Abstract
This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff(R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11±0.45, 1.26±0.11, and 0.85±0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.
Highlights
The Himalayan mountain glaciers encompasses the largest reservoirs of freshwater on Earth outside of the polar regions
We examine the errors and uncertainties in key terrestrial water budget variables of precipitation, evapotranspiration, runoff, terrestrial water storage, and snow cover over High Mountain Asia (HMA) using a suite of uncoupled Land surface models (LSMs) simulations forced with prescribed meteorology
All datasets capture the spatial pattern of increased rainfall over the central and eastern regions compared to the west and the relatively dry regions of the Tibetan Plateau
Summary
The Himalayan mountain glaciers encompasses the largest reservoirs of freshwater on Earth outside of the polar regions. Increasing trends in SWE, during the winter time, are observed over the western HMA and declining trends in other regions These studies emphasize the significant spatial heterogeneity and uncertainty in the trend estimates due to the limitations of the data sources and limitations in the process understanding of the dominant climate systems. The specific goals of the study include: (1) to develop simultaneous assessments of the uncertainty and accuracy of precipitation (modeling inputs) and terrestrial water budget components (modeling outputs) over HMA from remote sensing, model analysis, and merged products; (2) to quantify the spatial variability of the precipitation uncertainties and errors in these products; (3) to assess the long-term trends in the mean and extremes of these precipitation products; and (4) evaluate the uncertainty in the terrestrial water budget estimates and the consistency of the long-term trends relative to those in the input meteorology.
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