Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil

Isadora Araújo Sousa; Cassiano Antonio Bortolozo; Tatiana Sussel Gonçalves Mendes; Marcio Roberto Magalhães de Andrade; Giovanni Dolif Neto; Daniel Metodiev; Tristan Pryer; Noel Howley; Silvio Jorge Coelho Simões; Rodolfo Moreda Mendes

doi:10.1016/j.jsames.2023.104631

Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil

Isadora Araújo Sousa, Cassiano Antonio Bortolozo, Tatiana Sussel Gonçalves Mendes, Marcio Roberto Magalhães de Andrade, Giovanni Dolif Neto, Daniel Metodiev, Tristan Pryer, Noel Howley, Silvio Jorge Coelho Simões, Rodolfo Moreda Mendes

Research output: Contribution to journal › Article › peer-review

2 Citations (SciVal)

Abstract

Climate change has increased the frequency of extreme weather events and, consequently, the number of occurrences of natural disasters. In Brazil, among these disasters, floods, flash floods, and landslides account for the highest number of deaths, the latter being the most lethal. Bearing in mind the importance of monitoring areas susceptible to disasters, the REMADEN/REDEGEO project of the National Center for Monitoring and Natural Disaster Alerts (Cemaden) has promoted the installation of a network of soil moisture sensors in regions with a long history of landslides. This network was used in the present paper as a base to develop a system for moisture forecasting in those critical zones. The time series of rainfall and moisture were used in an inversion algorithm to obtain the geotechnical parameters of the soil. Then the geotechnical model was used in a forward calculation with the rainfall prediction to obtain the soil moisture forecast. The landslide events of March 2020 and May 2022 in Guarujá and Recife, respectively, were used as study cases for the developed system. The obtained results indicate that the proposed methodology has the potential to be used as an important tool in the decision-making process for issuing landslide alerts.

Original language	English
Article number	104631
Journal	Journal of South American Earth Sciences
Volume	131
Early online date	28 Sept 2023
DOIs	https://doi.org/10.1016/j.jsames.2023.104631
Publication status	Published - 30 Nov 2023

Bibliographical note

Funding Information:
Isadora Araújo Sousa thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Cemaden for the Scientific Initiation Scholarship (grants 112651/2022-4 and 120035/2022-7 ). Cassiano Bortolozo thanks to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the Postdoctoral Scholarship (grant 152269/2022-3 ), for the Research Fellowship Program (grant 301201/2022-6 ) and also for the Research Financial Support ( Universal Project grant 433481/2018-8 ). Tristan Pryer and Noel Howley are grateful to the Institute for Mathematical Innovation for supporting this work. All authors thank FINEP (Financiadora de Estudos e Projetos) for financing the REDEGEO project (Carta Convite MCTI/FINEP/FNDCT 01/2016 ), responsible for the PCD Geo network installation.

Funding Information:
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Cassiano Antonio Bortolozo reports financial support was provided by National Council for Scientific and Technological Development. Isadora Araujo Sousa reports financial support was provided by National Council for Scientific and Technological Development. Daniel Metodiev reports financial support was provided by National Council for Scientific and Technological Development.Isadora Araújo Sousa thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Cemaden for the Scientific Initiation Scholarship (grants 112651/2022-4 and 120035/2022-7). Cassiano Bortolozo thanks to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the Postdoctoral Scholarship (grant 152269/2022-3), for the Research Fellowship Program (grant 301201/2022-6) and also for the Research Financial Support (Universal Project grant 433481/2018-8). Tristan Pryer and Noel Howley are grateful to the Insitute for Mathematical Innovation for supporting this work. All authors thank FINEP (Financiadora de Estudos e Projetos) for financing the REDEGEO project (Carta Convite MCTI/FINEP/FNDCT 01/2016), responsible for the PCD Geo network installation.

Keywords

Brazil
Data inversion
Landslides
Sensor network
Soil moisture modeling

ASJC Scopus subject areas

Geology
Earth-Surface Processes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.jsames.2023.104631

Cite this

Sousa, I. A., Bortolozo, C. A., Gonçalves Mendes, T. S., de Andrade, M. R. M., Neto, G. D., Metodiev, D., Pryer, T., Howley, N., Coelho Simões, S. J., & Mendes, R. M. (2023). Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil. Journal of South American Earth Sciences, 131, Article 104631. https://doi.org/10.1016/j.jsames.2023.104631

Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil. / Sousa, Isadora Araújo; Bortolozo, Cassiano Antonio; Gonçalves Mendes, Tatiana Sussel et al.
In: Journal of South American Earth Sciences, Vol. 131, 104631, 30.11.2023.

Research output: Contribution to journal › Article › peer-review

Sousa, IA, Bortolozo, CA, Gonçalves Mendes, TS, de Andrade, MRM, Neto, GD, Metodiev, D, Pryer, T, Howley, N, Coelho Simões, SJ & Mendes, RM 2023, 'Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil', Journal of South American Earth Sciences, vol. 131, 104631. https://doi.org/10.1016/j.jsames.2023.104631

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abstract = "Climate change has increased the frequency of extreme weather events and, consequently, the number of occurrences of natural disasters. In Brazil, among these disasters, floods, flash floods, and landslides account for the highest number of deaths, the latter being the most lethal. Bearing in mind the importance of monitoring areas susceptible to disasters, the REMADEN/REDEGEO project of the National Center for Monitoring and Natural Disaster Alerts (Cemaden) has promoted the installation of a network of soil moisture sensors in regions with a long history of landslides. This network was used in the present paper as a base to develop a system for moisture forecasting in those critical zones. The time series of rainfall and moisture were used in an inversion algorithm to obtain the geotechnical parameters of the soil. Then the geotechnical model was used in a forward calculation with the rainfall prediction to obtain the soil moisture forecast. The landslide events of March 2020 and May 2022 in Guaruj{\'a} and Recife, respectively, were used as study cases for the developed system. The obtained results indicate that the proposed methodology has the potential to be used as an important tool in the decision-making process for issuing landslide alerts.",

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note = "Funding Information: Isadora Ara{\'u}jo Sousa thanks Conselho Nacional de Desenvolvimento Cient{\'i}fico e Tecnol{\'o}gico (CNPq) and Cemaden for the Scientific Initiation Scholarship (grants 112651/2022-4 and 120035/2022-7 ). Cassiano Bortolozo thanks to the Conselho Nacional de Desenvolvimento Cient{\'i}fico e Tecnol{\'o}gico (CNPq) for the Postdoctoral Scholarship (grant 152269/2022-3 ), for the Research Fellowship Program (grant 301201/2022-6 ) and also for the Research Financial Support ( Universal Project grant 433481/2018-8 ). Tristan Pryer and Noel Howley are grateful to the Institute for Mathematical Innovation for supporting this work. All authors thank FINEP (Financiadora de Estudos e Projetos) for financing the REDEGEO project (Carta Convite MCTI/FINEP/FNDCT 01/2016 ), responsible for the PCD Geo network installation. Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Cassiano Antonio Bortolozo reports financial support was provided by National Council for Scientific and Technological Development. Isadora Araujo Sousa reports financial support was provided by National Council for Scientific and Technological Development. Daniel Metodiev reports financial support was provided by National Council for Scientific and Technological Development.Isadora Ara{\'u}jo Sousa thanks Conselho Nacional de Desenvolvimento Cient{\'i}fico e Tecnol{\'o}gico (CNPq) and Cemaden for the Scientific Initiation Scholarship (grants 112651/2022-4 and 120035/2022-7). Cassiano Bortolozo thanks to the Conselho Nacional de Desenvolvimento Cient{\'i}fico e Tecnol{\'o}gico (CNPq) for the Postdoctoral Scholarship (grant 152269/2022-3), for the Research Fellowship Program (grant 301201/2022-6) and also for the Research Financial Support (Universal Project grant 433481/2018-8). Tristan Pryer and Noel Howley are grateful to the Insitute for Mathematical Innovation for supporting this work. All authors thank FINEP (Financiadora de Estudos e Projetos) for financing the REDEGEO project (Carta Convite MCTI/FINEP/FNDCT 01/2016), responsible for the PCD Geo network installation. ",

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Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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