Decadal-scale morphologic variability of managed coastal dunes

Coastal dunes located in densely populated areas provide various services to man, such as protection against flooding during storm surges. Since coastal dunes are dynamic features, the level of protection they provide varies in time. Therefore, management interventions are often undertaken to stabilize the dunes to reduce the natural variability. This study provides quantitative insight into the morphologic variability of managed foredunes over time spans of decades. We used Empirical Orthogonal Function (EOF) analysis on a 45 year data set of annually surveyed dune profiles along 97 km of the Netherlands' coast. On average, 70% of the deviations from the time-averaged profiles could be related to cross-shore coherent changes in foredune shape as mapped onto EOF 1. These changes are often largely due to morphologic developments occurring near the dunefoot. Changes in dune shape were coherent over time as well as in the longshore direction albeit with different characteristic patterns along the coast. These results show that managed foredunes may still exhibit considerable morphologic variability that should not be ignored in long-term dune safety assessment studies.     

Upwelling in the Alaskan Beaufort Sea: Atmospheric forcing and local versus non-local response

The spin up and relaxation of an autumn upwelling event on the Beaufort slope is investigated using a combination of oceanic and atmospheric data and numerical models. The event occurred in November 2002 and was driven by an Aleutian low storm. The wind field was strongly influenced by the pack-ice distribution, resulting in enhanced winds over the open water of the Chukchi Sea. Flow distortion due to the Brooks mountain range was also evident. Mooring observations east of Barrow Canyon show that the Beaufort shelfbreak jet reversed to the west under strong easterly winds, followed by upwelling of Atlantic Water onto the shelf. After the winds subsided a deep eastward jet of Atlantic Water developed, centered at 250 m depth. An idealized numerical model reproduces these results and suggests that the oceanic response to the local winds is modulated by a propagating signal from the western edge of the storm. The disparity in wave speeds between the sea surface height signal—traveling at the fast barotropic shelf wave speed—versus the interior density signal—traveling at the slow baroclinic wave speed—leads to the deep eastward jet. The broad-scale response to the storm over the Chukchi Sea is investigated using a regional numerical model. The strong gradient in windspeed at the ice edge results in convergence of the offshore Ekman transport, leading to the establishment of an anti-cyclonic gyre in the northern Chukchi Sea. Accordingly, the Chukchi shelfbreak jet accelerates to the east into the wind during the storm, and no upwelling occurs west of Barrow Canyon. Hence the storm response is fundamentally different on the Beaufort slope (upwelling) versus the Chukchi slope (no upwelling). The regional numerical model results are supported by additional mooring data in the Chukchi Sea.     

Sea Levels and Coastal Inundation Due to Tropical Cyclones in Indian Coastal Regions of Andhra and Orissa

Coastal inundation associated with extreme sea levels is the main factor which leads to the loss of life and property whenever a severe tropical cyclonic storm hits the Indian coasts. The Andhra and Orissa coasts are most vulnerable for coastal inundation due to extreme rise in sea levels associated with tropical cyclones. Loss of life may be minimized if extreme sea levels and associated coastal flooding is predicted well in advance. Keeping this in view, location specific coastal inundation models are developed and applied for the Andhra and Orissa coasts of India. Several numerical experiments are carried out using the data of past severe cyclones that struck these regions. The simulated inland inundation distances are found to be in general agreement with the reported flooding.     

Impact of Satellite Altimetry on Simulations of Sea Level Variability by an Indian Ocean Model

This article describes the impact of satellite altimeter data on the simulations of sea level variability (SLV) by a nonlinear reduced gravity model of the entire Indian Ocean. The model has been forced by 6-hourly analyzed wind stress data containing SSM/I observations and has been able to produce realistic circulation features. However, SLV values observed by Topex/Poseidon altimeter do not fit these simulations because of imperfect initial data. Hence an attempt has been made to initialize the model using altimeter data. The initialized model-generated SLVvalues have been compared with SLV derived by altimeter for monsoon as well as nonmonsoon months of 1996. Experimental runs have been performed for 10 days, 20 days, and one month. It has been found that the initialized model results on the final day of these experiments are in very good agreement with altimeter data of the same day. It is thus possible, in principle, to hindcast and forecast sea level variations in the time scale of 10 days to one month with the availability of good quality wind data for forcing the model and altimeter observations of sea level for initializing it.     

Accuracy Assessment of the TOPEX/Poseidon Ionosphere Measurements

We conducted an assessment of the TOPEX dual-frequency nadir ionosphere observations in the TOPEX/Poseidon (T/P) GDR by comparing TOPEX with the Center for Orbit Determination in Europe (CODE) Global Ionosphere Map (GIM), the climatological model IRI2001, and the DORIS (onboard T/P) relative ionosphere delays. We investigated the TOPEX (TOPEX Side A and TOPEX Side B altimeters, TSA and TSB, respectively) ionosphere observations for the time period 1995–2001, covering periods of low, intermediate, and high solar activity. Here, we use absolute path delays (at Ku-band frequency of the TOPEX altimeter and with positive signs) rather than Total Electron Content (TEC). We found significant biases between GIM and TOPEX (GIM–TOPEX) nadir ionosphere path delays: ?8.1 ± 0.4 {mm} formal uncertainties and equivalent to 3.7 TECu) and ?9.0 ± 0.7 {mm} (4.1 TECu) for TSA and TSB, respectively, indicating that the TOPEX path delay is longer (or with higher TECu) than GIM. The estimated relative biases vary with latitude and with daytime or nighttime passes. The estimated biases in the path delays (DORIS–TOPEX) are: ?10.9 ± 0.4 {mm} (5.0 TECu) and ?14.8 ± 0.6 {mm} (6.7 TECu), for TSA and TSB, respectively. There is a distinct jump of the DORIS path delays (?3.9 ± 0.7 {mm}, TSA delays longer than TSB delays) at the TSB altimeter switch in February 1999, presumably due to inconsistent DORIS processing. The origin of the bias between GIM (GPS, L-band) and TOPEX (radar altimeter, Ku-band) is currently unknown and warrants further investigation. Finally, the estimated drift rates between GIM and TSA, DORIS and TSA ionosphere path delays for the 6-year study span are ?0.4 mm/yr and ?0.8 mm/yr, respectively, providing a possible error bound for the TOPEX/Poseidon sea level observations during periods of low and intermediate solar activity.     

Evidence That Sea State Bias is Different for Ascending and Descending Tracks

Jason-1 and TOPEX/Poseidon (T/P) measured sea-surface heights (SSHs) are compared for five regions during the verification tandem phase. The five regions are of similar latitude and spatial extent and include the Gulf of Mexico, Arabian Sea, Bay of Bengal, and locations in the Pacific and Atlantic Oceans away from land. In all five regions, a bias, defined as Jason SSH—TOPEX-B SSH, exists that is different for ascending and descending tracks. For example, in the Gulf of Mexico the bias for ascending tracks was ?0.13 cm and the bias for descending tracks was 2.19 cm. In the Arabian Sea the bias for ascending tracks was ?2.45 cm and the bias for descending tracks was ?1.31 cm. The bias was found to depend on track orientation and significant wave height (SWH), indicating an error in the sea state bias (SSB) model for one or both altimeters. The bias in all five regions can be significantly reduced by calculating separate corrections for ascending and descending tracks in each region as a function of SWH. The correction is calculated by fitting a second-order polynomial to the bias as a function of SWH separately for ascending and descending tracks. An additional constraint is required to properly apply the correction, and we chose to minimize the sum of the TOPEX-B and Jason-1 root-mean-square (rms) crossover differences to be consistent with present SSB models. Application of this constraint shows that the correction, though consistent within each region, is different for each region and that each satellite contributes to the bias. One potential source that may account for a portion of the difference in bias is the leakage in the wave forms in TOPEX-B due to differing altitude rates for ascending and descending tracks. Global SSB models could be improved by separating the tracks into ascenders and descenders and calculating a separate SSB model for each track.     

Analysis of the Tsunami of December 26, 2004, on the Kerala Coast of India—Part I: Amplitudes

The Indian Ocean tsunami of December 26, 2004, not only affected the Bay of Bengal coast of India but also part of the Arabian Sea coast of India. In particular, the tsunami caused loss of life and heavy damage on some parts of the Kerala coast in southwest India. The tsunami traveled west, south of Sri Lanka, and some of the tsunami energy was diffracted around Sri Lanka and the southern tip of India and moved northward into the Arabian Sea. However, tsunami, being a long gravity wave with a wave length of a few hundred kilometers, has to take a wide turn. In that process, it missed the very southern part of the Kerala coast and did not achieve large amplitudes there. However, further north, the tsunami achieved amplitudes of upto 5 m and caused loss of life and significant damage. Here we identify the physical oceanographic processes that were responsible for selective amplification of the tsunami in certain locations.     

Identifying Rocky Seabed Using GIS-Modeled Predictor Variables

Mapping the seabed along the Norwegian coast is costly and time consuming. Hence, finding a modeling method to separate rocky seabed from other substrate types will provide digital maps that may be used to develop cost-effective sampling designs to predict species and habitat distribution. Our approach was to use geophysical data that were quantitative and objectively defined, generalized additive models (GAMs), and Akaike information criterion (AIC) to develop statistical models and select among them. We found that slope, terrain curvature, wave exposure, and depth predicted rocky seabed occurrence with a high degree of certainty.     

Vulnerability Assessment at Village Level Due to Tides,Surges and Wave Setup

An assessment of cyclone risk and vulnerability at the village level has evolved, which is an important component of the information system for local level development action plans for preparedness and mitigation. Here, a case study for the Nellore district along the east coast of India is considered. Using maximum probable surges along the coast, total water level (TWL) due to the combined effect of surge, tide, and wind wave is computed for the most vulnerable coastal villages of the Nellore district due to any tropical cyclones. The computations suggest that the TWL along the Nellore coast varies from 2 m in the south to 4 m in the north.     

Contribution of Tides to Sea Level Variations Along Visakhapatnam,India

The sea level variations along Visakhapatnam coast are governed by astronomical tides and nontidal oscillations including atmospheric pressure, winds, coastal currents, Ekman Pumping, and river influx. Tidal and nontidal sea level oscillations are usually studied separately because of the vastly different ways in which they are forced. In this study the tidal oscillations along Visakhapatnam are analyzed using GOTIC2 tidal model. The correlation between monthly mean sea level and monthly mean tides is 47% (r = 0.68) and increases to 54% (r = 0.74) when applied for inverse-barometric effect. The major six partial tides are computed and presented. The tidal variations from Neap tide to Spring tide are studied.