4 research outputs found
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Comprehensive multiyear carbon budget of a temperate headwater stream
Headwater streams comprise nearly 90% of the total length of perennial channels in global catchments. They mineralize organic carbon entering from terrestrial systems, evade terrestrial carbon dioxide (CO₂ ), and generate and remove carbon through in-stream primary production and respiration. Despite their importance, headwater streams are often neglected in global carbon budgets primarily because of a lack of available data. We measured these processes, in detail, over a 10 year period in a stream draining a 96 ha forested watershed in western Oregon, USA. This stream, which represents only 0.4% of the watershed area, exported 159 kg C ha⁻¹ yr⁻¹, similar to the global exports for large rivers. Stream export was dominated by downstream transport of dissolved inorganic carbon (63 kg C ha⁻¹ yr⁻¹) and by evasion of CO₂ to the atmosphere (42 kg C ha⁻¹ yr⁻¹), leaving the remainder of 51 kg C ha⁻¹ yr⁻¹ for downstream transport of organic carbon (17 kg C ha⁻¹ yr⁻¹ and 34 kg C ha⁻¹ yr⁻¹ in dissolved and particulate form, respectively)This is the publisher’s final pdf. The article is copyrighted by the American Geophysical Union and published by John Wiley & Sons, Inc. It can be found at: http://agupubs.onlinelibrary.wiley.com/agu/jgr/journal/10.1002/%28ISSN%292169-8961/Keywords: carbon, evasion, carbon fluxes, stream, biogeochemistr
Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations
Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (>18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables
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ArgerichComprehensiveMultiyearCarbon.pdf
Headwater streams comprise nearly 90% of the total length of perennial channels in global catchments. They mineralize organic carbon entering from terrestrial systems, evade terrestrial carbon dioxide (CO₂ ), and generate and remove carbon through in-stream primary production and respiration. Despite their importance, headwater streams are often neglected in global carbon budgets primarily because of a lack of available data. We measured these processes, in detail, over a 10 year period in a stream draining a 96 ha forested watershed in western Oregon, USA. This stream, which represents only 0.4% of the watershed area, exported 159 kg C ha⁻¹ yr⁻¹, similar to the global exports for large rivers. Stream export was dominated by downstream transport of dissolved inorganic carbon (63 kg C ha⁻¹ yr⁻¹) and by evasion of CO₂ to the atmosphere (42 kg C ha⁻¹ yr⁻¹), leaving the remainder of 51 kg C ha⁻¹ yr⁻¹ for downstream transport of organic carbon (17 kg C ha⁻¹ yr⁻¹ and 34 kg C ha⁻¹ yr⁻¹ in dissolved and particulate form, respectively)Keywords: carbon, evasion, carbon fluxes, stream, biogeochemistryKeywords: carbon, evasion, carbon fluxes, stream, biogeochemistr
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ArgerichComprehensiveMultiyearCarbonSupportingInfo.pdf
Headwater streams comprise nearly 90% of the total length of perennial channels in global catchments. They mineralize organic carbon entering from terrestrial systems, evade terrestrial carbon dioxide (CO₂ ), and generate and remove carbon through in-stream primary production and respiration. Despite their importance, headwater streams are often neglected in global carbon budgets primarily because of a lack of available data. We measured these processes, in detail, over a 10 year period in a stream draining a 96 ha forested watershed in western Oregon, USA. This stream, which represents only 0.4% of the watershed area, exported 159 kg C ha⁻¹ yr⁻¹, similar to the global exports for large rivers. Stream export was dominated by downstream transport of dissolved inorganic carbon (63 kg C ha⁻¹ yr⁻¹) and by evasion of CO₂ to the atmosphere (42 kg C ha⁻¹ yr⁻¹), leaving the remainder of 51 kg C ha⁻¹ yr⁻¹ for downstream transport of organic carbon (17 kg C ha⁻¹ yr⁻¹ and 34 kg C ha⁻¹ yr⁻¹ in dissolved and particulate form, respectively)Keywords: evasion, carbon fluxes, biogeochemistry, carbon, strea