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.

中文翻译：

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时变敏感性分析揭示了强年际变化地区流域气候与年参数重要性变化之间的关系

气候变化对水文气候的影响在世界范围内越来越明显。尚不清楚降水和气温的年度变化如何改变模型推断的水文过程的重要性，以及在整个流域如何变化。为了检验这一点，我们使用了简约的降雨径流模型，并对33个加利福尼亚州流域（1981-2014）进行了时变敏感性分析。我们针对五个性能指标计算了年度总订单敏感度指标：Kling-Gupta效率（KGE）和模拟水文特征时的模型误差（径流比，流量持续时间曲线的斜率，基本流量指数和流量质心的时间）。水文签名对参数的敏感性因签名而异，而关于KGE的参数重要性则在时空上具有更大的可变性。相对于KGE的参数敏感性变化与气温（内华达山脉中以雪为主的站点）或降水（低海拔站点<1,300 m）相关。在误差度量和签名中，对于一部分研究地点，干湿年之间的参数敏感性差异很大。尽管参数重要性随时间变化，但整个流域的参数灵敏度变化更为明显。这表明对模型性能的参数控制更多地是分水岭性质的反映，而不是受降水和气温变化的影响。