Ecosystem respiration and its components in a rainfed spring maize cropland in the Loess Plateau, China

Gao, Xiang; Mei, Xurong; Gu, Fangyi; Hao, Weiping; Li, Haoru; Gong, Daozhi

doi:10.1038/s41598-017-17866-1

Cited by 14 publications

(3 citation statements)

References 44 publications

(121 reference statements)

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“…NEE, GPP, and R eco , which represent the net carbon exchange, total carbon assimilation, and total respiration in a specific ecosystem, respectively, play vital roles in the response of the terrestrial ecosystem to global climate change (Jung et al, 2017;Reichstein et al, 2005;Xu et al, 2004). Particularly, increases in R eco may contribute to accelerating global warming through positive feedbacks to the atmosphere (Cox et al, 2000;Gao et al, 2017;IPCC, 2019;Suleau et al, 2011); estimating and monitoring R eco over relevant spatiotemporal scales is challenging. As described below, there are many different strategies for measuring and estimating ET and R eco , each of which has advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Chen

Dafflon

Tran

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (Reco). However, accurate estimation of ET and Reco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. 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 ET and Reco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate 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, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and Reco and enhances process understanding at sparsely monitored watersheds.

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Section: Introductionmentioning

confidence: 99%

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Chen

Dafflon

Tran

et al. 2021

Hydrol. Earth Syst. Sci.

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“…Soil respiration ( R s ) or soil CO 2 emissions is a major component of the terrestrial carbon cycle 8 , 9 . Globally, agricultural soils account for nearly 25% of CO 2 released to the atmosphere from anthropogenic sources 10 , 11 . This efflux of CO 2 from soils to the atmosphere includes two major respiratory fluxes: autotrophic respiration from live plant roots and heterotrophic respiration due to microbial decomposition of organic matter 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

Long-term tillage effect on with-in season variations in soil conditions and respiration from dryland winter wheat and soybean cropping systems

Zapata

Rajan

Mowrer

et al. 2021

Sci Rep

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Soil respiration from agricultural soils is a major anthropogenic source of CO2 to the atmosphere. With-in season emission of soil CO2 from croplands are affected by changes in weather, tillage, plant row spacing, and plant growth stage. Tillage involves physical turning of soils which accelerate residue decomposition and CO2 emission. No-tillage lacks soil disturbance and residues undergo slower decomposition at the surface. In this study, we compared with-in season soil conditions (temperature and moisture) and soil respiration from two major crops (soybean and winter wheat) by making high temporal frequency measurements using automated chambers at half-hourly intervals. The experiment lasted for 179 days. Total number of measurements made from conventional and no-tillage soybean and winter wheat plots were 6480 and 4456, respectively. Average flux after the winter-dormancy period of wheat was 37% higher in tilled soil compared to no-till soil. However, average flux during the soybean growing season was 8% lower in conventional till compared to no-till soil. This differential response of soil respiration in wheat and soybean was primarily due to tillage-induced changes in surface characteristics (residue cover) and soil environmental conditions (soil temperature and soil moisture). Results from this study can help elucidate relationships for modeling and assessment of field-scale soil CO2 emissions from dryland wheat and soybean crops grown in sub-tropics.

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“…As long term exchanges in have pivotal influences over the climate system (Cox et al, 2000;Gao et al, 2017;IPCC, 2019;Suleau et al, 2011), approaches are needed to estimate and monitor over relevant spatiotemporal scales. As described below, there are many different strategies for measuring and estimating ET and , each of which has advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

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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Ecosystem respiration and its components in a rainfed spring maize cropland in the Loess Plateau, China

Cited by 14 publications

References 44 publications

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Long-term tillage effect on with-in season variations in soil conditions and respiration from dryland winter wheat and soybean cropping systems

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

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