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We created a near-term iterative lake water temperature forecasting system that uses 13 sensors, data assimilation, and hydrodynamic modeling 14 15 • FLARE quantifies the uncertainty in each daily forecast and provides an open-source, 16 generalizable system for water quality forecasting 17 18• 16-day forecasted temperatures were within 0.91℃ over 100 days in a reservoir case Abstract 22 Freshwater ecosystems are experiencing greater variability due to human activities, necessitating 23 new tools to anticipate future water quality. In response, we developed and operationalized a 24 near-term iterative water temperature forecasting system (FLARE -Forecasting Lake And 25 Reservoir Ecosystems) that is generalizable for lakes and reservoirs. FLARE is composed of: 26 water quality and meteorology sensors that wirelessly stream data, a data assimilation algorithm 27 that uses sensor observations to update predictions from a hydrodynamic model and calibrate 28 model parameters, and an ensemble-based forecasting algorithm to generate forecasts that 29 include uncertainty. Importantly, FLARE quantifies the contribution of different sources of 30 uncertainty (parameters, driver data, initial conditions, and process) to each daily forecast of 31 water temperature at multiple depths. We applied FLARE to a temperate reservoir during a 100-32 day period that encompassed stratified and mixed thermal conditions and found that daily 33 forecasted water temperatures were on average within 0.91℃ at all depths of the reservoir over a 34 16-day forecast horizon. FLARE successfully predicted the onset of fall turnover eight days in 35 advance, and identified meteorology driver data and downscaling as the dominant sources of 36 forecast uncertainty. Overall, FLARE provides an open-source and easily-generalizable system 37 for water quality forecasting for lakes and reservoirs to improve management. 39Lake Model, Water temperature 41 45 freshwater ecosystems, which have been more degraded than any other ecosystem on the planet 46 [Millennium Ecosystem Assessment 2005], are seeking new tools to anticipate future change and 47 ensure clean water for drinking, fisheries, irrigation, industry, and recreation [Brookes et al. 48 2014]. 49 In response to this need, near-term iterative ecological forecasting has emerged as a 50 solution to provide stakeholders, managers, and policy-makers crucial information about future 51 ecosystem conditions [Clark et al. 2001, Dietze et al. 2018, Luo et al. 2011. Here, we define a 52 near-term iterative forecast as a projection of future ecosystem states with fully-specified 53 uncertainties, generated from predictive models that can be constantly updated with new data as 54 they become available [Clark et al. 2001]. Importantly, a near-term iterative forecast is not 55 created from merely one ecosystem simulation, but an ensemble of simulations that enable 56 quantification of the uncertainty in the forecast contributed by different sources [i.e., parameters, 57 driver data, initi...

Abstract-FutureGrid provides novel computing capabilities that enable reproducible experiments while simultaneously supporting dynamic provisioning. This paper describes the FutureGrid experiment management framework to create and execute large scale scientific experiments for researchers around the globe. The experiments executed are performed by the various users of FutureGrid ranging from administrators, software developers, and end users. The Experiment management framework will consist of software tools that record user and system actions to generate a reproducible set of tasks and resource configurations. Additionally, the experiment management framework can be used to share not only the experiment setup, but also performance information for the specific instantiation of the experiment. This makes it possible to compare a variety of experiment setups and analyze the impact Grid and cloud software stacks have.

Virtual private networking (VPN) has become an increasingly important component of a collaboration environment because it ensures private, authenticated communication among participants, using existing collaboration tools, where users are distributed across multiple institutions and can be mobile. The majority of current VPN solutions are based on a centralized VPN model, where all IP traffic is tunneled through a VPN gateway. Nonetheless, there are several use case scenarios that require a model where end-to-end VPN links are tunneled upon existing Internet infrastructure in a peer-to-peer (P2P) fashion, removing the bottleneck of a centralized VPN gateway. We propose a novel virtual network -TinCan -based on peerto-peer private network tunnels. It reuses existing standards and implementations of services for discovery notification (XMPP), reflection (STUN) and relaying (TURN), facilitating configuration. In this approach, trust relationships maintained by centralized (or federated) services are automatically mapped to TinCan links. In one use scenario, TinCan allows unstructured P2P overlays connecting trusted end-user devices -while only requiring VPN software on user devices and leveraging online social network (OSN) infrastructure already widely deployed. This paper describes the architecture and design of TinCan and presents an experimental evaluation of a prototype supporting Windows, Linux, and Android mobile devices. Results quantify the overhead introduced by the network virtualization layer, and the resource requirements imposed on services needed to bootstrap TinCan links.

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