Articles | Volume 23, issue 12
https://doi.org/10.5194/nhess-23-3805-2023
© Author(s) 2023. 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-23-3805-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling
Anne Felsberg
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Zdenko Heyvaert
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Jean Poesen
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Faculty of Earth Sciences and Spatial Management, Maria Curie-Skłodowska University, Lublin, Poland
Thomas Stanley
GESTAR II, University of Maryland Baltimore County, Baltimore, MD, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Gabriëlle J. M. De Lannoy
CORRESPONDING AUTHOR
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
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Jonas Mortelmans, Anne Felsberg, Gabriëlle J. M. De Lannoy, Sander Veraverbeke, Robert D. Field, Niels Andela, and Michel Bechtold
Nat. Hazards Earth Syst. Sci., 24, 445–464, https://doi.org/10.5194/nhess-24-445-2024, https://doi.org/10.5194/nhess-24-445-2024, 2024
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With global warming increasing the frequency and intensity of wildfires in the boreal region, accurate risk assessments are becoming more crucial than ever before. The Canadian Fire Weather Index (FWI) is a renowned system, yet its effectiveness in peatlands, where hydrology plays a key role, is limited. By incorporating groundwater data from numerical models and satellite observations, our modified FWI improves the accuracy of fire danger predictions, especially over summer.
Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 22, 3063–3082, https://doi.org/10.5194/nhess-22-3063-2022, https://doi.org/10.5194/nhess-22-3063-2022, 2022
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In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in, for example, meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
Lucas Boeykens, Devon Dunmire, Jonas-Frederik Jans, Willem Waegeman, Gabriëlle De Lannoy, Ezra Beernaert, Niko E. C. Verhoest, and Hans Lievens
EGUsphere, https://doi.org/10.5194/egusphere-2025-3327, https://doi.org/10.5194/egusphere-2025-3327, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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We used AI to better estimate the height of the snowpack present on the ground across the European Alps, by using novel satellite data, complemented by weather information or snow depth estimates from a computer model. We found that both combinations improve the accuracy of our AI-based snow depth estimates, performing almost equally well. This helps us better monitor how much water is stored as snow, which is vital for drinking water, farming, and clean energy production in Europe.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2025-2550, https://doi.org/10.5194/egusphere-2025-2550, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Hunter N. Jimenez, Erkan Istanbulluoglu, Tolga Gorum, Thomas A. Stanley, Pukar M. Amatya, Hakan Tanyas, Mehmet C. Demirel, Aykut Akgun, and Deniz Bozkurt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3011, https://doi.org/10.5194/egusphere-2025-3011, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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After a major earthquake struck near the Türkiye/Syria border in February 2023, a powerful storm brought intense rainfall to the region, triggering additional landslides. We used satellite data and a physics-based model to map probabilistic landslide hazard using both coseismic and hydrologic drivers. We also explored how the sequence of these disasters affected landslide risk. Finally, we offer a method for seasonal forecasting of landslide hazard in at-risk areas using the historic climate.
Devon Dunmire, Michel Bechtold, Lucas Boeykens, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2025-2306, https://doi.org/10.5194/egusphere-2025-2306, 2025
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Snow is vital for society and the climate, yet estimates of snowpack remain uncertain due to observational and modeling limitations. Data assimilation (DA) helps by integrating observations with models. Here, we integrate snow depth retrievals into a physically-based snow model across the European Alps. This work offers advancements for snow data assimilation, such as incorporating a dynamic observational uncertainty, which is essential for forecasting and water resource management.
Anne Springer, Gabriëlle De Lannoy, Matthew Rodell, Yorck Ewerdwalbesloh, Helena Gerdener, Mehdi Khaki, Bailing Li, Fupeng Li, Maike Schumacher, Natthachet Tangdamrongsub, Mohammad J. Tourian, Wanshu Nie, and Jürgen Kusche
EGUsphere, https://doi.org/10.5194/egusphere-2025-2058, https://doi.org/10.5194/egusphere-2025-2058, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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The GRACE and GRACE Follow-On satellites monitor changes in Earth's water storage by observing gravity variations. By integrating these observations into hydrological models through data assimilation, estimates of groundwater, soil moisture, and hydrological trends are improved, helping to monitor droughts, floods, and human water use. This review highlights recent advances in GRACE data assimilation, identifies key challenges, and discusses future directions with upcoming satellite missions.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
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The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Preprint archived
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
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To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
Jonas Mortelmans, Anne Felsberg, Gabriëlle J. M. De Lannoy, Sander Veraverbeke, Robert D. Field, Niels Andela, and Michel Bechtold
Nat. Hazards Earth Syst. Sci., 24, 445–464, https://doi.org/10.5194/nhess-24-445-2024, https://doi.org/10.5194/nhess-24-445-2024, 2024
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With global warming increasing the frequency and intensity of wildfires in the boreal region, accurate risk assessments are becoming more crucial than ever before. The Canadian Fire Weather Index (FWI) is a renowned system, yet its effectiveness in peatlands, where hydrology plays a key role, is limited. By incorporating groundwater data from numerical models and satellite observations, our modified FWI improves the accuracy of fire danger predictions, especially over summer.
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
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We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Ioanna S. Panagea, Antonios Apostolakis, Antonio Berti, Jenny Bussell, Pavel Čermak, Jan Diels, Annemie Elsen, Helena Kusá, Ilaria Piccoli, Jean Poesen, Chris Stoate, Mia Tits, Zoltan Toth, and Guido Wyseure
SOIL, 8, 621–644, https://doi.org/10.5194/soil-8-621-2022, https://doi.org/10.5194/soil-8-621-2022, 2022
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The potential to reverse the negative effects caused in topsoil by inversion tillage, using alternative agricultural practices, was evaluated. Reduced and no tillage, and additions of manure/compost, improved topsoil structure and OC content. Residue retention had a positive impact on structure. We concluded that the negative effects of inversion tillage can be mitigated by reducing tillage intensity or adding organic materials, optimally combined with non-inversion tillage.
Sara Modanesi, Christian Massari, Michel Bechtold, Hans Lievens, Angelica Tarpanelli, Luca Brocca, Luca Zappa, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 4685–4706, https://doi.org/10.5194/hess-26-4685-2022, https://doi.org/10.5194/hess-26-4685-2022, 2022
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Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for global water resource management and the comprehension of human impacts on the water cycle and identifies main challenges and outlooks for future research.
Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 22, 3063–3082, https://doi.org/10.5194/nhess-22-3063-2022, https://doi.org/10.5194/nhess-22-3063-2022, 2022
Short summary
Short summary
In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in, for example, meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
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
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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.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, https://doi.org/10.5194/hess-25-6283-2021, 2021
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Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
Shannon de Roos, Gabriëlle J. M. De Lannoy, and Dirk Raes
Geosci. Model Dev., 14, 7309–7328, https://doi.org/10.5194/gmd-14-7309-2021, https://doi.org/10.5194/gmd-14-7309-2021, 2021
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A spatially distributed version of the field-scale crop model AquaCrop v6.1 was developed for applications at various spatial scales. Multi-year 1 km simulations over central Europe were evaluated against biomass and surface soil moisture products derived from optical and microwave satellite missions, as well as in situ observations of soil moisture. The regional version of the AquaCrop model provides a suitable setup for subsequent satellite-based data assimilation.
Michiel Maertens, Gabriëlle J. M. De Lannoy, Sebastian Apers, Sujay V. Kumar, and Sarith P. P. Mahanama
Hydrol. Earth Syst. Sci., 25, 4099–4125, https://doi.org/10.5194/hess-25-4099-2021, https://doi.org/10.5194/hess-25-4099-2021, 2021
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In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.
Jianxiu Qiu, Jianzhi Dong, Wade T. Crow, Xiaohu Zhang, Rolf H. Reichle, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 1569–1586, https://doi.org/10.5194/hess-25-1569-2021, https://doi.org/10.5194/hess-25-1569-2021, 2021
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The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use a machine learning method to analyze how the efficiency of the L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to Tb innovation, followed by error in precipitation forcing and microwave soil roughness. Since the L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM–RZSM coupling strength.
Robert Emberson, Dalia Kirschbaum, and Thomas Stanley
Nat. Hazards Earth Syst. Sci., 20, 3413–3424, https://doi.org/10.5194/nhess-20-3413-2020, https://doi.org/10.5194/nhess-20-3413-2020, 2020
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Landslides cause thousands of fatalities and cost billions of dollars of damage worldwide every year, but different inventories of landslide events can have widely diverging completeness. This can lead to spatial biases in our understanding of the impacts. Here we use a globally homogeneous model of landslide hazard and exposure to provide consistent estimates of where landslides are most likely to cause damage to people, roads and other critical infrastructure at 1 km resolution.
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Short summary
The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of...
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