Trends in tidal range around the U.S. and potential implications for flooding occurrence

2021 ◽  
Author(s):  
Francesco De Leo ◽  
Stefan A. Talke
Keyword(s):  
Time Series ◽  
San Francisco ◽  
Tidal Range ◽  
Connecticut River ◽  
Climate Change Effects ◽  
Spatial Coverage ◽  
Tidal Changes ◽  
Anthropogenic Development ◽  
Tidal Change ◽  
The U.S

<p>Many locations in the U.S. have experienced large trends in their tidal range since the 19<sup>th</sup> century, often in response to altered coastal and estuarine morphology.  Such tidal changes may enhance the vulnerability of an area towards flooding. In this contribution, >1000 estimates of tidal range from around the contiguous United States are digitized from the published tide tables of 1899 and compared to the tide table of 2020. Our approach enables much greater spatial coverage than previous studies. Tidal range has more than doubled in many regions due to anthropogenic development, including Miami, the Saint Johns River, and the Connecticut River. Important changes are noted in other tidal rivers, including the Sacramento, Savannah, and James Rivers. On average, gauges located inland experienced the largest changes in tidal range, followed by estuary stations; coastal stations showed the least variability. Amplified tidal range increases the prevalence of minor (nuisance) flooding.  As shown by case studies of San Francisco, Wilmington (North Carolina) and Miami (Florida), the prevalence of minor (nuisance) flooding events has greatly increased due to tidal evolution. In locations without historical time-series, we infer the changed flooding using a statistical model that estimates changes to tidal constituents based on the observed change in tidal range and known constituent ratios.  Results show that tidal change may be a previously underappreciated factor in the increasing prevalence of nuisance flooding in cities like Miami and Jacksonville, Florida, where long time series of data back to the 19<sup>th</sup> century are not available.  Understanding the reasons for tidal change may provide planners and engineers with new tools to adapt to climate change effects like sea-level rise.</p>

A time-series analysis of the U.S. kidney transplantation and the waiting list: donor substitution effects

2010 ◽  
Vol 42 (1) ◽  
pp. 261-277 ◽  
Author(s):  
T. Randolph Beard ◽  
John D. Jackson ◽  
David Kaserman ◽  
Hyeongwoo Kim
Keyword(s):  
Time Series ◽  
Kidney Transplantation ◽  
Time Series Analysis ◽  
Waiting List ◽  
Substitution Effects ◽  
Series Analysis ◽  
The U.S

Classification of landslide activity based on spaceborne interferometric SAR at the Moselle Valley, Germany

2021 ◽  
Author(s):  
Andre C. Kalia
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Statistical Tests ◽  
Spatial Density ◽  
Deformation Pattern ◽  
Visual Interpretation ◽  
Landslide Activity ◽  
Spatial Coverage ◽  
Interferometric Sar

<p>Landslide activity is an important information for landslide hazard assessment. However, an information gap regarding up to date landslide activity is often present. Advanced differential interferometric SAR processing techniques (A-DInSAR), e.g. Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) are able to measure surface displacements with high precision, large spatial coverage and high spatial sampling density. Although the huge amount of measurement points is clearly an improvement, the practical usage is mainly based on visual interpretation. This is time-consuming, subjective and error prone due to e.g. outliers. The motivation of this work is to increase the automatization with respect to the information extraction regarding landslide activity.</p><p>This study focuses on the spatial density of multiple PSI/SBAS results and a post-processing workflow to semi-automatically detect active landslides. The proposed detection of active landslides is based on the detection of Active Deformation Areas (ADA) and a subsequent classification of the time series. The detection of ADA consists of a filtering of the A-DInSAR data, a velocity threshold and a spatial clustering algorithm (Barra et al., 2017). The classification of the A-DInSAR time series uses a conditional sequence of statistical tests to classify the time series into a-priori defined deformation patterns (Berti et al., 2013). Field investigations and thematic data verify the plausibility of the results. Subsequently the classification results are combined to provide a layer consisting of ADA including information regarding the deformation pattern through time.</p>

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

2021 ◽  
Author(s):  
Aidin Tamhidi ◽  
Nicolas Kuehn ◽  
S. Farid Ghahari ◽  
Arthur J. Rodgers ◽  
Monica D. Kohler ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Ground Motion ◽  
San Francisco ◽  
Earthquake Engineering ◽  
Seismic Analysis ◽  
Ground Motions ◽  
Free Field ◽  
Gaussian Process Regression ◽  
Motion Time

ABSTRACT Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

Crustal structure southwest of the San Andreas fault from quarry blasts

1964 ◽  
Vol 54 (1) ◽  
pp. 67-77
Author(s):  
Robert M. Hamilton ◽  
Alan Ryall ◽  
Eduard Berg
Keyword(s):  
San Francisco ◽  
Crustal Structure ◽  
San Andreas Fault ◽  
P Wave ◽  
Geological Survey ◽  
San Andreas ◽  
Crustal Model ◽  
Crystalline Crust ◽  
The U.S

abstract To determine a crustal model for the southwest side of the San Andreas fault, six large quarry blasts near Salinas, California, were recorded at 27 seismographic stations in the region around Salinas, and along a line northwest of the quarry toward San Francisco. Data from these explosions are compared with results of explosion-seismic studies carried out by the U.S. Geological Survey on a profile along the coast of California from San Francisco to Camp Roberts. The velocity of Pg, the P wave refracted through the crystalline crust, in the Salinas region is 6.2 km/sec and the velocity of Pn is about 8.0 km/sec. Velocities of the direct P wave in near-sur-face rocks vary from one place to another, and appear to correlate well with gross geologic features. The thickness of the crust in the region southwest of the San Andreas fault from Salinas to San Francisco is about 22 kilometers.

scholarly journals Deglacial carbon cycle changes observed in a compilation of 127 benthic <i>δ</i><sup>13</sup>C time series (20–6 ka)

2018 ◽  
Vol 14 (8) ◽  
pp. 1229-1252 ◽  
Author(s):  
Carlye D. Peterson ◽  
Lorraine E. Lisiecki
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Carbon Storage ◽  
Large Scale ◽  
Deep Ocean ◽  
Atmospheric Co2 ◽  
Last Deglaciation ◽  
Terrestrial Biosphere ◽  
Spatial Coverage ◽  
Ocean Carbon

Abstract. We present a compilation of 127 time series δ13C records from Cibicides wuellerstorfi spanning the last deglaciation (20–6 ka) which is well-suited for reconstructing large-scale carbon cycle changes, especially for comparison with isotope-enabled carbon cycle models. The age models for the δ13C records are derived from regional planktic radiocarbon compilations (Stern and Lisiecki, 2014). The δ13C records were stacked in nine different regions and then combined using volume-weighted averages to create intermediate, deep, and global δ13C stacks. These benthic δ13C stacks are used to reconstruct changes in the size of the terrestrial biosphere and deep ocean carbon storage. The timing of change in global mean δ13C is interpreted to indicate terrestrial biosphere expansion from 19–6 ka. The δ13C gradient between the intermediate and deep ocean, which we interpret as a proxy for deep ocean carbon storage, matches the pattern of atmospheric CO2 change observed in ice core records. The presence of signals associated with the terrestrial biosphere and atmospheric CO2 indicates that the compiled δ13C records have sufficient spatial coverage and time resolution to accurately reconstruct large-scale carbon cycle changes during the glacial termination.

CONVERGENCE AND THE U.S. STATES: A TIME-SERIES ANALYSIS*

1996 ◽  
Vol 36 (4) ◽  
pp. 597-616 ◽  
Author(s):  
Gerald Carlino ◽  
Leonard Mills
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
The U.S

Forecasting Performance Comparison by Using Power Transformation between VAR and Bayesian VAR Models

2014 ◽  
Vol 529 ◽  
pp. 621-624
Author(s):  
Syang Ke Kung ◽  
Chi Hsiu Wang
Keyword(s):  
Time Series ◽  
Industrial Production ◽  
Multivariate Time Series ◽  
Performance Comparison ◽  
Power Transformation ◽  
Var Models ◽  
Bayesian Var ◽  
Forecasting Performance ◽  
The U.S ◽  
Forecasting Practice

This article is devoted to examine the performance of power transformation in VAR and Bayesian VAR (BVAR) forecasts, in comparison with log-transformation. The effect of power transformation in multivariate time series model forecasts is still untouched in the literature. We examined the U.S. macroeconomic data from 1960 to 1987 and the Taiwan’s technology industrial production from 1990 to 2000. Our results showed that the power transformation provides outperforming forecasts in both VAR and BVAR models. Moreover, the non-informative prior BAVR with power transformation is the best predictive model and is recommendable to forecasting practice.

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