Evaluating Flexible Configurations of the Shyft Hydrologic Model Framework Across Mainland Norway

Abstract. The development and application of numerous hydrological models have played an indispensable role in advancing our understanding of hydrological processes, improving forecasting capabilities, supporting the design and operation of water conservancy projects, and facilitating water resource assessments. However, due to the spatial heterogeneity and temporal variability of climate and basin characteristics, the inherent complexity of hydrological processes, and data limitations, hydrological modeling faces two major bottlenecks: first, no single model is universally applicable to all river basins; second, further improvement in simulation accuracy of existing fixed-structure models remain challenging. As a result, the emergence of hydrological modeling frameworks with flexible structures and configurable components represents the next generation in the model development. Shyft is one of such flexible modeling frameworks fulfilling the above-mentioned purpose. It is cross platform and open source, jointly developed by academic and industrial partners. The framework allows uncertainty analysis, streamflow simulations, and forecasting. Most evaluation efforts of the framework to date have focused on smaller basins, but there is also a need to benchmark model performance more comprehensively. Here, we present a public benchmark for discharge simulation for 109 catchments across mainland Norway. Five model configurations are evaluated containing two different evapotranspiration routines (Priestley-Taylor and Penman-Monteith), two runoff methods (Kirchner and HBV) and two snow modules (temperature-index and semi-physical). The models are calibrated with 10 variants of target goal functions: KGE-based family, consisting of KGE, LKGE, bcKGE, KGE_LKGE, KGE_bcKGE, and NSE-based family, with NSE, LNSE, bcNSE, NSE_LNSE, NSE_bcNSE. The simulations are divided into two major groups: without precipitation correction and with precipitation correction. The evaluation is performed from 1981 to 2020 (approx. 40 years) at a daily time step. Using KGE, NSE and percent bias (PBIAS) as main evaluation metrics, the model configurations are compared against each other and against climatological benchmarks. The results show that all selected models were able to beat both mean and median flow benchmarks for the majority of catchments in all the target goal function set ups. 89 % of catchments gain higher performance scores with precipitation correction, but the picture is mixed for different metrics and models. The KGE and NSE performance metrics reveal that models, which combine temperature-index snow-tiles model and Kirchner runoff (-STK), perform best, but require precipitation correction to improve PBIAS. The models, which have semi-physical gamma-snow routine (-GSK), show relatively low performance with KGE and NSE scores, especially in Mountain and Inland hydrological regimes, but have the lowest |PBIAS|if no precipitation correction is applied. Precipitation correction shows limited effect on the -GSK models, even deteriorating some of the scores. The model, which combines temperature index snow-tiles and HBV runoff instead of Kirchner (-STHBV), is the most sensitive to precipitation correction: it has the worst PBIAS score across all models without precipitation correction, but jumps to third place in all three metrics, if the correction is applied. The study highlights that KGE-based goal functions reduce PBIAS more than any of the NSE-based goal functions. The study confirms that logarithmic transformation on streamflows, both if LKGE and LNSE are used as target goal functions, generate parameter sets with majority of outliers (KGE scores lower than -0.41). This new benchmark has potential to help with diagnosing problems, improving algorithms and further development within hydrological part of Shyft. Modeling results are made publicly available for further investigation.