Integration of observed and model-derived groundwater levels in landslide threshold models in Rwanda
| Autor: | J. Uwihirwe, M. Hrachowitz, T. Bogaard |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Natural Hazards and Earth System Sciences, Vol 22, Pp 1723-1742 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1561-8633 1684-9981 |
| DOI: | 10.5194/nhess-22-1723-2022 |
| Popis: | The 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 and (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 %. The 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 is thus 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 can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence. |
| Databáze: | Directory of Open Access Journals |
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