The melting of snow and glaciers in high mountain asia (HMA) contributes to the water needs of approximately 1 billion people in the region. There is an increasing array of remote sensing observations…

This is a federal grant award to the University of Washington (UW) from the National Aeronautics and Space Administration (NASA) Shared Services Center. The award, valued at $1,297,158.51, is for a project titled "Validating a Glacier Melt Toolbox for High Mountain Asia Using a Remote-Sensing-Driven Data Integration Framework." The project aims to integrate remote sensing data and modeling approaches to better understand the water budget in the High Mountain Asia region, which is critical for the water needs of approximately 1 billion people.

The grant will allow UW to work with the High Mountain Asia Team (HiMAT) to validate and calibrate a Glacier Melting Toolbox (GMELT) simulation across multiple spatial and temporal scales, using data from the Gravity Recovery and Climate Experiment (GRACE) as the primary calibration tool. The effort also includes developing new methods for parameterizing groundwater changes and producing high-resolution DEM time series to better constrain seasonal and interannual changes in glacier and snow mass. There is no set-aside designation for this award. The grant will support UW's expertise in remote sensing, hydrology, and cross-disciplinary data integration to provide decision support for stakeholders in the High Mountain Asia region.

The melting of snow and glaciers in high mountain asia (HMA) contributes to the water needs of approximately 1 billion people in the region. There is an increasing array of remote sensing observations and modeling approaches to estimate the HMA water budget. However, we lack coordinated efforts to link model components and effectively integrate remote sensing data in ways that provide decision support to local stakeholders. Moreover, the application of complex models in regions with limited observational constraints can provide misleading results. Here we will work with the high mountain asia team (HIMAT) to validate and calibrate glacier melting toolbox (GMELT) simulations across multiple temporal and spatial scales. Data from the gravity recovery and climate experiment (GRACE) will be our primary calibration tool. Our team will integrate GMELT water balance estimates associated with cryospheric, groundwater and other hydrological change, in an effort to account for as much of the known mass variability in the HMA region as possible. We will then forward model the estimated mass change as part of the GRACE mascon level 1B processing. By forward modeling this signal, we can minimize the magnitude of the inter satellite residuals and updates to the gravity field, thereby reducing the well known problem of temporal aliasing, and limiting the portion of the estimated gravity field that is subject to the fundamental spatial resolution of the GRACE determined solutions. Through iteration on this process we can utilize the full capabilities of GRACE and constrain the entire regional water balance, with robust estimates on errors in each component of the signal. Recognizing that groundwater changes (due to irrigation and other factors) greatly complicate the interpretation of GRACE in the HMA region, we will dedicate effort to better representing this portion of the water budget. We will develop new methods for parameterizing groundwater changes and perform sensitivity tests to assess their impact on GRACE observations. In addition, to achieve more robust constraints on seasonal and interannual changes in glacier and terrestrial snowpack mass we will produce 40 high resolution worldview/geoeye DEM time series, offering pan HMA spatial coverage with monthly, seasonal, annual and decadal temporal resolution. These observations will offer a new understanding of processes responsible for the significant regional variability in HMA mass balance, which is essential to further constrain HMA water balances within our GRACE calibration framework. Utilizing GRACE as a tool for constraining regional hydrology models is an inherently cross disciplinary task. Our team therefore proposes to lead the design of a data information framework within which GMELT tools can be developed and tested. We will guide GMELT to be composed of interchangeable modules that will be linked together in a cloud computing environment. This will enable US to make use of the widest possible scope of existing algorithmic tools and test numerous implementations of GMELT data and models in our forward modeling exercises. In addition to improving the quality and scope of the HIMAT teams earth science objectives, our proposed framework will provide the basis for policy and decision support across the HMA region.