Using isotopes to constrain water flux and age estimates in snow-influenced catchments using the STARR (Spatially distributed Tracer-Aided Rainfall-Runoff) model

Acknowledgements. This work was funded by the NERC/JPI SIWA project (NE/M019896/1) and the European Research Council ERC (project GA 335910 VeWa). Numerical simulations were performed using the Maxwell High Performance Computing Cluster of the University of Aberdeen IT Service, provided by Dell Inc. and supported by Alces Software. The isotope work in Krycklan is funded by the KAW Branch-Point project together with SKB and SITES. We would like to thank Marjolein van Hui- jgevoort for her help with the STARR code, and Masaki Hayashi and two anonymous reviewers for their insightful suggestions that significantly improved the paper. The Supplement related to this article is available online at https://doi.org/10.5194/hess-21-5089-2017-supplement.Peer reviewedPublisher PD

An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks

Edge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Google products such as Coral and Pixel devices. In this paper, we first discuss the major microarchitectural details of Edge TPUs. Then, we extensively evaluate three classes of Edge TPUs, covering different computing ecosystems, that are either currently deployed in Google products or are the product pipeline, across 423K unique convolutional neural networks. Building upon this extensive study, we discuss critical and interpretable microarchitectural insights about the studied classes of Edge TPUs. Mainly, we discuss how Edge TPU accelerators perform across convolutional neural networks with different structures. Finally, we present our ongoing efforts in developing high-accuracy learned machine learning models to estimate the major performance metrics of accelerators such as latency and energy consumption. These learned models enable significantly faster (in the order of milliseconds) evaluations of accelerators as an alternative to time-consuming cycle-accurate simulators and establish an exciting opportunity for rapid hard-ware/software co-design.Comment: 11 pages, 15 figures, submitted to ISCA 202

Modeling the Isotopic Evolution of Snowpack and Snowmelt: Testing a Spatially Distributed Parsimonious Approach

Use of stable water isotopes has become increasingly popular in quantifying water flow paths and travel times in hydrological systems using tracer-aided modeling. In snow-influenced catchments, snowmelt produces a traceable isotopic signal, which differs from original snowfall isotopic composition because of isotopic fractionation in the snowpack. These fractionation processes in snow are relatively well understood, but representing their spatiotemporal variability in tracer-aided studies remains a challenge. We present a novel, parsimonious modeling method to account for the snowpack isotope fractionation and estimate isotope ratios in snowmelt water in a fully spatially distributed manner. Our model introduces two calibration parameters that alone account for the isotopic fractionation caused by sublimation from interception and ground snow storage, and snowmelt fractionation progressively enriching the snowmelt runoff. The isotope routines are linked to a generic process-based snow interception-accumulation-melt model facilitating simulation of spatially distributed snowmelt runoff. We use a synthetic modeling experiment to demonstrate the functionality of the model algorithms in different landscape locations and under different canopy characteristics. We also provide a proof-of-concept model test and successfully reproduce isotopic ratios in snowmelt runoff sampled with snowmelt lysimeters in two long-term experimental catchment with contrasting winter conditions. To our knowledge, the method is the first such tool to allow estimation of the spatially distributed nature of isotopic fractionation in snowpacks and the resulting isotope ratios in snowmelt runoff. The method can thus provide a useful tool for tracer-aided modeling to better understand the integrated nature of flow, mixing, and transport processes in snow-influenced catchments

A preliminary assessment of water partitioning and ecohydrological coupling in northern headwaters using stable isotopes and conceptual runoff models

Funded by European Research Council ERC. Grant Number: GA 335910 VEWA Swedish Science Foundation (SITES) Future Forest Formas (ForWater) SKB the Kempe foundation Environment Canada the Garfield Weston Foundation the Natural Sciences and Engineering Research Council of Canada (NSERC) the Northwest Territories Cumulative Impacts Monitoring ProgramPeer reviewedPublisher PD

Modeling the isotopic evolution of snowpack and snowmelt : Testing a spatially distributed parsimonious approach

This work was funded by the NERC/JPI SIWA project (NE/M019896/1) and the European Research Council ERC (project GA 335910 VeWa). The Krycklan part of this study was supported by grants from the Knut and Alice Wallenberg Foundation (Branch-points), Swedish Research Council (SITES), SKB and Kempe foundation. The data and model code is available upon request. Authors declare that they have no conflict of interest. We would like to thank the three anonymous reviewers for their constructive comments that improved the manuscript.Peer reviewedPublisher PD

The essential value of long-term experimental data for hydrology and water management

We would like to thank the European Research Council ERC for funding the VeWa project and most of Tetzlaff's time (project GA 335910 VeWa). No data were used in producing this manuscript.Peer reviewedPublisher PD

Linking high-frequency DOC dynamics to the age of connected water sources

Acknowledgments The authors would like to thank our NRI colleagues for all their help with field and laboratory work, especially Audrey Innes, Jonathan Dick, and Ann Porter. We would like to also thank Iain Malcolm (Marine Scotland Science) for providing AWS data and the European Research Council ERC (project GA 335910 VEWA) for funding the VeWa project. Please contact the authors for access to the data used in this paper. We would also like to thank the Natural Environment Research Council NERC (project NE/K000268/1) for funding.Peer reviewedPublisher PD

Stable isotopes of water reveal differences in plant – soil water relationships across northern environments

Funding Information: We thank the European Research Council ERC for funding (VeWa project GA 335910). Contributions from CS were supported by the Leverhulme Trust through the ISO-LAND project (RPG 2018 375). Support for MJK and JPM were provided by the US National Science Foundation (EAR0842367) and Boise State University. We thank Dr. Samantha Evans for technical support. Thanks to the Dorset Environmental Science Centre for provision of meteorological data. The work conducted in Krycklan was partly financed by SITES (VR) and the KAW Branch-Point project. We would like to acknowledge Dr. Nadine Shatilla for collection of the Wolf Creek samples and the Global Water Futures program for financial support. We also would like to sincerely thank Jeff McDonnell for his support throughout the VeWa project and all participants in the different VeWa workshops esp. Tanya Doody and Marco Maneta for their invaluable input into the discussions.Peer reviewedPublisher PD