Preprints
https://doi.org/10.5194/nhess-2021-222
https://doi.org/10.5194/nhess-2021-222

  02 Aug 2021

02 Aug 2021

Review status: this preprint is currently under review for the journal NHESS.

Integration of observed and model derived groundwater levels in landslide threshold models in Rwanda

Judith Uwihirwe1,2, Markus Hrachowitz1, and Thom Bogaard1 Judith Uwihirwe et al.
  • 1Section Hydrology, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, PO Box 5048, 2600 GA, Delft, The Netherlands
  • 2Department of Irrigation and Drainage, School of Agricultural Engineering, College of Agriculture Animal Sciences and Veterinary Medicine, University of Rwanda, PO Box 210, Musanze, Rwanda

Abstract. Incorporation of specific regional hydrological characteristics in empirical statistical landslide threshold models has considerable potential to improve the quality of landslide predictions towards reliable early warning systems. The objective of this research was to test the value of regional groundwater level information, as a proxy for water storage fluctuations, to improve regional landslide predictions with empirical models based on the concept of threshold levels. Specifically, we investigated: i) the use of a data driven time series approach to model the regional groundwater levels based on short duration monitoring observations; ii) the predictive power of single variable and bilinear threshold landslide prediction models derived from groundwater levels and precipitation. Based on statistical measures of the model fit (R2 and RMSE), the groundwater level dynamics estimated by the transfer function noise time series model are broadly consistent with the observed groundwater levels. The single variable threshold models derived from groundwater levels exhibited the highest landslide prediction power with 82–93 % of true positive alarms despite the quite high rate of false alarms with about 26–38 %. Further combination as bilinear threshold models reduced the rate of false alarms by about 18–28 % at the expense of reduced true alarms by about 9–29 % and thus, being less advantageous than single variable threshold models. In contrast to precipitation based thresholds, relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.

Judith Uwihirwe et al.

Status: open (until 13 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-222', Roberto Valentino, 12 Sep 2021 reply
    • AC1: 'Reply on RC1', Judith Uwihirwe, 14 Sep 2021 reply

Judith Uwihirwe et al.

Judith Uwihirwe et al.

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Short summary
This research tested the value of regional groundwater level information to improve landslide predictions with empirical models based on the concept of threshold levels. In contrast to precipitation based thresholds, the results indicated that relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.
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