Evapotranspiration (ET) is a measure of plant water use that is utilized regionally for drought detection and monitoring, and locally for agricultural water resource management. Understanding the uncertainty associated with this measurement is vital for science predictions and analysis and for water resource management decision making. In this manuscript, the uncertainty in disaggregated Atmosphere-Land Exchange (disALEXI) is quantified; disALEXI is an ET algorithm that utilizes land surface temperature (LST) derived from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), as well ancillary inputs for landcover, elevation, vegetation parameters, and meteorological inputs. Since each of these inputs has an associated, and potentially unknown, uncertainty, in this study a Monte Carlo simulation based on a spatial statistical model is used to determine the algorithm's sensitivity to each of its inputs, and to quantify the probability distribution of algorithm outputs. Analysis shows that algorithm is most sensitive to LST (the input derived from ECOSTRESS). Significantly, the output uncertainty distribution is non-Gaussian, due to the non-linear nature of the algorithm. This means that ET uncertainty cannot be prescribed by accuracy and precision alone. Here, uncertainty was represented using five quantiles of the output distribution. The distribution was consistent across five different datasets (mean offset is 0.01 mm/day, and 95% of the data is contained within 0.3 mm/day). An additional two datasets with low ET, showed higher uncertainty (95% of the data is within 1 mm/day), and a positive bias (i.e., ET was overestimated by an average of 0.12 mm/day when ET was low).

蒸散量（ET）是一种植物水分利用的量度，该量度在区域内用于干旱检测和监测，在本地用于农业水资源管理。了解与此测量相关的不确定性对于科学预测和分析以及水资源管理决策至关重要。在本手稿中，对大气-土地交换分类（disALEXI）中的不确定性进行了量化；disALEXI是一种ET算法，它利用从ECOsystem空间站上的空间辐射热辐射计实验（ECOSTRESS）得出的地表温度（LST），以及用于土地覆盖，海拔，植被参数和气象输入的辅助输入。由于这些输入中的每一个都有相关的不确定性，因此可能是未知的，在这项研究中，基于空间统计模型的蒙特卡洛模拟用于确定算法对其每个输入的敏感度，并量化算法输出的概率分布。分析表明，该算法对LST（来自ECOSTRESS的输入）最敏感。明显地，由于算法的非线性特性，输出不确定性分布是非高斯分布的。这意味着ET不确定性不能仅由准确性和精度来规定。在这里，不确定性用输出分布的五分位数表示。在五个不同的数据集中分布是一致的（平均偏移为0.01毫米/天，而95％的数据包含在0.3毫米/天之内）。另外两个具有低ET的数据集显示出更高的不确定性（95％的数据在1毫米/天之内），