The Surge, Wave, and Tide Hydrodynamics (SWaTH) network of the U.S. Geological Survey—Past and future implementation of storm-response monitoring, data collection, and data delivery

Verdi, Richard J.; Lotspeich, R. Russell; Robbins, Jeanne C.; Busciolano, R.; Mullaney, John R.; Massey, Andrew; Banks, William S.L.; Roland, Mark A.; Jenter, Harry L.; Peppler, Marie C.; Suro, Thomas P.; Schubert, Christopher E.; Nardi, Mark R.

doi:10.3133/cir1431

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…After the 1 -2 days of heavy rainfall, the amount of streamflow increased dramatically. The heavy rains increase the accumulated runoff from the upstream and finally is drained to the downstream (Dunne and Black, 1970;Verdi et al, 2017). In conclusions, the simulated streamflow was close to the observed streamflow data.…”

Section: Streamflowsupporting

confidence: 81%

Evaluation of landslide occurrence using hydrological model in Huai Nam Phung Subbasin, Thailand

Chansorn

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Huai Nam Phung subbasin, which is located at the boundary between Loei and Phetchabun provinces, is the area where landslides occur frequently. Therefore, this study aims to evaluate the occurrence of landslides in this area by using the Digital Elevation Model (DEM), Meteorological data hydrological data and physical properties of soils processed with the Geographic Information System (GIS) to analyze the physical data of the study area and use the TOPography based hydrological MODEL (TOPMODEL) combined with the theory of factor of safety in slope stability to simulate the slope stability between 2016 and 2017. According to the satellite imageries and field survey, there were 63 shallow landslide points. These points have the slope higher than 20 degrees and the landslides at these points occurred in 2017. The model simulation shows that the volume of accumulated water in 2017 was much higher than in 2016. The volume of water was in accordance with the meteorological and hydrological data. From the calibration of parameters in the TOPMODEL, there are 3 most sensitive parameters, the logarithm of areal average of saturated soil transmissivity (lnTe), surface hydraulic conductivity (k0) and capillary drive (CD). From the analysis of slope stability, the slope became unstable from high slope areas and the instability expanded to lower slope areas. Most of becoming unstable of landslide points occurred in July 2017 and the unstable areas were highest on 30 October 2017. From the analysis of slope stability in areas with a slope higher than 20 degrees on that day, there were 4 sub-catchments that most of the areas were unstable, Huai Nam Ko (92.49%), the upper part of Huai Nam Phung (90.14%), Huai Nam Hia (89.97%), and Huai Nam Krang (87.38%). These sub-catchments were located on the areas of the mountain ranges in the northern and western parts of the study area. Moreover, it is found that consecutive heavy rain over a long time can cause the slope to become unstable and immediate heavy rainfall events can also cause the slope to become unstable immediately. This instability can lead to landslide occurrence. In addition, the author created a landslide susceptibility map of the Huai Nam Phung subbasin from slope stability on 30 October 2017 to be a way to cope landslides in the future.

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Section: Streamflowsupporting

confidence: 81%

Evaluation of landslide occurrence using hydrological model in Huai Nam Phung Subbasin, Thailand

Chansorn

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“…This should be accompanied by effective communication with local, regional and state planners and policy makers concerning specific needs and challenges. NOAA's U.S. IOOS Program Office has sponsored programs such as the Coastal and Ocean Modeling Testbed (COMT; Luettich et al, 2013Luettich et al, , 2017 to transition advances by the modeling research community to the community at large, especially partners such as U.S. IOOS Regional Associations, NOAA Center for Operational Oceanographic Products and Services, U.S. Geological Survey (Verdi et al, 2017), Environmental Protection Agency, and U.S. Army Corps of Engineers.…”

Section: Next Generation Numerical Modelsmentioning

confidence: 99%

Collaborative Science to Enhance Coastal Resilience and Adaptation

Nichols¹,

Wright

Bainbridge

et al. 2019

Front. Mar. Sci.

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Impacts from natural and anthropogenic coastal hazards are substantial and increasing significantly with climate change. Coasts and coastal communities are increasingly at risk. In addition to short-term events, long-term changes, including rising sea levels, increasing storm intensity, and consequent severe compound flooding events are degrading coastal ecosystems and threatening coastal dwellers. Consequently, people living near the coast require environmental intelligence in the form of reliable shortterm and long-term predictions in order to anticipate, prepare for, adapt to, resist, and recover from hazards. Risk-informed decision making is crucial, but for the resulting information to be actionable, it must be effectively and promptly communicated to planners, decision makers and emergency managers in readily understood terms and formats. The information, critical to forecasts of extreme weather and flooding, as well as long-term projections of future risks, must involve synergistic interplay between observations and models. In addition to serving data for assimilation into models, the observations are also essential for objective validation of models via hind casts. Linked observing and modeling programs that involve stakeholder input and integrate engineering, environmental, and community vulnerability are needed to evaluate conditions prior to and following severe storm events, to update baselines, and to plan for future changes over the long term. In contrast to most deep-sea phenomena, coastal vulnerabilities are locally and regionally specific and prioritization of the most important observational data and model predictions must rely heavily on input from local and regional communities and decision makers. Innovative technologies and nature-based solutions are already helping to reduce vulnerability from coastal hazards in some localities but more focus on local circumstances, as opposed to global solutions, is needed. Agile and spatially distributed response capabilities will assist operational organizations in predicting, preparing for and mitigating potential community-wide disasters. This white paper outlines the rationale, synthesizes recent literature and summarizes some data-driven approaches to coastal resilience.

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Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England

Lombard¹,

Olson²,

Sturtevant³

et al. 2021

Scientific Investigations Report

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The Surge, Wave, and Tide Hydrodynamics (SWaTH) network of the U.S. Geological Survey—Past and future implementation of storm-response monitoring, data collection, and data delivery

Cited by 3 publications

References 3 publications

Evaluation of landslide occurrence using hydrological model in Huai Nam Phung Subbasin, Thailand

Evaluation of landslide occurrence using hydrological model in Huai Nam Phung Subbasin, Thailand

Collaborative Science to Enhance Coastal Resilience and Adaptation

Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England

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