Evaluation of global hydrological models for climate change impact assessment: How well can they translate interannual climate variability into streamflow variability?

Abstract. Global hydrological models (GHMs) are widely used in climate change impact assessments to estimate future changes in freshwater availability, droughts, and floods. Their suitability is often evaluated by comparing simulated daily or monthly streamflow with historical observations. However, since climate change impacts are usually expressed as changes relative to a reference period, biases in flow magnitude do not necessarily prevent GHMs from accurately representing hydrological change.

Here, we propose an alternative method for evaluating the suitability of GHMs for quantifying the impact of climate change on water resources. This method is based on the assumption that GHMs that are more effective in capturing the timing and magnitude of the annual streamflow anomaly during a historical period (which is mainly caused by climate variability) are likely to produce more plausible hydrological responses to climate change. Therefore, it compares simulated and observed absolute and relative streamflow anomalies instead of streamflow magnitudes themselves; it considers annually aggregated streamflow, which, compared with daily or monthly streamflow, is much less impacted by reservoir operations and human water use — two important drivers of streamflow that are difficult to model using GHMs.

In this study, we test this new evaluation method as an example for three GHMs (H08, MIROC-INTEG-LAND, and WaterGAP2.2e), forced by two climate datasets (20CRv3–ERA5 and 20CRv3–W5E5). We use streamflow observations from 589 gauging stations worldwide to evaluate the method. Magnitude-based evaluation shows large performance differences among the GHMs, with negative median Nash–Sutcliffe Efficiency (NSE) values for the two uncalibrated ones due to large biases. In contrast, all GHMs achieve positive NSE for annual streamflow anomalies. Regarding relative anomalies, the land surface model MIROC-INTEG-LAND performs worst with an NSE of about 0.3, whereas the two water resources models perform very similarly and achieve median NSE values above 0.5. Both models show very similar correlation, but WaterGAP simulates the standard deviation of the absolute and relative annual streamflow anomaly better than H08. The differences in performance between the two climate forcings are smaller than the differences among the models. In terms of the ability of GHMs to simulate the years in which extreme wet and dry anomalies occur, there is exact agreement between the observed and simulated extreme years at only 7–13 % of analysis locations. Meanwhile, the observed extreme year is identified among the five most extreme simulated years at 40–59 % of analysis locations. MIROC-INTEG-LAND also shows the lowest performance regarding extreme anomalies. GHM evaluation based on annual streamflow anomalies, particularly relative anomalies, is suitable for assessing how GHMs translate annual climate variability and, to a certain extent, climate change into hydrological changes.

However, the proposed GHM evaluation method does not consider the vegetation response to increased atmospheric CO₂ concentrations and climatic changes that may strongly affect the hydrological response to climate change. Therefore, multi-model ensemble assessments of hydrological impacts of climate change should include even lower-performing GHMs, provided that these GHMs take the vegetation response into account.