scholarly journals Time-Series Prediction of Sea Level Change in the East Coast of Peninsular Malaysia from the Supervised Learning Approach

2020 ◽  
Vol 15 (3) ◽  
pp. 409-415
Author(s):  
Vivien Lai ◽  
Marlinda Malek ◽  
Samsuri Abdullah ◽  
Sarmad Latif ◽  
Ali Ahmed
Keyword(s):  
Time Series ◽  
Supervised Learning ◽  
Sea Level ◽  
East Coast ◽  
Time Series Prediction ◽  
Sea Level Change ◽  
Peninsular Malaysia ◽  
Learning Approach ◽  
Level Change

scholarly journals Impacts of Future Sea-Level Rise under Global Warming Assessed from Tide Gauge Records: A Case Study of the East Coast Economic Region of Peninsular Malaysia

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1382
Author(s):  
Milad Bagheri ◽  
Zelina Z. Ibrahim ◽  
Mohd Fadzil Akhir ◽  
Bahareh Oryani ◽  
Shahabaldin Rezania ◽  
...  
Keyword(s):  
Neural Network ◽  
Global Warming ◽  
Sea Level Rise ◽  
Sea Level ◽  
East Coast ◽  
Tide Gauge ◽  
Sea Level Change ◽  
Peninsular Malaysia ◽  
Tide Gauge Records ◽  
Level Change

The effects of global warming are putting the world’s coasts at risk. Coastal planners need relatively accurate projections of the rate of sea-level rise and its possible consequences, such as extreme sea-level changes, flooding, and coastal erosion. The east coast of Peninsular Malaysia is vulnerable to sea-level change. The purpose of this study is to present an Artificial Neural Network (ANN) model to analyse sea-level change based on observed data of tide gauge, rainfall, sea level pressure, sea surface temperature, and wind. A Feed-forward Neural Network (FNN) approach was used on observed data from 1991 to 2012 to simulate and predict the sea level change until 2020 from five tide gauge stations in Kuala Terengganu along the East Coast of Malaysia. From 1991 to 2020, predictions estimate that sea level would increase at a pace of roughly 4.60 mm/year on average, with a rate of 2.05 ± 7.16 mm on the East Coast of Peninsular Malaysia. This study shows that Peninsular Malaysia’s East Coast is vulnerable to sea-level rise, particularly at Kula Terengganu, Terengganu state, with a rate of 1.38 ± 7.59 mm/year, and Tanjung Gelang, Pahang state, with a rate of 1.87 ± 7.33 mm/year. As a result, strategies and planning for long-term adaptation are needed to control potential consequences. Our research provides crucial information for decision-makers seeking to protect coastal cities from the risks of rising sea levels.

Global sea-level and ocean-mass budgets using advanced data products and uncertainty characterisation

2021 ◽  
Author(s):  
Martin Horwath ◽  
Anny Cazenave ◽  
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Sea Level Change ◽  
Mass Change ◽  
Level Change ◽  
Ocean Mass ◽  
Global Mean Sea Level ◽  
Global Sea Level ◽  
Global Mean

<p>Studies of the global sea-level budget (SLB) and ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributors. The SLB is considered closed if the observed sea-level change agrees with the sum of independently assessed steric and mass contributions. The OMB is considered closed if the observed ocean-mass change is compatible with the sum of assessed mass contributions. </p><p>Here we present results from the Sea-Level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). We used data products from CCI projects as well as newly-developed products based on CCI products and on additional data sources. Our focus on products developed in the same framework allowed us to exercise a consistent uncertainty characterisation and its propagation to the budget closure analyses, where the SLB and the OMB are assessed simultaneously. </p><p>We present time series of global mean sea-level changes from satellite altimetry; new time series of the global mean steric component generated from Argo drifter data with incorporation of sea surface temperature data; time series of ocean-mass change derived from GRACE satellite gravimetry; time series of global glacier mass change from a global glacier model; time series of mass changes of the Greenland Ice Sheet and the Antarctic Ice Sheet both from satellite radar altimetry and from GRACE; as well as time series of land water storage change from the WaterGAP global hydrological model. Our budget analyses address the periods 1993–2016 (covered by the satellite altimetry records) and 2003–2016 (covered by GRACE and the Argo drifter system). In terms of the mean rates of change (linear trends), the SLB is closed within uncertainties for both periods, and the OMB, assessable for 2003–2016 only, is also closed within uncertainties. Uncertainties (1-sigma) arising from the combined uncertainties of the elements of the different budgets considered are between 0.26 mm/yr and 0.40 mm/yr, that is, on the order of 10% of the magnitude of global mean sea-level rise, which is 3.05 ± 0.24 mm/yr and 3.65 ± 0.26 mm/yr for 1993-2016 and 2003-2016, respectively. We also assessed the budgets on a monthly time series basis. The statistics of monthly misclosure agrees with the combined uncertainties of the budget elements, which amount to typically 2-3 mm for the 2003–2016 period. We discuss possible origins of the residual misclosure.</p>

Coastal Sea Level Change from in the North Eastern Atlantic

2020 ◽  
Author(s):  
Luciana Fenoglio-Marc ◽  
Bernd Uebbing ◽  
Jürgen Kusche ◽  
Salvatore Dinardo
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Sea Level Change ◽  
Altimeter Data ◽  
Level Change ◽  
The North ◽  
North Eastern ◽  
Coastal Sea

<p>A significant part of the World population lives in the coastal zone, which is affected by coastal sea level rise and extreme events. Our hypothesis is that the most accurate sea level height measurements are derived from the Synthetic Aperture Altimetry (SAR) mode. This study analyses the output of dedicated processing and assesses their impacts on the sea level change of the North-Eastern Atlantic. </p><p>It will be shown that SAR altimetry reduces the minimum usable distance from five to three kilometres when the dedicated coastal retrackers SAMOSA+ and SAMOSA++ are applied to data processed in SAR mode. A similar performance is achieved with altimeter data processed in pseudo low resolution mode (PLRM) when the Spatio-Temporal Altimeter sub-waveform Retracker (STAR) is used. Instead the Adaptive Leading Edge Sub-waveform retracker (TALES) applied to PLRM is less performant. SAR processed altimetry can recover the sea level heights with 4 cm accuracy up to 3-4 km distance to coast. Thanks to the low noise of SAR mode data, the instantaneous SAR and in-situ data have the highest agreement, with the smallest standard deviation of differences and the highest correlation. A co-location of the altimeter data near the tide gauge is the best choice for merging in-situ and altimeter data. The r.m.s. (root mean squared) differences between altimetry and in-situ heights remain large in estuaries and in coastal zone with high tidal regimes, which are still challenging regions. The geophysical parameters derived from CryoSat-2 and Sentinel-3A measurements have similar accuracy, but the different repeat cycle of the two missions locally affects the constructed time-series.</p><p>The impact of these new SAR observations in climate change studies is assessed by evaluating regional and local time series of sea level. At distances to coast smaller than 10 Kilometers the sea level change derived from SAR and LRM data is in good agreement. The long-term sea level variability derived from monthly time-series of LRM altimetry and of land motion-corrected tide gauges agrees within 1 mm/yr for half of in-situ German stations. The long-term sea level variability derived from SAR data show a similar behaviour with increasing length of the time series.</p><p> </p>

Closing the global and regional sea level budgets by combining multi-mission altimetry and GRACE(-FO) data

2020 ◽  
Author(s):  
Bernd Uebbing ◽  
Christina Lück ◽  
Roelof Rietbroek ◽  
Kristin Vielberg ◽  
Jürgen Kusche
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Sea Level Change ◽  
Inversion Method ◽  
Data Sets ◽  
Sea Level Changes ◽  
Steric Sea Level ◽  
Level Change ◽  
Spatial Coverage ◽  
Regional Sea Level

<p>Understanding present day sea level changes and their drivers requires the separation of the total sea level change into individual mass and steric related contributions. Total sea level rise has been observed continuously since 1993 providing a more than 25 year long time series of global and regional sea level variations. However, direct monitoring of ocean mass change has only been done since the start of the Gravity Recovery And Climate Experiment (GRACE) mission in 2002. It ended in 2017 and was succeeded by the follow-on mission (GRACE-FO) in 2018 leaving a gap of about 1 year. In the same time period of GRACE, since the early 2000s, a global array of freely drifting Argo floats samples temperature and salinity profiles of up to 2000m depth which can be converted to steric sea level change.</p><p>By combining altimetry, GRACE(-FO) and Argo data sets it is possible to derive global and regional sea level budgets. The conventional approach is to analyze at least two of the data sets and derive the residual, or compare with the third one. A more recent approach is the global joint inversion method (Rietbroek et al., 2016) which fits forward-modeled spatial fingerprints to a combination of GRACE gravity data and Jason-1/-2 satellite altimetry data. This enables us, additionally, to separate altimetric sea level change into mass contributions from terrestrial hydrology, the melting of land glaciers and the ice-sheets in Greenland and Antarctica as well as contributions from steric sea level changes due to variations in ocean temperature and salinity. It also allows to include a data weighting scheme in the analysis.</p><p>Here, we present global and regional sea level budget results from an updated inversion based on multi-mission altimetry (Jason-1/-2/-3, Envisat, Cryosat-2, Sentinel-3, …) providing better spatial coverage as well as new RL06 GRACE and GRACE-FO data which enables us to extend the time series of individual components of the sea level budget beyond the GRACE era from 2002-04 till 2019-06. The presented sea level budget is closed on global scale with a residual (unexplained) contribution of about 0.1 mm/yr, globally, originating in eddy-active regions. We provide consistent validation of our results against conventionally analyzed altimetry and GRACE data sets where we find agreement on global scales to be better than 0.1 mm/yr but a larger disagreement at regional scales as well as the implications of our results for deriving ocean heat content. We will also provide first results for filling the gap in the sea level budget estimates due to the gap between the GRACE and GRACE-FO missions by additionally incorporating time-variable gravity information from the Swarm mission as well as from Satellite Laser Ranging (SLR) to 5 satellites (Lageos-1/-2, Stella, Starlette, Ajisai).</p>

scholarly journals A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment

2020 ◽  
Vol 8 (7) ◽  
Author(s):  
William B. Hawley ◽  
Carling C. Hay ◽  
Jerry X. Mitrovica ◽  
Robert E. Kopp
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Sea Level Change ◽  
Variable Time ◽  
Level Change ◽  
Artificial Water ◽  
Water Impoundment

Vertical land motion in the Iberian Atlantic coast and its implications for sea level change evaluation

2020 ◽  
Vol 14 (3) ◽  
pp. 361-378
Author(s):  
V. B. Mendes ◽  
S. M. Barbosa ◽  
D. Carinhas
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Atlantic Coast ◽  
Sea Level Change ◽  
Tide Gauges ◽  
Tide Gauge Records ◽  
Level Change ◽  
Vertical Land Motion

AbstractIn this study, we estimate vertical land motion for 35 stations primarily located along the coastline of Portugal and Spain, using GPS time series with at least eight years of observations. Based on this set of GPS stations, our results show that vertical land motion along the Iberian coastline is characterized, in general, by a low to moderate subsidence, ranging from −2.2 mm yr−1 to 0.4 mm yr−1, partially explained by the glacial isostatic adjustment geophysical signal. The estimates of vertical land motion are subsequently applied in the analysis of tide gauge records and compared with geocentric estimates of sea level change. Geocentric sea level for the Iberian Atlantic coast determined from satellite altimetry for the last three decades has a mean of 2.5 ± 0.6 mm yr−1, with a significant range, as seen for a subset of grid points located in the vicinity of tide gauge stations, which present trends varying from 1.5 mm yr−1 to 3.2 mm yr−1. Relative sea level determined from tide gauges for this region shows a high degree of spatial variability, that can be partially explained not only by the difference in length and quality of the time series, but also for possible undocumented datum shifts, turning some trends unreliable. In general, tide gauges corrected for vertical land motion produce smaller trends than satellite altimetry. Tide gauge trends for the last three decades not corrected for vertical land motion range from 0.3 mm yr−1 to 5.0 mm yr−1 with a mean of 2.6 ± 1.4 mm yr−1, similar to that obtained from satellite altimetry. When corrected for vertical land motion, we observe a reduction of the mean to ∼1.9 ± 1.4 mm yr−1. Actions to improve our knowledge of vertical land motion using space geodesy, such as establishing stations in co-location with tide gauges, will contribute to better evaluate sea level change and its impacts on coastal regions.

scholarly journals Revisiting Vertical Land Motion and Sea Level Trends in the Northeastern Adriatic Sea Using Satellite Altimetry and Tide Gauge Data

2020 ◽  
Vol 8 (11) ◽  
pp. 949 ◽  
Author(s):  
Francesco De Biasio ◽  
Giorgio Baldin ◽  
Stefano Vignudelli
Keyword(s):  
Inverse Problem ◽  
Time Series ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Adriatic Sea ◽  
Tide Gauge ◽  
Sea Level Change ◽  
Linear Inverse Problem ◽  
Level Change ◽  
Vertical Land Motion

We propose a revisited approach to estimating sea level change trends based on the integration of two measuring systems: satellite altimetry and tide gauge (TG) time series of absolute and relative sea level height. Quantitative information on vertical crustal motion trends at six TG stations of the Adriatic Sea are derived by solving a constrained linear inverse problem. The results are verified against Global Positioning System (GPS) estimates at some locations. Constraints on the linear problem are represented by estimates of relative vertical land motion between TG couples. The solution of the linear inverse problem is valid as long as the same rates of absolute sea level rise are observed at the TG stations used to constrain the system. This requirement limits the applicability of the method with variable absolute sea level trends. The novelty of this study is that we tried to overcome such limitations, subtracting the absolute sea level change estimates observed by the altimeter from all relevant time series, but retaining the original short-term variability and associated errors. The vertical land motion (VLM) solution is compared to GPS estimates at three of the six TGs. The results show that there is reasonable agreement between the VLM rates derived from altimetry and TGs, and from GPS, considering the different periods used for the processing of VLM estimates from GPS. The solution found for the VLM rates is optimal in the least square sense, and no longer depends on the altimetric absolute sea level trend at the TGs. Values for the six TGs’ location in the Adriatic Sea during the period 1993–2018 vary from −1.41 ± 0.47 mm y−1 (National Research Council offshore oceanographic tower in Venice) to 0.93 ± 0.37 mm y−1 (Rovinj), while GPS solutions range from −1.59 ± 0.65 (Venice) to 0.10 ± 0.64 (Split) mm y−1. The absolute sea level rise, calculated as the sum of relative sea level change rate at the TGs and the VLM values estimated in this study, has a mean of 2.43 mm y−1 in the period 1974–2018 across the six TGs, a mean standard error of 0.80 mm y−1, and a sample dispersion of 0.18 mm y−1.

Sea-level change in the Northern Mediterranean Sea from long-period tide gauge time series

2017 ◽  
Vol 167 ◽  
pp. 72-87 ◽  
Author(s):  
Susanna Zerbini ◽  
Fabio Raicich ◽  
Claudio Maria Prati ◽  
Sara Bruni ◽  
Sara Del Conte ◽  
...  
Keyword(s):  
Time Series ◽  
Mediterranean Sea ◽  
Sea Level ◽  
Tide Gauge ◽  
Sea Level Change ◽  
Level Change ◽  
Long Period
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