Estimating Soil Respiration in a Subalpine Landscape Using Point, Terrain, Climate, and Greenness Data

Berryman, E.; Vanderhoof, Melanie K.; Bradford, John B.; Hawbaker, Todd J.; Henne, Paul D.; Burns, Sean P.; Frank, J. M.; Birdsey, Richard A.; Ryan, Michael G.

doi:10.1029/2018jg004613

Cited by 18 publications

(14 citation statements)

References 91 publications

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“…The 2 between 8-day aggregated HPMbased ET estimation and data retrieved from Mu et al (2013) achieves 0.65 (Figure S1). Berryman et al (2018) developed a random forest model to predict growing season soil respiration at subalpine forests in the Southern Rocky Watershed for validation, our results are comparable to other studies that focus on sites within the same ecoregion (e.g., Berryman et al, 2018). shows the absolute value of monthly mean difference in ET (Fig.…”

Section: Use Case 2: Ecoregion-based Data-driven Hpm Model For Et and Estimationsupporting

confidence: 81%

“…Our (Berryman et al, 2018;Mu et al, 2013). Annual differences between evergreen forests and deciduous forests are around 50 −2 , which is comparable to Berryman et al 2018). Similar dynamics were also observed at regions that are have different climate conditions.…”

Section: Discussionmentioning

confidence: 78%

“…Li and Xiao (2019) developed a data-driven model for gross primary production at a spatial and temporal resolution of 0.05° and days using MODIS and meterological reanalysis data. Berryman et al (2018) demonstrated a Random Forest model to predict growing season soil respiration from subalpine forests in the Southern Rocky Mountains ecoregion. Jung et al (2009) developed a model tree ensemble approach to upscale FLUXNET data, where they have successfully estimated ET and GPP.…”

Section: Introductionmentioning

confidence: 99%

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A Deep-Learning Hybrid-Predictive-Modeling Approach for Estimating Evapotranspiration and Ecosystem Respiration

Chen¹,

Dafflon²,

Tran³

et al. 2020

Preprint

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Abstract. Gradual changes in meteorological forcings (such as temperature and precipitation) are reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including water and carbon fluxes. Estimating evapotranspiration (ET) and ecosystem respiration (RECO) is essential for analyzing the effect of climate change on ecosystem behavior. To obtain a better understanding of these processes, we need to improve our estimation of water and carbon fluxes over space and time, which is difficult within ecosystems where we have only sparse data. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically-based model simulation results, meteorological forcings, and remote sensing datasets to estimate evapotranspiration (ET) and ecosystem respiration (RECO) in high space-time resolution. HPM relies on a deep learning algorithm-long short term memory (LSTM) – as well as direct measurements or outputs from physically-based models. We tested and validated HPM estimation results at sites within various mountainous regions, given their importance for water resources, their vulnerability to climate change, and the recognized difficulties in estimating ET and RECO in mountainous regions. We benchmarked estimates of ET and RECO obtained from the HPM method against measurements made at FLUXNET stations and outputs from the Community Land Model (CLM) at Rocky Mountain SNOTEL stations. At the mountainous East River Watershed site in the Upper Colorado River Basin, we explored how ET and RECO dynamics estimated from the new HPM approach vary with different vegetation and meteorological forcings. The results of this study indicate that HPM is capable of identifying complicated interactions among meteorological forcings, ET, and RECO variables, as well as providing reliable estimation of ET and RECO across relevant spatiotemporal scales. With HPM estimation of ET and RECO at the East River Watershed, we found that abiotic factors of temperature and radiation predominantly explained ET spatial variability; whereas RECO variability was largely controlled by biotic factors, such as vegetation type. In general, our study demonstrated that the HPM approach can circumvent the typical lack of spatiotemporally dense data needed to estimate ET and RECO over space and time, as well as the parametric and structural uncertainty inherent in mechanistic models. While the current limitations of the HPM approach are driven by the temporal and spatial resolution of available datasets (such as NDVI), ongoing advances in remote sensing are expected to further improve accuracy and resolution of ET and RECO estimation using HPM.

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Section: Use Case 2: Ecoregion-based Data-driven Hpm Model For Et and Estimationsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

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A Deep-Learning Hybrid-Predictive-Modeling Approach for Estimating Evapotranspiration and Ecosystem Respiration

Chen¹,

Dafflon²,

Tran³

et al. 2020

Preprint

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“…A good agreement (R 2 : 0.80-0.97; RMSE: 279-978 g CO2 m −2 yr −1 ) between simulated and observed ecosystem respiration (RECO) was observed (Figure 8). Although the simulative accuracy was good compared to previous studies, for example, studies on soil respiration simulation at global and ecosystem scales which showed RMSE values of 696-2010 g CO2 m −2 yr −1 [3,28,62,63]. It is worth note that the present RMSE values were still quite high (12-43% of the observed respiration), which calls for more efforts on ecosystem/soil respiration modeling.…”

Section: Uncertaintiescontrasting

confidence: 51%

A Method for Estimating Annual Cumulative Soil/Ecosystem Respiration and CH4 Flux from Sporadic Data Collected Using the Chamber Method

Yang

et al. 2019

Atmosphere

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Measurements of greenhouse gas fluxes over many ecosystems have been made as part of the attempt to quantify global carbon and nitrogen cycles. In particular, annual flux observations are of great value for regional flux assessments, as well as model development and optimization. The chamber method is a popular approach for soil/ecosystem respiration and CH4 flux observations of terrestrial ecosystems. However, in situ flux chamber measurements are usually made with non-continuous sampling. To date, efficient methods for the application of such sporadic data to upscale temporally and obtain annual cumulative fluxes have not yet been determined. To address this issue, we tested the adequacy of non-continuous sampling using multi-source data aggregation. We collected 330 site-years monthly soil/ecosystem respiration and 154 site-years monthly CH4 flux data in China, all obtained using the chamber method. The data were randomly divided into a training group and verification group. Fluxes of all possible sampling months of a year, i.e., 4094 different month combinations were used to obtain the annual cumulative flux. The results showed a good linear relationship between the monthly flux and the annual cumulative flux. The flux obtained during the warm season from May to October generally played a more important role in annual flux estimations, as compared to other months. An independent verification analysis showed that the monthly flux of 1 to 4 months explained up to 67%, 89%, 94%, and 97% of the variability of the annual cumulative soil/ecosystem respiration and 92%, 99%, 99%, and 99% of the variability of the annual cumulative CH4 flux. This study supports the use of chamber-observed sporadic flux data, which remains the most commonly-used method for annual flux estimating. The flux estimation method used in this study can be used as a guide for designing sampling programs with the intention of estimating the annual cumulative flux.

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“…Several studies have recently been conducted to model the spatial distribution of soil respiration (R s ) at alpine grasslands [25] and forests [26,27] using the satellite-based products including land surface temperature (LST) and spectral vegetation index (NDVI or EVI) or leaf area index (LAI). By incorporating the terrain information, Berryman et al [28] estimated R s in a typical of the Southern Rocky Mountains with a coefficient of determination (R 2 ) of 0.45. Jägermeyr et al [29] firstly developed the models of global R e according to forested and non-forested biomes.…”

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confidence: 99%

Remotely monitoring ecosystem respiration from various grasslands along a large-scale east–west transect across northern China

Tang

Zhou

et al. 2020

Carbon Balance Manage

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Background: Grassland ecosystems play an important role in the terrestrial carbon cycles through carbon emission by ecosystem respiration (R e) and carbon uptake by plant photosynthesis (GPP). Surprisingly, given R e occupies a large component of annual carbon balance, rather less attention has been paid to developing the estimates of R e compared to GPP. Results: Based on 11 flux sites over the diverse grassland ecosystems in northern China, this study examined the amounts of carbon released by R e as well as the dominant environmental controls across temperate meadow steppe, typical steppe, desert steppe and alpine meadow, respectively. Multi-year mean R e revealed relatively less CO 2 emitted by the desert steppe in comparison with other grassland ecosystems. Meanwhile, C emissions of all grasslands were mainly controlled by the growing period. Correlation analysis revealed that apart from air and soil temperature, soil water content exerted a strong effect on the variability in R e , which implied the great potential to derive R e using relevant remote sensing data. Then, these field-measured R e data were up-scaled to large areas using time-series MODIS information and remote sensing-based piecewise regression models. These semi-empirical models appeared to work well with a small margin of error (R 2 and RMSE ranged from 0.45 to 0.88 and from 0.21 to 0.69 g C m −2 d −1 , respectively). Conclusions: Generally, the piecewise models from the growth period and dormant season performed better than model developed directly from the entire year. Moreover, the biases between annual mean R e observations and the remotely-derived products were usually within 20%. Finally, the regional R e emissions across northern China's grasslands was approximately 100.66 Tg C in 2010, about 1/3 of carbon fixed from the MODIS GPP product. Specially, the desert steppe exhibited the highest ratio, followed by the temperate meadow steppe, typical steppe and alpine meadow. Therefore, this work provides a novel framework to accurately predict the spatio-temporal patterns of R e over large areas, which can greatly reduce the uncertainties in global carbon estimates and climate projections.

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Estimating Soil Respiration in a Subalpine Landscape Using Point, Terrain, Climate, and Greenness Data

Cited by 18 publications

References 91 publications

A Deep-Learning Hybrid-Predictive-Modeling Approach for Estimating Evapotranspiration and Ecosystem Respiration

A Deep-Learning Hybrid-Predictive-Modeling Approach for Estimating Evapotranspiration and Ecosystem Respiration

A Method for Estimating Annual Cumulative Soil/Ecosystem Respiration and CH4 Flux from Sporadic Data Collected Using the Chamber Method

Remotely monitoring ecosystem respiration from various grasslands along a large-scale east–west transect across northern China

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