Articles | Volume 22, issue 11
https://doi.org/10.5194/nhess-22-3679-2022
© 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/nhess-22-3679-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2022
Research article |
| 15 Nov 2022
| 15 Nov 2022
Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments
Axel A. J. Deijns
CORRESPONDING AUTHOR
Department of Earth Sciences, Royal Museum for Central Africa, 3080
Tervuren, Belgium
Department of Hydrology and Hydraulic Engineering, Earth System
Sciences, Vrije Universiteit Brussel, 1050 Elsene, Belgium
Olivier Dewitte
Department of Earth Sciences, Royal Museum for Central Africa, 3080
Tervuren, Belgium
Wim Thiery
Department of Hydrology and Hydraulic Engineering, Earth System
Sciences, Vrije Universiteit Brussel, 1050 Elsene, Belgium
Nicolas d'Oreye
Department of Geophysics/Astrophysics, National Museum of Natural
History, 7256 Walferdange, Luxembourg
European Center for Geodynamics and Seismology, 7256 Walferdange,
Luxembourg
Jean-Philippe Malet
Institut Terre et Environnement de Strasbourg/ITES – UMR 7063 CNRS, École et Observatoire des Sciences de la Terre, Université de Strasbourg, 5 rue Descartes, 676084 Strasbourg, France
François Kervyn
Department of Earth Sciences, Royal Museum for Central Africa, 3080
Tervuren, Belgium
Related authors
No articles found.
Seppe Lampe, Lukas Gudmundsson, Basil Kraft, Stijn Hantson, Douglas Kelley, Vincent Humphrey, Bertrand Le Saux, Emilio Chuvieco, and Wim Thiery
Geosci. Model Dev., 19, 955–988, https://doi.org/10.5194/gmd-19-955-2026, https://doi.org/10.5194/gmd-19-955-2026, 2026
Short summary
Short summary
We introduce BuRNN (BUrned area modelling by Recurrent Neural Networks), a model which estimates monthly burned area based on satellite observations and climate, vegetation, and socio-economic data using machine learning. BuRNN outperforms existing process-based fire models, which improves our capabilities of modelling past and future burned areas.
Simon Scheiter, Jinfeng Chang, Philippe Ciais, Marie Dury, Louis Francois, Matthew Forrest, Alexandra Henrot, Christopher P. O. Reyer, Sonia Seneviratne, Jörg Steinkamp, Wim Thiery, Wenfang Xu, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2026-221, https://doi.org/10.5194/egusphere-2026-221, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Our study shows how climate change may reshape the world's major biomes, such as forests, grasslands, and tundra. Using dynamic vegetation models, machine learning and real-world observations, we found that many biomes are likely to shift toward the poles as temperatures rise. Even under low-emission scenarios, large areas could change. These results help identify regions most susceptible to climate change and support global efforts to protect and manage natural ecosystems in the future.
Robert Reinecke, Tanjila Akhter, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Wim Thiery, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
Geosci. Model Dev., 19, 523–542, https://doi.org/10.5194/gmd-19-523-2026, https://doi.org/10.5194/gmd-19-523-2026, 2026
Short summary
Short summary
Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP (The Inter-Sectoral Impact Model Intercomparison Project) groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
Ni Li, Wim Thiery, Shorouq Zahra, Mariana Madruga de Brito, Koffi Worou, Murathan Kurfalı, Seppe Lampe, Paul Muñoz, Clare Flynn, Camila Trigoso, Joakim Nivre, Jakob Zscheischler, and Gabriele Messori
EGUsphere, https://doi.org/10.5194/egusphere-2025-4891, https://doi.org/10.5194/egusphere-2025-4891, 2025
Short summary
Short summary
Climate extremes threaten society and ecosystems. Understanding impacts is critical, despite open databases like EM-DAT and DesInventar, reliable impact data remain scattered across various text sources. Wikimpacts 1.0, using GPT4o, provides comprehensive socio-economic impact data on 2,928 events from 1034 to 2024. It offers broader storm coverage and finer spatial resolution impact data than EM-DAT, showcasing the potential of natural language processing to enhance climate impact datasets.
Antoine Dille, Olivier Dewitte, Jente Broeckx, Koen Verbist, Andile Sindiso Dube, Jean Poesen, and Matthias Vanmaercke
EGUsphere, https://doi.org/10.5194/egusphere-2025-5056, https://doi.org/10.5194/egusphere-2025-5056, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
In mountain regions, intense rainfall can trigger thousands of landslides within hours. Yet, while most efforts focus on where landslides start, the worst impacts often occur far downstream because slope material can mix with large runoffs. Studying Cyclone Idai’s impacts in eastern Zimbabwe, we found that landslide sources explain only one-fifth of total population exposure, highlighting the need to consider the full landslide–flood continuum to better protect people and plan safer landscapes.
Manon Maisonnier, Maoyuan Feng, David Bastviken, Sandra Arndt, Ronny Lauerwald, Aidin Jabbari, Goulven Gildas Laruelle, Murray D. MacKay, Zeli Tan, Wim Thiery, and Pierre Regnier
Earth Syst. Dynam., 16, 1779–1808, https://doi.org/10.5194/esd-16-1779-2025, https://doi.org/10.5194/esd-16-1779-2025, 2025
Short summary
Short summary
A new process-based modelling framework, FLaMe-v1.0 (Fluxes of Lake Methane version 1.0), is developed to simulate methane (CH4) emissions from lakes at large scales. FLaMe couples the dynamics of organic carbon, oxygen and methane in lakes and rests on an innovative, computationally efficient lake clustering approach for the simulation of CH4 emissions across a large number of lakes. The model evaluation suggests that FLaMe captures the sub-annual and spatial variability of CH4 emissions well.
Derrick Muheki, Bas Vercruysse, Krishna Kumar Thirukokaranam Chandrasekar, Christophe Verbruggen, Julie M. Birkholz, Koen Hufkens, Hans Verbeeck, Pascal Boeckx, Seppe Lampe, Ed Hawkins, Peter Thorne, Dominique Kankonde Ntumba, Olivier Kapalay Moulasa, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-3779, https://doi.org/10.5194/egusphere-2024-3779, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Archives worldwide host vast records of observed weather data crucial for understanding climate variability. However, most of these records are still in paper form, limiting their use. To address this, we developed MeteoSaver, an open-source tool, to transcribe these records to machine-readable format. Applied to ten handwritten temperature sheets, it achieved a median accuracy of 74%. This tool offers a promising solution to preserve records from archives and unlock historical weather insights.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Suqi Guo, Felix Havermann, Steven J. De Hertog, Fei Luo, Iris Manola, Thomas Raddatz, Hongmei Li, Wim Thiery, Quentin Lejeune, Carl-Friedrich Schleussner, David Wårlind, Lars Nieradzik, and Julia Pongratz
Earth Syst. Dynam., 16, 631–666, https://doi.org/10.5194/esd-16-631-2025, https://doi.org/10.5194/esd-16-631-2025, 2025
Short summary
Short summary
Land cover and land management changes (LCLMCs) can alter climate even in intact areas, causing carbon changes in remote areas. This study is the first to assess these effects, finding they substantially alter global carbon dynamics, changing terrestrial stocks by up to dozens of gigatonnes. These results are vital for scientific and policy assessments, given the expected role of LCLMCs in achieving the Paris Agreement's goal to limit global warming below 1.5 °C.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
Short summary
Short summary
Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Cristina Deidda, Arpita Khanna, Wolfgang Schade, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2025-1697, https://doi.org/10.5194/egusphere-2025-1697, 2025
Short summary
Short summary
Climate extremes like floods, heatwaves, droughts, and wildfires are disrupting Europe’s transport. This study combines a review of past events and future projections to assess current losses and the future exposure of transport infrastructure. Exposure to extremes is projected to rise, but lower emissions can reduce it. Urgent action is needed to adapt the Trans-European Transport Network to a changing climate.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Floriane Provost, Dimitri Zigone, Emmanuel Le Meur, Jean-Philippe Malet, and Clément Hibert
The Cryosphere, 18, 3067–3079, https://doi.org/10.5194/tc-18-3067-2024, https://doi.org/10.5194/tc-18-3067-2024, 2024
Short summary
Short summary
The recent calving of Astrolabe Glacier in November 2021 presents an opportunity to better understand the processes leading to ice fracturing. Optical-satellite imagery is used to retrieve the calving cycle of the glacier ice tongue and to measure the ice velocity and strain rates in order to document fracture evolution. We observed that the presence of sea ice for consecutive years has favoured the glacier extension but failed to inhibit the growth of fractures that accelerated in June 2021.
Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff
Earth Surf. Dynam., 12, 641–656, https://doi.org/10.5194/esurf-12-641-2024, https://doi.org/10.5194/esurf-12-641-2024, 2024
Short summary
Short summary
Natural disasters such as landslides and rockfalls are mostly difficult to study because of the impossibility of making in situ measurements due to their destructive nature and spontaneous occurrence. Seismology is able to record the occurrence of such events from a distance and in real time. In this study, we show that, by using a machine learning approach, the mass and velocity of rockfalls can be estimated from the seismic signal they generate.
Derrick Muheki, Axel A. J. Deijns, Emanuele Bevacqua, Gabriele Messori, Jakob Zscheischler, and Wim Thiery
Earth Syst. Dynam., 15, 429–466, https://doi.org/10.5194/esd-15-429-2024, https://doi.org/10.5194/esd-15-429-2024, 2024
Short summary
Short summary
Climate change affects the interaction, dependence, and joint occurrence of climate extremes. Here we investigate the joint occurrence of pairs of river floods, droughts, heatwaves, crop failures, wildfires, and tropical cyclones in East Africa under past and future climate conditions. Our results show that, across all future warming scenarios, the frequency and spatial extent of these co-occurring extremes will increase in this region, particularly in areas close to the Nile and Congo rivers.
Steven J. De Hertog, Carmen E. Lopez-Fabara, Ruud van der Ent, Jessica Keune, Diego G. Miralles, Raphael Portmann, Sebastian Schemm, Felix Havermann, Suqi Guo, Fei Luo, Iris Manola, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
Earth Syst. Dynam., 15, 265–291, https://doi.org/10.5194/esd-15-265-2024, https://doi.org/10.5194/esd-15-265-2024, 2024
Short summary
Short summary
Changes in land use are crucial to achieve lower global warming. However, despite their importance, the effects of these changes on moisture fluxes are poorly understood. We analyse land cover and management scenarios in three climate models involving cropland expansion, afforestation, and irrigation. Results show largely consistent influences on moisture fluxes, with cropland expansion causing a drying and reduced local moisture recycling, while afforestation and irrigation show the opposite.
Rosa Pietroiusti, Inne Vanderkelen, Friederike E. L. Otto, Clair Barnes, Lucy Temple, Mary Akurut, Philippe Bally, Nicole P. M. van Lipzig, and Wim Thiery
Earth Syst. Dynam., 15, 225–264, https://doi.org/10.5194/esd-15-225-2024, https://doi.org/10.5194/esd-15-225-2024, 2024
Short summary
Short summary
Heavy rainfall in eastern Africa between late 2019 and mid 2020 caused devastating floods and landslides and drove the levels of Lake Victoria to a record-breaking maximum in May 2020. In this study, we characterize the spatial extent and impacts of the floods in the Lake Victoria basin and investigate how human-induced climate change influenced the probability and intensity of the record-breaking lake levels and flooding by applying a multi-model extreme event attribution methodology.
Celray James Chawanda, Albert Nkwasa, Wim Thiery, and Ann van Griensven
Hydrol. Earth Syst. Sci., 28, 117–138, https://doi.org/10.5194/hess-28-117-2024, https://doi.org/10.5194/hess-28-117-2024, 2024
Short summary
Short summary
Africa's water resources are being negatively impacted by climate change and land-use change. The SWAT+ hydrological model was used to simulate the hydrological cycle in Africa, and results show likely decreases in river flows in the Zambezi and Congo rivers and highest flows in the Niger River basins due to climate change. Land cover change had the biggest impact in the Congo River basin, emphasizing the importance of including land-use change in studies.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
Short summary
Short summary
Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Steven J. De Hertog, Felix Havermann, Inne Vanderkelen, Suqi Guo, Fei Luo, Iris Manola, Dim Coumou, Edouard L. Davin, Gregory Duveiller, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
Earth Syst. Dynam., 14, 629–667, https://doi.org/10.5194/esd-14-629-2023, https://doi.org/10.5194/esd-14-629-2023, 2023
Short summary
Short summary
Land cover and land management changes are important strategies for future land-based mitigation. We investigate the climate effects of cropland expansion, afforestation, irrigation and wood harvesting using three Earth system models. Results show that these have important implications for surface temperature where the land cover and/or management change occur and in remote areas. Idealized afforestation causes global warming, which might offset the cooling effect from enhanced carbon uptake.
Steven J. De Hertog, Carmen E. Lopez-Fabara, Ruud van der Ent, Jessica Keune, Diego G. Miralles, Raphael Portmann, Sebastian Schemm, Felix Havermann, Suqi Guo, Fei Luo, Iris Manola, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2023-953, https://doi.org/10.5194/egusphere-2023-953, 2023
Preprint archived
Short summary
Short summary
Land cover and management changes can affect the climate and water availability. In this study we use climate model simulations of extreme global land cover changes (afforestation, deforestation) and land management changes (irrigation) to understand the effects on the global water cycle and local to continental water availability. We show that cropland expansion generally leads to higher evaporation and lower amounts of precipitation and afforestation and irrigation expansion to the opposite.
Francisco José Cuesta-Valero, Hugo Beltrami, Almudena García-García, Gerhard Krinner, Moritz Langer, Andrew H. MacDougall, Jan Nitzbon, Jian Peng, Karina von Schuckmann, Sonia I. Seneviratne, Wim Thiery, Inne Vanderkelen, and Tonghua Wu
Earth Syst. Dynam., 14, 609–627, https://doi.org/10.5194/esd-14-609-2023, https://doi.org/10.5194/esd-14-609-2023, 2023
Short summary
Short summary
Climate change is caused by the accumulated heat in the Earth system, with the land storing the second largest amount of this extra heat. Here, new estimates of continental heat storage are obtained, including changes in inland-water heat storage and permafrost heat storage in addition to changes in ground heat storage. We also argue that heat gains in all three components should be monitored independently of their magnitude due to heat-dependent processes affecting society and ecosystems.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
Short summary
Short summary
Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Blaise Mafuko Nyandwi, Matthieu Kervyn, François Muhashy Habiyaremye, François Kervyn, and Caroline Michellier
Nat. Hazards Earth Syst. Sci., 23, 933–953, https://doi.org/10.5194/nhess-23-933-2023, https://doi.org/10.5194/nhess-23-933-2023, 2023
Short summary
Short summary
Risk perception involves the processes of collecting, selecting and interpreting signals about the uncertain impacts of hazards. It may contribute to improving risk communication and motivating the protective behaviour of the population living near volcanoes. Our work describes the spatial variation and factors influencing volcanic risk perception of 2204 adults of Goma exposed to Nyiragongo. It contributes to providing a case study for risk perception understanding in the Global South.
Jean-Claude Maki Mateso, Charles L. Bielders, Elise Monsieurs, Arthur Depicker, Benoît Smets, Théophile Tambala, Luc Bagalwa Mateso, and Olivier Dewitte
Nat. Hazards Earth Syst. Sci., 23, 643–666, https://doi.org/10.5194/nhess-23-643-2023, https://doi.org/10.5194/nhess-23-643-2023, 2023
Short summary
Short summary
This research highlights the importance of human activities on the occurrence of landslides and the need to consider this context when studying hillslope instability patterns in regions under anthropogenic pressure. Also, this study highlights the importance of considering the timing of landslides and hence the added value of using historical information for compiling an inventory.
François Noël, Michel Jaboyedoff, Andrin Caviezel, Clément Hibert, Franck Bourrier, and Jean-Philippe Malet
Earth Surf. Dynam., 10, 1141–1164, https://doi.org/10.5194/esurf-10-1141-2022, https://doi.org/10.5194/esurf-10-1141-2022, 2022
Short summary
Short summary
Rockfall simulations are often performed to make sure infrastructure is safe. For that purpose, rockfall trajectory data are needed to calibrate the simulation models. In this paper, an affordable, flexible, and efficient trajectory reconstruction method is proposed. The method is tested by reconstructing trajectories from a full-scale rockfall experiment involving 2670 kg rocks and a flexible barrier. The results highlight improvements in precision and accuracy of the proposed method.
Steven J. De Hertog, Felix Havermann, Inne Vanderkelen, Suqi Guo, Fei Luo, Iris Manola, Dim Coumou, Edouard L. Davin, Gregory Duveiller, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
Earth Syst. Dynam., 13, 1305–1350, https://doi.org/10.5194/esd-13-1305-2022, https://doi.org/10.5194/esd-13-1305-2022, 2022
Short summary
Short summary
Land cover and land management changes are important strategies for future land-based mitigation. We investigate the climate effects of cropland expansion, afforestation, irrigation, and wood harvesting using three Earth system models. Results show that these have important implications for surface temperature where the land cover and/or management change occurs and in remote areas. Idealized afforestation causes global warming, which might offset the cooling effect from enhanced carbon uptake.
Louise Busschaert, Shannon de Roos, Wim Thiery, Dirk Raes, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 3731–3752, https://doi.org/10.5194/hess-26-3731-2022, https://doi.org/10.5194/hess-26-3731-2022, 2022
Short summary
Short summary
Increasing amounts of water are used for agriculture. Therefore, we looked into how irrigation requirements will evolve under a changing climate over Europe. Our results show that, by the end of the century and under high emissions, irrigation water will increase by 30 % on average compared to the year 2000. Also, the irrigation requirement is likely to vary more from 1 year to another. However, if emissions are mitigated, these effects are reduced.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
Short summary
Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Aine M. Gormley-Gallagher, Sebastian Sterl, Annette L. Hirsch, Sonia I. Seneviratne, Edouard L. Davin, and Wim Thiery
Earth Syst. Dynam., 13, 419–438, https://doi.org/10.5194/esd-13-419-2022, https://doi.org/10.5194/esd-13-419-2022, 2022
Short summary
Short summary
Our results show that agricultural management can impact the local climate and highlight the need to evaluate land management in climate models. We use regression analysis on climate simulations and observations to assess irrigation and conservation agriculture impacts on warming trends. This allowed us to distinguish between the effects of land management and large-scale climate forcings such as rising CO2 concentrations and thus gain insight into the impacts under different climate regimes.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
Short summary
Short summary
We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
Short summary
Short summary
We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Arthur Depicker, Gerard Govers, Liesbet Jacobs, Benjamin Campforts, Judith Uwihirwe, and Olivier Dewitte
Earth Surf. Dynam., 9, 445–462, https://doi.org/10.5194/esurf-9-445-2021, https://doi.org/10.5194/esurf-9-445-2021, 2021
Short summary
Short summary
We investigated how shallow landslide occurrence is impacted by deforestation and rifting in the North Tanganyika–Kivu rift region (Africa). We developed a new approach to calculate landslide erosion rates based on an inventory compiled in biased © Google Earth imagery. We find that deforestation increases landslide erosion by a factor of 2–8 and for a period of roughly 15 years. However, the exact impact of deforestation depends on the geomorphic context of the landscape (rejuvenated/relict).
Robert Reinecke, Hannes Müller Schmied, Tim Trautmann, Lauren Seaby Andersen, Peter Burek, Martina Flörke, Simon N. Gosling, Manolis Grillakis, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Wim Thiery, Yoshihide Wada, Satoh Yusuke, and Petra Döll
Hydrol. Earth Syst. Sci., 25, 787–810, https://doi.org/10.5194/hess-25-787-2021, https://doi.org/10.5194/hess-25-787-2021, 2021
Short summary
Short summary
Billions of people rely on groundwater as an accessible source of drinking water and for irrigation, especially in times of drought. Groundwater recharge is the primary process of regenerating groundwater resources. We find that groundwater recharge will increase in northern Europe by about 19 % and decrease by 10 % in the Amazon with 3 °C global warming. In the Mediterranean, a 2 °C warming has already lead to a reduction in recharge by 38 %. However, these model predictions are uncertain.
Cited articles
Aimaiti, Y., Liu, W., Yamazaki, F., and Maruyama, Y.: Earthquake-induced
landslide mapping for the 2018 Hokkaido Eastern Iburi earthquake using
PALSAR-2 data, Remote Sens., 111, 2351, https://doi.org/10.3390/rs11202351, 2019.
Ali, K., Bajracharyar, R. M., and Raut, N.: Advances and challenges in flash
flood risk assessment: A review, J. Geogr. Nat. Disast., 7, 1–6,
https://doi.org/10.4172/2167-0587.1000195, 2017.
Bai, J.: Estimating multiple breaks one at a time, Econ. Theory, 13, 315–352, https://doi.org/10.1017/S0266466600005831, 1997.
Balzter, H.: Forest mapping and monitoring with interferometric synthetic
aperture radar (InSAR), Prog. Phys. Geogr., 25, 159–177,
https://doi.org/10.1177/030913330102500201, 2001.
Bamler, R.: Principles of synthetic aperture radar, Surv. Geophys., 21,
147–157, https://doi.org/10.1023/A:1006790026612, 2000.
Barrett, B., Whelan, P., and Dwyer, E.: The use of C-and L-band repeat-pass
interferometric SAR coherence for soil moisture change detection in
vegetated areas, Open Remote Sens. J., 5, 37–53,
https://doi.org/10.2174/1875413901205010037, 2012.
Behling, R., Roessner, S., Kaufmann, H., and Kleinschmit, B.: Automated
spatiotemporal landslide mapping over large areas using rapideye time series
data, Remote Sens., 6, 8026–8055, https://doi.org/10.3390/rs6098026, 2014.
Behling, R., Roessner, S., Golovko, D., and Kleinschmit, B.: Derivation of
long-term spatiotemporal landslide activity – A multi-sensor time series
approach, Remote Sens. Environ., 186, 88–104, https://doi.org/10.1016/j.rse.2016.07.017, 2016.
Bonfils, S.: Trend analysis of the mean annual temperature in Rwanda during
the last fifty two years, J. Environ. Protect., 3, 20077,
https://doi.org/10.4236/jep.2012.36065, 2012.
Bradshaw, C. J. A., Sodhi, N. S., Peh, K. S. H., and Brook, B. W.: Global evidence that deforestation amplifies flood risk and severity in the developing world, Global Change Biol., 13, 2379–2395,
https://doi.org/10.1111/j.1365-2486.2007.01446.x, 2007.
Brancato, V., Liebisch, F., and Hajnsek, I.: Impact of plant surface moisture
on differential interferometric observables: A controlled electromagnetic
experiment, IEEE T. Geosci. Remote, 55, 3949–3964, https://doi.org/10.1109/TGRS.2017.2684814, 2017.
Burrows, K., Walters, R. J., Milledge, D., Spaans, K., and Densmore, A. L.: A
new method for large-scale landslide classification from satellite radar,
Remote Sens., 11, 237, https://doi.org/10.3390/rs11030237, 2019.
Burrows, K., Walters, R. J., Milledge, D., and Densmore, A. L.: A systematic
exploration of satellite radar coherence methods for rapid landslide
detection, Nat. Hazards Earth Syst. Sci., 20, 3197–3214,
https://doi.org/10.5194/nhess-20-3197-2020, 2020.
Burrows, K., Marc, O., and Remy, D.: Using Sentinel-1 radar amplitude time
series to constrain the timings of individual landslides: a step towards
understanding the controls on monsoon-triggered landsliding, Nat. Hazards
Earth Syst. Sci., 22, 2637–2653, https://doi.org/10.5194/nhess-22-2637-2022, 2022.
Casagli, N., Frodella, W., Morelli, S., Tofani, V., Ciampalini, A.,
Intrieri, E., Raspini, F., Rossi, G., Tanteri, L., and Lu, P.: Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning, Geoenviron. Disast., 4, 9, https://doi.org/10.1186/s40677-017-0073-1, 2017.
Chen, X. L., Liu, C. G., Chang, Z. F., and Zhou, Q.: The relationship between
the slope angle and the landslide size derived from limit equilibrium
simulations, Geomorphology, 253, 547–550, https://doi.org/10.1016/j.geomorph.2015.01.036, 2016.
Colesanti, C. and Wasowski, J.: Investigating landslides with space-borne
Synthetic Aperture Radar (SAR) interferometry, Eng. Geol., 88, 173–199,
https://doi.org/10.1016/j.enggeo.2006.09.013, 2006.
Covello, F., Battazza, F., Coletta, A., Lopinto, E., Fiorentino, C., Pietranera, L., Valentini, G., and Zoffoli, S.: COSMO-SkyMed an existing
opportunity for observing the Earth, J. Geodyn., 49, 171–180,
https://doi.org/10.1016/j.jog.2010.01.001, 2010.
Copernicus: Sentinel-1: Copernicus Sentinel data, ASF DAAC [data set], https://search.asf.alaska.edu/#/ (last access: 7 November 2022), 2022a.
Copernicus: Sentinel-2: Copernicus Sentinel data, Google Earth Engine, https://developers.google.com/earth-engine/datasets (last access: 7 November 2022), 2022b.
Deijns, A. A. J.: Deijns et al. NHESS – SAR Timing – Scripts, Zenodo [code], https://doi.org/10.5281/zenodo.7198346, 2022a.
Deijns, A. A. J.: Deijns et al. NHESS – SAR Timing – GH Event Inventories, Zenodo [data set], https://doi.org/10.5281/zenodo.7198322, 2022b.
Deijns, A. A. J., Bevington, A. R., van Zadelhoff, F., de Jong, S. M.,
Geertsema, M., and McDougall, S.: Semi-automated detection of landslide
timing using harmonic modelling of satellite imagery, Buckinghorse River,
Canada, Int. J. Appl. Earth Obs. Geoinf., 84, 101943,
https://doi.org/10.1016/j.jag.2019.101943, 2020.
Depicker, A., Jacobs, L., Mboga, N., Smets, B., Van Rompaey, A., Lennert,
M., Wolff, E., Kervyn, F., Michellier, C., Dewitte, O., and Govers, G.:
Historical dynamics of landslide risk from population and forest-cover
changes in the Kivu Rift, Nat. Sustain., 4, 965–974,
https://doi.org/10.1038/s41893-021-00757-9, 2021.
Derauw, D., Libert, L., Barbier, C., Orban, A., Kervyn, F., Samsonov, S., and
d'Oreye, N.: The CSL InSAR Suite processor: specificities of a command line
InSAR processing software specifically adapted for automated time series
processing, in: ESA Living Planet Symposium 2019, 13–17 May 2019, Milano, Italy, https://lps19.esa.int/NikalWebsitePortal/living-planet-symposium-2019/lps19/Agenda/AgendaItemDetail?id=f879ca21-f0cf-4800-9473-e6882c23016d (last access: 7 November 2022), 2019.
Derauw, D., Jaspard, M., Caselli, A., and Samsonov, S.: Ongoing automated
ground deformation monitoring of Domuyo-Laguna del Maule area (Argentina)
using Sentinel-1 MSBAS time series: Methodology description and first
observations for the period 2015–2020, J. S. Am. Earth Sci., 104, 102850,
https://doi.org/10.1016/j.jsames.2020.102850, 2020
DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. W., and Lang, M. W.: Rapid and robust monitoring of flood events using Sentinel-1 and Landsat
data on the Google Earth Engine, Remote Sens. Environ., 240, 111664,
https://doi.org/10.1016/j.rse.2020.111664, 2020.
Dewitte, O., Dille, A., Depicker, A., Kubwimana, D., Maki Mateso, J.-C.,
Mugaruka Bibentyo, T., Uwihirwe, J., and Monsieurs, E.: Constraining landslide timing in a data-scarce context: from recent to very old processes in the tropical environment of the North Tanganyika-Kivu Rift region, Landslides, 18, 161–177, https://doi.org/10.1007/s10346-020-01452-0, 2021.
Dewitte, O., Depicker, A., Moeyersons, J., and Dille, A.: Mass Movements in
Tropical Climates: Treatise on Geomorphology, in: vol. 5, edited by: Shroder, J. J. F., Elsevier, Academic Press, 338–349, https://doi.org/10.1016/B978-0-12-818234-5.00118-8, 2022.
Dobson, M. C. and Ulaby, F. T.: Active microwave soil moisture research, IEEE
T. Geosci. Remote, 1, 23–36, https://doi.org/10.1109/TGRS.1986.289585, 1986.
d'Oreye, N., Derauw, D., Libert, L., Samsonov, S., Dille, A., Nobile, A.,
Monsieurs, E., Dewitte, O., and Kervyn, F.: Automatization of InSAR mass
processing using CSL InSAR Suite (CIS) software for Multidimensional Small
Baseline Subset (MSBAS) analysis: example combining Sentinel-1 and Cosmo-SkyMed SAR data for landslides monitoring in South Kivu, DR Congo, in: ESA Living Planet Symposium, 13–17 May 2019, Milano, Italy, https://lps19.esa.int/NikalWebsitePortal/living-planet-symposium-2019/lps19/Agenda/AgendaItemDetail?id=f879ca21-f0cf-4800-9473-e6882c23016d (last access: 7 November 2022), 2019.
d'Oreye, N., Derauw, D., Samsonov, S., Jaspard, M., and Smittarello, D.:
MasTer: A Full Automatic Multi-Satellite InSAR Mass Processing Tool for
Rapid Incremental 2D Ground Deformation Time Series, in: Int. Geosci. Remote
Sens. Symp. (IGARSS), 12–16 July 2021, Brussels, 1899–1902, https://doi.org/10.1109/IGARSS47720.2021.9553615, 2021.
Dubois, P. C., Van Zyl, J., and Engman, T.: Measuring soil moisture with
imaging radars, IEEE T. Geosci. Remote, 33, 915–926, https://doi.org/10.1109/36.406677, 1995.
Dzurisin, D.: Volcano deformation: new geodetic monitoring techniques,
Springer, https://doi.org/10.1007/978-3-540-49302-0, 2006.
Emberson, R., Kirschbaum, D., and Stanley, T.: New global characterisation of
landslide exposure, Nat. Hazards Earth Syst. Sci., 20, 3413–3424,
https://doi.org/10.5194/nhess-20-3413-2020, 2020.
Emberson, R., Kirschbaum, D. B., Amatya, P., Tanyas, H., and Marc, O.:
Insights from the topographic characteristics of a large global catalog of
rainfall-induced landslide event inventories, Nat. Hazards Earth Syst. Sci.,
22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, 2022.
ESA: Climate Change Initiative–Land Cover Project 2017, 20 m Resolution,
European Space Agency, https://2016africalandcover20m.esrin.esa.int/ (last access: 7 November 2022), 2016.
Esposito, G., Marchesini, I., Mondini, A. C., Reichenbach, P., Rossi, M.,
and Sterlacchini, S.: A spaceborne SAR-based procedure to support the
detection of landslides, Nat. Hazards Earth Syst. Sci., 20, 2379–2395,
https://doi.org/10.5194/nhess-20-2379-2020, 2020.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315,
https://doi.org/10.1002/joc.5086, 2017.
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley Jr., R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Hughes, M. J., and Laue, B.: Cloud detection algorithm comparison and validation for operational Landsat data products, Remote Sens. Environ., 194, 379–390,
https://doi.org/10.1016/j.rse.2017.03.026, 2017.
Foody, G. M. and Mathur, A.: The use of small training sets containing mixed
pixels for accurate hard image classification: Training on mixed spectral
responses for classification by a SVM, Remote Sens. Environ., 103, 179–189,
https://doi.org/10.1016/j.rse.2006.04.001, 2006.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004
to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181,
https://doi.org/10.5194/nhess-18-2161-2018, 2018.
Fryzlewicz, P.: Wild binary segmentation for multiple change-point detection, Ann. Stat., 42, 2243–2281, https://doi.org/10.1214/14-AOS1245, 2014.
Ge, P., Gokon, H., Meguro, K., and Koshimura, S.: Study on the intensity and
coherence information of high-resolution ALOS-2 SAR images for rapid massive
landslide mapping at a pixel level, Remote Sens., 11, 2808,
https://doi.org/10.3390/rs11232808, 2019.
Giertz, S., Junge, B., and Diekkrüger, B.: Assessing the effects of land
use change on soil physical properties and hydrological processes in the
sub-humid tropical environment of West Africa, Phys. Chem. Earth, 30,
485–496, https://doi.org/10.1016/j.pce.2005.07.003, 2005.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: he rainfall
intensity–duration control of shallow landslides and debris flows: an
update, Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1, 2008.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M.,
and Chang, K.-T.: Landslide inventory maps: New tools for an old problem,
Earth-Sci. Rev., 112, 42–66, https://doi.org/10.1016/j.earscirev.2012.02.001, 2012.
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I., Rossi, M., and Melillo, M.: Geographical landslide early warning systems,
Earth-Science Rev., 200, 102973, https://doi.org/10.1016/j.earscirev.2019.102973, 2020.
Hagberg, J. O., Ulander, L. M., and Askne, J.: Repeat-p–340,
https://doi.org/10.1109/TGRS.1995.8746014, 1995.
Handwerger, A. L., Huang, M.-H., Jones, S. Y., Amatya, P., Kerner, H. R., and Kirschbaum, D. B.: Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine, Nat. Hazards Earth Syst. Sci., 22, 753–773, https://doi.org/10.5194/nhess-22-753-2022, 2022.
Hanssen, R. F.: Radar interferometry: data interpretation and error analysis,
in: vol. 2, Springer, https://doi.org/10.1007/0-306-47633-9, 2001.
Heri-Kazi, A. B. and Bielders, C. L.: Cropland characteristics and extent of
soil loss by rill and gully erosion in smallholder farms in the KIVU highlands, D. R. Congo, Geoderma Reg., 26, e00404, https://doi.org/10.1016/j.geodrs.2021.e00404, 2021.
Intrieri, E., Raspini, F., Fumagalli, A., Lu, P., Del Conte, S., Farina, P.,
Allievi, J., Ferretti, A., and Casagli, N.: The Maoxian landslide as seen
from space: detecting precursors of failure with Sentinel-1 data, Landslides, 15, 123–133, https://doi.org/10.1007/s10346-017-0915-7, 2018.
Jacobs, L., Maes, J., Mertens, K., Sekajugo, J., Thiery, W., Van Lipzig, N.,
Poesen, J., Kervyn, M., and Dewitte, O.: Reconstruction of a flash flood event through a multi-hazard approach: focus on the Rwenzori Mountains, Uganda, Nat. Hazards, 84, 851–876, https://doi.org/10.1007/s11069-016-2458-y, 2016a.
Jacobs, L., Dewitte, O., Poesen, J., Delvaux, D., Thiery, W., and Kervyn, M.:
The Rwenzori Mountains, a landslide-prone region?, Landslides, 13, 519–536,
https://doi.org/10.1007/s10346-015-0582-5, 2016b.
Jacobs, L., Kabaseke, C., Bwambale, B., Katutu, R., Dewitte, O., Mertens, K., Maes, J., and Kervyn, M.: The geo-observer network: A proof of concept on
participatory sensing of disasters in a remote setting, Sci. Total Environ.,
670, 245–261, https://doi.org/10.1016/j.scitotenv.2019.03.177, 2019.
Jacquemart, M. and Tiampo, K.: Leveraging time series analysis of radar
coherence and normalized difference vegetation index ratios to characterize
pre-failure activity of the Mud Creek landslide, California, Nat. Hazards
Earth Syst. Sci., 21, 629–642, https://doi.org/10.5194/nhess-21-629-2021, 2021.
Joyce, K. E., Belliss, S. E., Samsonov, S. V., McNeill, S. J., and Glassey, P. J.: A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters, Prog. Phys. Geogr., 33, 183–207, https://doi.org/10.1177/0309133309339563, 2009.
Jung, J. and Yun, S. H.: Evaluation of coherent and incoherent landslide
detection methods based on Synthetic Aperture Radar for rapid response: A case study for the 2018 Hokkaido landslides, Remote Sens., 12, 265,
https://doi.org/10.3390/rs12020265, 2020.
Kennedy, R. E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen,
W. B., and Healey, S.: Implementation of the LandTrendr algorithm on google earth engine, Remote Sens., 10, 691, https://doi.org/10.3390/rs10050691, 2018.
Kjekstad, O. and Highland, L.: Economic and Social Impacts of Landslides, in:
Landslides – Disaster Risk Reduction, edityed by: Sassa, K. and Canuti, P.,
Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-69970-5_30, 2009.
Komac, M., Holley, R., Mahapatra, P., van der Marel, H., and Bavec, M.: Coupling of GPS/GNSS and radar interferometric data for a 3D surface displacement monitoring of landslides, Landslides, 12, 241–257,
https://doi.org/10.1007/s10346-014-0482-0, 2015.
Konishi, T. and Suga, Y.: Landslide detection using COSMO-SkyMed images: A
case study of a landslide event on Kii Peninsula, Japan, Eur. J. Remote
Sens., 51, 205–221, https://doi.org/10.1080/22797254.2017.1418185, 2018.
Korup, O., Densmore, A. L., and Schlunegger, F.: The role of landslides in
mountain range evolution, Geomorphology, 120, 77–90,
https://doi.org/10.1016/j.geomorph.2009.09.017, 2010.
Kubwimana, D., Ait Brahim, L., Nkurunziza, P., Dille, A., Depicker, A.,
Nahimana, L., Abdelouafi, A., and Dewitte, O.: Characteristics and Distribution of Landslides in the Populated Hillslopes of Bujumbura,
Burundi, Geosciences, 11, 259, https://doi.org/10.3390/geosciences11060259, 2021.
Le Cozannet, G., Kervyn, M., Russo, S., Ifejika Speranza, C., Ferrier, P.,
Foumelis, M., Lopez, T., and Modaressi, H.: Space-Based Earth Observations for Disaster Risk Management, Surv. Geophys., 41, 1209–1235,
https://doi.org/10.1007/s10712-020-09586-5, 2020.
Liu, C., Guo, L., Ye, L., Zhang, S., Zhao, Y., and Song, T.: A review of
advances in China's flash flood early-warning system, Nat. Hazards, 92,
619–634, https://doi.org/10.1007/s11069-018-3173-7, 2018.
Marc, O., Stumpf, A., Malet, J.-P., Gosset, M., Uchida, T., and Chiang, S.-H.: Initial insights from a global database of rainfall-induced landslide
inventories: the weak influence of slope and strong influence of total storm
rainfall, Earth Surf. Dynam., 6, 903–922, https://doi.org/10.5194/esurf-6-903-2018, 2018.
Marengo, J. A. and Alves, L. M.: The 2011 intense rainfall and floods in Rio
De Janeiro, B. Am. Meteorol. Soc., 93, 1–282, 2012.
Martinis, S., Kuenzer, C., Wendleder, A., Huth, J., Twele, A., Roth, A., and
Dech, S.: Comparing four operational SAR-based water and flood detection
approaches, Int. J. Remote Sens., 36, 3519–3543,
https://doi.org/10.1080/01431161.2015.1060647, 2015.
Mohan, A., Singh, A. K., Kumar, B., and Dwivedi, R.: Review on remote sensing
methods for landslide detection using machine and deep learning, Trans.
Emerg. Telecommun. Technol. T., 32, 3998, https://doi.org/10.1002/ett.3998, 2021.
Mondini, A. C.: Measures of spatial autocorrelation changes in multitemporal
SAR images for event landslides detection, Remote Sens., 9, 554,
https://doi.org/10.3390/rs9060554, 2017.
Mondini, A. C., Santangelo, M., Rocchetti, M., Rossetto, E., Manconi, A., and
Monserrat, O.: Sentinel-1 SAR amplitude imagery for rapid landslide detection, Remote Sens., 11, 760, https://doi.org/10.3390/rs11070760, 2019.
Mondini, A. C., Guzzetti, F., Chang, K. T., Monserrat, O., Martha, T. R., and
Manconi, A.: Landslide failures detection and mapping using Synthetic
Aperture Radar: Past, present and future, Earth-Sci. Rev., 216, 103574,
https://doi.org/10.1016/j.earscirev.2021.103574, 2021.
Monsieurs, E.: The potential of satellite rainfall estimates in assessing
regional landslide hazard in Central Africa, Doctoral dissertation,
Université de Liège, Liège, Belgium,
https://hdl.handle.net/2268/245576 (last access: 7 November 2022), 2020.
Monsieurs, E., Jacobs, L., Michellier, C., Basimike Tchangaboba, J.,
Bamulezi Ganza, G., Kervyn, F., Maki Mateso, J.-C., Mugaruka Bibentyo, T.,
Kalikone Buzera, C., Nahimana, L., Ndayisenga, A., Nkurunziza, P., Thiery,
W., Demoulin, A., Kervyn, M., and Dewitte, O.: Landslide inventory for hazard
assessment in a data-poor context: a regional-scale approach in a tropical
African environment, Landslides, 15, 2195–2209, https://doi.org/10.1007/s10346-018-1008-y, 2018a.
Monsieurs, E., Kirschbaum, D. B., Tan, J., Maki Mateso, J.-C., Jacobs, L.,
Plisnier, P.-D., Thiery, W., Umutoni, A., Musoni, D., Mugaruka Bibentyo, T.
Bamulezi Ganza, G., Ilombe Mawe, G., Bagalwa, L., Kankurize, C., Michellier,
C., Stanley, T., Kervyn, F., Kervyn, M., Demoulin, A., and Dewitte, O.:
Evaluating TMPA rainfall over the sparsely gauged East African Rift, J.
Hydrometeorol., 19, 1507–1528, https://doi.org/10.1175/JHM-D-18-0103.1, 2018b.
Monsieurs, E., Dewitte, O., and Demoulin, A.: A susceptibility-based
rainfall threshold approach for landslide occurrence, Nat. Hazards Earth
Syst. Sci., 19, 775–789, https://doi.org/10.5194/nhess-19-775-2019, 2019.
Nakulopa, F., Vanderkelen, I., Van de Walle, J., Van Lipzig, N. P., Tabari,
H., Jacobs, L., Tweheyo, C., Dewitte, O., and Thiery, W.: Evaluation of
high-resolution precipitation products over the Rwenzori Mountains (Uganda),
J. Hydrometeorol., 23, 747–768, https://doi.org/10.1175/JHM-D-21-0106.1, 2022.
Nicholson, S. E.: Climate and climatic variability of rainfall over eastern
Africa, Rev. Geophys., 5, 590–635, https://doi.org/10.1002/2016RG000544, 201.
NISAR: NASA-ISRO SAR (NISAR) Mission Science Users' Handbook, NASA Jet
Propulsion Laboratory, 261, 18–1893, https://nisar.jpl.nasa.gov/resources/documents (last access: 7 November 2022), 2018.
Nobile, A., Dille, A., Monsieurs, E., Basimike, J., Mugaruka Bibentyo, T.,
d'Oreye, N., Kervyn, F., and Dewitte, O.: Multi-temporal DInSAR to characterise landslide ground deformations in a tropical urban environment:
Focus on Bukavu (DR Congo), Remote Sens., 10, 626, https://doi.org/10.3390/rs10040626, 2018.
Nolan, M. and Fatland, D. R.: Penetration depth as a DInSAR observable and
proxy for soil moisture, IEEE T. Geosci. Remote, 41, 532–537,
https://doi.org/10.1109/TGRS.2003.809931, 2003.
Park, S. E. and Lee, S. G.: On the use of single-, dual-, and quad-polarimetric SAR observation for landslide detection, ISPRS Int. J.
Geo-Inf., 8, 384, https://doi.org/10.3390/ijgi8090384, 2019.
Petersen, M. S.: Impacts of Flash Floods. Coping With Flash Floods, in: NATO
Science Series, vol. 77, edited by: Gruntfest, E. and Handmer, J., Springer,
Dordrecht, https://doi.org/10.1007/978-94-010-0918-8_2, 2001.
Peterson, M., Mach, D., and Buechler, D.: A Global LIS/OTD Climatology of
Lightning Flash Extent Density, J. Geophys. Res.-Atmos., 126, e2020JD033885, https://doi.org/10.1029/2020JD033885, 2021.
Planet Team: Planet Application Program Interface: In Space for Life on
Earth, San Francisco, CA, https://api.planet.com (last access: 7 November 2022), 2017.
Psomiadis, E.: October. Flash flood area mapping utilising SENTINEL-1 radar
data, Earth Resour. Environ. Remote Sens./GIS App. VII, 10005, 100051G,
https://doi.org/10.1117/12.2241055, 2016.
Rengers, F. K., McGuire, L. A., Kean, J. W., Staley, D. M., and Hobley, D. E. J.: Model simulations of flood and debris flow timing in steep catchments after wildfire, Water Resour. Res., 52, 6041–6061, https://doi.org/10.1002/2015WR018176, 2016.
Roback, K., Clark, M. K., West, A. J., Zekkos, D., Li, G., Gallen, S. F.,
Chamlagain, D., and Godt, J. W.: The size, distribution, and mobility of
landslides caused by the 2015 Mw 7.8 Gorkha earthquake, Nepal,
Geomorphology, 301, 121–138, https://doi.org/10.1016/j.geomorph.2017.01.030, 2018.
Robinson, T. R., Rosser, N.,and Walters, R. J.: The Spatial and Temporal
Influence of Cloud Cover on Satellite-Based Emergency Mapping of Earthquake
Disasters, Sci. Rep. 9, 1–9, https://doi.org/10.1038/s41598-019-49008-0, 2019.
Rocca, F., Prati, C., Monti Guarnieri, A., and Ferretti, A.: SAR interferometry and its applications, Surv. Geophys., 21, 159–176,
https://doi.org/10.1023/A:1006710731155, 2000.
Samsonov, S. and d'Oreye, N.: Multidimensional time-series analysis of ground
deformation from multiple InSAR data sets applied to Virunga Volcanic
Province, Geophys. J. Int., 191, 1095–1108, https://doi.org/10.1111/j.1365-246X.2012.05669.x, 2012.
Scott, C. P., Lohman, R. B., and Jordan, T. E.: InSAR constraints on soil
moisture evolution after the March 2015 extreme precipitation event in
Chile, Sci. Rep., 7, 1–9, https://doi.org/10.1038/s41598-017-05123-4, 2017.
Sekajugo, J., Kagoro-Rugunda, G., Mutyebere, R., Kabaseke, C., Namara, E.,
Dewitte, O., Kervyn, M., and Jacobs, L.: Can citizen scientists provide a
reliable geo-hydrological hazard inventory? An analysis of biases, sensitivity and precision for the Rwenzori Mountains, Uganda, Environ. Res.
Lett., 17, 045011, https://doi.org/10.1088/1748-9326/ac5bb5, 2022.
Shibayama, T., Yamaguchi, Y., and Yamada, H.: Polarimetric scattering
properties of landslides in forested areas and the dependence on the local
incidence angle, Remote Sens., 7, 15424–15442, https://doi.org/10.3390/rs71115424, 2015.
Small, D.: Flattening gamma: Radiometric terrain correction for SAR imagery,
IEEE T. Geosci. Remote, 49, 3081–3093, https://doi.org/10.1109/TGRS.2011.2120616, 2011.
Srivastava, H. S., Patel, P., and Navalgund, R. R.: How far SAR has fulfilled
its expectation for soil moisture retrieval, Microwave Remote Sens. Atmos.
Environ., 6410, 641001 https://doi.org/10.1117/12.693946, 2006.
Strozzi, T., Dammert, P. B., Wegmuller, U., Martinez, J. M., Askne, J. I.,
Beaudoin, A., and Hallikainen, N. T.: Landuse mapping with ERS SAR interferometry, IEEE T. Geosci. Remote, 38, 766–775, https://doi.org/10.1109/36.842005, 2000.
Stumpf, A., Malet, J. P., Allemand, P., and Ulrich, P.: Surface reconstruction and landslide displacement measurements with Pléiades satellite images, ISPRS J. Photogram. Remote Sens., 95, 1–12,
https://doi.org/10.1016/j.isprsjprs.2014.05.008, 2014.
Tessari, G., Floris, M., and Pasquali, P.: Phase and amplitude analyses of
SAR data for landslide detection and monitoring in non-urban areas located
in the North-Eastern Italian pre-Alps, Environ. Earth Sci., 76, 85, https://doi.org/10.1007/s12665-017-6403-5, 2017
Thiery, W., Davin, E. L., Panitz, H.-J., Demuzere, M., Lhermitte, S., and van Lipzig, N. P. M.: The impact of the African Great Lakes on the regional
climate, J. Climate, 28, 4061–4085, https://doi.org/10.1175/JCLI-D-14-00565.1, 2015.
Thiery, W., Davin, E. L., Seneviratne, S. I., Bedka, K., Lhermitte, S., and van Lipzig, N. P. M.: Hazardous thunderstorm intensification over Lake Victoria, Nat. Comm., 7, 12786, https://doi.org/10.1038/ncomms12786, 2016.
Thiery, W., Gudmundsson, L., Bedka, K., Semazzi, F. H. M., Lhermitte, S.,
Willems, P., van Lipzig, N. P. M., and Seneviratne, S. I.: Early warnings of
hazardous thunderstorms over Lake Victoria, Environ. Res. Lett., 12, 074012,
https://doi.org/10.1088/1748-9326/aa7521, 2017.
Truong, C., Oudre, L., and Vayatis, N.: Selective review of offline change
point detection methods, Signal Process., 167, 107299,
https://doi.org/10.1016/j.sigpro.2019.107299, 2020.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150,
https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
Turkington, T., Ettema, J., van Westen, C. J., and Breinl, K.: Empirical
atmospheric thresholds for debris flows and flash floods in the southern
French Alps, Nat. Hazards Earth Syst. Sci., 14, 1517–1530,
https://doi.org/10.5194/nhess-14-1517-2014, 2014.
Twele, A., Cao, W., Plank, S., and Martinis, S.: Sentinel-1-based flood
mapping: a fully automated processing chain, Int. J. Remote Sens., 37,
2990–3004, https://doi.org/10.1080/01431161.2016.1192304, 2016.
Tzouvaras, M., Danezis, C., and Hadjimitsis, D. G.: Small scale landslide
detection using Sentinel-1 interferometric SAR coherence, Remote Sens., 12,
1560, https://doi.org/10.3390/rs12101560, 2020.
Ulaby, F. T., Dubois, P. C., and Van Zyl, J.: Radar mapping of surface soil
moisture, J. Hydrol., 184, 57–84, https://doi.org/10.1016/0022-1694(95)02968-0, 1996.
US Geological Survey: Landsat 8 imagery, Google Earth Engine, US Geological Survey, https://developers.google.com/earth-engine/datasets, last access: 7 November 2022.
Van de Walle, J., Thiery, W., Brousse, O., Souverijns, N., Demuzere, M., and
van Lipzig, N. P. M.: A convection-permitting model for the Lake Victoria
Basin: Evaluation and insight into the mesoscale versus synoptic atmospheric
dynamics, Clim. Dynam., 54, 1779–1799, https://doi.org/10.1007/s00382-019-05088-2, 2020.
van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for
landslide susceptibility, hazard, and vulnerability assessment: An overview,
Eng. Geol., 102, 112–131, https://doi.org/10.1016/j.enggeo.2008.03.010, 2008.
Weydahl, D. J.: Analysis of ERS SAR coherence images acquired over vegetated
areas and urban features, Int. J. Remote Sens., 22, 2811–2830,
https://doi.org/10.1080/01431160010006412, 2001.
Zebker, H. A. and Villasenor, J.: Decorrelation in interferometric radar
echoes, IEEE T. Geosci. Remote, 30, 950–959, https://doi.org/10.1109/36.175330, 1992.
Zhong, C., Li, C., Gao, P., and Li, H.: Discovering Vegetation Recovery and
Landslide Activities in the Wenchuan Earthquake Area with Landsat Imagery, Sensors, 21, 5243, https://doi.org/10.3390/s21155243, 2021.
Short summary
Landslides and flash floods are rainfall-induced processes that often co-occur and interact, generally very quickly. In mountainous cloud-covered environments, determining when these processes occur remains challenging. We propose a regional methodology using open-access satellite radar images that allow for the timing of landslide and flash floods events, in the contrasting landscapes of tropical Africa, with an accuracy of up to a few days. The methodology shows potential for transferability.
Landslides and flash floods are rainfall-induced processes that often co-occur and interact,...
Altmetrics
Final-revised paper
Preprint