Cross-scale evaluation of catchment- and global-scale hydrological model simulations of drought characteristics across eight large river catchments

Abstract Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated runoff simulations from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed monthly runoff and hydrological droughts over 31 years (1971–2001). Observed and simulated hydrological drought events were identified using the Standardised Runoff Index (SRI) and were classified based on intensity. Our results show that for all eight catchments, CHMs out-performed GHMs in monthly runoff estimation showing a better representation of observed runoff than GHMs. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means present limited ability to accurately simulate severe drought events in all eight catchments, in terms of their timing and intensity. By analysing the monthly runoff time-series for several extreme droughts over the historical period, we identify room for improvement in the models so that extreme droughts may ultimately be better represented by both CHMs and GHMs.