Sensitivity and Uncertainty Quantification for the ECOSTRESS Evapotranspiration Algorithm - DisALEXI

Evapotranspiration (ET) is a measure of plant water use that is utilized re- gionally 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 wa- ter resource management decision making. In this manuscript, the uncer- tainty in disaggregated Atmosphere-Land Exchange (disALEXI) is quanti- ed; disALEXI is an ET algorithm that utilizes land surface temperature (LST) derived from the ECOsystem Spaceborne Thermal Radiometer Exper- iment 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 algorithms sensitivity to each of its inputs, and to quan- tify the probability distribution of algorithm outputs. Analysis shows that algorithm is most sensitive to LST (the input derived from ECOSTRESS). Signicantly, the output uncertainty distribution is non-Gaussian, due to the non-linear nature of the algorithm. This means that ET uncertainty can not be prescribed by accuracy and precision alone. Here, uncertainty was represented using ve quantiles of the output distribution. The distribution was consistent across ve dierent datasets (mean oset 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).