

Spinning and rolling torques were measured in an angular-contact ball bearing with and without a cage under several lubrication regimes in a modified NASA spinning torque apparatus. Two lubricants were used - a di-2 ethylhexyl sebacate and a synthetic paraffinic oil, at shaft speeds of 1000, 2000, and 3000 rpm and bearing loads from 45 newtons (10 lb) to 403 newtons (90 lb). An analytical model was developed from previous spinning friction models to include rolling with spinning under lubrication regimes from thin film to flooded conditions. The bearing torque values have a wide variation, under any condition of speed and load, depending on the amount of lubricant present in the bearing. The analytical model compared favorably with experimental results under several lubrication regimes.

Townsend, D. P.↗

NASA NTRS2005

Global Precipitation Measurement Mission: Architecture and Mission Concept

The Global Precipitation Measurement (GPM) Mission is a collaboration between the National Aeronautics and Space Administration (NASA) and the Japanese Aerospace Exploration Agency (JAXA), and other partners, with the goal of monitoring the diurnal and seasonal variations in precipitation over the surface of the earth. These measurements will be used to improve current climate models and weather forecasting, and enable improved storm and flood warnings. This paper gives an overview of the mission architecture and addresses some of the key trades that have been completed, including the selection of the Core Observatory s orbit, orbit maintenance trades, and design issues related to meeting orbital debris requirements.

Bundas, David↗

NASA NTRS2006

Global Precipitation Measurement (GPM) Mission Core Spacecraft Systems Engineering Challenges

The Global Precipitation Measurement (GPM) Mission is a collaboration between the National Aeronautics and Space Administration (NASA) and the Japanese Aerospace Exploration Agency (JAXA), and other US and international partners, with the goal of monitoring the diurnal and seasonal variations in precipitation over the surface of the earth. These measurements will be used to improve current climate models and weather forecasting, and enable improved storm and flood warnings. This paper gives an overview of the mission architecture and addresses the status of some key trade studies, including the geolocation budgeting, design considerations for spacecraft charging, and design issues related to the mitigation of orbital debris.

Bundas, David J.↗

NASA NTRS2008

Assessing the Relative Performance of Microwave-based Satellite Rain Rate Retrievals using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecast of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (AQUA) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparison with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellites is examined via comparisons with GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm hr-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Wolff, David B.↗

NASA NTRS2010

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Wolff, David B.↗

NASA NTRS2011

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Wolff, David B.↗

NASA NTRS2016

Evaluation of Dynamic Coastal Response to Sea-level Rise Modifies Inundation Likelihood

Sea-level rise (SLR) poses a range of threats to natural and built environments, making assessments of SLR-induced hazards essential for informed decision making. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30m resolution predictions for more than 38,000 sq km of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.

Climate-change impacts↗

NASA NTRS2015

A-Train Based Observational Metrics for Model Evaluation in Extratropical Cyclones

Extratropical cyclones contribute most of the precipitation in the midlatitudes, i.e. up to 70 during winter in the northern hemisphere, and can generate flooding, extreme winds, blizzards and have large socio-economic impacts. As such, it is important that general circulation models (GCMs) accurately represent these systems so their evolution in a warming climate can be understood. However, there are still uncertainties on whether warming will increase their frequency of occurrence, their intensity and how much rain or snow they bring. Part of the issue is that models have trouble representing their strength, but models also have biases in the amount of clouds and precipitation they produce. This is caused by potential issues in various aspects of the models: convection, boundary layer, and cloud scheme to only mention a few. In order to pinpoint which aspects of the models need improvement for a better representation of extratropical cyclone precipitation and cloudiness, we will present A-train based observational metrics: cyclone-centered, warm and cold frontal composites of cloud amount and type, precipitation rate and frequency of occurrence. Using the same method to extract similar fields from the model, we will present an evaluation of the GISS-ModelE2 and the IPSL-LMDZ-5B models, based on their AR5 and more recent versions. The AR5 version of the GISS model underestimates cloud cover in extratropical cyclones while the IPSL AR5 version overestimates it. In addition, we will show how the observed CloudSat-CALIPSO cloud vertical distribution across cold fronts changes with moisture amount and cyclone strength, and test if the two models successfully represent these changes. We will also show how CloudSat-CALIPSO derived cloud type (i.e. convective vs. stratiform) evolves across warm fronts as cyclones age, and again how this is represented in the models. Our third process-based analysis concerns cumulus clouds in the post-cold frontal region and how their amount relates to the stability of the boundary layer. This test uses Aqua cloud and vertical atmospheric profiles and when applied to the model output can help assess the accuracy of the convection, boundary layer and cloud scheme.

Convection clouds↗

NASA NTRS2014

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of-the- art cloud computing and crowdsourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters.

CyberFlood↗

NASA NTRS1976

Water resources planning for rivers draining into mobile bay

A hydrodynamic model describing water movement and tidal elevation is formulated, computed, and used to provide basic data about water quality in natural systems. The hydrodynamic model is based on two-dimensional, unsteady flow equations. The water mass is considered to be reasonably mixed such that integration (averaging) in the depth direction is a valid restriction. Convective acceleration, the Coriolis force, wind and bottom interactions are included as contributing terms in the momentum equations. The solution of the equations is applied to Mobile Bay, and used to investigate the influence that river discharge rate, wind direction and speed, and tidal condition have on water circulation and holdup within the bay. Storm surge conditions, oil spill transport, artificial island construction, dredging, and areas subject to flooding are other topics which could be investigated using the mathematical modeling approach.

Ng, S.↗

NASA NTRS2021

Northern Great Plains Disasters: Using Earth Observations to Enhance Flood Monitoring on Tribal Lands in the Northern Great Plains

In 2019, the Great Plains experienced unprecedented catastrophic flooding. Large flood events are predicted to increase in frequency and severity, posing risks to communities in this region, particularly Tribal Nations. We used data from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), imagery from the Sentinel-2 MultiSpectral Instrument (MSI), and digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM) within Google Earth Engine to map historical floods in the region beginning in 2014 with particular attention to the Rosebud Sioux Reservation and the tribal lands of other Great Plains Tribal Water Alliance members. This historical mapping used C-SAR for a combined method approach with a Z-score algorithm in addition to an index for flooded short vegetation. We also developed a flood risk map by weighting different flood predictor variables according to flood risk literature. These variables included soil drainage from the Soil Survey Geographic Database (SSURGO); elevation, slope, and Topographic Wetness Index (TWI) derived from digital elevation models; precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS); land cover from the National Land Cover Database (NLDC); and Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 Operational Land Imager (OLI). From the flood extent and risk maps, we identified widespread flooding in short vegetation (including cropland) and noted flood susceptibility in regions exhibiting high social vulnerability and low community resilience (FEMA indices). We created an ArcGIS Online StoryMap to share project background, results, and data. Additionally, we provided a written tutorial so partners may replicate the flood mapping for future flood events.

Anna Ballasiotes↗