Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, realtime freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential opensource option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.
Abstract. Empirical evidence demonstrates that lakes and reservoirs are warming across
the globe. Consequently, there is an increased need to project future
changes in lake thermal structure and resulting changes in lake
biogeochemistry in order to plan for the likely impacts. Previous studies of
the impacts of climate change on lakes have often relied on a single model
forced with limited scenario-driven projections of future climate for a
relatively small number of lakes. As a result, our understanding of the
effects of climate change on lakes is fragmentary, based on scattered
studies using different data sources and modelling protocols, and mainly
focused on individual lakes or lake regions. This has precluded
identification of the main impacts of climate change on lakes at global and
regional scales and has likely contributed to the lack of lake water quality
considerations in policy-relevant documents, such as the Assessment Reports
of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a
simulation protocol developed by the Lake Sector of the Inter-Sectoral
Impact Model Intercomparison Project (ISIMIP) for simulating climate change
impacts on lakes using an ensemble of lake models and climate change
scenarios for ISIMIP phases 2 and 3. The protocol prescribes lake
simulations driven by climate forcing from gridded observations and
different Earth system models under various representative greenhouse gas
concentration pathways (RCPs), all consistently bias-corrected on a
0.5∘ × 0.5∘ global grid. In ISIMIP phase 2, 11 lake
models were forced with these data to project the thermal structure of 62
well-studied lakes where data were available for calibration under
historical conditions, and using uncalibrated models for 17 500 lakes
defined for all global grid cells containing lakes. In ISIMIP phase 3, this
approach was expanded to consider more lakes, more models, and more
processes. The ISIMIP Lake Sector is the largest international effort to
project future water temperature, thermal structure, and ice phenology of
lakes at local and global scales and paves the way for future simulations of
the impacts of climate change on water quality and biogeochemistry in lakes.
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