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Abstract The increasing knowledge in the capabilities of satellite imagery to hazard applications is especially useful in emergency situations where timing and ability to cover large areas are of the essence. For optical imagery, cloud coverage can corrupt an image rendering it unusable for intended emergency analyses. This study proposes the use of Synthetic Aperture Radar (SAR) imagery for burn severity analysis for western United States sites, as an alternative to its optical based counterpart, differenced normalized burn ratio (dNBR). Unlike optical sensors, the radar sensor is an active sensor that is able to penetrate clouds and smoke, an attribute that is crucial in emergency situations where immediate burn severity data are needed to assess the vulnerability of fire affected areas to post-fire hazards. Using C5 decision tree algorithm we developed a SAR-based metric that attempts to classify burn severities of fire affected locations in the western USA. We then compared the performance of this developed metric to that obtained by the existing dNBR metric, to determine if there is any merit to its adoption as an alternative for the western USA landscape. The results showed the SAR approach to produce higher validation metrics in comparison to the dNBR. It had an overall accuracy and kappa of 60% and 0.35, respectively, in comparison to the 35% and 0.1 of the dNBR approach. This shows an improved ability to quickly obtain burn severity data and make better informed decisions in emergency situations. [ABSTRACT FROM AUTHOR]

Accurate large-area Digital Terrain Models (DTMs) are crucial for many science applications. Spaceborne Synthetic Aperture Radar (SAR) platforms are often used to create these DTMs as they provide an effective tool to collect surface elevations across a wide extent. However, SAR-derived digital elevation models (DEMs) cannot accurately measure ground elevations in the presence of forests. This work demonstrates an approach to estimate terrain elevations from 12 m TanDEM-X by using a convolutional neural network (CNN) trained with ground elevations from ICESat-2 – a spaceborne laser altimeter. This approach demonstrated the ability to estimate terrain elevations from TanDEM-X DEMs for the greater North Carolina area. The CNN estimated terrain saw an improvement in RMSE from 11.28 m to 4.42 m within the entire area of interest, and a focused improvement in RMSE from 12.78 m to 4.95 m in forested areas when compared to ICESat-2. The CNN model outperformed linear, random forest, and gradient boosted regression models using comparable model inputs. This work combines 12-m TanDEM-X data with ICESat-2 profiles, resulting in a new DTM product with accuracy approaching that of reference elevations obtained from satellite laser altimetry in the southeastern United States. • Investigates use of CNN to create DTM with TanDEM-X and ICESat-2 in North Carolina. • CNN outperformed linear, random forest, and gradient boosted regression models tested. • CNN reduced RMSE from 11.28 m to 4.42 m with convolutional neural network. • Establishes possible framework for creating global DTMs. [ABSTRACT FROM AUTHOR]

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g. , AMSR-E), current (e.g. , AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers. [Display omitted] • A multi-sensor semi-physical algorithm is applied to retrieve vegetation moisture. • The water component in vegetation optical depth is disentangled from dry biomass. • Vegetation moisture retrievals at X- and Ku-bands capture the in situ dynamics. • L-band has poorer performance due to lack of in situ samples in wood canopy layers. [ABSTRACT FROM AUTHOR]

Abstract In this paired study, we apply a recently developed high-resolution bathymetry estimation algorithm ("cBathy") to X-band marine radar observations at two nearshore field sites. The algorithm exploits observations of the spatial structure of wave phase to attain wavenumber estimates, inverts the linear water wave dispersion relation for depth, and then applies a Kalman filter to objectively update the bathymetry estimates. Previously, performance has only been tested using optical video observations. In this first of two papers, performance of the algorithm using X-band radar image time series is tested at two disparate barred beach environments: Duck, NC, USA, and Benson Beach, WA, USA. Each of the test beaches is either co-located with (Duck, NC) or geographically close to (Benson Beach, WA) those utilized in the original algorithm verification. Concurrent echosounder surveys are used as ground truth. The bulk performance of the radar-derived bathymetry estimate at Duck, NC, achieves 0.49 m root-mean-square error (RMSE) with 0.26 m bias deep. This compares well with the bulk performance of the concurrent estimate derived using optical video (0.44 m RMSE and 0.23 m bias deep). The radar-derived bathymetry estimate performance at Benson Beach, is similar (0.35 m RMSE and 0.11 m bias shallow), and is comparable to that of an optical video derived estimate at a similar Pacific Northwest beach (0.56 m RMSE and 0.41 m bias shallow). At both beaches, significantly higher performance is achieved at locations deeper than 2 m (offshore of the surfzone) than at locations shallower than 2 m (surfzone), where errors are often a large fraction of the total depth. Lastly, several weeks of observations are utilized to assess the sensitivity of algorithm quality control to environmental conditions. Thresholds based on the shoreward component of wind stress and offshore wave steepness are identified and shown to impact the areal coverage of radar-derived bathymetric estimates. Overall, these results demonstrate the viability of marine radar observations as input to the cBathy algorithm and delineate some environmental constraints on algorithm performance. In the companion paper, the algorithm is extended to areas where tidal currents are important, including an ebb tidal shoal and an estuary mouth. Highlights • Depth estimation algorithm, cBathy, is applied to X-band radar image time series. • Performance is assessed at two beaches (North Carolina and Washington, USA). • Accuracy via radar is comparable to video-based performance (reported previously). • Frequency of updates increases with wave height and when wind blows toward shore. [ABSTRACT FROM AUTHOR]

• Precipitation annual maxima estimated from NEXRAD are evaluated over the CONUS. • Accuracy of radar-based estimates varies with magnitude and duration. • Uncertainty in radar-based IDF curves exhibits climate dependence. The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002–2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes. [ABSTRACT FROM AUTHOR]

No-precipitation reflectivity observations from Doppler radar contain valuable information on areas lacking precipitation; however, they are often ignored in four-dimensional variational (4DVar) radar data assimilation (DA). This study incorporated a neighborhood-based scheme to assimilate no-precipitation observations as a mechanism to suppress spurious convection. The impact of the scheme on convective forecasting using 4DVar was evaluated by comparing the performance of experiments with and without assimilation (ExpCTL) using eight diverse storm cases that occurred over the central United States during summer 2016. Three no-precipitation assimilation experiments with different neighborhood radiuses of 10 (ExpR10), 30 (ExpR30), and 50 km (ExpR50) were conducted to examine the sensitivity of the scheme to neighborhood size. Results indicated that all the no-precipitation assimilation experiments significantly improved quantitative precipitation forecast skill with large reduction of the bias and the false alarm ratio from ExpCTL, as well as improving representation of the intensity and coverage of the precipitation. The horizontal wind, temperature, and water vapor were also improved, especially the latter. The scheme was found sensitive to neighborhood size and greater benefit was found in ExpR30 in comparison with ExpR10 and ExpR50. Analysis revealed that ExpR30 reduced low-level cooling and mid-level warming corresponding to decreased water vapor in areas of overpredicted and false precipitation, and it was more effective in conserving the total water content balance during cycled radar DA. The findings of this study could provide reference information for assimilation of no-precipitation observations into the Weather Research and Forecasting model using 4DVar, which would be valuable for severe weather prediction. • A neighborhood-based scheme was incorporated into 4DVar to assimilate no-precipitation observations from Doppler Radar. • The scheme significantly reduced spurious convection, and a moderate neighborhood size resulted in the best results. • The no-precipitation assimilation helps conserve balance of water content, which led to improved precipitation forecast. [ABSTRACT FROM AUTHOR]

Data gathered by electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to produce a three-dimensional image of subsurface soft-sediment deformation structures developed on the modern Lake Powell delta at Hite, Utah. ERT and GPR lines were run orthogonal across the crater. ERT resolved a low-resistivity layer up to 2 m thick in the area near the vents within the crater. This low-resistivity layer thins toward the margins representing clays ejected from the vents. Below and adjacent to this layer is a high-resistivity layer that reflects delta top and pro-delta sands. The deepest zone recognized in the ERT profiles consists of a low-resistivity layer, clay deposits that accumulated during the maximum lake high stand. This clay zone is connected to the vent within the crater by a conduit that changes diameter vertically. GPR profiles recognized the presence of collapse features restricted to the proximity of the vent. The geometry of the model is consistent with those proposed for marine pockmarks that can be generated seismically or aseismically with the exception of subaerial exposure after the dome stage development. [ABSTRACT FROM AUTHOR]

Lack of accurate and complete utility records is a root cause of utility strikes that occur on average once every minute in the United States, leading to hundreds of deaths and billions of dollars in financial losses. Ground-penetrating radar (GPR) has emerged as a promising tool to detect, locate, and measure underground pipes. Many algorithms have been devised to process GPR scans to determine pipe location and burying depth. Their accuracy depends on the relative angles between the GPR survey trajectory and the buried pipes. Perpendicular-to-pipe scanning yields the highest detectability, and along-pipe scanning yields the highest planimetric and depth accuracy. However, the challenge in practice is to maintain such ideal angles while not knowing the exact orientation of the pipes. This paper devises a novel spatial reasoning mechanism to enable the automation of GPR survey trajectory planning and adjustment based on ill-shaped and incomplete GPR signatures to achieve ideal angles in real time. The spatial reasoning mechanism provides trajectory adjustment suggestions based on the connection between the GPR signatures extracted from GPR scans and the relative angles between the GPR trajectory and underground pipes. The adjustment process continues until the GPR signatures from synthetic data under ideal angles and from field survey converge, and further adjustment no longer improves the results. Both indoor and field experiments have been conducted for validation. The results show that the newly developed method is capable of guiding the adjustment process in real time to achieve ideal angles and collect high-quality GPR data, leading to a more accurate estimation of pipe locations and burying depths. It has the great potential to support the operation of robotic unmanned ground vehicles (UGV) or unmanned aircraft system (UAS) to fully automate the GPR field survey. • A spatial reasoning process that guides survey robots to adaptively adjust trajectory in field • The trajectory planning of robotic GPR-UGV is near real-time. • The correlation of GPR signatures and spatial configurations are modeled and established. • Indoor and outdoor experiments were performed to validate the new method. • The new method achieves high accuracy and efficacy in mapping underground pipes. [ABSTRACT FROM AUTHOR]

Highlights • Evaluates utility of scatterometer data for estimation of urban built-up volume. • Dense Sampling Method-processed scatterometer data are compared to airborne lidar. • Analysis conducted for nine urban areas in the United States. • Strong, linear correlations observed between radar- and lidar-derived datasets. • Results promote three-dimensional approach to land use/land cover analyses. Abstract Accurately mapping urban infrastructure and extent is a high priority for resource management and service allocation as well as for addressing environmental, socioeconomic, and geopolitical concerns. Most available data products only document surficial (two-dimensional) land use and land cover (LULC), yet a substantial component of urban growth occurs in the vertical dimension. Light detection and ranging (lidar) data offer the potential for monitoring three-dimensional (3D) change, but the extreme lack of systematic lidar coverage worldwide inflicts considerable gaps in both spatial and temporal coverage. Satellite scatterometer (radar) data may serve as an alternative data source for characterizing urban growth and development in both the horizontal and vertical directions. The accuracy of these radar-based datasets for estimating building volumes remains to be validated quantitatively. For nine U.S. cities, we test whether scatterometer data can be used to estimate 3D urban built-up volume. We found strong, linear correlations between the lidar-derived and radar-derived building volume estimates for all cities with r2 values as high as 0.98 when using spatial trend analysis. Given the high expense that limits lidar data acquisition to small areas at sporadic points in time, satellite scatterometer data provide a breakthrough method for monitoring both vertical growth and horizontal expansion of cities across the world with a continuous decadal time scale. [ABSTRACT FROM AUTHOR]

Wastewater injection over the past decade has increased seismicity in the central USA, in some cases accompanied by detectable surface uplift. Here, we use this uplift to constrain subsurface properties and pore pressure evolution. We apply an advanced multitemporal interferometric algorithm to 35 synthetic aperture radar images acquired by ALOS satellite over four years before the 2012 earthquake sequence in east Texas, where large volumes of wastewater are disposed at depths of ∼800 m and ∼1800 m. To solve for the hydraulic diffusivity of the injection layers, we jointly inverted the injected volume and uplift data, considering a poroelastic layered half space. We find diffusivity values of 0.3 ± 0.1 m2/s and 0.7 ± 0.15 m2/s for shallow and deep injection layers, respectively, which combined with seismically-derived bulk moduli yields permeability values of 5.5 ± 2.6 × 10 − 14 m2 and 1.9 ± 0.25 × 10 − 13 m2 for these layers, consistent with permeability range reported for Rodessa formation and well test values. Hydraulic conductivity determines the evolution of pore pressure and thus the origin and location of induced seismicity. This study highlights the value of geodetic observations to constrain key hydrogeological properties of injection layers and to monitor the evolution of the subsurface pressure change. • Fluid injection near Timpson, East Texas caused an uplift detectable by InSAR. • Surface deformation data are inverted to solve for hydraulic diffusivity of injection layer. • Pore pressure increase due to injection has triggered the 2012 M w 4.8 earthquake. [ABSTRACT FROM AUTHOR]

Satellite Radar Interferometry (InSAR) is suited to monitoring ground deformation on the scale of volcanic arcs, providing insight into the eruptive cycle over both long and short time periods. However, these measurements are often contaminated with atmospheric artefacts caused by changes in the refractivity of the atmosphere. Here, we test the use of two large-scale atmospheric models, ERA-Interim (ERA-I) and North American Regional Reanalysis (NARR), to correct atmospheric uncertainties in InSAR data from the Cascades Volcanic Arc, United States. At Lassen Volcanic Center, we find that NARR reduces interferogram standard deviation in 79% of cases by an average of 22%. Using NARR, we develop a strategy to produce a priori estimates of atmospheric uncertainties on an arc-wide basis. We show that in the Cascades, the RMS variation in range change is dependent upon volcano topography and increases by 0.7 cm per kilometre of relief. We use this to estimate detection thresholds for long-term monitoring of small magnitude (1 cm/yr) deformation signals, and short-term monitoring of ground deformation associated with pre-eruptive unrest. This new approach of assessing atmospheric uncertainties a priori is widely applicable to other volcanic arcs, and provides realistic estimates of atmospheric uncertainties suitable for use in near-real-time analysis of InSAR data during periods of volcanic unrest. [ABSTRACT FROM AUTHOR]

• Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. • Noncontact radar-based discharge estimates can be as accurate as in-situ, conventional streamgauges. • There was a notable phase sequencing between the surface velocity and stage, indicative of unsteady flows. • Real-time alerting capabilities of an impending flash flood are demonstrated in a vulnerable, ungauged basin. Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams. [ABSTRACT FROM AUTHOR]

• Mesic vegetation is a necessary indicator of water availability in semi-arid regions. • Sentinel data improve detection of water at meaningful temporal and spatial scales. • We capture intra-seasonal dynamics that existing mesic vegetation products do not. • We present a replicable workflow with open data on an open-access platform. Semi-arid and arid systems cover one third of the earth's land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water resources product for a semi-arid mountainous region of the western United States, which includes monthly maps of both a) surface water and b) mesic vegetation at 10 m spatial resolution using freely available Earth observation data on an open access platform. We applied random forest classifiers to optical data from the Sentinel-2 time series, synthetic aperture radar (SAR) data from the Sentinel-1 time series, and topographic variables. We compared our SF product with three commonly used and publicly available datasets in the western U.S. We found that our surface water class contained fewer omission errors than a leading global surface water product in (94 % producer's accuracy (PA) vs 84 %) and comparable user's accuracy (UA) (91 % vs 97 %) with commission errors occurring largely in mixed water pixels. Our mesic vegetation class had up to 43 % higher PAs compared to the National Wetlands Inventory (NWI) estimates and up to 78 % higher UAs over the Sage Grouse Initiative mesic resources maps during the most critical part of the water year. We found that while inclusion of SAR data from the C-band Sentinel-1 sensor consistently improved estimates of water resources in each of the last four months of the 2021 water year when compared to optical-only + topographic variables, only in September did those improvements lie outside of the 95 % confidence interval. With nine times finer spatial resolution and more frequent image collection, our SF maps characterize intra-annual dynamics of smaller water bodies (<30 m wide) and mesic vegetation integral to ecosystem functioning in semi-arid systems compared to leading Landsat-derived products. Further, our workflow is easily reproducible using freely available data on an open access platform, and can be adopted to help guide land use decisions related to water resources by farmers, ranchers, and conservationists in semi-arid environments. [ABSTRACT FROM AUTHOR]

The northeastern U.S. experienced land use/land cover (LULC) changes over the last 300 years, including widespread deforestation during the 17th and 19th centuries, followed by reforestation in the 20th century. Reconstructing the extent of historic land use is crucial to understanding longer term impacts of anthropogenic activity. However, LULC datasets around the globe are typically only available for the last 50 years. This study aims to reconstruct the spatial distribution of cleared land with area estimation at two time periods based on stone walls using light detection and ranging (lidar), historic aerial photography, and deep learning. The two time periods are (1) the peak of agricultural activity period (mid-19th century) and (2) early-20th century. We utilized deep learning methods for (1) mapping the distribution of stone walls from lidar derivatives and 1934 aerial photography and (2) mapping historic land use from pre-satellite era aerial photography. The results of area estimation demonstrate strong consistency with the area indicated in the census record for both time periods. Notably, the incorporation of stone walls from 1934 orthomosaic plays a key role in significantly increasing the agreement of cleared area in the mid-19th century, as it compensated for the absence of walls that have been removed. By adding new historical time intervals of cleared land with our method, it allows for investigating the duration of land used for agricultural activities and the spatial pattern of LULC transition over the last 300 years. • Spatial reconstruction of historic land use in the northeastern U.S. using deep learning. • Filling the gap of pre-satellite period using anthropogenic landforms through lidar. • Potential to monitoring of long-term land use change. [ABSTRACT FROM AUTHOR]

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m 2 , 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ± 0.004 Mg/ha). Estimated C stocks for predefined jurisdictional areas ranged from 1023 ± 39 Mg in the Nisqually National Wildlife Refuge in Washington to 507,761 ± 14,822 Mg in the Terrebonne and St. Mary Parishes in Louisiana. This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included in the coastal wetland section of the U.S. National Greenhouse Gas Inventory. With the increased availability of free post-processed satellite data, we provide a tractable means of modeling tidal marsh aboveground biomass and carbon at the global extent as well. [ABSTRACT FROM AUTHOR]

Planned satellite launches will provide open access and operational L-band radar data streams at space-time resolutions not previously available. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band (24 cm) prototype science products. Time series flights flew over four independent areas during the growing season of 2019 while crop ground measurements were collected. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of machine learning (ML) algorithms (random forest, feedforward fully connected neural network, support vector machine), the recently developed Multi-temporal Binary Tree Classification (MBTC), and a phenology (Coefficient of Variation; CoV) approach to synergistically assess performance, scattering mechanisms, and limitations. Specific objectives of this research application included 1.) evaluation of L-band mapping performance across multiple independent agricultural production areas with field scale training data, and 2.) assessment of the CoV approach for the generation of prototype NISAR Level 2 science products. Collectively, SAR terms with sensitivity to volume scattering performed well and consistently across CoV mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Dynamic phenology classes, such as herbaceous wetlands, had some confusion with CoV agriculture requiring further regionalized training optimization. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and 0.85, respectively, for crop type by late growth stages for L-band observations. As expected, time series information was more valuable compared to any single ML technique, site, or crop schema. Ultimately, as more SAR platforms launch, the user community should leverage physical contributions of different wavelengths and polarizations along with growing open access time series for efficient and meaningful agricultural products. • CoV and machine learning methods are deployed to understand L-band crop Monitoring. • CoV achieves an overall accuracy greater than 80% for the NISAR L2 product. • Time series and the cross-pol term are the most useful for discrimination. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) remote sensing has been widely used for flood mapping and monitoring. Nevertheless, flood detection in urban areas still proves to be particularly challenging by using SAR. In this paper, we assess the roles of SAR intensity and interferometric coherence in urban flood detection using multi-temporal TerraSAR-X data. We further introduce an active self-learning convolution neural network (A-SL CNN) framework to alleviate the effect of a limited annotated training dataset. The proposed framework selects informative unlabeled samples based on a temporal-ensembling CNN model. These samples are subsequently pseudo-labeled by a multi-scale spatial filter. Consistency regularization is introduced to penalize incorrect labels caused by pseudo-labeling. We show results for a case study that is centered on flooded areas in Houston, USA, during hurricane Harvey in August 2017. Our experiments show that multi-temporal intensity (pre- and co-event) plays the most important role in urban flood detection. Adding multi-temporal coherence can increase the reliability of the inundation map considerably. Meanwhile, encouraging results are achieved by the proposed A-SL CNN framework: the к statistic is improved from 0.614 to 0.686 in comparison to its supervised counterpart. [ABSTRACT FROM AUTHOR]

We analyzed 4-years of Advanced Scatterometer (ASCAT) incidence angle normalized C-band backscatter, Next-Generation Radar (NEXRAD) precipitation and United States Department of Agriculture (USDA) crop yield data over the contiguous United States (U.S.). Large negative anomalies in backscatter are evident over the U.S. during the 2011 and 2012 growing seasons, coincident with two instances of severe drought. The backscatter anomalies were correlated with diminished growing season precipitation and with reduced crop yields reflecting where drought-induced impacts on above-ground biomass production occurred. During periods of acute drought, differences in diurnal backscatter were reduced and in some cases reversed reflecting diminished nocturnal leave rehydration. The results indicate that the C-band, VV-polarized ASCAT backscatter data is sensitive to interannual variability in agricultural productivity rather than soil moisture, and can offer an important geophysical monitoring tool capable of identifying and assessing drought-related vegetation responses independent of and complementary to satellite optical/near-infrared remote sensing data. This technique is flexible and adaptable, providing a capability to address region-specific needs. The twice-daily, weather independent and near-real time global monitoring capabilities of ASCAT and similar microwave instruments (e.g. RapidSCAT, Sentinel-1) can help improve drought preparedness and mitigation planning allowing improved water resource management for agriculture on the local as well as the state level. [ABSTRACT FROM AUTHOR]

Abstract The Northeast United States, particularly New York State has experienced an increase in extreme daily precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy, have revealed vulnerability to the intense precipitation within the transportation sector. In the scale of New York City, where transit system is the most dominant mode of transportation and daily mobility of millions of passengers depends on it, any disruption in the transit service would result in gridlocks and massive delays. To assess the impacts of rainfall on the subway ridership, we merged high resolution radar rainfall and subway ridership data to conduct a detailed analysis for each of the 116 subway stations at the borough of Manhattan. The analysis is carried out on both hourly and daily resolution level, where a spatial-temporal Bayesian multi-level regression model is used to capture the underlying dependency between the parameters. The estimation results are obtained through Markov Chain Monte Carlo sampling method. The results for daily analysis indicate that during weekdays, transit ridership in the stations located in commercial zones are less sensitive to the rainfall compared to the ones in residential zones. Highlights • Impacts of spatial variability of rainfall on subway ridership is assessed using large scale ridership and rainfall data. • A unified multi scale model, combining time and spatial scales for ridership is proposed. • The study framework can be used by agencies to improve system efficiency and resilience at the station level. [ABSTRACT FROM AUTHOR]

Building height is one of the key variables in studying anthropogenic activities and built environments in the urban system. However, the information of urban building height over large areas is still limited due to the lack of available data. In this study, we developed an approach to estimate building height using the Sentinel-1 Ground Range Detected data. First, we proposed an indicator of VVH that integrates the dual-polarization information (i.e., VV and VH) from the Sentinel-1 synthetic aperture radar data. Second, we developed a building height model using the indicator of VVH and the reference building height from airborne light detection and ranging (LiDAR) images in seven cities in the United States (US). Third, we estimated building height of major cities with area larger than 500 km2 in the US using the developed model. The root mean square error between estimated and reference height is 1.5 m in seven study cities in the US. The comparison with the Laser observations from the Ice, Cloud, and land Elevation Satellite data in available footprints also indicates that the estimated building height from Sentinel-1 data is reliable. The estimated building height from Sentinel-1 data shows a better agreement with the reference data compared to the Advanced Land Observing Satellite data in seven US cities. Our results of building height in major US cities indicate that there are more cities with tall buildings in the Northeastern US compared to the Western and Southern US, with more aggregated tall buildings in central business districts of cities. The information of building height from this study is of great value in urban studies, such as estimating energy consumption and carbon emissions in urban areas. • We developed a building height model using Sentinel-1 data. • We estimated building height of major cities in the US. • The model performs well in estimating building height with a root mean square error of 1.5 m. • The information of building height is valuable in urban studies such as energy consumption. [ABSTRACT FROM AUTHOR]

Objectives: To review assessments from the Institute for Clinical and Economic Review (ICER) and describe how cost-effectiveness, other benefits or disadvantages, and contextual considerations affect Council members' assessments of value.Methods: Assessments published by the ICER between December 2014 and April 2019 were reviewed. Data on the assessment, intervention, results from cost-effectiveness analyses, and Council members' votes were extracted. Voting data were examined using bar charts and radar plots. Spearman's correlations between the number of votes for other benefits and contextual considerations were estimated. Two case studies (tisagenlecleucel and voretigene neparvovec) explored the relationship between different aspects of value and the vote.Results: Thirty-one ICER assessments were reviewed, which included 51 value votes and 17 votes on other benefits and contextual considerations. On average, interventions with lower cost-effectiveness ratios received a higher proportion of high and intermediate value votes; however, there was heterogeneity across assessments. Of other benefits or disadvantages, having a novel mechanism of action received the most votes (n = 138), and reducing health disparities received the fewest (n = 24). Of contextual considerations, treating a condition that has a severe impact on length and quality of life received the most votes (n = 164). There was a strong positive correlation between votes for reduced caregiver/family burden and improving return to work/productivity (ρ = 0.88, P < .05). Two case studies highlighted that factors beyond cost-effectiveness can lead to lower (tisagenlecleucel) or higher (voretigene neparvovec) assessments of value.Conclusion: Council members' judgments about the value of interventions are influenced by other benefits or disadvantages and contextual considerations but anchored by cost-effectiveness. [ABSTRACT FROM AUTHOR]

Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final product (V06B) with a ground-based radar product, Multi-Radar Multi-Sensor (MRMS), using both pixel-based and object-based approaches. This study is focused on the eastern United States (land-only) during the hurricane days that occurred in 2016–2018. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area with a bias reduction of 75% and 65%, respectively, when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS compared to temporally averaged MRMS data (MRMS-Averaged). PMW observations tend to show storms with smaller areas in the IMERG Final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1 mm/h) in the object-based approach, hurricane objects in the IMERG Final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates have smaller areas with higher average intensity during the PMW observations in the IMERG Final product compared to data estimated by Morphed or IR (morph/IR) observations, which is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. In addition, with the longer absence of PMW observations, the quality of morph/IR estimates in IMERG Final product deteriorates with a decreasing correlation coefficient, a growth in precipitation area and a downward trend in average precipitation intensity. Finally, the inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area. • PMW estimates in IMERG have better agreement with MRMS when matched instantaneously. • PMW sensors often represent the storms with smaller areas in IMERG compared to MRMS. • PMW sensors show the storms smaller with higher intensity compared to morphed/IR • Morphed/IR data quality in IMERG decreases with the longer absence of PMW sensors. • PMW sensors in IMERG should be prioritized based on the precipitation properties. [ABSTRACT FROM AUTHOR]

The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007–2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from “observational data.” The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48 h, without a consistently best or worst NWP model. [ABSTRACT FROM AUTHOR]

In October 7–9, 2016, Hurricane Matthew moved along the southeastern coast of the U.S., causing major flooding and significant damage, even to locations farther north well away from the storm’s winds. Various observations, such as tide gauge data, cable measurements of the Florida Current (FC) transport, satellite altimeter data and high-frequency radar data, were analyzed to evaluate the impact of the storm. The data show a dramatic decline in the FC flow and increased coastal sea level along the U.S. coast. Weakening of the Gulf Stream (GS) downstream from the storm’s area contributed to high coastal sea levels farther north. Analyses of simulations of an operational hurricane-ocean coupled model reveal the disruption that the hurricane caused to the GS flow, including a decline in transport of ∼20 Sv (1 Sv = 10 6 m 3 s −1 ). In comparison, the observed FC reached a maximum transport of ∼40 Sv before the storm on September 10 and a minimum of ∼20 Sv after the storm on October 12. The hurricane impacts both the geostrophic part of the GS and the wind-driven currents, generating inertial oscillations with velocities of up to ±1 m s −1 . Analysis of the observed FC transport since 1982 indicated that the magnitude of the current weakening in October 2016 was quite rare (outside 3 standard deviations from the mean). Such a large FC weakening in the past occurred more often in October and November, but is extremely rare in June-August. Similar impacts on the FC from past tropical storms and hurricanes suggest that storms may contribute to seasonal and interannual variations in the FC. The results also demonstrated the extended range of coastal impacts that remote storms can cause through their influence on ocean currents. [ABSTRACT FROM AUTHOR]

The International Charter ‘Space and Major Disasters’ (“Charter”) is a collaboration among Earth observation satellite owners and operators to provide remotely sensed (RS) data following natural and man-made disasters. Charter members provided post-flood and pre-flood or archived RS data after the April 2011 floods in the Midwestern US. These multispectral and RADAR images provided valuable information to end users and first responders in the flood affected states for their planning and relief efforts. This effort was successful because of the process established by the Charter for soliciting requests, and acquiring and distributing data through a network of international and national agencies, and data vendors. This paper describes the a) role played by the Charter to acquire and distribute RS data through six of its members, b) value of archived satellite data, c) need for image processing experts to process these near real-time data, and transfer them in a timely manner, and d) data acquisition and distribution policies of the Charter. Lessons learned from this response along with the policy recommendations will be helpful in future activations for maximizing the value of RS data and services provided by the Charter. [ABSTRACT FROM AUTHOR]

Background Factors underlying a possible excess of amyotrophic lateral sclerosis (ALS) among military veterans remain unidentified. Limitations of previous studies on this topic include reliance on ALS mortality as a surrogate for ALS incidence, low statistical power, and sparse information on military-related factors. Objectives We evaluated associations between military-related factors and ALS using data from a case-control study of U.S. military veterans. Methods From 2005 to 2010, we identified medical record-confirmed ALS cases via the National Registry of Veterans with ALS and controls via the Veterans Benefits Administration's Beneficiary Identification and Records Locator System database. In total, we enrolled 621 cases and 958 frequency-matched controls in the Genes and Environmental Exposures in Veterans with Amyotrophic Lateral Sclerosis study. We collected information on military service and deployments and 39 related exposures. We used unconditional logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs). We used inverse probability weighting to adjust for potential bias from confounding, missing covariate data, and selection arising from a case group that disproportionately included long-term survivors and a control group that may or may not differ from U.S. military veterans at large. Results The odds of ALS did not differ for veterans of the Air Force, Army, Marines, and Navy. We found higher odds of ALS for veterans whose longest deployment was World War II or the Korean War and a positive trend with total years of all deployments (OR = 1.27; 95% CI: 1.06, 1.52). ALS was positively associated with exposure to herbicides for military purposes, nasopharyngeal radium, personal pesticides, exhaust from heaters or generators, high-intensity radar waves, contaminated food, explosions within one mile, herbicides in the field, mixing and application of burning agents, burning agents in the field, and Agent Orange in the field, with ORs between 1.50 and 7.75. Conclusions Although our results need confirmation, they are potentially important given the large number of U.S. military veterans, and they provide clues to potential factors underlying the apparent increase of ALS in veteran populations. [ABSTRACT FROM AUTHOR]

Summary Topography plays a major role in determining the accuracy of flood inundation areas. However, many areas in the United States and around the world do not have access to high quality topographic data in the form of Digital Elevation Models (DEM). For such areas, an improved understanding of the effects of DEM properties such as horizontal resolution and vertical accuracy on flood inundation maps may eventually lead to improved flood inundation modeling and mapping. This study attempts to relate the errors arising from DEM properties such as spatial resolution and vertical accuracy to flood inundation maps, and then use this relationship to create improved flood inundation maps from coarser resolution DEMs with low accuracy. The results from the five stream reaches used in this study show that water surface elevations (WSE) along the stream and the flood inundation area have a linear relationship with both DEM resolution and accuracy. This linear relationship is then used to extrapolate the water surface elevations from coarser resolution DEMs to get water surface elevations corresponding to a finer resolution DEM. Application of this approach show that improved results can be obtained from flood modeling by using coarser and less accurate DEMs, including public domain datasets such as the National Elevation Dataset and Shuttle Radar Topography Mission (SRTM) DEMs. The improvement in the WSE and its application to obtain better flood inundation maps is dependent on the study reach characteristics such as land use, valley shape, reach length and width. Application of the approach presented in this study on more reaches may lead to development of guidelines for flood inundation mapping using coarser resolution and less accurate topographic datasets. [ABSTRACT FROM AUTHOR]

Seasonal snow-derived water is a critical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow-water-equivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water management efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging publicly available data sources and open-source software. The model demonstrates the application of a limited feature space in a relatively simple ML architecture without the need for process-based formulations to effectively estimate spatially continuous SWE at a daily temporal resolution. Beginning with in situ SWE measurements (i.e., SNOTEL), lidar-derived terrain features, and temporal variables, we employ localized feature engineering and optimization via gradient-boosting decision trees to identify regionally unique drivers of snowpack dynamics and use the optimal features to train regionally independent artificial neural networks to estimate regional SWE at a 1 km spatial resolution. The model results yield respectable skill in reconstructed 1 km gridded SWE magnitudes in a hindcast simulation of the 2019 water year that is independent of the training and testing data. Comparing model estimates to over 6200 observations, the model demonstrates a weighted RMSE of 15.4 cm, Kling-Gupta Efficiency metric of 0.86, and a percent bias of 0.71% across 23 snow-influenced regions in the western U.S. The model simulation produces peak SWE estimates within 10 cm for twenty of the twenty-three regions, demonstrating capability in effectively capturing regional snow accumulation processes. The demonstration of low-error ML workflows capable of providing near-real-time, spatially continuous SWE estimates at a high spatial resolution provides proof-of-concept and a foundation to effectively update snow state variables that drive water supply forecasts in snow-dominated regions. • A new Machine Learning-based SWE estimation model for the Western United States. • Utilization of open-source tools and up-to-date in-situ observations to estimate SWE. • Reconstructed SWE magnitudes exhibit respectable skill in a hindcast simulation. • Output may serve as a supplemental state-of-snowpack input to hydrologic models. [ABSTRACT FROM AUTHOR]

Subsurface drainage pipes are commonly used in the Midwestern United States to reduce excess soil moisture and improve crop yields. However, they are the considerable source of nonpoint pollution due to nutrient losses. Detecting the locations of drainage pipes is crucial for water quality management, but information about drainage pipe maps is often privately owned and unavailable. In this study, we propose an ensemble learning approach that uses eight fully convolutional networks (FCNs), including well-known architectures such as Unet, DenseNet, and Wnet, to detect subsurface drainage pipe locations from aerial images. Each FCN model is trained and validated using an aerial image dataset, taking a 256 × 256 × 3 pixel aerial image patch as input and outputting a pixel-wise drainage pipe detection map. Weighted averaging is then applied to the individual FCN outputs to create a unified drain pipe detection map. The performance of the proposed approach is evaluated using large-scale aerial image data that has not been used during the training and validation phases. The results demonstrate that the proposed approach provides accurate and more robust drain pipe detection over the case of using an individual FCN model. We further explore the effects of image resolution for the effective use of the proposed drain pipe detection approach. • Our ensemble learning method detects subsurface pipes accurately and robustly. • The ensemble model outperforms image processing and single machine learning models. • An optimal resolution can enhance the performance of the ensemble learning model. [ABSTRACT FROM AUTHOR]

Smoother pavement provides a comfortable, safe and economical riding experience to the public and prolongs the life of the pavement with low maintenance costs. Hence, obtaining a smooth pavement is considered a necessity by many transportation agencies around the world. In order to achieve a smooth pavement, smoothness specifications are developed. Smoothness specifications mainly consist of four parts: smoothness measuring equipment, smoothness indices and analysis method, acceptance limits, and incentive/disincentive payment schemes. This paper summarizes smoothness specifications in the United States and various countries around the world and provide an overview on the comparison of smoothness measurement. The smoothness measuring devices, indices, limits and payment schemes vary from country-to-country International Roughness Index is the most used smoothness index in the US and around the world. Inertial profilers are used to measure the pavement profile for smoothness. [ABSTRACT FROM AUTHOR]

The article comments on the review made by Dr. Kwonil Jung and Dr. Linda Saif on the etiology, epidemiology, pathogenesis , and immunoprophylaxis of porcine epidemic diarrhea virus (PEDV) infection. Topics discussed include the reason that coronaviruses such as PEDV continues to remain on the radar of global health professionals, characteristics of the disease associated with PEDV, and finding on the PEDV strains identified in U.S. pigs in 2013.

The Energy Act of 2020 authorizes $1 billion over five years from 2021 to 2025 to support energy storage development in the United States. In addition, the Federal Energy Regulatory Commission (FERC) Orders 841 and 2222 opened the wholesale energy markets for distributed energy resources, including energy storage. The statute and orders pave the way for novel energy storage technologies to participate in electricity markets as the qualifying facilities, thereby expanding opportunities for energy storage development. However, the existing policy, legal, and regulatory regime, including these much-welcomed newcomers, fails to recognize and support the entire spectrum of benefits that some forms of energy storage create. In this paper, we focus on Pumped Underground Storage Hydro (PUSH), a variant of pumped hydro storage (PHS), which currently provides over 90% of the world's energy storage capacity. PUSH operates with the same principle as PHS; however, it is an entirely underground variant of PHS. In addition to becoming competitive in the wholesale electricity market, PUSH facilities can be developed as community infrastructure in the postindustrial landscape, particularly in abandoned underground mines. Given federal energy law and policy development, this paper identifies how communities with abandoned mines, technically feasible for PUSH facilities and operating as municipal-owned utilities or cooperatives, can participate and take advantage of federal legislation. We further look into the implications through the lens of technical, economic, and social aspects of energy justice. We consider energy justice as a conceptual framework that seeks to explain the occurrence of injustice within energy system decisions and outcomes and how policymakers can respond to these injustices. We use it as a conceptual tool for understanding policy formulation and detailing the energy system's missing ethical and justice dimension. We argue that as a technically mature technology, PUSH facilities can act as a potential means to speed up the energy transition in the United States. The federal and state law along with utility market structures are vital in shaping the potential opportunities and barriers for energy storage facilities like PUSH. We show that although it supports PUSH development, there are gaps in the current market structure, specifically in the regulatory framework, when seen through the lens of justice and valuation of just energy services. These gaps limit the realization of utility-scale energy storage technology's potential to fully contribute to a decarbonized energy system that is more resilient and more just than the incumbent system. [ABSTRACT FROM AUTHOR]

Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and flood duration with geospatial and geomorphologic information. The availability of a representative and long archive of flooding events spanning 78 years over a variety of hydroclimatic regions results in a spatially and temporally comprehensive flood characterization over the continental U.S. This study, for the first time, employs a large-event database that is based on actual National Weather Service (NWS) definitions of floods instead of the frequently-adopted case study or frequentist approach, allowing us to base our findings on real definitions of floods. It examines flooding characteristics to identify how space and time scales of floods vary with climatic regimes and geomorphology. Flood events were characterized by linking flood response variables in gauged basins to spatially distributed variables describing climatology, geomorphology, and topography. The primary findings of this study are that the magnitude of flooding is highest is regions such as West Coast and southeastern U.S. which experience the most extraordinary precipitation. The seasonality of flooding varies greatly from maxima during the cool season on the West Coast, warm season in the desert Southwest, and early spring in the Southeast. The fastest responding events tend to be in steep basins of the arid Southwest caused by intense monsoon thunderstorms and steep terrain. The envelope curves of unit peak discharge are consistent with those reported for Europe and worldwide. But significant seasonal variability was observed in floods of the U.S. compared to Europe that is attributed to the diversity of causative rainfall ranging from synoptic scales with orographic enhancements in the West Coast, monsoon thunderstorms in the desert Southwest, to land-falling tropical storms and localized, intense thunderstorms in the Southeast. [ABSTRACT FROM AUTHOR]

Summary Flash flooding is one of the most costly and deadly natural hazards in the United States and across the globe. This study advances the use of high-resolution quantitative precipitation forecasts (QPFs) for flash flood forecasting. The QPFs are derived from a stormscale ensemble prediction system, and used within a distributed hydrological model framework to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Before creating the PFFFs, it is important to characterize QPF uncertainty, particularly in terms of location which is the most problematic for hydrological use of QPFs. The SAL methodology (Wernli et al., 2008), which stands for structure, amplitude, and location, is used for this error quantification, with a focus on location. Finally, the PFFF methodology is proposed that produces probabilistic hydrological forecasts. The main advantages of this method are: (1) identifying specific basin scales that are forecast to be impacted by flash flooding; (2) yielding probabilistic information about the forecast hydrologic response that accounts for the locational uncertainties of the QPFs; (3) improving lead time by using stormscale NWP ensemble forecasts; and (4) not requiring multiple simulations, which are computationally demanding. [ABSTRACT FROM AUTHOR]

Summary Several different factors external to the natural hazard of flash flooding can contribute to the type and magnitude of their resulting damages. Human exposure, vulnerability, fatality and injury rates can be minimized by identifying and then mitigating the causative factors for human impacts. A database of flash flooding was used for statistical analysis of human impacts across the U.S. 21,549 flash flood events were analyzed during a 6-year period from October 2006 to 2012. Based on the information available in the database, physical parameters were introduced and then correlated to the reported human impacts. Probability density functions of the frequency of flash flood events and the PDF of occurrences weighted by the number of injuries and fatalities were used to describe the influence of each parameter. The factors that emerged as the most influential on human impacts are short flood durations, small catchment sizes in rural areas, vehicles, and nocturnal events with low visibility. Analyzing and correlating a diverse range of parameters to human impacts give us important insights into what contributes to fatalities and injuries and further raises questions on how to manage them. [ABSTRACT FROM AUTHOR]