Developing Simultaneous Assessments of the Uncertainty and Accuracy of Precipitation and Terrestrial Water Budget Components over High Mountain Asia

We focus on exploring the uncertainties in the individual water budget components of precipitation, evapotranspiration (ET), runoff, terrestrial water storage (TWS), snow depth/snow water equivalent, and snow cover over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations forced with prescribed meteorology. Precipitation is one of the most important forcing variables in LSM simulation. Thus, the ten gridded precipitation products spanning 1980 – 2017, including APHRODITE, CHIRPS, CMORPH , ECMWF, ERA-Interim/Land, GDAS, HAR, IMD, MERRA-2, and TRMM are evaluated first to assess which precipitation dataset tends to be more suitable for LSM simulations in HMA. Inter-comparison with each other and a triple collocation (TC) analysis are applied to quantify the systematic and random error in these precipitation datasets. TC analysis is a method for estimating the unknown error standard deviations of three mutually independent measurement systems. It may not require the availability of a reference dataset and is therefore useful methods for HMA severely limits the availability of traditional ground-based meteorological observations. The consistency of their long-term trends is also evaluated. The nonparametric Mann-Kendall test is used to detect monotonic trends in precipitation datasets. Then an ensemble of LSM simulations with the selected precipitation dataset that provides the most consistent representation is performed to produce the terrestrial water budget estimates and their uncertainties. In this study, three different land surface models (i.e., Noah 3.3, Noah-MP 3.6, and Catchment F2.5) in the NASA Land Information System are employed. To evaluate the uncertainties in water budget estimates, reference measurements from remote sensing and reanalysis products (e.g., ALEXI and GLEAM for ET, GRACE for TWS, and MODIS snow products for snow cover) are used. Together, these evaluations provide a consistent assessment of the uncertainties in the water balance over HMA, bounded by available observations.