Timeline Visualization Uncovers Gaps in Archived Tsunami Water Level Data

We demonstrate that data abstraction via a timeline visualization is highly effective at allowing one to discover patterns in the underlying data. We describe the rapid identification of data gaps in the archival time-series records of deep-ocean pressure and coastal water level observations collected to support the NOAA Tsunami Program and successful measures taken to rescue these data. These data gaps had persisted for years prior to the development of timeline visualizations to represent when data were collected. This approach can be easily extended to all types of time-series data and the author recommends this type of temporal visualization become a routine part of data management, whether one collects data or archives data.

On The Determinations of Weather, Seasonal, Sub-Seasonal and Climate Scale Variability and Overall Trends in the Atmosphere and Ocean

The traditional concepts and definitions of multi-scale “weather”, “seasonal variability”, “sub-seasonal variability”, “climate variability”, “trends” and “climate change” for both the global atmosphere and the global ocean are considered. We build upon existing literature and present new evidence that atmospheric and oceanic temporal multi-scale variability are the result of a mix of well-known frequency and amplitude modulated nonlinear and phenomena that occur simultaneously [1-3]. We harvest representative atmospheric temperature and wind data, oceanic temperature and coastal water level from United States (U.S.) and United Kingdom (U.K.) agency archives, collected via in-situ and satellite remotely sensed data and employ a mathematical methodology that can decompose nonlinear data. The data decomposition reveals a continuum of well-defined, modulated, internal modes of oscillations, each with broad spectral peaks and each representative of naturally occurring phenomena. We reveal that the conventional notions of weather and seasonal to subseasonal to climate variability, actually constitute an over-lapping continuum, with shorter period oscillations commuting with longer period oscillations onto overall record length trends. We relate these internal, intrinsic modes of variability to naturally occurring causal agents, from relatively high frequency weather to lower frequency seasonal to sub-seasonal to climate scale variability. Correlative relationships between climate factors reveal causal couplings of the oceanic and atmospheric systems.

Compounding Effects of Riverine and Coastal Floods and Its Implications for Coastal Urban Flood Resilience

<p>Globally more than 600 million people reside in the low elevation (< 10 meters elevation) coastal zone. The densely populated low-lying deltas are vulnerable to flooding primarily in two ways: (1) Due to extreme coastal water level (ECWL) because of either storm surges or heavy rain-induced river floods generated by a severe storm episode. (2) Co-occurrence or successive occurrence of ECWL and river floods as a result of storm-producing synoptic weather conditions leading to compound floods that causes a severe impact than when each of these extremes occurs in an isolation at different times. Most of the earlier assessments that analyzed compound floods, often do not consider the delay between rainfall and streamflow events. River runoff, which also includes subsurface groundwater recharge component, cannot be adequately described by extreme precipitation alone. While most of the literature is limited to analyzing joint dependence between variables considering only central dependence, challenges to flood hazard assessment include difficulty in delineating the severity of riverine floods, especially due to long upper tails of the variables that influence interdependencies between underlying drivers. Despite uncertainties, utilizing the rich database of northwestern Europe, here we assess compound flood severity and its trend by examining spatial interdependencies between annual maxima coastal water level (as an indicator of ECWL) and d-day lagged peak discharge within ±7 days of the occurrence of the ECWL event. Our analysis reveals a spatially coherent dependence pattern with strong positive dependence for gauges located between 52° and 60°N latitude, whereas a weak positive dependence across gauges in > 60°N latitude. Based on a newly proposed index, Compound Hazard Ratio (CHR) that compares the severity of compound floods with at-site design floods, our proof-of-principal analysis suggests nearly half of the stream gauges show amplifications in fluvial flood hazard during 2013/2014’s catastrophic winter storm Xaver that affected most of northern Europe. Furthermore, a multi-decadal (1889 – 2014) temporal evolution of compound flood reveals the existence of a flood-rich period between 1960s and 1980s, especially for the mid-latitude gauges (located within 47° to 60°N), which might be closely linked to the North Atlantic Oscillation (NAO) teleconnection pattern prevailing in the region. On the other hand, gauges at high-latitude (> 60°N) show decreasing to no trend in compound floods. The approach presented here can serve as a basis for developing coastal urban flood risk management portfolios aiding improved resilience and reduce vulnerability in the affected areas.  </p>

Ensemble Water Level Prediction System: Improving the Representation of Model Uncertainty

<p>Concern over increased flooding and the need for earlier and more reliable risk forecasts motivate the continued development of operational forecasts of coastal water level. We report here on results from a year long ensemble of total water level forecasts calculated using a dynamical ocean model forced with ensemble atmospheric forcing and tidal boundary conditions. We focus on the east coast of Canada. The domain includes the Gulf of St. Lawrence, the Labrador Shelf, the Scotian Shelf, and the Gulf of Maine. The water level ensemble is made of a control and 20 perturbed members. Individual forecasts are produced twice daily for 16 days.</p><p> </p><p>The novelty of the present study is in the exploration of perturbations of the ocean contributions. In addition to examining how uncertainty in atmospheric forcing maps into flood risk, we also explore the feasibility, and impact, of perturbing the ocean tides. We use a recent case study to demonstrate our findings.</p><p> </p>

A REVIEW OF MODELLING APPROACHES ON TIDAL ANALYSIS AND PREDICTION

Tide height depends on both long-term astronomical effects that are principally affected by the moon and sun and short-term meteorological effects caused by severe weather conditions which are very important tasks for human activities, safe marine navigation in shallow areas, oceans and coastal engineering work. Conventional tidal forecasting techniques are based on harmonic analysis, which is a superposition of many sinusoidal constituents with three parameters amplitudes, Phase and frequencies using the least squares method to determine the harmonic parameters. However, harmonic analysis required a large number of parameters and long-term tidal measured for precise tidal level predictions. Furthermore, what seems to stand out by the other researchers on traditional harmonic methods, was its limitation when short data are involved and rely on based on the analysis of astronomical components and they can be insufficient when the influence of non-astronomical components such as the weather, is important. Therefore, conventional harmonic analysis alone does not adequately predict the coastal water level variation, in order to deal with these situations and provide predictions with the desired accuracy, with respect to the length of the available tidal record, an alternative approach has been developed by various tidalist. In this study the state - of - art for tidal analysis and prediction techniques that have proven to be successful in a variety of circumstances have been reviewed in a systematic and consistent way for holistic understanding with a view to provide a reference for future work, showing their main mathematical concepts, model capabilities for tidal analysis and prediction with their limitations.

Resolving Hydrometeorological Data Discontinuities along an International Border

Monitoring, understanding, and forecasting the hydrologic cycle of large freshwater basins often requires a broad suite of data and models. Many of these datasets and models, however, are susceptible to variations in monitoring infrastructure and data dissemination protocols when watershed, political, and jurisdictional boundaries do not align. Reconciling hydrometeorological monitoring gaps and inconsistencies across the international Laurentian Great Lakes–St. Lawrence River basin is particularly challenging because of its size and because the basin’s dominant hydrologic feature is the vast surface waters of the Great Lakes.For tens of millions of Canadian and U.S. residents that live within the Great Lakes basin, seamless binational datasets are needed to better understand and predict coastal water-level fluctuations and other conditions that could potentially threaten human and environmental health. Binational products addressing this need have historically been developed and maintained by the Coordinating Committee on Great Lakes Basic Hydraulic and Hydrologic Data (Coordinating Committee). The Coordinating Committee recently held its one-hundredth semiannual meeting and reflected on a range of historical accomplishments while setting goals for future work. This article provides a synthesis of those achievements and goals. Particularly significant legacy and recently developed datasets of the Coordinating Committee include historical Great Lakes surface water elevations, basin-scale tributary inflow to the Great Lakes, and basin-scale estimates of both over-lake and over-land precipitation. Moving forward, members of the Coordinating Committee will work toward customizing state-of-the-art hydrologic and meteorological forecasting systems across the entire Great Lakes basin and toward promoting their products and protocols as templates for successful binational coordination across other large binational freshwater basins.

TOWARDS A PROBABILISTIC FLOOD BASE MODEL FOR CARIBBEAN COASTS

An estimate of the total water level setup at the coast is examined at a monthly resolution to assess the base combined contribution from wind, wave and pressure setup in the absence of cyclonic activity. A stochastic approach is used to estimate the total values of water level setup from the three parameters and the results indicate that while the total coastal water level setup closely follows the wave setup due to a difference of an order of magnitude of two with the wind and pressure setup from the inverse barometer effect, the latter two parameters have a greater impact during the first seven months of the year – the dry season with an approximate two month overlap into the rainy season. Using the generated moments from monthly combined probability density functions, we show that the average coastal setup from the three driving forces show a general decreasing trend from the highest coastal setup months of December, January and February.

Development of an Atlantic Canadian Coastal Water Level Neural Network Model

Coastal water-level information is essential for coastal zone management, navigation, and oceanographic research. However, long-term water-level observations are usually only available at a limited number of locations. This study discusses a complementary and simple neural network (NN) approach, to predict water levels at a specified coastal site from the data gathered at other nearby or remote permanent stations. A simple three-layer, feed-forward, back-propagation network and a neural network ensemble, named Atlantic Canadian Coastal Water Level Neural Network (ACCSLENNT) models, was developed to correlate the nonlinear relationship of sea level data among stations by learning from their historical characteristics. Instantaneous hourly observations of water level from five stations along the coast of Atlantic Canada—Argentia, Belledune, Halifax, North Sydney, and St. John’s—are used to formulate and validate the ACCSLENNT models. Qualitative and quantitative comparisons of the network output with target observations showed that despite significant changes in sea level amplitudes and phases in the study area, appropriately trained NN models could provide accurate and robust long-term predictions of both tidal and nontidal (tide subtracted) water levels when only short-term data are available. The robust results indicate that the NN models in conjunction with limited permanent stations are able to supplement long-term historical water-level data along the Atlantic Canadian coast. Because field data collection is usually expensive, the ACCSLENNT models provide a cost-effective alternative to obtain long-term data along Atlantic Canada.