Encoding diel hysteresis and the Birch effect in dryland soil respiration models through knowledge-guided deep learning

Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of the Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models

A systematic review of patient reported factors associated with uptake and completion of cardiovascular lifestyle behaviour change

Background: Healthy lifestyles are an important facet of cardiovascular risk management. Unfortunately many individuals fail to engage with lifestyle change programmes. There are many factors that patients report as influencing their decisions about initiating lifestyle change. This is challenging for health care professionals who may lack the skills and time to address a broad range of barriers to lifestyle behaviour. Guidance on which factors to focus on during lifestyle consultations may assist healthcare professionals to hone their skills and knowledge leading to more productive patient interactions with ultimately better uptake of lifestyle behaviour change support. The aim of our study was to clarify which influences reported by patients predict uptake and completion of formal lifestyle change programmes. Methods: A systematic narrative review of quantitative observational studies reporting factors (influences) associated with uptake and completion of lifestyle behaviour change programmes. Quantitative observational studies involving patients at high risk of cardiovascular events were identified through electronic searching and screened against pre-defined selection criteria. Factors were extracted and organised into an existing qualitative framework. Results: 374 factors were extracted from 32 studies. Factors most consistently associated with uptake of lifestyle change related to support from family and friends, transport and other costs, and beliefs about the causes of illness and lifestyle change. Depression and anxiety also appear to influence uptake as well as completion. Many factors show inconsistent patterns with respect to uptake and completion of lifestyle change programmes. Conclusion: There are a small number of factors that consistently appear to influence uptake and completion of cardiovascular lifestyle behaviour change. These factors could be considered during patient consultations to promote a tailored approach to decision making about the most suitable type and level lifestyle behaviour change support

ECOSTRESS: NASA's next generation mission to measure evapotranspiration from the International Space Station

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station ECOSTRESS) was launched to the International Space Station on June 29, 2018. The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as level‐3 (L3) latent heat flux (LE) data products. These data are generated from the level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are over‐represented. The 70 m high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1 km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data

Mechanistic links between underestimated CO2 fluxes and non-closure of the surface energy balance in a semi-arid sagebrush ecosystem

The surface energy balance non-closure problem in eddy covariance (EC) studies has been largely attributed to the influence of large-scale turbulent eddies (hereafter large eddies) on latent and sensible heat fluxes. However, how such large eddies concurrently affect CO _2 fluxes remains less studied and mechanistic links between the energy balance non-closure and CO _2 fluxes are not well understood. Here, using turbulence data collected from an EC tower over a sagebrush ecosystem during two growing seasons, we decomposed the turbulence data into small and large eddies at a cutoff frequency and analyzed their contributions to the fluxes. We found that the magnitude of CO _2 fluxes decreased concurrently with decreased sensible and latent heat fluxes (and thus increased energy balance non-closure), primarily caused by large turbulent eddies. The contributions of such large eddies to fluxes are dependent not only upon their magnitudes of vertical velocity ( w ) and scalars (i.e. temperature, water vapor density, and CO _2 concentration), but also upon the phase differences between the large eddies of w and scalars via their covariances. Enlarged phase differences between large eddies of w and these scalars simultaneously led to reductions in the magnitudes of both CO _2 and heat fluxes, linking the lower CO _2 fluxes to energy balance non-closure. Such increased phase differences of large eddies were caused by changes in the structures of large eddies from unstable to near neutral conditions. Given widespread observations in non-closure in the flux community, the processes identified here may bias CO _2 fluxes at many sites and cause upscaled regional and global budgets to be underestimated. More studies are needed to investigate how landscape heterogeneity influences CO _2 fluxes through the influence of associated large eddies on flux exchange

DataSheet1_Encoding diel hysteresis and the Birch effect in dryland soil respiration models through knowledge-guided deep learning.PDF

Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of the Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.</p

A novel approach to evaluate soil heat flux calculation: An analytical review of nine methods

This dataset is presented in the paper submitted to Journal of Geophysical Research-Atmospheres: A novel approach to evaluate soil heat flux calculation: An analytical review of nine methods. This dataset includes all the measured and modeled data used in the paper. For the detailed data description, please refer to the paper