Articles | Volume 22, issue 7
https://doi.org/10.5194/nhess-22-2317-2022
© 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-22-2317-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jul 2022
Research article |
| 27 Jul 2022
| 27 Jul 2022
Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars
Chuxuan Li
CORRESPONDING AUTHOR
Department of Earth and Planetary Sciences, Northwestern University, Evanston, IL 60208, USA
Alexander L. Handwerger
Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Jiali Wang
Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
Wei Yu
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, CO 80309, USA
Global Systems Laboratory, NOAA, Denver, CO 80305-3328, USA
Xiang Li
Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL 60208, USA
Noah J. Finnegan
Department of Earth and Planetary Sciences, University of California Santa Cruz, Santa Cruz, CA 95064, USA
Yingying Xie
Program in Environmental Sciences, Northwestern University, Evanston, IL 60208, USA
Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
Giuseppe Buscarnera
Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL 60208, USA
Daniel E. Horton
Department of Earth and Planetary Sciences, Northwestern University, Evanston, IL 60208, USA
Related authors
No articles found.
Chunyong Jung, Pengfei Xue, Chenfu Huang, William Pringle, Mrinal Biswas, Geeta Nain, and Jiali Wang
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-47, https://doi.org/10.5194/wes-2025-47, 2025
Preprint under review for WES
Short summary
Short summary
This study introduces a system that combines weather, ocean, and wave models to better understand their interactions during tropical storms and their impact on offshore structures like wind turbines. Tested using Hurricane Henri (2021), the system improves storm predictions by including how waves and ocean cooling affect storm strength and wind patterns. The results show this approach helps assess risks to offshore infrastructure during severe weather, making it more accurate and reliable.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Kyle Peco, Jiali Wang, Chunyong Jung, Gökhan Sever, Lindsay Sheridan, Jeremy Feinstein, Rao Kotamarthi, Caroline Draxl, Ethan Young, Avi Purkayastha, and Andrew Kumler
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-13, https://doi.org/10.5194/wes-2025-13, 2025
Preprint under review for WES
Short summary
Short summary
This study presents a new wind dataset, generated by a climate model, that can help facilitate efforts in wind energy. By providing data across much of North America, this dataset can offer insights into the wind patterns in more understudied regions. By validating the dataset against actual wind observations, we have demonstrated that this dataset is able to accurately capture the wind patterns of different geographic areas, which can help identify locations for wind energy farms.
Vrinda D. Desai, Alexander L. Handwerger, and Karen E. Daniels
EGUsphere, https://doi.org/10.22541/essoar.172494370.04413277/v1, https://doi.org/10.22541/essoar.172494370.04413277/v1, 2025
Short summary
Short summary
Landslide events occur when soil, rock, and debris on slopes become unstable and move downhill, often triggered by heavy rain that reduces friction. Our research evaluates landslide vulnerability using a method that analyzes the spatiotemporal dynamics of landslide-prone areas. We've developed a statistical metric to track changing conditions in these regions. This approach can aid in early warning systems, helping communities and authorities take preventive measures and minimize damage.
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, and Ethan Young
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-115, https://doi.org/10.5194/wes-2024-115, 2024
Revised manuscript under review for WES
Short summary
Short summary
Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and inter-annual trends in the wind resource to support customers interested in small and midsize wind energy.
Matthew C. Morriss, Benjamin Lehmann, Benjamin Campforts, George Brencher, Brianna Rick, Leif S. Anderson, Alexander L. Handwerger, Irina Overeem, and Jeffrey Moore
Earth Surf. Dynam., 11, 1251–1274, https://doi.org/10.5194/esurf-11-1251-2023, https://doi.org/10.5194/esurf-11-1251-2023, 2023
Short summary
Short summary
In this paper, we investigate the 28 June 2022 collapse of the Chaos Canyon landslide in Rocky Mountain National Park, Colorado, USA. We find that the landslide was moving prior to its collapse and took place at peak spring snowmelt; temperature modeling indicates the potential presence of permafrost. We hypothesize that this landslide could be part of the broader landscape evolution changes to alpine terrain caused by a warming climate, leading to thawing alpine permafrost.
Jeffrey S. Munroe and Alexander L. Handwerger
Hydrol. Earth Syst. Sci., 27, 543–557, https://doi.org/10.5194/hess-27-543-2023, https://doi.org/10.5194/hess-27-543-2023, 2023
Short summary
Short summary
Rock glaciers are mixtures of ice and rock debris that are common landforms in high-mountain environments. We evaluated the role of rock glaciers as a component of mountain hydrology by collecting water samples during the summer and fall of 2021. Our results indicate that the water draining from rock glaciers late in the melt season is likely derived from old buried ice; they further demonstrate that this water collectively makes up about a quarter of streamflow during the month of September.
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022, https://doi.org/10.5194/ascmo-8-205-2022, 2022
Short summary
Short summary
We study wind conditions and their potential future changes across the U.S. via a statistical conditional framework. We conclude that changes between historical and future wind directions are small, but wind speeds are generally weakened in the projected period, with some locations being intensified. Moreover, winter wind speeds are projected to decrease in the northwest, Colorado, and the northern Great Plains (GP), while summer wind speeds over the southern GP slightly increase in the future.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
Short summary
Short summary
In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Alexander L. Handwerger, Mong-Han Huang, Shannan Y. Jones, Pukar Amatya, Hannah R. Kerner, and Dalia B. Kirschbaum
Nat. Hazards Earth Syst. Sci., 22, 753–773, https://doi.org/10.5194/nhess-22-753-2022, https://doi.org/10.5194/nhess-22-753-2022, 2022
Short summary
Short summary
Rapid detection of landslides is critical for emergency response and disaster mitigation. Here we develop a global landslide detection tool in Google Earth Engine that uses satellite radar data to measure changes in the ground surface properties. We find that we can detect areas with high landslide density within days of a triggering event. Our approach allows the broader hazard community to utilize these state-of-the-art data for improved situational awareness of landslide hazards.
Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 6355–6372, https://doi.org/10.5194/gmd-14-6355-2021, https://doi.org/10.5194/gmd-14-6355-2021, 2021
Short summary
Short summary
Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
George Brencher, Alexander L. Handwerger, and Jeffrey S. Munroe
The Cryosphere, 15, 4823–4844, https://doi.org/10.5194/tc-15-4823-2021, https://doi.org/10.5194/tc-15-4823-2021, 2021
Short summary
Short summary
We use satellite InSAR to inventory and monitor rock glaciers, frozen bodies of ice and rock debris that are an important water resource in the Uinta Mountains, Utah, USA. Our inventory contains 205 rock glaciers, which occur within a narrow elevation band and deform at 1.94 cm yr-1 on average. Uinta rock glacier movement changes seasonally and appears to be driven by spring snowmelt. The role of rock glaciers as a perennial water resource is threatened by ice loss due to climate change.
Alexander L. Handwerger, Shannan Y. Jones, Mong-Han Huang, Pukar Amatya, Hannah R. Kerner, and Dalia B. Kirschbaum
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-315, https://doi.org/10.5194/nhess-2020-315, 2020
Manuscript not accepted for further review
Short summary
Short summary
The rapid and accurate mapping of landslides is critical for emergency response, disaster mitigation, and understanding landslide processes. Here we present a new approach to detect landslides anywhere in the world using freely available synthetic aperture radar data and open source tools in Google Earth Engine. Importantly, our methods do not require specialized processing software or training, which allows the broader hazards community to utilize these state-of-the-art remote sensing tools.
Giuseppe Buscarnera, Yanni Chen, José Lizárraga, and Ruiguo Zhang
Proc. IAHS, 382, 421–425, https://doi.org/10.5194/piahs-382-421-2020, https://doi.org/10.5194/piahs-382-421-2020, 2020
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
Short summary
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Noah J. Finnegan, Kiara N. Broudy, Alexander L. Nereson, Joshua J. Roering, Alexander L. Handwerger, and Georgina Bennett
Earth Surf. Dynam., 7, 879–894, https://doi.org/10.5194/esurf-7-879-2019, https://doi.org/10.5194/esurf-7-879-2019, 2019
Short summary
Short summary
In some settings, landslides trigger valley blockages that impound huge volumes of sediment, often drastically changing river habitat and habitability. In other settings, landslides appear to have little effect on rivers. In this study, we explore what governs the different sensitivity of rivers to blocking from landslide debris. We accomplish this by comparing two sites in California with dramatic differences in blocking from otherwise similar slow-moving landslides.
Jiali Wang, Cheng Wang, Vishwas Rao, Andrew Orr, Eugene Yan, and Rao Kotamarthi
Geosci. Model Dev., 12, 3523–3539, https://doi.org/10.5194/gmd-12-3523-2019, https://doi.org/10.5194/gmd-12-3523-2019, 2019
Short summary
Short summary
WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However, when applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed in parallel. This study ported an independent calibration tool (parameter estimation tool – PEST) to high-performance computing clusters and adapted it to work with WRF-Hydro. The results show significant speedup for model calibration.
Related subject area
Landslides and Debris Flows Hazards
From rockfall source area identification to susceptibility zonation: a proposed workflow tested on El Hierro (Canary Islands, Spain)
Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping
Topographic controls on landslide mobility: modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico
Characterizing the scale of regional landslide triggering from storm hydrometeorology
A participatory approach to determine the use of road cut slope design guidelines in Nepal to lessen landslides
An integrated method for assessing vulnerability of buildings caused by debris flows in mountainous areas
Identifying unrecognised risks to life from debris flows
Predicting the thickness of shallow landslides in Switzerland using machine learning
Unraveling landslide failure mechanisms with seismic signal analysis for enhanced pre-survey understanding
Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer
Limit analysis of earthquake-induced landslides considering two strength envelopes
The vulnerability of buildings to a large-scale debris flow and outburst flood hazard cascade that occurred on 30 August 2020 in Ganluo, southwest China
Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
Is higher resolution always better? Open-access DEM comparison for Slope Units delineation and regional landslide prediction
Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar
Size scaling of large landslides from incomplete inventories
InSAR-informed in situ monitoring for deep-seated landslides: insights from El Forn (Andorra)
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
Temporal clustering of precipitation for detection of potential landslides
Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China
Comparative Analysis of μ (I) and Voellmy-Type Grain Flow Rheologies in Geophysical Mass Flows: Insights from Theoretical and Real Case Studies
Exploring implications of input parameter uncertainties on GLOF modelling results using the state-of-the-art modelling code, r.avaflow
Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
A regional analysis of paraglacial landslide activation in southern coastal Alaska
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Assessing landslide damming susceptibility in Central Asia
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
Evaluation of debris-flow building damage forecasts
Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA
Morphological characteristics and conditions of drainage basins contributing to the formation of debris flow fans: an examination of regions with different rock strength using decision tree analysis
Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps
Simulation analysis of 3D stability of a landslide with a locking segment: a case study of the Tizicao landslide in Maoxian County, southwest China
Brief Communication: Weak correlation between building damage and loss of life from landslides
Space–time landslide hazard modeling via Ensemble Neural Networks
Optimization strategy for flexible barrier structures: investigation and back analysis of a rockfall disaster case in southwestern China
Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment
Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling
A new analytical method for stability analysis of rock blocks with basal erosion in sub-horizontal strata by considering the eccentricity effect
Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Lessons learnt from a rockfall time series analysis: data collection, statistical analysis, and applications
The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
Roberto Sarro, Mauro Rossi, Paola Reichenbach, and Rosa María Mateos
Nat. Hazards Earth Syst. Sci., 25, 1459–1479, https://doi.org/10.5194/nhess-25-1459-2025, https://doi.org/10.5194/nhess-25-1459-2025, 2025
Short summary
Short summary
This study proposes a novel systematic workflow that integrates source area identification, deterministic runout modelling, the classification of runout outputs to derive susceptibility zonation, and robust procedures for validation and comparison. The proposed approach enables the integration and comparison of different modelling, introducing a robust and consistent workflow/methodology that allows us to derive and verify rockfall susceptibility zonation, considering different steps.
Matthias Schlögl, Anita Graser, Raphael Spiekermann, Jasmin Lampert, and Stefan Steger
Nat. Hazards Earth Syst. Sci., 25, 1425–1437, https://doi.org/10.5194/nhess-25-1425-2025, https://doi.org/10.5194/nhess-25-1425-2025, 2025
Short summary
Short summary
Communicating uncertainties is a crucial yet challenging aspect of spatial modelling – especially in applied research, where results inform decisions. In disaster risk reduction, susceptibility maps for natural hazards guide planning and risk assessment, yet their uncertainties are often overlooked. We present a new type of landslide susceptibility map that visualizes both susceptibility and associated uncertainty alongside guidelines for creating such maps using free and open-source software.
Dianne L. Brien, Mark E. Reid, Collin Cronkite-Ratcliff, and Jonathan P. Perkins
Nat. Hazards Earth Syst. Sci., 25, 1229–1253, https://doi.org/10.5194/nhess-25-1229-2025, https://doi.org/10.5194/nhess-25-1229-2025, 2025
Short summary
Short summary
Landslide runout zones are the areas downslope or downstream of landslide initiation. People often live and work in these areas, leading to property damage and deaths. Landslide runout may occur on hillslopes or in channels, requiring different modeling approaches. We develop methods to identify potential runout zones and apply these methods to identify susceptible areas for three municipalities in Puerto Rico.
Jonathan Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
Nat. Hazards Earth Syst. Sci., 25, 1037–1056, https://doi.org/10.5194/nhess-25-1037-2025, https://doi.org/10.5194/nhess-25-1037-2025, 2025
Short summary
Short summary
Rainfall-induced landslides result in deaths and economic losses annually across the globe. However, it is unclear how storm severity relates to landslide severity across large regions. Here we develop a method to dynamically map landslide-affected areas, and we compare this to meteorological estimates of storm severity. We find that preconditioning by earlier storms and the location of rainfall bursts, rather than atmospheric storm strength, dictate landslide magnitude and pattern.
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
Nat. Hazards Earth Syst. Sci., 25, 949–973, https://doi.org/10.5194/nhess-25-949-2025, https://doi.org/10.5194/nhess-25-949-2025, 2025
Short summary
Short summary
Slopes excavated alongside roads in Nepal frequently fail (a landslide), resulting in substantial losses. Our participatory approach study with road engineers aimed to assess how road slope design guidelines in Nepal can be improved. Our study revealed inconsistent guideline adherence due to a lack of user-friendliness and inadequate training. We present general recommendations to enhance road slope management, as well as technical recommendations to improve the guidelines.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci., 25, 709–726, https://doi.org/10.5194/nhess-25-709-2025, https://doi.org/10.5194/nhess-25-709-2025, 2025
Short summary
Short summary
We propose an integrated method using a combination of a physical vulnerability matrix and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on collected historical data on the Qinghai–Tibet Plateau region.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
Nat. Hazards Earth Syst. Sci., 25, 647–656, https://doi.org/10.5194/nhess-25-647-2025, https://doi.org/10.5194/nhess-25-647-2025, 2025
Short summary
Short summary
Debris flows occur infrequently, with average recurrence intervals (ARIs) ranging from decades to millennia. Consequently, they pose an underappreciated hazard. We describe how to make a preliminary identification of debris-flow-susceptible catchments, estimate threshold ARIs for debris flows that pose an unacceptable risk to life, and identify the “window of non-recognition” where debris flows are infrequent enough that their hazard is unrecognised yet frequent enough to pose a risk to life.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025, https://doi.org/10.5194/nhess-25-467-2025, 2025
Short summary
Short summary
We developed a machine-learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables, including metrics on terrain, geomorphology, vegetation height, and lithology, and used data from two Swiss field inventories to calibrate and test the models. The best-performing machine learning model consistently reduced the mean average error by at least 20 % compared to previous models.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
Nat. Hazards Earth Syst. Sci., 25, 451–466, https://doi.org/10.5194/nhess-25-451-2025, https://doi.org/10.5194/nhess-25-451-2025, 2025
Short summary
Short summary
The study on the Cilan landslide (CL) demonstrates the utilization of seismic analysis results as preliminary data for geologists during field surveys. Spectrograms revealed that the first event of CL consisted of four sliding failures accompanied by a gradual reduction in landslide volume. The second and third events were minor toppling and rockfalls. Then combining the seismological-based knowledge and field survey results, the spatiotemporal variation in landslide evolution is proposed.
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci., 25, 183–206, https://doi.org/10.5194/nhess-25-183-2025, https://doi.org/10.5194/nhess-25-183-2025, 2025
Short summary
Short summary
The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
Short summary
Short summary
Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci., 25, 119–146, https://doi.org/10.5194/nhess-25-119-2025, https://doi.org/10.5194/nhess-25-119-2025, 2025
Short summary
Short summary
This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing 8 years of data from Taiwan's Lushan, improving early warning and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Di Wu, Yuke Wang, and Xin Chen
Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
Short summary
Short summary
This paper proposes a 3D limit analysis for seismic stability of soil slopes to address the influence of earthquakes on slope stabilities with nonlinear and linear criteria. Comparison results illustrate that the use of a linear envelope leads to the non-negligible overestimation of steep-slope stability, and this overestimation will be significant with increasing earthquakes. Earthquakes have a smaller influence on slope slip surfaces with a nonlinear envelope than those with a linear envelope.
Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
Short summary
Short summary
The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
Short summary
Short summary
Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Mahnoor Ahmed, Giacomo Titti, Sebastiano Trevisani, Lisa Borgatti, and Mirko Francioni
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-211, https://doi.org/10.5194/nhess-2024-211, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Elevation models are compared with a true dataset for terrain characteristics which selects a better ranking model to test with different parameters for partitioning the terrain. The partitioning of the terrain is measured by how well a partitioned unit can support the mapped landslide area and number of landslides. The effect of this relationship is reflected with different metrics in the susceptibility maps.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
Short summary
Short summary
Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
Short summary
Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
Short summary
Short summary
This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
Short summary
Short summary
The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
Short summary
Short summary
The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary
Short summary
Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
Short summary
Short summary
The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-87, https://doi.org/10.5194/nhess-2024-87, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
This study reformulates the μ(I) rheology into a Voellmy-type relationship to elucidate its physical implications. The μ(I) rheology, incorporating a dimensionless inertial number, mimics granular temperature effects, reflecting shear thinning behavior of mass flows. However, its constant Coulomb friction coefficient limits accuracy in modeling deposition. Comparing μ(I) with Voellmy-type rheologies reveals strengths and limitations, enhancing mass flow modeling and engineering applications.
Sonam Rinzin, Stuart Dunning, Rachel Carr, Ashim Sattar, and Martin Mergili
EGUsphere, https://doi.org/10.5194/egusphere-2024-1819, https://doi.org/10.5194/egusphere-2024-1819, 2024
Short summary
Short summary
We evaluated the sensitivity of model outputs to input parameter uncertainties by performing multiple GLOF simulations using the r.avaflow model. We found out that GLOF modelling outputs are highly sensitive to six parameters: volume of mass movements entering lakes, DEM datasets, origin of mass movements, mesh size, basal frictional angle, and entrainment coefficient. Future modelling should carefully consider the output uncertainty from these sensitive parameters.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
Short summary
Short summary
Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
Short summary
Short summary
Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
Short summary
Short summary
Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
Short summary
Short summary
In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1086, https://doi.org/10.5194/egusphere-2024-1086, 2024
Short summary
Short summary
In a study of eight landslides adjacent to glaciers in Alaska, we found that landslide movement increased as the glacier retreated past the landslide at four sites. Movement at other sites coincided with heavy precipitation or increased glacier thinning, and two sites showed little-to-no motion. We suggest that landslides next to water-terminating glaciers may be especially vulnerable to acceleration, which we guess is due to faster retreat rates and water replacing ice at the landslide edge.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
Short summary
Short summary
With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
Short summary
Short summary
Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
Short summary
Short summary
We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
Short summary
Short summary
Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
Short summary
Short summary
Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
Short summary
Short summary
Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
Short summary
Short summary
We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
Short summary
Short summary
We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-40, https://doi.org/10.5194/nhess-2024-40, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides- such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management on landslide risk.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
Short summary
Short summary
We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
Short summary
Short summary
We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
Short summary
Short summary
Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
Short summary
Short summary
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
Short summary
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.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
Short summary
Short summary
A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
Short summary
Short summary
Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
Short summary
Short summary
The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
Short summary
Short summary
Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
Short summary
Short summary
Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Cited articles
Alfieri, L., Salamon, P., Pappenberger, F., Wetterhall, F., and Thielen, J.:
Operational early warning systems for water-related hazards in Europe, Environ. Sci. Policy, 21, 35–49, https://doi.org/10.1016/j.envsci.2012.01.008, 2012.
Andersson, J. C. M., Arheimer, B., Traoré, F., Gustafsson, D., and Ali, A.:
Process refinements improve a hydrological model concept applied to the Niger River basin, Hydrol. Process., 31, 4540–4554, https://doi.org/10.1002/hyp.11376, 2017.
Anagnostou, M. N., Kalogiros, J., Anagnostou, E. N., Tarolli, M., Papadopoulos, A., and Borga, M: Performance evaluation of high-resolution rainfall estimation by X-band dual-polarization radar for flash flood applications in mountainous basins, J. Hydrol., 394, 4–16, https://doi.org/10.1016/j.jhydrol.2010.06.026, 2010.
Arattano, M. and Franzi, L.:
On the application of kinematic models to simulate the diffusive processes of debris flows, Nat. Hazards Earth Syst. Sci., 10, 1689–1695, https://doi.org/10.5194/nhess-10-1689-2010, 2010.
Brabb, E. E.: Innovative approaches to landslide hazard and risk mapping, in: International Landslide Symposium Proceedings, Toronto, Canada, 16–21, https://pubs.er.usgs.gov/publication/70197529
(last access: 27 June 2021), 1984.
Brown, E. K., Wang, J., and Feng, Y.:
U. S. wildfire potential: a historical view and future projection using high-resolution climate data, Environ. Res. Lett., 16, 034060, https://doi.org/10.1088/1748-9326/aba868, 2020.
Brunkal, H. and Santi, P. M.:
Exploration of design parameters for a dewatering structure for debris flow mitigation, Eng. Geol., 208, 81–92, https://doi.org/10.1016/j.enggeo.2016.04.011, 2016.
Burned Area Emergency Response: Dolan Postfire BAER Soil Burn Severity Map Release, U.S. Forest Service, https://inciweb.nwcg.gov/incident/7231/ (last access: 27 November 2021), 2020.
Bytheway, J. L., Hughes, M., Mahoney, K., and Cifelli, R.:
On the Uncertainty of High-Resolution Hourly Quantitative Precipitation Estimates in California, J. Hydrometeorol., 21, 865–879, https://journals.ametsoc.org/view/journals/hydr/21/5/jhm-d-19-0160.1.xml (last access: 27 November 2021), 2020.
Canli, E., Mergili, M., Thiebes, B., and Glade, T.:
Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology, Nat. Hazards Earth Syst. Sci., 18, 2183–2202, https://doi.org/10.5194/nhess-18-2183-2018, 2018.
Cannon, S. H., Gartner, J. E., Parrett, C., and Parise, M.:
Wildfire-related debris-flow generation through episodic progressive sediment-bulking processes, western USA, in: Debris-Flow Hazards Mitigation: Mechanics, Prediction, and Assessment, edited by: Rickenmann, D. and Chen, C. L., Davos, Switzerland, 10–12 September 2003, Millpress, Rotterdam, 71–82, 2003.
Cannon, S. H., Gartner, J., Wilson, R., Bowers, J., and Laber, J.:
Storm Rainfall Conditions for Floods and Debris Flows from Recently Burned Basins in Southwestern Colorado and Southern California, Geomorphology, 96, 250–269, https://doi.org/10.1016/j.geomorph.2007.03.019, 2008.
Cannon, S. H., Gartner, J. E., Rupert, M. G., Michael, J. A., Rea, A. H., and Parrett, C.:
Predicting the probability and volume of postwildfire debris flows in the intermountain western United States, Geol. Soc. Am. Bull., 122, 127–144, https://doi.org/10.1130/B26459.1, 2010.
Cannon, S. H., Boldt, E. M., Laber, J. L., Kean, J. W., and Staley, D. M.: Rainfall intensity–duration thresholds for postfire debris flow emergency-response planning, Nat. Hazards, 59, 209–236, https://doi.org/10.1007/s11069-011-9747-2, 2011.
Cavagnaro, D., Delgado, N., East, A. E., Finnegan, N. J., Kostelnik, J., Lindsay, D., McCoy, S. W., Suter, I., Thomas, M. A., and Winner, A.:
Variability in hydrologic response to rainfall across a burn scar: observations from the Dolan Fire, California, in: AGU Fall Meeting 2021, New Orleans, Louisiana, USA, 12–17 December 2021, H55W-1005 2021.
Chen, F. and Dudhia, J.:
Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chen, L., Berli, M., and Chief, K.:
Examining Modeling Approaches for the Rainfall-Runoff Process in Wildfire-Affected Watersheds: Using San Dimas Experimental Forest, J. Am. Water Resour. As., 49, 851–866, https://doi.org/10.1111/jawr.12043, 2013.
Claessens, L., Schoorl, J. M., and Veldkamp, A.:
Modelling the location of shallow landslides and their effects on landscape dynamics in large watersheds: An application for Northern New Zealand, Geomorphology, 87, 16–27, https://doi.org/10.1016/j.geomorph.2006.06.039, 2007.
Coe, J., Godt, J., Parise, M., and Moscariello, A.:
Estimating debris flow probability using fan stratigraphy, historic records, and drainage-basin morphology, Interstate 70 highway corridor, central Colorado, USA, Paper presented at the Debris flow Hazards Mitigation: Mechanics, Prediction, and Assessment, edited by: Rickenmann, D. and Cheng, Ch., Proceedings 3rd International DFHM Conference, Davos, Switzerland, 10–12 September 2003, 2, 1085–1096, 2003.
Coe, J. A., Kinner, D. A., and Godt, J. W. J. G.:
Initiation conditions for debris flows generated by runoff at Chalk Cliffs, central Colorado, Geomorphology, 96, 270–297, https://doi.org/10.1016/j.geomorph.2007.03.017, 2008.
Collow, A. B. M., Mersiovsky, H., Bosilovich, M. G.:
Large-Scale Influences on Atmospheric River–Induced Extreme Precipitation Events along the Coast of Washington State, J. Hydrometeorol., 21, 2139–2156, https://doi.org/10.1175/JHM-D-19-0272.1, 2020.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.:
A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils, Water Resour. Res., 20, 682–690, https://doi.org/10.1029/WR020i006p00682, 1984.
Cydzik, K. and Hogue, T. S.:
Modeling postfire response and recovery using the hydrologic engineering center hydrologic modeling system (HEC-HMS), J. Am. Water Resour. As., 45, 702–714, https://doi.org/10.1111/j.1752-1688.2009.00317.x, 2009.
Daly, C., Slater, M. E., Roberti, J. A., Laseter, S. H., and Swift Jr., L. W.:
High-resolution precipitation mapping in a mountainous watershed; Ground truth for evaluating uncertainty in a national precipitation dataset, Int. J. Climatol., 37, 124–137, https://doi.org/10.1002/joc.4986, 2017.
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio, P. N., Fiore, A., Frankignoul, C. , Fyfe, J. C., Horton, D. E., Kay, J. E., Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S., Randerson, J., Screen, J. A., Simpson, I. R., and Ting, M.:
Insights from Earth system model initial-condition large ensembles and future prospects, Nat. Clim. Change, 10, 277–286, https://doi.org/10.1038/s41558-020-0731-2, 2020. Di Cristo, C., Iervolino, M., Moramarco, T., and Vacca, A.:
Applicability of Diffusive model for mud-flows: An unsteady analysis, J. Hydrol., 600, 126512, https://doi.org/10.1016/j.jhydrol.2021.126512, 2021.
Eldardiry, H., Mahmood, A., Chen, X., Hossain, F., Nijssen, B., Lettenmaier, D. P.:
Atmospheric River–Induced Precipitation and Snowpack during the Western United States Cold Season, J. Hydrometeorol., 20, 613–630, https://doi.org/10.1175/JHM-D-18-0228.1, 2019.
Friedel, M. J.:
A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty, Environ. Modell. Softw., 26, 1583–1598, https://doi.org/10.1016/j.envsoft.2011.07.014, 2011a.
Friedel, M. J.:
Modeling hydrologic and geomorphic hazards across post-fire landscapes using a self-organizing map approach, Environ. Modell. Softw., 26, 1660–1674, https://doi.org/10.1016/j.envsoft.2011.07.001, 2011b.
Gartner, J. E., Cannon, S. H., and Santi, P. M.:
Empirical models for predicting volumes of sediment deposited by debris flows and sediment-laden floods in the transverse ranges of southern California, Eng. Geol., 176, 45–56, https://doi.org/10.1016/j.enggeo.2014.04.008, 2014.
George, D. L. and Iverson, R. M.:
A depth-averaged debris flow model that includes the effects of evolving dilatancy. II. Numerical predictions and experimental tests, P. Roy. Soc. A-Math. Phy., 470, 20130820. https://doi.org/10.1098/rspa.2013.0820, 2014.
Germann, U., Galli, G., Boscacci, M., and Bolliger, M.:
Radar Precipitation Measurement in a Mountainous Region, Q. J. Roy. Meteor. Soc., 132, 1669–1692, https://doi.org/10.1256/qj.05.190, 2007.
Gochis, D. J. and Chen, F.:
Hydrological Enhancements to the Community Noah Land Surface Model (No. NCAR/TN-454+STR), University Corporation for Atmospheric Research, NCAR, technical report, https://doi.org/10.5065/D60P0X00, 2003.
Goodrich, D., Burns, I., Unkrich, C., Semmens, D., Guertin, D., Hernandez, M., Yatheendradas, S., Kennedy, J. R., and Levick, L.:
KINEROS2/AGWA: model use, calibration, and validation, T. ASABE, 55, 1561–1574, https://doi.org/10.13031/2013.42264, 2012.
Goss, M., Swain, D. L., Abatzoglou, J. T., Sarhadi, A., Kolden, C. A., Williams, A. P., and Diffenbaugh, N. S.:
Climate change is increasing the likelihood of extreme autumn wildfire conditions across California, Environ. Res. Lett., 15, 094016, https://doi.org/10.1088/1748-9326/ab83a7, 2020.
Gregoretti, C. and Dalla Fontana, G.:
The triggering of debris flow due to channel-bed failure in some alpine headwater basins of the Dolomites: analyses of critical runoff, Hydrol. Process., 22, 2248–2263, https://doi.org/10.1002/hyp.6821, 2008.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.:
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., and Ardizzone, F. J. G.:
Probabilistic landslide hazard assessment at the basin scale, Geomorphology, 72, 272–299 https://doi.org/10.1016/j.geomorph.2005.06.002, 2005.
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.
Hapuarachchi, H. A. P., Wang, Q. J., and Pagano, T. C.:
A review of advances in flash flood forecasting, Hydrol. Process., 25, 2771–2784 https://doi.org/10.1002/hyp.8040, 2011.
Hecht, C. W. and Cordeira, J. M.:
Characterizing the influence of atmospheric river orientation and intensity on precipitation distributions over North Coastal California, Geophys. Res. Lett., 44, 9048–9058, https://doi.org/10.1002/2017GL074179, 2017.
Huang, M. and Handwerger, A. L.: GEE_scripts_for_Handwerger_et_al_2022_NHESS, GitHub [code], https://github.com/alhandwerger/GEE_scripts_for_Handwerger_et_al_2022_NHESS, last access: 27 June 2022.
Huang, X., Stevenson, S., and Hall, A. D.:
Future warming and intensification of precipitation extremes: A “double whammy” leading to increasing flood risk in California, Geophys. Res. Lett., 47, e2020GL088679, https://doi.org/10.1029/2020GL088679, 2020a.
Huang, X., Swain, D. L., and Hall, A. D.:
Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California, Sci. Adv., 6, eaba1323, https://doi.org/10.1126/sciadv.aba1323, 2020b.
Jolly, W. M., Cochrane, M. A., Freeborn, P. H., Holden, Z. A., Brown, T. J., Williamson, G. J., and Bowman, D. M. J. S.:
Climate-induced variations in global wildfire danger from 1979 to 2013, Nat. Commun., 6, 7537, https://doi.org/10.1038/ncomms8537, 2015.
Julien, P. Y., Saghafian, B., and Ogden, F. L.:
Raster-based hydrologic modeling of spatially-varied surface runoff, J. Am. Water Resour. As., 31, 523–536, https://doi.org/10.1111/j.1752-1688.1995.tb04039.x, 1995.
Kean, J. W. and Staley, D. M.:
Forecasting the Frequency and Magnitude of Postfire Debris Flows Across Southern California, Earths Future, 9, e2020EF001735, https://doi.org/10.1029/2020EF001735, 2021.
Kean, J. W., Staley, D. M., and Cannon, S. H.:
In situ measurements of postfire debris flows in southern California: Comparisons of the timing and magnitude of 24 debris flow events with rainfall and soil moisture conditions, J. Geophys. Res.-Earth, 116, F04019, https://doi.org/10.1029/2011JF002005, 2011.
Kling, H., Fuchs, M., and Paulin, M.:
Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Lahmers, T. M., Gupta, H., Castro, C. L., Gochis, D. J., Yates, D., Dugger, A., Goodrich, D., and Hazenberg, P.: Enhancing the structure of the WRF-Hydro hydrologic model for semiarid environments, J. Hydrometeorol, 20, 691–714, https://doi.org/10.1175/JHM-D-18-0064.1, 2019.
Larsen, I. J., MacDonald, L. H., Brown, E., Rough, D., Welsh, M. J., Pietraszek, J. H., Libohova, Z., de Dios Benavides-Solorio, J., and Schaffrath, K.:
Causes of Postfire Runoff and Erosion: Water Repellency, Cover, or Soil Sealing?, Soil Sci. Soc. Am. J., 73, 1393–1407, https://doi.org/10.2136/sssaj2007.0432, 2009.
Leopold, L. B., Wolman, M. G., and Miller, J. P.:
Fluvial Processes in Geomorphology, W. H. Freeman, New York, Courier Dover Publications, 1964.
Li, C.: Use of WRF-Hydro in postfire debris-flow hazard simulation, Zenodo [data set], https://doi.org/10.5281/zenodo.5544083, 2021.
Li, C.: Modification of WRF-Hydro source code to output overland flow, Zenodo [code], https://doi.org/10.5281/zenodo.6766928, 2022.
MacDonald, L. H. and Huffman, E. L.:
Postfire soil water repellency: Persistence and soil moisture thresholds, Soil Sci. Soc. Am. J., 68, 1729–1734, https://doi.org/10.2136/sssaj2004.1729, 2004.
Martin, D. and Moody, J.:
Comparison of Soil Infiltration Rates in Burned and Unburned Mountainous Watersheds, Hydrol. Process., 15, 2893–2903, https://doi.org/10.1002/hyp.380, 2001.
McCormick, B. C., Eshleman, K. N., Griffith, J. L., and Townsend, P. A.:
Detection of flooding responses at the river basin scale enhanced by land use change, Water Resour. Res., 45, W08401, https://doi.org/10.1029/2008WR007594, 2009.
McGuire, L. A. and Youberg, A. M.:
What drives spatial variability in rainfall intensity-duration thresholds for post-wildfire debris flows? Insights from the 2018 Buzzard Fire, NM, USA, Landslides, 17, 2385–2399, https://doi.org/10.1007/s10346-020-01470-y, 2020.
McGuire, L. A., Kean, J. W., Staley, D. M., Rengers, F. K., and Wasklewicz, T. A.:
Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales, J. Geophys. Res.-Earth, 121, 2211–2237, https://doi.org/10.1002/2016JF003867, 2016.
McGuire, L. A., Rengers, F. K., Kean, J. W., and Staley, D. M.:
Debris flow initiation by runoff in a recently burned basin: Is grain-by-grain sediment bulking or en masse failure to blame?, Geophys. Res. Lett., 44, 7310–7319, https://doi.org/10.1002/2017GL074243, 2017.
McMichael, C. E. and Hope, A. S.:
Predicting streamflow response to fire-induced landcover change: implications of parameter uncertainty in the MIKE SHE model, J. Environ. Manage., 84, 245–256, https://doi.org/10.1016/j.jenvman.2006.06.003, 2007.
National Aeronautics and Space Administration (NASA), and the European Space Agency (ESA): Copernicus program, remote sensing data, Google Earth Engine, https://earthengine.google.com/, last access: 27 June 2021.
National Centers for Environmental Prediction (NCEP): Multi-Radar/Multi-Sensor (MRMS) Precipitation Data, Version 1.0, UCAR/NCAR, Earth Observing Laboratory [data set], https://doi.org/10.26023/4WM5-Y4PF-8D03, 2018.
National Interagency Fire Center (NCEP): WFIGS – Wildland Fire Perimeters Full History, National Interagency Fire Center [data set], https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-wildland-fire-perimeters-full-history/explore?location=0.000000,0.000000,1.81 (last acces: 28 June 2022),
2021.
Neiman, P. J., Ralph, F. M., Wick, G. A., Lundquist, J. D., and Dettinger, M. D.:
Meteorological Characteristics and Overland Precipitation Impacts of Atmospheric Rivers Affecting the West Coast of North America Based on Eight Years of SSM/I Satellite Observations, J. Hydrometeorol., 9, 22–47, https://doi.org/10.1175/2007JHM855.1, 2008.
Miller, D. A. and White, R. A.:
A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling, Earth Interact., 2, 1–26, https://doi.org/10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2, 1998.
Moody, J. A., Shakesby, R. A., Robichaud, P. R., Cannon, S. H., and Martin, D. A.:
Current research issues related to post-wildfire runoff and erosion processes, Earth-Sci Rev., 122, 10–37. https://doi.org/10.1016/j.earscirev.2013.03.004, 2013.
Nash, J. E. and Sutcliffe, J. V.:
River flow forecasting through conceptual models part I — A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
NOAA Physical Sciences Laboratory (PSL): Surface Meteorology and Physics Data, NOAA [data set], https://psl.noaa.gov/data/obs/datadisplay/, last access: 28 June 2022.
Nikolopoulos, E. I., Destro, E., Bhuiyan, M. A. E., Borga, M., and Anagnostou, E. N.:
Evaluation of predictive models for post-fire debris flow occurrence in the western United States, Nat. Hazards Earth Syst. Sci., 18, 2331–2343, https://doi.org/10.5194/nhess-18-2331-2018, 2018.
Nikolopoulos, E. I., Schwartz, C., Zhang, X, and Anagnostou, E. N.:
Rainfall estimation uncertainty and early warning procedures for
post-fire debris flows, EGU General Assembly, Vienna, Austria, 7–12 April 2019, Abstracts, 2019.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek,
M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.:
The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Oakley, N. S.:
A warming climate adds complexity to postfire hydrologic hazard planning, Earths Future, 9, e2021EF002149, https://doi.org/10.1029/2021EF002149, 2021.
Oakley, N. S., Lancaster, J. T., Kaplan, M. L., and Ralph, F. M.:
Synoptic conditions associated with cool season post-fire debris flows in the Transverse Ranges of southern California, Nat. Hazards, 88, 327–354 https://doi.org/10.1007/s11069-017-2867-6, 2017.
Oakley, N. S., Cannon, F., Munroe, R., Lancaster, J. T., Gomberg, D., and Ralph, F. M.:
Brief communication: Meteorological and climatological conditions associated with the 9 January 2018 post-fire debris flows in Montecito and Carpinteria, California, USA, Nat. Hazards Earth Syst. Sci., 18, 3037–3043, https://doi.org/10.5194/nhess-18-3037-2018, 2018.
Ogden, F. L.:
CASC2D reference manual, Department of Civil & Environmental Engineering, University of Connecticut, Storrs, 1997.
Palmer, J.:
The devastating mudslides that follow forest fires, Nature, 601, 184–186, https://www.nature.com/articles/d41586-022-00028-3 (last access: 27 November 2021), 2022.
Parise, M. and Cannon, S. H.:
The effects of wildfires on erosion and debris-flow generation in Mediterranean climatic areas: a first database, Proceedings of 1st World Landslide Forum, Tokyo, Japan, 18–21 November 2008, 465–468, 2008.
Parise, M. and Cannon, S. H.:
A database on post-fire erosion rates and debris flows in Mediterranean-Basin watersheds, EGU General Assembly, Vienna, Austria, 19–24 April 2009, Abstracts, p. 1530, 2009.
Pietsch, B.:
Part of Highway 1 in California Falls Into the Ocean, The New York Times, https://www.nytimes.com/2021/01/30/us/highway-one-mudslide.html (last access: 27 November 2021), 2021.
Polade, S. D., Gershunov, A., Cayan, D.R, Dettinger, M. D., and Pierce, D. W.:
Precipitation in a warming world: Assessing projected hydro-climate changes in California and other Mediterranean climate regions, Sci. Rep.-UK, 7, 10783, https://doi.org/10.1038/s41598-017-11285-y, 2017.
Qi, Y., Martinaitis, S., Zhang, J., and Cocks, S.:
A real-time automated quality control of hourly rain gauge data based on multiple sensors in MRMS system, J. Hydrometeorol., 17, 1675–1691, https://doi.org/10.1175/JHM-D-15-0188.1, 2016.
Raia, S., Alvioli, M., Rossi, M., Baum, R. L., Godt, J. W., and Guzzetti, F.:
Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach, Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, 2014.
Ralph, F. M., Neiman, P. J., and Wick, G. A.:
Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98, Mon. Weather Rev., 132, 1721–1745, https://doi.org/10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2, 2004.
Regmi, N. R., Giardino, J. R., and Vitek, J. D.:
Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA, Geomorphology, 115, 172–187, https://doi.org/10.1016/j.geomorph.2009.10.002, 2010.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.:
A review of statistically-based landslide susceptibility models, Earth-Sci. Rev., 180, 60–91 https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
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.
Reneau, S. L., Katzman, D., Kuyumjian, G. A., Lavine, A., and Malmon, D. V.:
Sediment delivery after a wildfire, Geology, 35, 151–154, https://doi.org/10.1130/G23288A.1, 2007.
Reynolds, C.:
Highway 1 washout near Big Sur expected to be fixed by summer, Los Angeles Times, https://www.latimes.com/travel/story/2021-02-25/highway-1-to-big-sur-will-reopen-by-summer-caltrans-says (last access: 27 November 2021), 2021.
Robichaud, P. R.: Fire and erosion: evaluating the effectiveness of a post-fire rehabilitation treatment, contour-felled logs, in: Watershed Management and Operations Management 2000, 1–11, https://doi.org/10.1061/40499(2000)36, 2000.
Rosso, R., Rulli, M. C., and Bocchiola, D.:
Transient catchment hydrology after wildfires in a Mediterranean basin: runoff, sediment and woody debris, Hydrol. Earth Syst. Sci., 11, 125–140, https://doi.org/10.5194/hess-11-125-2007, 2007.
Rulli, M. C. and Rosso, R.:
Hydrologic response of upland catchments to wildfires, Adv. Water Resour., 30, 2072–2086, https://doi.org/10.1016/j.advwatres.2006.10.012, 2007.
Santi, P. M. and Morandi, L.:
Comparison of debris flow volumes from burned and unburned areas, Landslides, 10, 757–769, 2013.
Santi, P. M., deWolfe, V. G., Higgins, J. D., Cannon, S. H., and Gartner, J. E.:
Sources of debris flow material in burned areas, Geomorphology, 96, 310–321, https://doi.org/10.1016/j.geomorph.2007.02.022, 2008.
Schaefli, B. and Gupta, H. V.:
Do Nash values have value?, Hydrol. Process., 21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007.
Scheip, C., and Wegmann, K.: HazMapper: v1.0 source code (Version 1.0), Zenodo [code], https://doi.org/10.5281/zenodo.4103348, 2020.
Scheip, C. M. and Wegmann, K. W.:
HazMapper: a global open-source natural hazard mapping application in Google Earth Engine, Nat. Hazards Earth Syst. Sci., 21, 1495–1511, https://doi.org/10.5194/nhess-21-1495-2021, 2021.
Shakesby, R. A. and Doerr, S. H.:
Wildfire as a hydrological and geomorphological agent, Earth-Sci. Rev., 74, 269–307, https://doi.org/10.1016/j.earscirev.2005.10.006, 2006.
Shen, H., Lynch, B.., Poulsen, C. J., and Yanites, B. J.:
A modeling framework (WRF-Landlab) for simulating orogen-scale climate-erosion coupling, Comput. Geosci., 146, 104625, https://doi.org/10.1016/j.cageo.2020.104625, 2021.
Staley, D. M., Negri, J. A., Kean, J. W., Laber, J. M., Tillery, A. C., and Youberg, A. M.: Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States: U.S. Geological Survey Open-File Report, 2016–1106, 13 pp., https://doi.org/10.3133/ofr20161106, 2016.
Staley, D. M., Negri, J. A., Kean, J. W., Laber, J. L., Tillery, A. C., and Youberg, A. M.:
Prediction of spatially explicit rainfall intensity–duration thresholds for post-fire debris-flow generation in the western United States, Geomorphology, 278, 149–162 https://doi.org/10.1016/j.geomorph.2016.10.019, 2017.
Stoof, C. R., Vervoort, R. W., Iwema, J., van den Elsen, E., Ferreira, A. J. D., and Ritsema, C. J.:
Hydrological response of a small catchment burned by experimental fire, Hydrol. Earth Syst. Sci., 16, 267–285, https://doi.org/10.5194/hess-16-267-2012, 2012.
Swain, D. L.:
A Shorter, Sharper Rainy Season Amplifies California Wildfire Risk, Geophys. Res. Lett., 48, e2021GL092843, https://doi.org/10.1029/2021GL092843, 2021.
Swain, D. L., Langenbrunner, B., Neelin, J. D., and Hall, A.:
Increasing precipitation volatility in twenty-first-century California, Nat. Clim. Change, 8, 427–433 https://doi.org/10.1038/s41558-018-0140-y, 2018.
Tang, H., McGuire, L. A., Rengers, F. K., Kean, J. W., Staley, D. M., and Smith, J. B.:
Developing and testing physically based triggering thresholds for runoff-generated debris flows, Geophys. Res. Lett., 46, 8830–8839. https://doi.org/10.1029/2019GL083623, 2019a.
Tang, H., McGuire, L. A., Rengers, F. K., Kean, J. W., Staley, D. M., and Smith, J. B.:
Evolution of debris-flow initiation mechanisms and sediment sources during a sequence of postwildfire rainstorms, J. Geophys. Res.-Earth, 124, 1572–1595. https://doi.org/10.1029/2018JF004837, 2019b.
Tognacca, C., Bezzola, G. R., and Minor, H. E.:
Threshold criterion for debris-flow initiation due to channel-bed failure, in: Debris-flow hazards mitigation: Mechanics, prediction and assessment, edited by: edited by:
Wieczorek, G. F. and Naeser, N. D., A.A.Balkema, Brookfield, the
Netherlands, 89–97, 2000.
U.S. Geological Survey (USGS): National Water Information System data, USGS Water Data for the Nation, USGS [data set], https://doi.org/10.5066/F7P55KJN, 2016.
U.S. Geological Survey (USGS): Postfire Debris flow Hazards: Dolan Fire (Los Padres National Forest, CA), Landslide Hazards Program, USGS,
https://landslides.usgs.gov/hazards/postfire_debrisflow/detail.php?objectid=312 (last access: 27 September 2021), 2021.
Wang, J., Wang, C., Rao, V., Orr, A., Yan, E., and Kotamarthi, R.:
A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the midwestern United States, Geosci. Model Dev., 12, 3523–3539, https://doi.org/10.5194/gmd-12-3523-2019, 2019.
Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman-Morales, J., Bishop, D. A., Balch, J. K., and Lettenmaier, D. P.:
Observed Impacts of Anthropogenic Climate Change on Wildfire in California, Earths Future, 7, 892–910, https://doi.org/10.1029/2019EF001210, 2019.
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.: NCEP/EMC, NLDAS Primary Forcing Data L4 Hourly 0.125×0.125 degree V002, Edited by David Mocko, NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/6J5LHHOHZHN4, 2009.
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.:
Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow, J. Geophys. Res-Atmos., 117, D03110, https://doi.org/10.1029/2011JD016051, 2012.
Young, A. M., Skelly, K. T., and Cordeira, J. M.:
High-impact hydrologic events and atmospheric rivers in California: An investigation using the NCEI Storm Events Database, Geophys. Res. Lett., 44, 3393–3401 https://doi.org/10.1002/2017GL073077, 2017.
Yin, D., Xue, Z. G., Gochis, D. J., Yu, W., Morales, M., and Rafieeinasab, A.:
A Process-Based, Fully Distributed Soil Erosion and Sediment Transport Model for WRF-Hydro, Water, 12, 1840, https://doi.org/10.3390/w12061840, 2020.
Zhang, J., Howard, K., Langston, C., Vasiloff, S., Kaney, B., Arthur, A., Van Cooten, S., Kelleher, K., Kitzmiller, D., Ding, F., Seo, D., Wells, E., and Dempsey, C.:
National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans, B. Am. Meteorol. Soc., 92, 1321–1338, https://doi.org/10.1175/2011BAMS-D-11-00047.1, 2011.
Zhang, J., Qi, Y., Langston, C., Kaney, B., and Howard, K.:
A real-time algorithm for merging radar QPEs with rain gauge observations and orographic precipitation climatology, J. Hydrometeorol., 15, 1794–1809, https://doi.org/10.1175/JHM-D-13-0163.1, 2014.
Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J., and Kitzmiller, D.:
Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities, B. Am. Meteorol. Soc., 97, 621–638, https://doi.org/10.1175/BAMS-D-14-00174.1, 2016.
Zhang, S., Zhao, L., Delgado-Tellez, R., and Bao, H.:
A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale, Nat. Hazards Earth Syst. Sci., 18, 969–982, https://doi.org/10.5194/nhess-18-969-2018, 2018.
Zhu, Y. and Newell, R. E.:
A Proposed Algorithm for Moisture Fluxes from Atmospheric Rivers, Mon. Weather Rev., 126, 725–735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2, 1998.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(26696 KB) - Full-text XML
Short summary
In January 2021 a storm triggered numerous debris flows in a wildfire burn scar in California. We use a hydrologic model to assess debris flow susceptibility in pre-fire and postfire scenarios. Compared to pre-fire conditions, postfire conditions yield dramatic increases in peak water discharge, substantially increasing debris flow susceptibility. Our work highlights the hydrologic model's utility in investigating and potentially forecasting postfire debris flows at regional scales.
In January 2021 a storm triggered numerous debris flows in a wildfire burn scar in California....
Altmetrics
Final-revised paper
Preprint