Runoff projection under future climate scenarios has been widely studied to investigate the impacts of climate change on regional water availability. However, uncertainty in runoff projection due to different ETp inputs has not been fully assessed. This study firstly adopted the physically-based Penman model, temperature-based Hargreaves model, and radiation-based Abtew, Jensen-Haise, and modified Makkink models to drive Xinanjiang (XAJ) model, thus investigating the influence of different potential evapotranspiration (ETp) inputs on runoff simulation. Then, we used the validated XAJ model to project runoff in North Johnstone catchment, northeast Australia. Lastly, we quantified the uncertainty caused by 34 global climate models (GCMs), different representative concentrative pathway (RCP) scenarios (RCP4.5 & RCP8.5), and different ETp models with the technique of three-way analysis of variance (ANOVA). We found that XAJ model performed well (R² ≥ 0.88, NSE ≥ 0.86) and showed low sensitivity to different ETp inputs in runoff simulation and projection. Under future climate scenarios, spring and winter runoff had a large decrease, which was mainly caused by the decrease in rainfall. The mean decreases in spring and winter runoff were 14.6% – 20.1% and 10.3% – 15.2% respectively by 2090s under RCP8.5. GCMs (50.9% – 67.4%) and their interaction with RCPs (35.4% – 46.6%) were the dominant factors resulting in uncertainty in runoff projection. Our study not only advanced the understanding of the impacts of different ETp inputs on runoff projection but also offered insights on the understanding of the roles different factors played in the uncertainty in runoff projection. We expect such knowledge can provide a way forward to narrow down the uncertainty in runoff projection, thus providing more robust information for policy makers in water management.

未来气候情景下的径流预测已被广泛研究，以研究气候变化对区域水资源可用性的影响。然而，由于 ETp 输入不同而导致径流预测的不确定性尚未得到充分评估。本研究首次采用基于物理的 Penman 模型、基于温度的 Hargreaves 模型和基于辐射的 Abtew、Jensen-Haise 和改进的 Makkink 模型驱动新安江（XAJ）模型，从而研究了不同潜在蒸散量（ETp）的影响。径流模拟的输入。然后，我们使用经过验证的 XAJ 模型来预测澳大利亚东北部北约翰斯通集水区的径流。最后，我们量化了由 34 个全球气候模型（GCM）、不同的代表性集中路径（RCP）情景（RCP4.5 和 RCP8.5）引起的不确定性，以及采用三因素方差分析 (ANOVA) 技术的不同 ETp 模型。我们发现 XAJ 模型表现良好（R² ≥ 0.88，NSE ≥ 0.86），并且在径流模拟和预测中对不同 ETp 输入的敏感性较低。未来气候情景下，春季和冬季径流量均出现较大减少，主要是由于降雨量减少所致。在 RCP8.5 下，到 2090 年代，春季和冬季径流的平均减少量分别为 14.6% - 20.1% 和 10.3% - 15.2%。GCMs (50.9% – 67.4%) 及其与 RCPs 的相互作用 (35.4% – 46.6%) 是导致径流预测不确定性的主要因素。我们的研究不仅加深了对不同 ETp 输入对径流预测的影响的理解，而且还为理解不同因素在径流预测不确定性中所起的作用提供了见解。我们期望这些知识可以提供一种方法来缩小径流预测的不确定性，