scholarly journals Integrated Ocean, Earth, and Atmospheric Observations for Resilience Planning in Hampton Roads, Virginia

2018 ◽  
Vol 52 (2) ◽  
pp. 68-83 ◽  
Author(s):  
Jon Derek Loftis ◽  
Molly Mitchell ◽  
Larry Atkinson ◽  
Ben Hamlington ◽  
Thomas R. Allen ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Water Levels ◽  
Data Sets ◽  
Atmospheric Conditions ◽  
Integrated Network ◽  
Hampton Roads ◽  
Wave Measurements ◽  
Land Cover Data ◽  
Atmospheric Observations ◽  
Private And Public

AbstractBuilding flood resilience in coastal communities requires a precise understanding of the temporal and spatial scales of inundation and the ability to detect and predict changes in flooding. In Hampton Roads, the Intergovernmental Pilot Project's Scientific Advisory Committee recommended an integrated network of ocean, earth, and atmospheric data collection from both private and public sector organizations that engage in active scientific monitoring and observing. Since its establishment, the network has grown to include monitoring of water levels, land subsidence, wave measurements, current measurements, and atmospheric conditions. High-resolution land elevation and land cover data sets have also been developed. These products have been incorporated into a number of portals and integrated tools to help support resilience planning. Significant challenges to building the network included establishing consistent data standards across organizations to allow for the integration of the data into multiple, unique products and funding the expansion of the network components. Recommendations to the network development in Hampton Roads include the need to continue to support and expand the publicly available network of sensors; enhance integration between ocean, earth, and atmospheric networks; and improve shallow water bathymetry data used in spatial flooding models.

scholarly journals StormSense: A New Integrated Network of IoT Water Level Sensors in the Smart Cities of Hampton Roads, VA

2018 ◽  
Vol 52 (2) ◽  
pp. 56-67 ◽  
Author(s):  
Jon Derek Loftis ◽  
David Forrest ◽  
Sridhar Katragadda ◽  
Kyle Spencer ◽  
Tammie Organski ◽  
...  
Keyword(s):  
Water Level ◽  
Smart Cities ◽  
Monitoring Network ◽  
Water Levels ◽  
United States Geological Survey ◽  
Integrated Network ◽  
Hampton Roads ◽  
Newport News ◽  
Starting Point ◽  
Level Sensors

AbstractPropagation of cost-effective water level sensors powered through the Internet of Things (IoT) has expanded the available offerings of ingestible data streams at the disposal of modern smart cities. StormSense is an IoT-enabled inundation forecasting research initiative and an active participant in the Global City Teams Challenge, seeking to enhance flood preparedness in the smart cities of Hampton Roads, VA, for flooding resulting from storm surge, rain, and tides. In this study, we present the results of the new StormSense water level sensors to help establish the “regional resilience monitoring network” noted as a key recommendation from the Intergovernmental Pilot Project. To accomplish this, the Commonwealth Center for Recurrent Flooding Resiliency's Tidewatch tidal forecast system is being used as a starting point to integrate the extant (NOAA) and new (United States Geological Survey [USGS] and StormSense) water level sensors throughout the region and demonstrate replicability of the solution across the cities of Newport News, Norfolk, and Virginia Beach within Hampton Roads, VA. StormSense's network employs a mix of ultrasonic and radar remote sensing technologies to record water levels during 2017 Hurricanes Jose and Maria. These data were used to validate the inundation predictions of a street level hydrodynamic model (5-m resolution), whereas the water levels from the sensors and the model were concomitantly validated by a temporary water level sensor deployed by the USGS in the Hague and crowd-sourced GPS maximum flooding extent observations from the sea level rise app, developed in Norfolk, VA.

scholarly journals A low-cost technique to measure bank erosion proces ses along middle-size river reaches

2018 ◽  
Author(s):  
Gonzalo Duró ◽  
Alessandra Crosato ◽  
Maarten G. Kleinhans ◽  
Wim S. J. Uijttewaal
Keyword(s):  
Laser Scanning ◽  
Spatial Scales ◽  
Low Cost ◽  
Bank Erosion ◽  
Water Levels ◽  
Small Scale ◽  
Observation Distance ◽  
River Bank ◽  
Erosion Processes ◽  
Rtk Gps

Abstract. Diverse methods are currently available to measure river bank erosion at broad-ranging temporal and spatial scales. Yet, no technique provides low-cost and high-resolution to survey small-scale bank processes along a river reach. We investigate the capabilities of Structure-from-Motion photogrammetry applied with imagery from an Unmanned Aerial Vehicle (UAV) to describe the evolution of riverbank profiles in middle-size rivers. The bank erosion cycle is used as a reference to assess the applicability of different techniques. We surveyed 1.2 km of a restored bank of the Meuse River eight times within a year, combining different photograph perspectives and overlaps to identify an efficient UAV flight to monitor banks. The accuracy of the Digital Surface Models (DSMs) was evaluated compared with RTK GPS points and an Airborne Laser Scanning (ALS) of the whole reach. An oblique perspective with eight photo overlaps was sufficient to achieve the highest relative precision to observation distance of ~1:1400, with 10 cm error range. A complementary nadiral view increased coverage behind bank toe vegetation. The DSM and ALS had comparable accuracies except on banks, where the latter overestimates elevations. Sequential DSMs captured signatures of the erosion cycle such as mass failures, slump-block deposition, and bank undermining. Although this technique requires low water levels and banks without dense vegetation, it is a low-cost method to survey reach-scale riverbanks in sufficient resolution to quantify bank retreat and identify morphological features of the bank failure and erosion processes.

The Three-Cornered Hat Method for Estimating Error Variances of Three or More Atmospheric Data Sets – Part II: Evaluating Radio Occultation and Radiosonde Observations, Global Model Forecasts, and Reanalyses

2021 ◽  
Author(s):  
Therese Rieckh ◽  
Jeremiah P. Sjoberg ◽  
Richard A. Anthes
Keyword(s):  
Radio Occultation ◽  
State Of The Art ◽  
Specific Humidity ◽  
Data Sets ◽  
Error Growth ◽  
Atmospheric Conditions ◽  
Error Statistics ◽  
Weather And Climate ◽  
Atmospheric Data ◽  
The Impact

AbstractWe apply the three-cornered hat (3CH) method to estimate refractivity, bending angle, and specific humidity error variances for a number of data sets widely used in research and/or operations: radiosondes, radio occultation (COSMIC, COSMIC-2), NCEP global forecasts, and nine reanalyses. We use a large number and combinations of data sets to obtain insights into the impact of the error correlations among different data sets that affect 3CH estimates. Error correlations may be caused by actual correlations of errors, representativeness differences, or imperfect co-location of the data sets. We show that the 3CH method discriminates among the data sets and how error statistics of observations compare to state-of-the-art reanalyses and forecasts, as well as reanalyses that do not assimilate satellite data. We explore results for October and November 2006 and 2019 over different latitudinal regions and show error growth of the NCEP forecasts with time. Because of the importance of tropospheric water vapor to weather and climate, we compare error estimates of refractivity for dry and moist atmospheric conditions.

scholarly journals CLASSIC: a semi-distributed rainfall-runoff modelling system

2007 ◽  
Vol 11 (1) ◽  
pp. 516-531 ◽  
Author(s):  
S. M. Crooks ◽  
P. S. Naden
Keyword(s):  
Spatial Scales ◽  
Digital Terrain Model ◽  
Flood Frequency ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Terrain Model ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
The Uk ◽  
Modelling System

Abstract. This paper describes the development of a semi-distributed conceptual rainfall–runoff model, originally formulated to simulate impacts of climate and land-use change on flood frequency. The model has component modules for soil moisture balance, drainage response and channel routing and is grid-based to allow direct incorporation of GIS- and Digital Terrain Model (DTM)-derived data sets into the initialisation of parameter values. Catchment runoff is derived from the aggregation of components of flow from the drainage module within each grid square and from total routed flow from all grid squares. Calibration is performed sequentially for the three modules using different objective functions for each stage. A key principle of the modelling system is the concept of nested calibration, which ensures that all flows simulated for points within a large catchment are spatially consistent. The modelling system is robust and has been applied successfully at different spatial scales to three large catchments in the UK, including comparison of observed and modelled flood frequency and flow duration curves, simulation of flows for uncalibrated catchments and identification of components of flow within a modelled hydrograph. The role of such a model in integrated catchment studies is outlined.

scholarly journals Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 367-374 ◽  
Author(s):  
R. R. Colditz ◽  
R. M. Llamas ◽  
R. A. Ressl
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Time Interval ◽  
Data Sets ◽  
Change Analysis ◽  
Dimensional Error ◽  
Analysis Technique ◽  
Coarse Spatial Resolution

Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

scholarly journals Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0

2021 ◽  
Vol 14 (8) ◽  
pp. 4865-4890
Author(s):  
Peter Uhe ◽  
Daniel Mitchell ◽  
Paul D. Bates ◽  
Nans Addor ◽  
Jeff Neal ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
River Flow ◽  
Climate Model ◽  
Climate Modeling ◽  
Flood Hazard ◽  
Spatial Scales ◽  
Computational Cost ◽  
Data Sets ◽  
Flood Inundation

Abstract. Riverine flood hazard is the consequence of meteorological drivers, primarily precipitation, hydrological processes and the interaction of floodwaters with the floodplain landscape. Modeling this can be particularly challenging because of the multiple steps and differing spatial scales involved in the varying processes. As the climate modeling community increases their focus on the risks associated with climate change, it is important to translate the meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. This is due to the complexity and uncertainties of model cascades and the computational cost of flood inundation modeling. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (∼90 m) river flooding (fluvial) hazards. Thus, this framework is designed to be an accessible, computationally efficient tool using freely available data to enable greater uptake of this type of modeling. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data sets and thus can be applied anywhere in the world, but we use the Brahmaputra River in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework. This framework is designed to be driven by meteorology from observational data sets or climate model output. In this study, only observations are used to drive the models, so climate changes are not assessed. However, by comparing current and future simulated climates, this framework can also be used to assess impacts of climate change.

scholarly journals Fractional snow-covered area: scale-independent peak of winter parameterization

2021 ◽  
Vol 15 (2) ◽  
pp. 615-632
Author(s):  
Nora Helbig ◽  
Yves Bühler ◽  
Lucie Eberhard ◽  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Hydrological Modeling ◽  
Spatial Scales ◽  
Empirical Relationship ◽  
Ground Surface ◽  
Model Parameters ◽  
Data Sets ◽  
Snow Covered Area ◽  
Covered Area ◽  
Geographical Regions

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of peak of winter parameterization for the standard deviation of snow depth σHS by evaluating it with 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 to 3 m. An enhanced performance (mean percentage errors, MPE, decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σHS and between 0 % and 1 % for fSCA. We performed a scale- and region-dependent evaluation of the parameterizations to assess the potential performances with independent data sets. This evaluation revealed that for the majority of the regions, the MPEs mostly lie between ±10 % for σHS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.

scholarly journals Determination of diurnal water level fluctuations in headwaters

2016 ◽  
Vol 47 (4) ◽  
pp. 888-901 ◽  
Author(s):  
Marek Marciniak ◽  
Anna Szczucińska
Keyword(s):  
Water Temperature ◽  
Water Level ◽  
Air Temperature ◽  
Hyporheic Zone ◽  
Water Pressure ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Diurnal Changes ◽  
Atmospheric Conditions ◽  
Diurnal Fluctuations

The aim of this paper is to study diurnal fluctuations of the water level in streams draining headwaters and to identify the controlling factors. The fieldwork was carried out in the Gryżynka River catchment, western Poland. The water levels of three streams draining into the headwaters via a group of springs were monitored in the years 2011–2014. Changes in the water pressure and water temperature were recorded by automatic sensors – Schlumberger MiniDiver type. Simultaneously, Barodiver type sensors were used to record air temperature and atmospheric pressure, as it was necessary to adjust the data collected by the MiniDivers calculate the water level. The results showed that diurnal fluctuations in water level of the streams ranged from 2 to 4 cm (approximately 10% of total water depth) and were well correlated with the changes in evapotranspiration as well as air temperature. The observed water level fluctuations likely have resulted from processes occurring in the headwaters. Good correlation with atmospheric conditions indicates control by daily variations of the local climate. However, the relationship with water temperature suggests that fluctuations are also caused by changes in the temperature-dependent water viscosity and, consequently, by diurnal changes in the hydraulic conductivity of the hyporheic zone.

The use of wavelet transforms for improved interpretation of airborne transient electromagnetic data

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. E117-E123 ◽  
Author(s):  
Vanessa Nenna ◽  
Adam Pidlisecky
Keyword(s):  
Wavelet Transforms ◽  
Oil And Gas ◽  
Spatial Scales ◽  
Data Sets ◽  
Data Set ◽  
Topographic Features ◽  
Transient Electromagnetic ◽  
Power Maps ◽  
Southern Alberta ◽  
Cultural Noise

The continuous wavelet transform (CWT) is used to create maps of dominant spatial scales in airborne transient electromagnetic (ATEM) data sets to identify cultural noise and topographic features. The introduced approach is applied directly to ATEM data, and does not require the measurements be inverted, though it can easily be applied to an inverted model. For this survey, we apply the CWT spatially to B-field and dB/dt ATEM data collected in the Edmonton-Calgary Corridor of southern Alberta. The average wavelet power is binned over four ranges of spatial scale and converted to 2D maps of normalized power within each bin. The analysis of approximately 2 million soundings that make up the survey can be run on the order of minutes on a 2.4 GHz Intel processor. We perform the same CWT analysis on maps of surface and bedrock topography and also compare ATEM results to maps of infrastructure in the region. We find that linear features identified on power maps that differ significantly between B-field and dB/dt data are well correlated with a high density of infrastructure. Features that are well correlated with topography tend to be consistent in power maps for both types of data. For this data set, use of the CWT reveals that topographic features and cultural noise from high-pressure oil and gas pipelines affect a significant portion of the survey region. The identification of cultural noise and surface features in the raw ATEM data through CWT analysis provides a means of focusing and speeding processing prior to inversion, though the magnitude of this affect on ATEM signals is not assessed.

Recent Progress in Applying Advanced Computation Methods to Radar-Based Wave Measurements

2021 ◽  
Author(s):  
Morten Loell Vinther ◽  
Torbjørn Eide ◽  
Aurelia Paraschiv ◽  
Dickon Bonvik-Stone
Keyword(s):  
Doppler Radar ◽  
Risk Mitigation ◽  
Weather Conditions ◽  
Data Access ◽  
Environmental Data ◽  
Data Sets ◽  
Wave Measurements ◽  
Current Profiler ◽  
Pulse Doppler ◽  
Calm Weather

Abstract High quality environmental data are critical for any offshore activity relying on data insights to form appropriate planning and risk mitigation routines under challenging weather conditions. Such data are the most significant driver of future footprint reduction in offshore industries, in terms of costs savings, as well as operational safety and efficiency, enabled through ease of data access for all relevant stakeholders. This paper describes recent advancements in methods used by a dual-footprint Pulse-Doppler radar to provide accurate and reliable ocean wave height measurements. Achieved improvements during low wind weather conditions are presented and compared to data collected from other sources such as buoys and acoustic doppler wave and current profiler (ADCP) or legacy. The study is based on comparisons of recently developed algorithms applied to different data sets recorded at various sites, mostly covering calm weather conditions.

Sign in / Sign up

Export Citation Format

Share Document