Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

Dou, Xiankang; Yang, Yongguo

doi:10.3390/f8120498

Cited by 11 publications

(4 citation statements)

References 86 publications

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“…Here, the SVM algorithm ranked a close second to RF (Figure 2), so it may also serve as an excellent model for water flux simulation. The performance of the BPNN algorithm was poor, in contrast to the findings of Tramontana et al (2016) and Dou and Yang (2017). This is most likely because BPNN has relatively poor stability, and small numerical perturbation can affect model performance (Zhang & Hu, 2011).…”

Section: Discussioncontrasting

confidence: 75%

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Xie

Luo

Hellwich

et al. 2021

Hydrological Processes

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The study of water fluxes is important to better understand hydrological cycles in arid regions. Data‐driven machine learning models have been recently applied to water flux simulation. Previous studies have built site‐scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter‐annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R2 = 0.25–0.90) was slightly worse than that of the RF algorithm (R2 = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R2 = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R2 = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non‐growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision‐making basis to the water management in desert and oasis ecosystems.

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Section: Discussioncontrasting

confidence: 75%

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Xie

Luo

Hellwich

et al. 2021

Hydrological Processes

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“…In this study, the accuracy of the ET prediction models was evaluated using several statistical criteria: root mean square error (RMSE), determination coefficient (R 2 ), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NS) (Dou and Yang, 2017;Kisi and Alizamir, 2018;Wunsch et al, 2018). These statistical indicators are defined as follows: To evaluate these statistical indicators, the following criteria were used: for RMSE, a lower value indicates better performance; for R 2 , a value greater than 0.5 indicates a satisfactory fit; for MAE, a lower value indicates better performance; and for NS, a value below 0.5 indicates an unsatisfactory fit.…”

Section: Performance Evaluationmentioning

confidence: 99%

Artificial Neural Networks Estimate Evapotranspiration For Miscanthus × Giganteus As Effectively As Empirical Model But With Fewer Inputs

Sungur,

Moore,

Bernacchi

et al. 2024

Preprint

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Estimating actual evapotranspiration (ET) is particularly crucial for addressing how vegetation affects the water balance of ecosystems. ET estimation can be complex with empirical models due to their many parameters and reliance on aridity. In contrast, artificial neural networks (ANNs) could potentially estimate ET with fewer and more common meteorological parameters. In this study, we trained two ANNs, one using a feed-forward approach (FFN) and the other a nonlinear auto-regressive network (NARX), to predict ET and compared them to the commonly used empirical model Granger and Gray (GG). We trained our models on a nine-year eddy covariance (EC) dataset for Miscanthus × giganteus (M. × giganteus) from Illinois (UIEF), then tested them using out-of-sample data from both UIEF and a different location in Iowa (SABR) to compare the accuracy of FFN, NARX, and GG models in estimating daily ET. A combination of air temperature (Ta) and solar radiation (Rs) was chosen as inputs due to the highest R2 for FFN (R2= 0.79, 0.81, and 0.79 for training, testing, and validation, respectively) and only Ta for NARX (R2= 0.70 for out-of-sample validation). The predictive power of the FFN model was superior to the NARX and GG models at the UIEF site (R2= 0.84, 0.70, and 0.83 for out-of-sample validation, respectively). Our analysis showed that ANN approaches are as accurate as empirical approaches for estimating ET but use fewer inputs.

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“…Currently, EC is the only standard measurement method that can directly measure the exchange of CO 2 between an ecosystem and the atmosphere. Although the monitoring system was first used in natural ecosystems such as grasslands, cropland, and forests [7][8][9][10], EC is now increasingly being used in cities [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…The majority of studies of CO 2 fluxes have concentrated on the seasonal changes of single vegetation-type ecosystems [7][8][9][10]. Measuring the CO 2 flux of urban vegetation still has many challenges such as the complexity of urban environments, the difficulty of determining soil CO 2 flux and the discrepancies of photosynthesis rates among trees [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

CO2 Flux Characteristics of Different Plant Communities in a Subtropical Urban Ecosystem

Zhang

Gong

et al. 2019

Sustainability

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Shanghai, China, is a city that is relatively representative of various cities in China due to its geographical location and socio-economic dynamics. The role of urban vegetation in the carbon cycle of urban developments in these types of cities is now being studied. We focus on identifying which urban plant community types have a greater influence on CO2 flux in cities, thus providing a scientific basis for low-carbon urban greening. Based on the eddy covariance (EC) observation system, ART Footprint Tool, plant inventory, and ecological community classification, we show that the CO2 flux characteristics of different plant communities vary temporally. The carbon sink duration during summer was the longest (up to 10 h) and the carbon sink duration was the shortest during winter (7.5 h). In addition, we discovered that the CO2 flux contribution rates of different plant community types are distinct. The annual average CO2 contribution rates of the Cinnamomum camphora-Trachycarpus fortunei community and the Metasequoia glyptostroboides+Sabina chinensis community are 11.88% and 0.93%, respectively. The CO2 flux contribution rate of the same plant community differs according to season. The CO2 contribution rate of the Cinnamomum camphora-Trachycarpus fortunei community exhibits local maxima during winter and summer, with a maximum difference of 11.16%. In contrast, the Metasequoia glyptostroboides+Sabina chinensis community has a CO2 contribution rate of 0.35% during the same period. In general, summer is the season with the lowest CO2 flux contribution rate of plant communities, and winter is the season with the highest CO2 flux contribution rate. However, the Cinnamomum camphora+Salix babylonica community and the Cinnamomum camphora+Sabina chinensis community present the opposite pattern. Finally, the diurnal variation characteristics of CO2 flux in different communities have the same trend, but the peak values differ significantly. Overall, daily CO2 flux peak value of the Metasequoia glyptostroboides community and the Cinnamomum camphora-Trachycarpus fortunei community indicate that these two plant communities exhibit a strong capacity for CO2 absorption in the study area. According to these research results, urban greening efforts in subtropical climates can increase the green space covered by the Cinnamomum camphora-Trachycarpus fortunei and the Metasequoia glyptostroboides community types when urban greening, so as to appropriately reduce the CO2 emitted into the atmosphere.

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Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

Cited by 11 publications

References 86 publications

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Simulation of site‐scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

Artificial Neural Networks Estimate Evapotranspiration For Miscanthus × Giganteus As Effectively As Empirical Model But With Fewer Inputs

CO2 Flux Characteristics of Different Plant Communities in a Subtropical Urban Ecosystem

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