Parameter transferability of a distributed hydrological model to droughts

Abstract. Hydrological models often have issues in simulating streamflow (Q) during droughts, because of hard-to-capture feedback mechanisms across precipitation deficit, actual evapotranspiration (ET), and terrestrial water storage anomalies (TWSA). To gain more insights into these performance drops and move toward more robust hydrological models in the anthropogenic era, we evaluated Q, ET, and TWSA simulations during droughts of different severity and their sensitivity to the climatic conditions of the calibration period. We used the distributed hydrological model Continuum over the heavily human-affected Po river basin (northern Italy, period 2010–2022) and independent ground- and remote sensing-based datasets of Q, ET, and TWSA as benchmarks. Across the 38 study sub-catchments, Continuum simulated Q comparably well during wet years (2014 and 2020) and moderate droughts (2012 and 2017) with mean KGE = 0.59±0.32 during wet years and = 0.55±0.25 during moderate droughts. The model simulated well Q for the outlet section of the basin also for the severe 2022 drought (KGE = 0.82). However, performances for 2022 declined across the other sub-catchments (mean KGE = 0.18±0.69, meaning the model still preserved some skill over a climatological mean). The model properly represented seasonality of Q, ET, and TWSA over the basin, as well as a declining trend in TWSA. We explained the performance drops in 2022 with an increased uncertainty in ET anomalies, in particular in human-affected croplands. Calibrating during a moderate drought (2017) did not improve model performances during the severe 2022 drought (mean KGE = 0.18±0.63), pointing to the fairly unique conditions of this period in terms of hydrological processes and human interference on the hydrological cycle. By highlighting increased uncertainty of hydrological models specifically during severe droughts which are expected to increase in frequency, these findings provide relevant guidelines for assessments of model robustness in a changing climate and so for informing water management, disaster risk reduction, and climate change adaptation strategies.