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Anthony Arendt

Research Scientist/Engineer - Principal





Department Affiliation

Polar Science Center


B.S. Earth & Atmospheric Sciences, University of Alberta, 1995

M.S. Earth & Atmospheric Sciences, University of Alberta, 1997

Ph.D. Geophysics, University of Alaska, 2006


2000-present and while at APL-UW

Water storage trends in high mountain Asia

Loomis, B.D., and 8 other including A.A. Arendt, "Water storage trends in high mountain Asia," Front. Earth Sci., 7, 235, doi:10.3389/feart.2019.00235, 2019.

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26 Sep 2019

Changes in terrestrial water storage (TWS) in High Mountain Asia (HMA) could have major societal impacts, as the region's large reservoirs of glaciers, snow, and groundwater provide a freshwater source to more than one billion people. We seek to quantify and close the budget of secular changes in TWS over the span of the GRACE satellite mission (2003–2016). To assess the TWS trend budget we consider a new high-resolution mass trend product determined directly from GRACE L1B data, glacier mass balance derived from Digital Elevation Models (DEMs), groundwater variability determined from confined and unconfined well observations, and terrestrial water budget estimates from a suite of land surface model simulations with the NASA Land Information System (LIS). This effort is successful at closing the aggregated TWS trend budget over the entire HMA region, the glaciated portion of HMA, and the Indus and Ganges basins, where the full-region trends are primarily due to the glacier mass balance and groundwater signals. Additionally, we investigate the closure of TWS trends at individual 1-arc-degree mascons (area ≈12,000 km2); a significant improvement in spatial resolution over previous analyses of GRACE-derived trends. This mascon-level analysis reveals locations where the TWS trends are well-explained by the independent datasets, as well as regions where they are not; identifying specific geographic areas where additional data and model improvements are needed. The accurate characterization of total TWS trends and its components presented here is critical to understanding the complex dynamics of the region, and is a necessary step toward projecting future water mass changes in HMA.

Converting snow depth to snow water equivalent using climatological variables

Hill, D.F., and 7 others including A.A. Arendt, "Converting snow depth to snow water equivalent using climatological variables," The Cryosphere, 13, 1767-1784, doi:10.5194/tc-13-1767-2019, 2019.

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4 Jul 2019

We present a simple method that allows snow depth measurements to be converted to snow water equivalent (SWE) estimates. These estimates are useful to individuals interested in water resources, ecological function, and avalanche forecasting. They can also be assimilated into models to help improve predictions of total water volumes over large regions. The conversion of depth to SWE is particularly valuable since snow depth measurements are far more numerous than costlier and more complex SWE measurements. Our model regresses SWE against snow depth (h), day of water year (DOY) and climatological (30-year normal) values for winter (December, January, February) precipitation (PPTWT), and the difference (TD) between mean temperature of the warmest month and mean temperature of the coldest month, producing a power-law relationship. Relying on climatological normals rather than weather data for a given year allows our model to be applied at measurement sites lacking a weather station. Separate equations are obtained for the accumulation and the ablation phases of the snowpack. The model is validated against a large database of snow pillow measurements and yields a bias in SWE of less than 2 mm and a root-mean-squared error (RMSE) in SWE of less than 60 mm. The model is additionally validated against two completely independent sets of data: one from western North America and one from the northeastern United States. Finally, the results are compared with three other models for bulk density that have varying degrees of complexity and that were built in multiple geographic regions. The results show that the model described in this paper has the best performance for the validation data sets.

Evaluating the uncertainty of terrestrial water budget components over high mountain Asia

Yoon, Y., and 15 others including A. Arendt, "Evaluating the uncertainty of terrestrial water budget components over high mountain Asia," Front. Earth Sci., 24, doi:10.3389/feart.2019.00120, 2019.

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24 May 2019

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.

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In The News

The water future of Earth's 'third pole'

NASA Explores, Carol Rasmussen

The most comprehensive survey ever made of snow, ice, and water in the high mountains of Asian and how they are changing is now underway. NASA's High Mountain Asia Team (HiMAT), led by Anthony Arendt is charged with integrating the many, varied types of satellite observations and existing numerical models to create an authoritative estimate of the water budget of this region and a set of products local policy makers can use in planning for a changing water supply.

26 Jun 2019

How many glaciers are in Alaska? There's no easy answer.

Anchorage Daily News, Ned Rozell

Anthony Arendt notes that mapmakers tend to give different names to several branches of an ice mass, all of which, by a more scientific definition, form part of a single glacier.

1 Jun 2019

Scientists unravel the ocean's mysteries with cloud computing

UW Information Technology, Elizabeth Sharpe

The OOI Cabled Array is delivering data on a scale that was previously not possible. More than 140 instruments are working simultaneously.

That’s why oceanographers teamed up with data and research computing experts to organize a unique event at the University of Washington in late August 2018 to help ocean scientists learn the computational tools, techniques, data management and analytical skills needed to handle this massive amount of data.

8 Nov 2018

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Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center