Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET${}_0$) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET${}_0$ was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET${}_0$. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE) $M=0.903$, $SD=0.034$ for KGE and $M=3.17$, $SD=2.97$ for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET${}_0$ increase in headwaters and a smaller increase in the coast.

中文翻译：

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半干旱地区蒸散量对气候变化的响应：以随机森林为多模型集成方法

大型的气候模型集越来越多地出现，要么是机会集成，要么是物理扰动集成，它们提供了大量额外的数据，这些数据对于改善对气候变化的适应策略可能具有帮助。在这项工作中，我们提出了一个框架来评估11种多模型集成方法（MME）的预测能力，包括随机森林（RF）来估计参考蒸散量（ET${}_0$）针对RCP4.5和RCP8.5场景使用10种AR5模型。这项研究是在塞古拉水文界线（西班牙SE）进行的，这是典型的地中海半干旱地区。ET${}_0$在历史情景（1970-2000年）中使用空间校准的Hargreaves模型进行了估算。MME的每日ET再现效果优于任何单个模型${}_0$。在验证中，RF结果比其他MME更准确（Kling–Gupta效率（KGE）$\mathrm{中号}=0。903$， $\mathrm{小号}d=0。034$ 对于KGE和 $\mathrm{中号}=3。17$， $\mathrm{小号}d=2。97$绝对百分比偏差）。RCP8.5沿21世纪观察到了统计上显着的正趋势，但RCP4.5的趋势在本世纪中叶趋于稳定。观察到的空间格局显示出更大的ET${}_0$ 上游水源增加，而海岸增加较小。