How evaluation of hydrological models influences results of climate impact assessment—an editorial

Abstract

This paper introduces the Special Issue (SI) “How evaluation of hydrological models influences results of climate impact assessment.” The main objectives were as follows: (a) to test a comprehensive model calibration/validation procedure, consisting of five steps, for regional-scale hydrological models; (b) to evaluate performance of global-scale hydrological models; and (c) to reveal whether the calibration/validation methods and the model evaluation results influence climate impacts in terms of the magnitude of the change signal and the uncertainty range. Here, we shortly describe the river basins and large regions used as case studies; the hydrological models, data, and climate scenarios used in the studies; and the applied approaches for model evaluation and for analysis of projections for the future. After that, we summarize the main findings. The following general conclusions could be drawn. After successful comprehensive calibration and validation, the regional-scale models are more robust and their projections for the future differ from those of the model versions after the conventional calibration and validation. Therefore, climate impacts based on the former models are more trustworthy than those simulated by the latter models. Regarding the global-scale models, using only models with satisfactory or good performance on historical data and weighting them based on model evaluation results is a more reliable approach for impact assessment compared to the ensemble mean approach that is commonly used. The former method provides impact results with higher credibility and reduced spreads in comparison to the latter approach. The studies for this SI were performed in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).

Hydrological and Meteorological Variability in the Volga River Basin under Global Warming by 1.5 and 2 Degrees

“…Assessments of climate change impact on the water regime of large rivers were based on regional, spatially distributed, process-based runoff generation models, whose inputs are given by GCMs ensemble (e.g., [56,57]). The results of the international project ISIMIP for river basins located in different continents and physiographic conditions show that regional hydrological models successfully tested with observational data [39,48] can significantly reduce the uncertainty of estimates of current and future changes in the water regime of rivers compared to runoff calculations from global hydrological models or GCMs [58,59]. Thus, the results of calculations of annual runoff changes based on the ECOMAG model allowed for refining the estimates obtained from GCMs or conceptual and water-balance hydrological models, showing a greater decrease in the Volga basin's runoff by 10 to 11%.…”

Anthropogenic influence on extreme temperature and precipitation in Central Asia

“…In the counterfactual ISIMIP ensemble, two sets of model simulations from CMIP6 with and without anthropogenic forcings labelled as “historical” and “hist-nat”, are bias-adjusted using a gridded observational dataset and statistically downscaled to a horizontal resolution. This resolution is appropriate for impact studies over river basins larger than 50,000 32 . For impact studies at smaller regional scales in CA, a higher resolution is required to have enough grid points of the model within those basins.…”

Changes in and Modelling of Hydrological Process for a Semi-Arid Catchment in the Context of Human Disturbance

“…Hydrological models are mathematical models constructed to simulate hydrological cycles and describe hydrophysical processes. They are essential means to study the laws of hydrology and nature (Xu, 2010;Krysanova et al, 2020) and effective tools to solve practical problems, e.g., hydrological forecasting, water resources management, and water conservancy project planning and design (Musuuza et al, 2020;Thatch et al, 2020;Turner et al, 2020). Liu et al (2019) applied the VIC model to forecast the annual maximum floods and annual first floods in the YarlungZangbo River based on precipitation and temperature data, and provided an early warning with extended lead time.…”