Modelling extreme pore-water pressures in clay under climate change for landslide risk assessment: the Göta Älv river valley, Sweden

The valley of the River Göta Älv in southwest Sweden is highly prone to landslides due to the presence of underlaying clay deposits. Landslides occur when driving forces and moments exceed resisting forces and moments, and this balance can be altered by elevated pore-water pressures thereby reducing effective stress. Pore pressure varies with climatic drivers (precipitation and evapotranspiration) and with changes in external loading, such as river stage fluctuations.Here we apply the impulse–response modelling framework Pastas to calibrate, validate, and simulate pore-pressure time series from piezometers installed along the Göta Älv valley. Since 2019, pore pressure has been monitored at multiple locations and depths. Model calibration used daily precipitation, temperature, potential evapotranspiration (PET), and river level time series. In total, 127 pore-pressure series were modelled using multiple combinations of impulse response functions and evapotranspiration formulations. Based on calibration and validation performance, 58 series were deemed suitable for climate-driven simulations. The most common causes of unsatisfactory model performance were (i) failure to reproduce rapid responses, (ii) threshold-like behaviour leading to underestimation of extreme high levels, (iii) short records limiting representation of inter-annual variability, (iv) shifted dynamics from the calibration to the validation period, and (v) potential outliers related to initialization of pressure sensors, measurement errors, or gas intrusion in instruments.The acceptable models were forced using the CMIP5 ensemble of EURO-CORDEX regional climate model (RCM) simulations for 1970–2100 (daily precipitation and temperature). Extreme high pore-pressure levels were quantified as 100-year return levels using a generalized extreme value (GEV) distribution under RCP8.5. For most series, the projected median change in the 100-year return level by 2100 is