The Everglades in south Florida supply fresh drinking water for more than 7 million people, host a National Park, and are classified as a Ramsar wetland of international distinction. Predicting trajectories of water flow and water storage changes in the future is important to managing the Congressionally authorized restoration of the Everglades. Here we describe the needed data sources and analysis approaches to build the inputs for biophysically based modeling that can protect water and ecological resources in the face of changing water management and climate conditions. A biophysical approach to modeling overland flow in the Everglades can help predict future outcomes for ecological habitat, water storage during droughts, and water conveyance during floods. The needed data include measurements of vegetation stem architecture, microtopography, and landscape pattern metrics. Stem architecture measurements present the opportunity to estimate flow roughness of distinct vegetation communities based on hydraulic principles. At a larger scale, the microtopography and the connectivity of the sloughs between ridges offer a way to quantify the effects of flow blockage and tortuous flow paths on overland flow. Combined with theory these data provide the capacity to simulate overland flow in both the historical, pre-drainage Everglades as well as in the present-day managed Everglades. Also provided are the hydrologic data, e.g., water slopes, water depths and overland flow velocities, that can be used to verify a biophysical model. Ultimately, the purpose is to anticipate how changing flow and water depth will interact with evolving vegetation and landscape conditions to influence future water availability for society and for the ecosystem, both in the Everglades and in other low-gradient floodplains.

Ecosystem metabolism quantifies the rate of production, maintenance, and decay of organic matter in terrestrial and aquatic systems. It is a fundamental measure of energy flow associated with biomass production by photosynthesizing organisms and biomass oxidation by respiring plants, animals, algae, and bacteria (Bernhardt et al., 2022) . Ecosystem metabolism also provides an understanding of energy flow to higher trophic levels that supports secondary and tertiary productivity, as well as helping to explain when aquatic ecosystems undergo out-of-balance behaviors such as harmful algal blooms and hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. Here we present RiverMET, a framework for estimation of river metabolism, with workflows to streamline data preparation, run a stream metabolism model, assess the model performance, and flag and censor final output data. The workflows are specifically tailored to use streamMetabolizer, a model for one-station calculations of stream metabolism that calculates gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600). We advise potential users of RiverMET to review core publications for the streamMetabolizer model (Appling et al., 2018 a, b, c) to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. We tested RiverMET by calculating GPP, ER, and K600 across 17 river sites in the Illinois River basin (ILRB). Each river had between one and nine years of sensor data appropriate for modeling metabolism. In total, metabolism was modeled on 15,176 days between 2005 and 2020. Overall confidence in the results was rated as high at nine river sites, medium at six river sites, and poor at two river sites. Twenty-nine percent of the total modeled days had performance metrics that triggered flags. Metrics used for daily flagging are provided with the final output, with an option to only retain the censored daily outputs with high confidence (representing 72 %, i.e., 10,938 days, of the total days modeled). This work was completed as part of the U.S. Geological Survey Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for harmful algal blooms (HABs), per- and polyfluoroalkyl substances (PFAS), and metals, at multiple spatial and temporal scales.