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Anthony Arendt

Research Scientist/Engineer - Principal

Email

arendta@apl.washington.edu

Phone

206-685-4551

Department Affiliation

Polar Science Center

Education

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

Publications

2000-present and while at APL-UW

Grand challenges of hydrological modeling for food–energy–water nexus security in high mountain Asia

Mishra, S.K., and 24 others including A. Arendt, "Grand challenges of hydrological modeling for food–energy–water nexus security in high mountain Asia," Front. Water, 3, doi:10.3389/frwa.2021.728156, 2021.

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5 Oct 2021

Climate-influenced changes in hydrology affect water-food-energy security that may impact up to two billion people downstream of the High Mountain Asia (HMA) region. Changes in water supply affect energy, industry, transportation, and ecosystems (agriculture, fisheries) and as a result, also affect the region's social, environmental, and economic fabrics. Sustaining the highly interconnected food-energy-water nexus (FEWN) will be a fundamental and increasing challenge under a changing climate regime. High variability in topography and distribution of glaciated and snow-covered areas in the HMA region, and scarcity of high resolution (in-situ) data make it difficult to model and project climate change impacts on individual watersheds. We lack basic understanding of the spatial and temporal variations in climate, surface impurities in snow and ice such as black carbon and dust that alter surface albedo, and glacier mass balance and dynamics. These knowledge gaps create challenges in predicting where and when the impact of changes in river flow will be the most significant economically and ecologically. In response to these challenges, the United States National Aeronautics and Space Administration (NASA) established the High Mountain Asia Team (HiMAT) in 2016 to conduct research to address knowledge gaps. This paper summarizes some of the advances HiMAT made over the past 5 years, highlights the scientific challenges in improving our understanding of the hydrology of the HMA region, and introduces an integrated assessment framework to assess the impacts of climate changes on the FEWN for the HMA region. The framework, developed under a NASA HMA project, links climate models, hydrology, hydropower, fish biology, and economic analysis. The framework could be applied to develop scientific understanding of spatio-temporal variability in water availability and the resultant downstream impacts on the FEWN to support water resource management under a changing climate regime.

Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

Crumley, R.L., D.F. Hill, K.W. Jones, G.J. Wolken, A.A. Arendt, C.M. Aragon, and C. Cosgrove, "Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations," Hydrol. Earth Syst. Sci., 25, 4651-4680, doi:10.5194/hess-25-4651-2021, 2021.

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31 Aug 2021

A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations (CSO; https://communitysnowobs.org/, last access: 11 August 2021) project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62% to 78% of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.

Automated dynamic mascot generation for GRACE and GRACE-FO harmonic processing

Mohajerani, Y., D. Shean, A. Arendt, and T.C. Sutterley, "Automated dynamic mascot generation for GRACE and GRACE-FO harmonic processing," Remote Sens., 13, doi:10.3390/rs13163134, 2021.

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7 Aug 2021

Commonly used mass-concentration (mascon) solutions estimated from Level-1B Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On data, provided by processing centers such as the Jet Propulsion Laboratory (JPL) or the Goddard Space Flight Center (GSFC), do not give users control over the placement of mascons or inversion assumptions, such as regularization. While a few studies have focused on regional or global mascon optimization from spherical harmonics data, a global optimization based on the geometry of geophysical signal as a standardized product with user-defined points has not been addressed. Finding the optimal configuration with enough coverage to account for far-field leakage is not a trivial task and is often approached in an ad-hoc manner, if at all. Here, we present an automated approach to defining non-uniform, global mascon solutions that focus on a region of interest specified by the user, while maintaining few global degrees of freedom to minimize noise and leakage. We showcase our approach in High Mountain Asia (HMA) and Alaska, and compare the results with global uniform mascon solutions from range-rate data. We show that the custom mascon solutions can lead to improved regional trends due to a more careful sampling of geophysically distinct regions. In addition, the custom mascon solutions exhibit different seasonal variation compared to the regularized solutions. Our open-source pipeline will allow the community to quickly and efficiently develop optimized global mascon solutions for an arbitrary point or polygon anywhere on the surface of the Earth.

More Publications

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

More News Items

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
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