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
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Wind Energ. Sci., 10, 1551–1574, https://doi.org/10.5194/wes-10-1551-2025, https://doi.org/10.5194/wes-10-1551-2025, 2025
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Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and interannual trends in the wind resource in coastal locations to support customers interested in small and midsize wind energy.
Lara Tobias-Tarsh, Chunyong Jung, Jiali Wang, Vishal Bobde, Akintomide A. Akinsanola, and V. Rao Kotamarthi
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We use a high-resolution regional climate model to better understand hurricanes in the North Atlantic over the past 20 years. The model closely matches observed storm frequency and captures stronger storms more accurately than traditional datasets. It also shows better performance in areas with limited data, like the Caribbean. These results can help improve local storm preparedness and planning for critical infrastructure.
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
Revised manuscript under review for WES
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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
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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
Revised manuscript accepted for WES
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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.
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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....
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