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

Carvalho, K.S.; Wang, Shuo

doi:10.1016/j.jhydrol.2018.11.072

Cited by 33 publications

(19 citation statements)

References 28 publications

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“…In the last decade, the adverse effects of global warming have resulted in a higher frequency of floods in various regions around the globe [57][58][59]; therefore, new studies to develop better tools for flood prediction are highly necessary. In this research, we proposed a new modeling approach, named the HFPS-RSTree, for the spatial prediction of flash flood susceptibility, with a case study of a high-frequency torrential rainfall area.…”

Section: Discussionmentioning

confidence: 99%

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

et al. 2020

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Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

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Section: Discussionmentioning

confidence: 99%

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

et al. 2020

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“…The Indian Ocean (Figures 5c and 5d) is dominated by a positive steric trend, which can be related to the increasing atmospheric temperature in the region (Carvalho & Wang, 2019). Around 10°S, the trends in all data sets are not statistically significant and reverse to −2 mm/year, accompanied by an increased uncertainty (~3 mm/year).…”

Section: Regional Steric Slcmentioning

confidence: 98%

Exploring Sources of Uncertainty in Steric Sea‐Level Change Estimates

et al. 2020

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Recent studies disagree about the contribution of variations in temperature and salinity of the oceans-steric change-to the observed sea-level change. This article explores two sources of uncertainty to both global mean and regional steric sea-level trends. First, we analyze the influence of different temperature and salinity data sets on the estimated steric sea-level change. Next, we investigate the impact of different stochastic noise models on the estimation of trends and their uncertainties. By varying both the data sets and noise models, the global mean steric sea-level trend and uncertainty can vary from 0.69 to 2.40 and 0.02 to 1.56 mm/year, respectively, for 1993-2017. This range is even larger on regional scales, reaching up to 30 mm/year. Our results show that a first-order autoregressive model is the most appropriate choice to describe the residual behavior of the ensemble mean of all data sets for the global mean steric sea-level change over the last 25 years, which consequently leads to the most representative uncertainty. Using the ensemble mean and the first-order autoregressive noise model, we find a global mean steric sea-level change of 1.36 ± 0.10 mm/year for 1993-2017 and 1.08 ± 0.07 mm/year for 2005-2015. Regionally, a combination of different noise models is the best descriptor of the steric sea-level change and its uncertainty. The spatial coherence in the noise model preference indicates clusters that may be best suited to investigate the regional sea-level budget. Plain Language Summary Ocean temperature and salinity variations lead to changes in sea level, known as steric sea-level change. Steric variations are important contributors to sea-level change and reflect how the oceans have been responding to global warming. For this reason, several recent studies have quantified the contribution of steric variations to global and regional sea-level change. However, the reported rates largely differ between studies. In this paper, we look at how the use of different temperature and salinity data sets can be one of the causes of the different estimates of steric sea-level change published so far. We also investigate how different methods (noise models) used to obtain the rate of change can be another source of different results. We find that the rate of change can vary up to 2 mm/year for the global mean as a result of different data sets and methods used. Regionally, differences can reach up to several tens of millimeters per year. We show that the noise models should always be carefully chosen for each region, so that the rate of change is accurately estimated.

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“…Floods are one of the major natural disasters inflicting catastrophic damages on society and ecosystems and even causing millions of deaths (Bevacqua et al., 2017; Carvalho & Wang, 2019; Mallakpour et al., 2019). The 2017 California floods, for example, forced evacuations for thousands of people, while the Mississippi River floods of 2019 caused up to $20 billion in economic losses and at least 12 deaths (Smith, 2020).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Projections of Multidimensional Flood Risks at a Convection‐Permitting Scale

Zhang

Wang

2021

Water Resources Research

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

Cited by 33 publications

References 28 publications

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

Exploring Sources of Uncertainty in Steric Sea‐Level Change Estimates

Probabilistic Projections of Multidimensional Flood Risks at a Convection‐Permitting Scale

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