Development of Tools for Coastal Management in Google Earth Engine: Uncertainty Bathtub Model and Bruun Rule

Lucas Terres De Lima,Luiz Magalhães Filho,João Francisco Gonçalves,Cristina Bernardes,Sandra Fernández-Fernández

doi:10.3390/rs13081424

Lucas Terres De Lima, Luiz Magalhães Filho + Show 3 more

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https://doi.org/10.3390/rs13081424

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Abstract

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of free and open-source models to estimate the sea-level impact can contribute to improve coastal management. This study aims to develop and validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE)—a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the sea-level rise impact module of TerrSet—Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses the Bruun rule formula implemented in GEE and can determine the coastline retreat of a profile by creatting a simple vector line from topo-bathymetric data. The model shows a very high correlation (0.97) with a classical Bruun rule study performed in the Aveiro coast (NW Portugal). Therefore, the achieved results disclose that the GEE platform is suitable to perform these analysis. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific community.

Highlights

Coastal areas are densely populated gathering more than 30% of the world’s population and this is increasing exponentially [1,2]
The results section is divided in two subsections: (i) the validation process of the uncertainty Bathub model and (ii) Bruun rule validation for Google Earth Engine model (BRGM) through Spearman correlation analysis and the example of the application of the Bruun rule for GEE model (BRGM) under a climate change scenario
This work presents and validates two models for the assessment of sea-level rise created on Google Earth Engine (GEE)

Summary

Introduction

Coastal areas are densely populated gathering more than 30% of the world’s population and this is increasing exponentially [1,2]. Climatic, and oceanographic conditions, coastal zones may present a high risk of flooding, erosion, and regional sea-level fluctuations among others. All these phenomena are natural and contributed to model present-day coastlines. In the last seventy years, the effect of these drivers has increased in both intensity and frequency [3,4], and it is expected that this rising trend keeps up in the future, being the anthropogenic forcing the main reason for the global average sea level rise since 1970 [5]. The sea-level rise is a common problem that affects about 70% of coastal zones worldwide that it is accelerating and it is expected to be worst in the future [6,7]. In the period 2006–2015, the GMSL rise rate was 3.6 mm yr−1 , about 2.5 times higher than in the period 1902–1990

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