Unusual catchment runoff in a high alpine karst environment influenced by a complex geological setting (Northern Calcareous Alps, Tyrol, Austria)

Garber Schlag (Q-GS) is one of the major springs of the Karwendel Mountains, Tyrol, Austria. This spring has a unique runoff pattern that is mainly controlled by the tectonic setting. The main aquifer is a moderately karstified and jointed limestone of the Wetterstein Formation that is underlain by nonkarstified limestone of the Reifling Formation, which acts as an aquitard. The aquifer and aquitard of the catchment of spring Q-GS form a large anticline that is bound by a major fault (aquitard) to the north. Discharge of this spring shows strong seasonal variations with three recharge origins, based on δ18O and electrical conductivity values. A clear seasonal trend is observed, caused by the continuously changing portions of water derived from snowmelt, rainfall and groundwater. At the onset of the snowmelt period in May, the discharge is composed mainly of groundwater. During the maximum snowmelt period, the water is dominantly composed of water derived from snowmelt and subordinately from rainfall. During July and August, water derived from snowmelt continuously decreases and water derived from rainfall increases. During September and October, the water released at the spring is mainly derived from groundwater and subordinately from rainfall. The distinct discharge plateau from August to December and the following recession until March is likely related to the large regional groundwater body in the fissured and moderately karstified aquifer of the Wetterstein Formation and the tectonic structures (anticline, major fault). Only a small portion of the water released at spring Q-GS is derived from permafrost.

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

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.