Data, knowledge and modeling challenges for science-informed management of river deltas

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.oneear.2023.02.010. This is version 2 of this Preprint.

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Authors

Rafael Jan Pablo Schmitt , Philip S. J. Minderhoud

Abstract

450 million people live on river deltas and thus on land that is precariously low above the sea level and sinking because of human activities and natural processes. Although global debates around coastal risk typically focus on sea level rise, it is sinking lands and rising seas that together endanger lives and livelihoods in river deltas. However, the ability to quantify and address those risks in an integrated manner remains limited. Herein, we identify four priority areas where a lack of data, models, and knowledge is limiting sustainable delta management, namely (1) developing practical models for delta-scale processes and nature-based solutions, (2) coupling models for basin and delta processes, (3) closing knowledge disparities between river deltas, and (4) integrating deltas in assessments of global change and vice versa. Addressing those challenges through global scientific effort is instrumental to identify local-to-global levers to design adaptation and mitigation measures for resilient river deltas.

DOI

https://doi.org/10.31223/X5Q94J

Subjects

Environmental Engineering, Environmental Sciences, Natural Resources and Conservation, Natural Resources Management and Policy, Other Civil and Environmental Engineering, Other Environmental Sciences, Sustainability, Water Resource Management

Keywords

River Deltas, climate adaptation, Climate Mitigation, Sea level rise, Land subsidence

Dates

Published: 2023-03-16 11:34

Last Updated: 2023-03-17 22:11

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License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
Data are available from: Schmitt, R.J.P. (2022). Socio-economic development of global river deltas from gridded data. https://doi.org/10.5281/zenodo.7483942