Abstract

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.

Highlights

  • High Mountain Asia (HMA) is a landscape of tundra, enormous glaciers, and alpine lakes, in addition to being a storehouse of freshwater central to the well-being of more than one billion people across Asia (Immerzeel et al, 2010)

  • We systematically evaluate the ability of the baseline Noah Multi-parameterization Land Surface Model (Noah-MP) model along with two data assimilation schemes to simulate surface temperature, soil temperature, snow depth, and snow water equivalent (SWE) states in HMA

  • A hyper-resolution (1 km) land data assimilation configuration is developed within the NASA Land Information System (LIS) using the Noah-MP forced by the ModernEra Retrospective analysis for Research and Applications (MERRA)-2

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Summary

Introduction

High Mountain Asia (HMA) is a landscape of tundra, enormous glaciers, and alpine lakes, in addition to being a storehouse of freshwater central to the well-being of more than one billion people across Asia (Immerzeel et al, 2010). Snow and glacier melt are important hydrologic processes in HMA (Immerzeel et al, 2009, 2010), and changes in surface temperature are expected to seriously affect the surface melt characteristics (Barnett et al, 2005; Immerzeel et al, 2010) as well as subsurface conditions, such as permafrost (Wu et al, 2013). A comprehensive knowledge of the regional spatiotemporal variability in the HMA environment might only be achieved by applying advanced modeling techniques, remote sensing products, and data assimilation (DA) methods at relatively high spatial and temporal resolutions

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