Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation using HydroMet

Abstract:

Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, landslide warning systems 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. We present HydroMet, a code developed in Python by the U.S. Geological Survey Landslide Hazards Program, 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 options, including controlling the format of the threshold equation, 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 failed alarms and a less conservative threshold to minimize false alarms. Users may also split their inventory data into calibration and evaluation subsets to further test the performance and accuracy of the decisions made during threshold development. We present several applications of HydroMet using datasets from landslide-prone areas in different U.S. States and Territories to demonstrate its utility and ability to produce thresholds with limited failed and false alarms for informing the next generation of reliable landslide warning systems.