Michael D. Morphew | Geology Reports

USGS2021

HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation

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.

Water↗

USGS2018

Developing hydro-meteorological thresholds for shallow landslide initiation and early warning

Consistent relations between shallow landslide initiation and associated rainfall characteristics remain difficult to identify, due largely to the complex hydrological and geological processes causing slopes to be predisposed to failure and those processes that subsequently trigger failures. Considering the importance of hillslope hydrology for rainfall-induced landsliding, we develop and test a method for identifying hybrid hydro-meteorological thresholds to assess landslide initiation potential. We outline a series of steps for using a landslide inventory in combination with triggering rainfall and antecedent wetness to identify empirical thresholds that can inform landslide early warning systems. The method is semi-automated but remains flexible enough to allow threshold developers to consider data inputs and various performance metrics with different priorities for balancing failed versus false alarms. We demonstrate the utility of our approach for two monitoring sites near Seattle, Washington and in Portland, Oregon, USA, to develop daily bilinear thresholds within a two-dimensional parameter space, which rely on accurate 24 h forecasts, measured recent rainfall and in situ soil saturation. Although there were no prior landslide thresholds for Portland, our new hybrid threshold for the Seattle area outperforms established rainfall-only thresholds for the same region. Introducing subsurface hydrologic monitoring into landslide initiation thresholds has the potential to greatly improve early warning capabilities and help reduce losses.

Oregon, Washington↗