Quantifying uncertainty from large‐scale model predictions of forest carbon dynamics

Miehle, P.; Livesley, Stephen J.; Li, Changsheng; Feikema, Paul; Adams, Mark A.; Arndt, Stefan K.

doi:10.1111/j.1365-2486.2006.01176.x

Cited by 61 publications

(37 citation statements)

References 47 publications

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“…Figure 5 shows an obvious linear relationship between NDVI and existing aboveground biomass (n=98, R=0.885, α=0.002). To verify the accuracy of the model established in this research, the author used data from 38 randomly selected sampling locations that were collected from 1989 to 2011 to conduct verification and analyzed the error in terms of root mean square error (RMSE) and mean absolute percent error (MAPE) [47].…”

Section: Simulation Of Existing Aboveground Biomassmentioning

confidence: 99%

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A Model for Simulating the Soil Organic Carbon Pool of Steppe Ecosystems

Zhou

et al. 2015

Environ Model Assess

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In this research, the improved Terrestrial Ecosystem Regional (TECO-R) model was adapted to steppe ecosystems and then utilized to simulate the soil organic carbon pool in the period from 1989 to 2011 (excluding 1994, 2002, 2009, and 2010) for a typical steppe in Xilingol League of Inner Mongolia in China. The improved TECO-R model is an ecological model in combination of remote sensing data, which allows the spatial scale for the analysis of soil organic carbon which is not limited to vegetation or soil type. The spatial and temporal resolution advantages of remote sensing image can be well utilized in this model. The results indicate that in addition to an accurate simulation of the soil carbon pool of a steppe ecosystem, the vegetation aboveground carbon pool, grazing intensity of herbivores, mowing coefficient, litter carbon pool, root carbon pools of different vegetation layers, root-shoot ratio, actual residence time of different carbon pools, and allocation coefficients of different carbon pools in corresponding years are also available from the TECO-R model. Some of the above data are difficult to obtain through macro-observation but can be simulated with the TECO-R model by combining the model with input data; this is very important for a correct understanding of the feedback relationships between the steppe ecosystem's carbon cycle and climate change (e.g., global warming) and human activities such as grazing.

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Section: Simulation Of Existing Aboveground Biomassmentioning

confidence: 99%

“…N is the number of sampling [41,42] and soil organic carbon data from 2011 were measured by the author of this paper) [47]. And the analysis results in Table 4 and Fig.…”

Section: Simulation Of Existing Aboveground Biomassmentioning

confidence: 99%

A Model for Simulating the Soil Organic Carbon Pool of Steppe Ecosystems

Zhou

et al. 2015

Environ Model Assess

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“…R 2 is used to test the agreement between the modeled results and observations, where a value closer to 1 indicates that the model provides a better explanation for the observed values (Willmott, 1982). The positive ME value indicates that the model prediction is better than the mean of observations, and the best model performance has ME value equal to 1 (Miehle, 2006). RMSE, R 2 and ME were calculated as follows:…”

Section: Model Validation and Sensitivity Testmentioning

confidence: 99%

“…There is a lack of consistent conclusions regarding the impact of grazing on the SOC concentration according to previous studies. Thus, some studies showed that the grazing intensity and SOC had a negative correlation (Derner et al, 1997;Bagchi and Ritchie, 2010;Wu et al, 2009) or no relationship (Milchunas and Lauenroth, 1993;Holt, 1997). By contrast, many other studies showed that grazing can increase the SOC (Schuman et al, 1999;Wienhold et al, 2001;Li et al, 2011).…”

Section: Effects Of Grazing Intensity On Biomass and Socmentioning

confidence: 99%

Modeling impacts of climate change and grazing effects on plant biomass and soil organic carbon in the Qinghai–Tibetan grasslands

Zhang

et al. 2017

Biogeosciences

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Abstract. The Qinghai Province supports over 40 % of the human population of the Qinghai-Tibetan Plateau (QTP) but occupies about 29 % of its land area, and thus it plays an important role in the plateau. The dominant land cover is grassland, which has been severely degraded over the last decade due to a combination of increased human activities and climate change. Numerous studies indicate that the plateau is sensitive to recent global climate change, but the drivers and consequences of grassland ecosystem change are controversial, especially the effects of climate change and grazing patterns on the grassland biomass and soil organic carbon (SOC) storage in this region. In this study, we used the DeNitrification-DeComposition (DNDC) model and two climate change scenarios (representative concentration pathways: RCP4.5 and RCP8.5) to understand how the grassland biomass and SOC pools might respond to different grazing intensities under future climate change scenarios. More than 1400 grassland biomass sampling points and 46 SOC points were used to validate the simulated results. The simulated above-ground biomass and SOC concentrations were in good agreement with the measured data (R 2 0.71 and 0.73 for above-ground biomass and SOC, respectively). The results showed that climate change may be the major factor that leads to fluctuations in the grassland biomass and SOC, and it explained 26.4 and 47.7 % of biomass and SOC variation, respectively. Meanwhile, the grazing intensity explained 6.4 and 2.3 % variation in biomass and SOC, respectively. The project average biomass and SOC between 2015 and 2044 was significantly smaller than past 30 years , and it was 191.17 g C m −2 , 63.44 g C kg −1 and 183.62 g C m −2 , 63.37 g C kg −1 for biomass and SOC under RCP4.5 and RCP8.5, respectively. The RCP8.5 showed the more negative effect on the biomass and SOC compared with RCP4.5. Grazing intensity had a negative relationship with biomass and positive relationship with SOC. Compared with the baseline, the biomass and SOC changed by 12.56 and −0.19 % for G 0 , 7.23 and 0.23 for G −50 , and −5.17 and 1.19 % for G +50 . In the future, more human activity and management practices should be coupled into the model simulation.

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“…2). A positive value of ME indicates that the model predictions are better than the mean of the measurements, and the best model performance has ME value equal to 1 (Miehle et al, 2006;Nash and Sutcliffe, 1970).…”

Section: Model Testsmentioning

confidence: 99%

Modeling nitrogen loading in a small watershed in southwest China using a DNDC model with hydrological enhancements

et al. 2011

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Abstract. The degradation of water quality has been observed worldwide, and inputs of nitrogen (N), along with other nutrients, play a key role in the process of contamination. The quantification of N loading from non-point sources at a watershed scale has long been a challenge. Processbased models have been developed to address this problem. Because N loading from non-point sources result from interactions between biogeochemical and hydrological processes, a model framework must include both types of processes if it is to be useful. This paper reports the results of a study in which we integrated two fundamental hydrologic features, the SCS (Soil Conservation Service) curve function and the MUSLE (Modified Universal Soil Loss), into a biogeochemical model, the DNDC. The SCS curve equation and the MUSLE are widely used in hydrological models for calculating surface runoff and soil erosion. Equipped with the new added hydrologic features, DNDC was substantially enhanced with the new capacity of simulating both vertical and horizontal movements of water and N at a watershed scale. A long-term experimental watershed in Southwest China was selected to test the new version of the DNDC. The target watershed's 35.1 ha of territory encompass 19.3 ha of croplands, 11.0 ha of forest lands, 1.1 ha of grassplots, and 3.7 ha of residential areas. An input database containing topographic data, meteorological conditions, soil properties, vegetation information, and management applications was established and linked to the enhanced DNDC. Driven by the input database, the DNDC simulated the surface runoff flow, the subsurface leaching flow, the soil erosion, and the N loadings from the target watershed. The modeled waterCorrespondence to: B. Zhu (bzhu@imde.ac.cn) flow, sediment yield, and N loading from the entire watershed were compared with observations from the watershed and yielded encouraging results. The sources of N loading were identified by using the results of the model. In 2008, the modeled runoff-induced loss of total N from the watershed was 904 kg N yr −1 , of which approximately 67 % came from the croplands. The enhanced DNDC model also estimated the watershed-scale N losses (1391 kg N yr −1 ) from the emissions of the N-containing gases (ammonia, nitrous oxide, nitric oxide, and dinitrogen). Ammonia volatilization (1299 kg N yr −1 ) dominated the gaseous N losses. The study indicated that process-based biogeochemical models such as the DNDC could contribute more effectively to watershed N loading studies if the hydrological components of the models were appropriately enhanced.

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Quantifying uncertainty from large‐scale model predictions of forest carbon dynamics

Cited by 61 publications

References 47 publications

A Model for Simulating the Soil Organic Carbon Pool of Steppe Ecosystems

A Model for Simulating the Soil Organic Carbon Pool of Steppe Ecosystems

Modeling impacts of climate change and grazing effects on plant biomass and soil organic carbon in the Qinghai–Tibetan grasslands

Modeling nitrogen loading in a small watershed in southwest China using a DNDC model with hydrological enhancements

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