Characterizing the Indian Ocean sea level changes and potential coastal flooding impacts under global warming

Abstract The Indian Ocean which is home to many islands and the low-lying coastal zones have attracted considerable attention due to regional sea level changes. In this study, we examine regional changes in sea level of the Indian Ocean and potential coastal flooding impacts by using tide gauge data. Various interpolation methods are evaluated to predict values at locations where data is unavailable. Based on the cross-validation analysis, the radial basis function is identified as the most optimal interpolation method and is used to analyze the spatial patterns of sea level changes. The analysis reveals that Bangladesh, Seychelles, and Cocos (Keeling) Islands have relatively high rates of sea level rise. These regions would thus be highly vulnerable to coastal flooding induced by the accelerating sea level rise in future decades, posing significant threats to coastal communities and ecosystems. Flooding impacts are examined through inundation mapping in a geographic information system (GIS) environment. In addition, relationships between regional factors (sea surface temperature, air temperature, and vertical land motions) affecting sea level rise are investigated. Our findings indicate that vertical land motion is an important factor affecting sea level changes for the regions of Seychelles and Cocos Islands. There is a strong relationship between air temperature and sea level rise for all studied regions. This study is a first attempt to examine regional changes in sea level of the Indian Ocean and potential coastal flooding impacts by using tide gauge data. The methods used in this study can be applied to other coastal regions around the world.

[1]  S. Wang,et al.  Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada , 2014 .

[2]  Xiaosheng Qin,et al.  Study of climate change impact on flood frequencies : a combined weather generator and hydrological modeling approach , 2014 .

[3]  K. Lambeck,et al.  Estimates of the Regional Distribution of Sea Level Rise over the 1950–2000 Period , 2004 .

[4]  G. Meehl,et al.  Patterns of Indian Ocean sea-level change in a warming climate , 2010 .

[5]  Jean Palutikof,et al.  GIS-based construction of baseline climatologies for the Mediterranean using terrain variables , 2000 .

[6]  Guohe Huang,et al.  Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation , 2018 .

[7]  C. Shum,et al.  Mean Sea Level Variation in the South China Sea from Four Decades of Tidal Records in Hong Kong , 2000 .

[8]  L. Fenoglio-Marc,et al.  Sea Level Change and Vertical Motion from Satellite Altimetry, Tide Gauges and GPS in the Indonesian Region , 2012 .

[9]  H. Baki Iz, C. K. Shum.  Mean Sea Level Variation in the South China Sea from Four Decades of Tidal Records in Hong Kong , 2000 .

[10]  Qing Dong,et al.  Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks , 2017, Remote. Sens..

[11]  J. Hunter,et al.  Sea-level rise at tropical Pacific and Indian Ocean islands , 2006 .

[12]  R. Kopp,et al.  Probabilistic reanalysis of twentieth-century sea-level rise , 2015, Nature.

[13]  Tariq Masood Ali Khan, O. P. Singh, Md. Sazedur Ra.  Recent Sea Level and Sea Surface Temperature Trends Along the Bangladesh Coast in Relation to the Frequency of Intense Cyclones , 2000 .

[14]  Guohe Huang,et al.  Probabilistic Inference Coupled with Possibilistic Reasoning for Robust Estimation of Hydrologic Parameters and Piecewise Characterization of Interactive Uncertainties , 2016 .

[15]  Robert E. Kopp,et al.  Global and Regional Sea Level Rise Scenarios for the United States , 2017 .

[16]  G. Huang,et al.  A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment , 2015 .

[17]  G. Wöppelmann,et al.  Performance of modern tide gauges: towards mm-level accuracy , 2012 .

[18]  Yuanlong Li,et al.  Decadal Sea Level Variations in the Indian Ocean Investigated with HYCOM: Roles of Climate Modes, Ocean Internal Variability, and Stochastic Wind Forcing* , 2015 .

[19]  M. Keskin,et al.  Comparing Spatial Interpolation Methods for Mapping Meteorological Data in Turkey , 2015 .

[20]  Arup Kumar Roy,et al.  Sea Level Rise and Coastal Vulnerability along the Eastern Coast of India through Geo-spatial Technologies , 2015 .

[21]  Merrifield,et al.  Sea level variability and change , 2017 .

[22]  M. Merrifield,et al.  Forcing of recent decadal variability in the Equatorial and North Indian Ocean , 2016 .

[23]  Steven B. Kraines,et al.  Uncertainties in Tidally Adjusted Estimates of Sea Level Rise Flooding (Bathtub Model) for the Greater London , 2016, Remote. Sens..

[24]  J. Gerlach.  Climate change and identification of terrestrial protected areas in the Seychelles Islands , 2008 .

[25]  James A. Carton,et al.  Sea level in ocean reanalyses and tide gauges , 2014 .

[26]  S. Masson,et al.  The Curious Case of Indian Ocean Warming , 2014 .

[27]  Nitin Muttil,et al.  Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments , 2017 .

[28]  M. Marcos,et al.  Vertical land motion as a key to understanding sea level change and variability , 2016 .