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This paper aims to investigate the uncertainty ranges of high flows under climate change in Jinhua River basin, eastern China. Four representative concentration pathways (RCPs), three global climate models (GCMs), 10 downscaling parameter sets and three hydrologic models are applied to simulate future discharges. Changes of annual maximum discharges are assessed for the baseline period (1961-1990) and future period (2011-2040). The uncertainties of annual maximum discharges are calculated for each uncertainty source and compared with different combinations of them. The minimum temperature will probably increase all year round in the future period and maximum temperature would increase in most cases. The changes of precipitation showed different directions by different models and emission scenarios. The annual maximum discharges would decrease for all RCPs. The order of uncertainty ranges of high flows due to different uncertainty sources from high to low is: hydrologic models, GCMs, parameter sets in the downscaling method and emission scenarios. It must be noted that the small uncertainty contribution from different emission scenarios is due to the study period when the differences in increase of radiative forcing and greenhouse gas concentration are less obvious between different RCPs compared to the second half of the 21st century. [ABSTRACT FROM AUTHOR]

Climate change impacts on hydroclimatology are becoming increasingly apparent around the world. It is unknown how annual variations in precipitation and air temperature alter the model‐inferred importance of hydrological processes and how this varies across watersheds. To examine this, we used parsimonious rainfall‐runoff model and applied time‐varying sensitivity analysis across 30 Californian watersheds for a 33‐year period (1981–2014). We calculated annual total order sensitivity indices for five performance metrics: Kling‐Gupta Efficiency (KGE) and model error in simulating hydrologic signatures (runoff ratio, slope of flow duration curve, baseflow index, and timing of streamflow centroid). Sensitivity of hydrological signatures to the parameters differed by signature, while parameter importance with respect to KGE was much more spatially and temporally variable. Variations in parameter sensitivity with respect to KGE were either correlated with air temperature (snow‐dominated sites in the Sierra Nevada) or precipitation (lower elevation sites < 1,300 m). Across error metrics and signatures, parameter sensitivity strongly differed between wet and dry years for a subset of our study sites. While parameter importance varied through time, parameter sensitivity variations across watersheds were much more pronounced. This suggests that parameter controls on model performance are much more a reflection of watershed properties as opposed to being dominantly shaped by shifts in precipitation and air temperature. These findings emphasize the importance of understanding simulated watershed responses to fluctuating annual conditions, as this conceptual knowledge is necessary to anticipate similarities and differences in response across even relatively proximal watersheds in the face of growing extreme conditions. Plain Language Summary: Computer models are useful tools that can be used to analyze what inputs matter most to ensuring that model output is similar to observed conditions. In order to understand how very wet years versus very dry years impact which model inputs (called parameters) are most important to matching good predictions, we simulated streamflow using a simple mathematical model many thousands of times. We used these outputs to determine how variations in predictions of streamflow (output from the model) are related to variations in model parameters, to identify which parameters are most important for ensuring reasonable model predictions. We focused on watersheds that are close together, but that may experience different average conditions and interannual variability. We found that which parameters are important varied across different watersheds and also varied with total annual precipitation and watershed elevation. In addition, we found that drought impacted the importance of processes in the model, especially in mountainous watersheds. Our results emphasize the importance of understanding how watersheds respond to varying annual conditions, as this type of knowledge is essential to anticipate the future of streamflow availability and inform the management style that needs to be implemented to predict water availability in these systems. Key Points: Annual time‐varying sensitivity analysis revealed unique patterns of parameter importance across 21 proximal study watershedsLow elevation parameter importance to Kling‐Gupta Efficiency varied with precipitation, while shifts at high elevations varied with elevationParameter sensitivity to select hydrological signatures was generally static through time, despite annual variations in observed values [ABSTRACT FROM AUTHOR]