Articles | Volume 29, issue 3
https://doi.org/10.5194/npg-29-301-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-301-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2022
Research article |
| 01 Aug 2022
| 01 Aug 2022
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Joko Sampurno
CORRESPONDING AUTHOR
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Department of Physics, Fakultas MIPA, Universitas Tanjungpura, Pontianak, 78124, Indonesia
Valentin Vallaeys
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Randy Ardianto
Pontianak Maritime Meteorological Station, Pontianak, 78111, Indonesia
Emmanuel Hanert
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Institute of Mechanics, Materials and Civil Engineering (iMMC), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Related authors
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Biogeosciences, 19, 2741–2757, https://doi.org/10.5194/bg-19-2741-2022, https://doi.org/10.5194/bg-19-2741-2022, 2022
Short summary
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This study is the first assessment to evaluate the interactions between river discharges, tides, and storm surges and how they can drive compound flooding in the Kapuas River delta. We successfully created a realistic hydrodynamic model whose domain covers the land–sea continuum using a wetting–drying algorithm in a data-scarce environment. We then proposed a new method to delineate compound flooding hazard zones along the river channels based on the maximum water level profiles.
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Biogeosciences, 19, 2741–2757, https://doi.org/10.5194/bg-19-2741-2022, https://doi.org/10.5194/bg-19-2741-2022, 2022
Short summary
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This study is the first assessment to evaluate the interactions between river discharges, tides, and storm surges and how they can drive compound flooding in the Kapuas River delta. We successfully created a realistic hydrodynamic model whose domain covers the land–sea continuum using a wetting–drying algorithm in a data-scarce environment. We then proposed a new method to delineate compound flooding hazard zones along the river channels based on the maximum water level profiles.
Philippe Delandmeter, Jonathan Lambrechts, Vincent Legat, Valentin Vallaeys, Jaya Naithani, Wim Thiery, Jean-François Remacle, and Eric Deleersnijder
Geosci. Model Dev., 11, 1161–1179, https://doi.org/10.5194/gmd-11-1161-2018, https://doi.org/10.5194/gmd-11-1161-2018, 2018
Short summary
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The discontinuous Galerkin (DG) finite element method is well suited for the modelling of three-dimensional flows exhibiting strong density gradients. Here, a vertical adaptive mesh method is developed for DG finite element methods and implemented into SLIM 3D. This technique increases drastically the accuracy of simulations including strong stratification, without affecting the simulation cost. SLIM 3D is then used to simulate the thermocline oscillations of Lake Tanganyika.
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Data-driven methods to estimate the committor function in conceptual ocean models
Downscaling of surface wind forecasts using convolutional neural networks
Exploring meteorological droughts' spatial patterns across Europe through complex network theory
Predicting sea surface temperatures with coupled reservoir computers
Using neural networks to improve simulations in the gray zone
The blessing of dimensionality for the analysis of climate data
Producing realistic climate data with generative adversarial networks
Identification of droughts and heatwaves in Germany with regional climate networks
Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico
Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation
Applications of matrix factorization methods to climate data
Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Valérian Jacques-Dumas, René M. van Westen, Freddy Bouchet, and Henk A. Dijkstra
Nonlin. Processes Geophys., 30, 195–216, https://doi.org/10.5194/npg-30-195-2023, https://doi.org/10.5194/npg-30-195-2023, 2023
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Computing the probability of occurrence of rare events is relevant because of their high impact but also difficult due to the lack of data. Rare event algorithms are designed for that task, but their efficiency relies on a score function that is hard to compute. We compare four methods that compute this function from data and measure their performance to assess which one would be best suited to be applied to a climate model. We find neural networks to be most robust and flexible for this task.
Florian Dupuy, Pierre Durand, and Thierry Hedde
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2023-13, https://doi.org/10.5194/npg-2023-13, 2023
Revised manuscript accepted for NPG
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Forecasting near-surface winds over complex terrain requires high-resolution numerical weather prediction models, which drastically increases the duration of simulations and hinders to run them on a routine basis. A faster alternative is statistical downscaling. In this work, we explore different ways of calculating near-surface wind speed and direction using artificial intelligence algorithms based on various convolutional neural networks in order to find the best approach for wind downscaling.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
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Despite being among the most severe climate extremes, it is still challenging to assess droughts’ features for specific regions. In this paper we study meteorological droughts in Europe using concepts derived from climate network theory. By exploring the synchronization in droughts occurrences across the continent we unveil regional clusters which are individually examined to identify droughts’ geographical propagation and source–sink systems, which could potentially support droughts’ forecast.
Benjamin Walleshauser and Erik Bollt
Nonlin. Processes Geophys., 29, 255–264, https://doi.org/10.5194/npg-29-255-2022, https://doi.org/10.5194/npg-29-255-2022, 2022
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As sea surface temperature (SST) is vital for understanding the greater climate of the Earth and is also an important variable in weather prediction, we propose a model that effectively capitalizes on the reduced complexity of machine learning models while still being able to efficiently predict over a large spatial domain. We find that it is proficient at predicting the SST at specific locations as well as over the greater domain of the Earth’s oceans.
Raphael Kriegmair, Yvonne Ruckstuhl, Stephan Rasp, and George Craig
Nonlin. Processes Geophys., 29, 171–181, https://doi.org/10.5194/npg-29-171-2022, https://doi.org/10.5194/npg-29-171-2022, 2022
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Our regional numerical weather prediction models run at kilometer-scale resolutions. Processes that occur at smaller scales not yet resolved contribute significantly to the atmospheric flow. We use a neural network (NN) to represent the unresolved part of physical process such as cumulus clouds. We test this approach on a simplified, yet representative, 1D model and find that the NN corrections vastly improve the model forecast up to a couple of days.
Bo Christiansen
Nonlin. Processes Geophys., 28, 409–422, https://doi.org/10.5194/npg-28-409-2021, https://doi.org/10.5194/npg-28-409-2021, 2021
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In geophysics we often need to analyse large samples of high-dimensional fields. Fortunately but counterintuitively, such high dimensionality can be a blessing, and we demonstrate how this allows simple analytical results to be derived. These results include estimates of correlations between sample members and how the sample mean depends on the sample size. We show that the properties of high dimensionality with success can be applied to climate fields, such as those from ensemble modelling.
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
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This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
Gerd Schädler and Marcus Breil
Nonlin. Processes Geophys., 28, 231–245, https://doi.org/10.5194/npg-28-231-2021, https://doi.org/10.5194/npg-28-231-2021, 2021
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We used regional climate networks (RCNs) to identify past heatwaves and droughts in Germany. RCNs provide information for whole areas and can provide many details of extreme events. The RCNs were constructed on the grid of the E-OBS data set. Time series correlation was used to construct the networks. Network metrics were compared to standard extreme indices and differed considerably between normal and extreme years. The results show that RCNs can identify severe and moderate extremes.
Jonathan M. Lilly and Paula Pérez-Brunius
Nonlin. Processes Geophys., 28, 181–212, https://doi.org/10.5194/npg-28-181-2021, https://doi.org/10.5194/npg-28-181-2021, 2021
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Long-lived eddies are an important part of the ocean circulation. Here a dataset for studying eddies in the Gulf of Mexico is created through the analysis of trajectories of drifting instruments. The method involves the identification of quasi-periodic signals, characteristic of particles trapped in eddies, from the displacement records, followed by the creation of a measure of statistical significance. It is expected that this dataset will be of use to other authors studying this region.
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91, https://doi.org/10.5194/npg-28-61-2021, https://doi.org/10.5194/npg-28-61-2021, 2021
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An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.
Dylan Harries and Terence J. O'Kane
Nonlin. Processes Geophys., 27, 453–471, https://doi.org/10.5194/npg-27-453-2020, https://doi.org/10.5194/npg-27-453-2020, 2020
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Different dimension reduction methods may produce profoundly different low-dimensional representations of multiscale systems. We perform a set of case studies to investigate these differences. When a clear scale separation is present, similar bases are obtained using all methods, but when this is not the case some methods may produce representations that are poorly suited for describing features of interest, highlighting the importance of a careful choice of method when designing analyses.
Jaqueline Lekscha and Reik V. Donner
Nonlin. Processes Geophys., 27, 261–275, https://doi.org/10.5194/npg-27-261-2020, https://doi.org/10.5194/npg-27-261-2020, 2020
Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis
Nonlin. Processes Geophys., 27, 23–34, https://doi.org/10.5194/npg-27-23-2020, https://doi.org/10.5194/npg-27-23-2020, 2020
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Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.
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Short summary
In this study, we successfully built and evaluated machine learning models for predicting water level dynamics as a proxy for compound flooding hazards in a data-scarce delta. The issues that we tackled here are data scarcity and low computational resources for building flood forecasting models. The proposed approach is suitable for use by local water management agencies in developing countries that encounter these issues.
In this study, we successfully built and evaluated machine learning models for predicting water...
