We assembled data from a global network of automated lake observatories to test hypotheses regarding the drivers of ecosystem metabolism. We estimated daily rates of respiration and gross primary production (GPP) for up to a full year in each lake, via maximum likelihood fits of a free-water metabolism model to continuous highfrequency measurements of dissolved oxygen concentrations. Uncertainties were determined by a bootstrap analysis, allowing lake-days with poorly constrained rate estimates to be down-weighted in subsequent analyses. GPP and respiration varied considerably among lakes and at seasonal and daily timescales. Mean annual GPP and respiration ranged from 0.1 to 5.0 mg O 2 L 21 d 21 and were positively related to total phosphorus but not dissolved organic carbon concentration. Within lakes, significant day-to-day differences in respiration were common despite large uncertainties in estimated rates on some lake-days. Daily variation in GPP explained 5% to 85% of the daily variation in respiration after temperature correction. Respiration was tightly coupled to GPP at a daily scale in oligotrophic and dystrophic lakes, and more weakly coupled in mesotrophic and eutrophic lakes. Background respiration ranged from 0.017 to 2.1 mg O 2 L 21 d 21 and was positively related to indicators of recalcitrant allochthonous and autochthonous organic matter loads, but was not clearly related to an indicator of the quality of allochthonous organic matter inputs.Gross primary production (GPP) and respiration are perhaps the two most fundamental processes in ecosystems. At the cellular or organismal level, they describe biochemical pathways that make organic carbon molecules and energy available to cells. When these cellular processes are integrated across an entire ecosystem, the result-ecosystemlevel GPP, ecosystem respiration, or collectively ecosystem metabolism-describes biogeochemical and trophic processes occurring at the system level.There is substantial interest in understanding the controls on ecosystem metabolism in aquatic (Mulholland et al.

Here we document the regional effects of Tropical Cyclone Irene on thermal structure and ecosystem metabolism in nine lakes and reservoirs in northeastern North America using a network of high-frequency, in situ, automated sensors. Thermal stability declined within hours in all systems following passage of Irene, and the magnitude of change was related to the volume of water falling on the lake and catchment relative to lake volume. Across systems, temperature change predicted the change in primary production, but changes in mixed-layer thickness did not affect metabolism. Instead, respiration became a driver of ecosystem metabolism that was decoupled from in-lake primary production, likely due to addition of terrestrially derived carbon. Regionally, energetic disturbance of thermal structure was shorter-lived than disturbance from inflows of terrestrial materials. Given predicted regional increases in intense rain events with climate change, the magnitude and longevity of ecological impacts of these storms will be greater in systems with large catchments relative to lake volume, particularly when significant material is available for transport from the catchment. This case illustrates the power of automated sensor networks and associated human networks in assessing both system response and the characteristics that mediate physical and ecological responses to extreme events.

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP)-based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R rs), (b) retrieval of particulate backscattering (b bp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b bp via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m 3 ], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in R rs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID

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It has been a long-standing goal to precisely measure water-leaving radiance (L(w), or its equivalent property, remote-sensing reflectance) in the field, but reaching this goal is quite a challenge. This is because conventional approaches do not provide a direct measurement of L(w), but rather measure various related components and subsequently derive this core property from these components. Due to many uncontrollable factors in the measurement procedure and imprecise post-measurement processing routines, the resulting L(w) is inherently associated with various levels of uncertainties. Here we present a methodology called the skylight-blocked approach (SBA) to measure L(w) directly in the field, along with results obtained recently in the Laurentian Great Lakes. These results indicate that SBA can measure L(w) in high precision. In particular, there is no limitation of water types for the deployment of SBA, and the requirement of post-measurement processing is minimum; thus high-quality L(w) for a wide range of aquatic environments can be acquired.