Uncertainty information associated with urban forest models are beneficial for model transparency, model development, effective communication of model output, and decision-making. However, compared with the extensive studies based on the applications of urban forest models, little attention has been paid to the uncertainty of the output from these models. In this study, bootstrap and Monte Carlo simulation were employed to explore the uncertainty of i-Tree Eco. We assess the uncertainties associated with input data, sampling methods and models throughout the processes of urban forest structure and function quantification, and we propagate and aggregate the three sources of uncertainty to derive an estimator of total uncertainty. The uncertainty magnitude is expressed as the coefficient of variation. By applying the uncertainty framework to a network of 15 cities across the United States, we find that the average magnitude of total uncertainty across 15 cities is 12.3 % for leaf area, 13.4 % for carbon storage, 11.1 % for carbon sequestration, 40.7 % for isoprene emissions, and 25.0 % for monoterpene emissions. For leaf and carbon estimators, the total uncertainty is primarily driven by sampling uncertainty; the magnitudes of all three sources of uncertainty are comparable across 15 cities. In contrast, input, sampling, and model uncertainties all contribute to the total uncertainty for isoprene and monoterpene emission estimators, and there are large variations in these three sources of uncertainty across the 15 cities. An analysis of a regression-based approach to estimate input and model error indicated only moderate improvements over using averages across sites when estimating total uncertainty.

与城市森林模型相关的不确定性信息有利于模型透明度，模型开发，模型输出的有效交流以及决策。但是，与基于城市森林模型应用的广泛研究相比，很少关注这些模型输出的不确定性。在这项研究中，引导程序和蒙特卡洛模拟被用来探索i-Tree Eco的不确定性。我们在城市森林结构和功能量化的整个过程中评估与输入数据，采样方法和模型相关的不确定性，并传播和汇总三种不确定性来源，以得出总不确定性的估计值。不确定度的大小表示为变化系数。通过将不确定性框架应用于美国15个城市的网络，我们发现15个城市的总不确定性的平均幅度为：叶面积的12.3％，碳储存的13.4％，碳封存的11.1％，碳储存的40.7％异戊二烯排放，单萜排放为25.0％。对于烟叶和碳估算器，总不确定性主要由采样不确定性驱动；在15个城市中，这三种不确定性来源的大小均具有可比性。相比之下，输入，采样和模型的不确定性都对异戊二烯和单萜排放估算器的总不确定性有所贡献，在这15个城市中，这三种不确定性来源存在很大差异。