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We apply two statistical techniques to satellite measurements to identify a relationship between terrestrial water storage (TWS) and El Niño-Southern Oscillation (ENSO). First, we modified and used the least-squares regression of a previous study using longer records. Second, we applied a cyclostationary empirical orthogonal function analysis (CSEOF). Although the CSEOF technique is distinct from the least-squares regression in that it does not consider proxies, each method produces two modes (decadal and interannual), showing consistency with each technique in spatial pattern and its evolution amplitudes. We also compared the results obtained by the two methods for thirty watersheds, of which five watersheds were compared with previous studies. The combination of the two modes explains the total variance in most watersheds showing the role that interannual and decadal ENSO-related signals in understanding terrestrial water storage variability. The results show that the decadal mode, along with the interannual mode, also plays an important role in describing the local TWS.

The two dominant drivers of the global mean sea level (GMSL) variability at interannual timescales are steric changes due to changes in ocean heat content and barystatic changes due to the exchange of water mass between land and ocean. With Gravity Recovery and Climate Experiment (GRACE) satellites and Argo profiling floats, it has been possible to measure the relative steric and barystatic contributions to GMSL since 2004. While efforts to “close the GMSL budget” with satellite altimetry and other observing systems have been largely successful with regards to trends, the short time period covered by these records prohibits a full understanding of the drivers of interannual to decadal variability in GMSL. One particular area of focus is the link between variations in the El Niño−Southern Oscillation (ENSO) and GMSL. Recent literature disagrees on the relative importance of steric and barystatic contributions to interannual to decadal variability in GMSL. Here, we use a multivariate data analysis technique to estimate variability in barystatic and steric contributions to GMSL back to 1982. These independent estimates explain most of the observed interannual variability in satellite altimeter-measured GMSL. Both processes, which are highly correlated with ENSO variations, contribute about equally to observed interannual GMSL variability. A theoretical scaling analysis corroborates the observational results. The improved understanding of the origins of interannual variability in GMSL has important implications for our understanding of long-term trends in sea level, the hydrological cycle, and the planet’s radiation imbalance.

Sea-Level data has been reconstructed for the Indian Ocean using a new multivariate CSEOF reconstruction technique for the period 1900-2018.This dataset is created using sea surface temperature and sea level pressure in addition to sea level data (to help overcome sparse spatio-temporal tide gauge sampling) and consists of improved 20th century reconstructed sea level estimates for the Indian Ocean.This is a .mat structure file (mvr) created using Matlab. Under the mvr structure, there are four variables:sla ==> Reconstructed sea level anomaly. [Units: Meters].lat ==> Latitude. [Resolution: 0.5 degrees].lon ==> Longitude.[Resolution: 0.5 degrees].t ==> Time. [Resolution: Monthly].*** NOTE:Due to the 12 month fitting window of the CSEOF basis functions, data (sla) for the first 6 months and last 6 months are absent.

Since the advent of the modern satellite altimeter era, the understanding of the sea level has increased dramatically. The satellite altimeter record, however, dates back only to the 1990s. The tide gauge record, on the other hand, extends through the 20th century but with poor spatial coverage when compared to the satellites. Many studies have been conducted to create a dataset with the spatial coverage of the satellite datasets and the temporal length of the tide gauge records by finding novel ways to combine the satellite data and tide gauge data in what is known as sea level reconstruction. However, most of the reconstructions of sea level were conducted on a global scale, leading to reduced accuracy on regional levels, especially when there are relatively few tide gauges. The seas around the Korean Peninsula are one such area with few tide gauges before 1960. In this study, new methods are proposed to reconstruct past sea level around the Korean Peninsula. Using spatial patterns obtained from a cyclostationary empirical orthogonal function decomposition of satellite data, we reconstruct sea level over the period from 1900 to 2014. Sea surface temperature data and altimeter data are used simultaneously in the reconstruction process, leading to an elimination of reliance on tide gauge data. Although we did not use the tide gauge data in the reconstruction process, the reconstructed sea level has a better agreement with the tide gauge observations in the region than previous studies that incorporated the tide gauge data. This study demonstrates a reconstruction technique that can potentially be used at regional levels, with particular emphasis on areas with poor tide gauge coverage.

In this paper, the method to assess the impact of sea level rise under regular waves, as proposed by Townend (1994) , is extended to irregular waves in order to estimate the changes in nearshore significant waves and the parameters related to hydraulic performance and stability of inclined coastal structures. The relative changes in wavelength, refraction coefficient, shoaling coefficient, and wave height for significant waves are presented as functions of the relative change in water depth. The calculated relative changes in wave characteristics are then used to estimate the effect of sea level rise on coastal structures by calculating the relative changes in wave run-up height, overtopping discharge, crest freeboard, and armor weight of the structures. The relative changes in wave characteristics are expressed as functions of relative water depth and deepwater wave angle or wave steepness. The relative changes in the structure-related parameters are expressed as functions of the relative change in wave height and the wave height and crest freeboard before the sea level rise.

For analyzing sand grain size, a specialized high-priced instrument has been used, such as sieve shaker, video camera, laser particle size analyzer, and microscope. Among these, the sieve shaker is commonly used because it is not only cheaper than others but also provides reasonable accuracy. However, it takes a long time and makes lots of dust and noise. In this study, a cheaper and easier method which can replace the sieve shaker is proposed. By using a commercial flat-bed scanner and a darkroom box, the sand size distribution can be analyzed. The darkroom box makes sand images clear and protects the glass of the scanner from being scratched. Comparison between the present method and sieve analysis shows that the present method not only has an accuracy comparable to the sieve analysis but also can save time and effort.

The effects of long-term sea-level rise and offshore wave-height increase due to climate change on the stability of caisson breakwaters constructed in different water depths are analyzed by using a time-dependent performance-based design method. An artificial neural network is combined with the wave transformation model to reduce the computation time in the Monte Carlo simulation. The breakwater is designed by the conventional safety-factor method, while its performance is evaluated by the expected sliding distance. In general, the stability of the breakwater is reduced if the climate change effects are included, but it shows different trends depending on water depth. Outside the surf zone, the effect of sea-level rise decreases with increasing water depth, whereas that of wave-height increase increases with water depth. Inside the surf zone, however, both effects decrease with decreasing water depth, with greater effect of wave-height increase than sea-level rise. In the design of a caisson breakwater of ordinary importance, it is not necessary to take into account the effect of sea-level rise, whereas the effect of wave-height increase should be taken into account if the breakwater is constructed far outside the surf zone. However, it should be noted that different results should be obtained if the breakwater were designed based on the expected sliding distance.

In this study, a method has been developed for estimating the change of nearshore random waves in response to sea-level rise, by extending the method proposed for regular waves by Townend in 1994. The relative changes in wavelength, refraction coefficient, shoaling coefficient, and wave height for random waves are presented as functions of relative change in water depth. The changes in wavelength and refraction coefficient are calculated by using the significant wave period and principal wave direction in the regular-wave formulas. On the other hand, the changes in shoaling coefficient and wave height are calculated by using the formulas proposed for shoaling and transformation of random waves in the nearshore area including surf zone. The results are proposed in the form of both formulas and graphs. In particular, the relative change in wave height is compared with the result for regular waves.

Although the existing performance-based design method for the vertical breakwater evaluates an average sliding distance during an arbitrary time, it does not calculate the probability of the first occurrence of an event exceeding an allowable sliding distance(i.e. the first-passage probability). Designers need information about the probability that the structure is damaged for the first time for not only design but also maintenance and operation of the structure. Therefore, in this study, a time-dependent reliability design method based on a stochastic process is developed to evaluate the first-passage probability of caisson sliding. Caisson sliding can be formulated by the Poisson spike process because both occurrence time and intensity of severe waves causing caisson sliding are random processes. The occurrence rate of severe waves is expressed as a function of the distribution function of sliding distance and mean occurrence rate of severe waves. These values simulated by a performance-based design method are expressed as multivariate regression functions of design variables. As a result, because the distribution function of sliding distance and the mean occurrence rate of severe waves are expressed as functions of significant wave height, caisson width, and water depth, the first-passage probability of caisson sliding can be easily evaluated.

In this paper, a method has been developed for estimating the change of nearshore significant waves in response to long-term sea level rise, by extending the method proposed for regular waves by Townend in 1994. The relative changes in wavelength, refraction coefficient, shoaling coefficient, and wave height for random waves are presented as functions of the relative change in water depth. The changes in wavelength and refraction coefficient are calculated by using the significant wave period and principal wave direction in the formulas for regular waves. On the other hand, the changes in shoaling coefficient and wave height are calculated by using the formulas proposed for transformation of random waves in the nearshore area including the surf zone. The results are presented in the form of both formulas and graphs. In particular, the relative change in significant wave height is compared with the result for regular waves.

This paper presents a new calibration method of wave heights obtained using marine X-band radar wave measurement system. Realtime information about sea state, such as wave height, period, and direction, is crucial for coastal protection as well as offshore operational management. In the last 30 years, routine sea state measurements have been carried out using different measuring devices such as mooring buoys, pressure gages, and acoustic type gages. Although such devices provide reliable measurements, they are subject to frequent damages and losses. Therefore, considerable interest has been shown lately in the use of remote sensing techniques to measure waves and surface currents. In recent years, it has been demonstrated that radar images of the ocean surface provide reliable information about the spatial behavior of wave fields. Many systems which utilize a commercial marine X-band radar for providing time series of radar backscatter images from the sea surface have been developed. (Borge et al., 2004, Plant and Zunk, 1997 ) Wave measurement system using marine X-band radar has many advantages over conventional wave gages, which are more robust in severe sea state, easy to maintain, economic maintenance cost, etc. However, one critical shortcoming of X-band radar system using in operational wave measurements is inaccuracy in the estimation of wave heights during weak local wind conditions. This is a critical weakness when people try to use measured wave data in the statistical estimation of design waves. It is not an easy task to overcome this critical shortcoming. It is because this problem resides in the X-band radar system’s inherent sea surface scanning mechanism, which is called Bragg Resonance mechanism. It is known that under various conditions signatures of the sea surface are visible in the near ranges (usually less than 5.4 km) of marine radar images. These signatures are known as sea clutter because they are undesirable for navigational purposes and generally suppressed by filter algorithm. Sea clutter is mainly due to the backscatter through Bragg resonance with ocean waves of wavelengths similar to those of the transmitted electromagnetic waves. The longer waves become visible in the radar images because they modulate the sea clutter signals. For the case of X-band radar (frequency of 9.4 GHz), sea surface with ripple waves (wavelength of about 2 cm) provide Bragg resonance, henceforth results in accurate estimation of wave heights. On the other hand, swell waves without ripples when no local wind available, are tend to be significantly underestimated.