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Technical Note

HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation

1
U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO 80401, USA
2
Colorado School of Mines, Golden, CO 80401, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Samuele Segoni
Water 2021, 13(13), 1752; https://doi.org/10.3390/w13131752
Received: 21 May 2021 / Revised: 21 June 2021 / Accepted: 22 June 2021 / Published: 25 June 2021
(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)
Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems. View Full-Text
Keywords: software; landslide early warning systems; hydrometeorological thresholds; automated optimization software; landslide early warning systems; hydrometeorological thresholds; automated optimization
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MDPI and ACS Style

Conrad, J.L.; Morphew, M.D.; Baum, R.L.; Mirus, B.B. HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation. Water 2021, 13, 1752. https://doi.org/10.3390/w13131752

AMA Style

Conrad JL, Morphew MD, Baum RL, Mirus BB. HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation. Water. 2021; 13(13):1752. https://doi.org/10.3390/w13131752

Chicago/Turabian Style

Conrad, Jacob L., Michael D. Morphew, Rex L. Baum, and Benjamin B. Mirus 2021. "HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation" Water 13, no. 13: 1752. https://doi.org/10.3390/w13131752

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