Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

He, Jinling; Zhang, Pengyan; Jing, Wenlong; Yan, Yuhang

doi:10.3390/su10103646

Cited by 16 publications

(13 citation statements)

References 66 publications

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“…The simulation results of the CASA model showed a larger zigzag shape, especially in the southern region. Obviously, NPP from TW model overestimated compared with the other two models, which is consistent with previous studies [ 24 , 33 , 36 , 41 ] As far as the input data is concerned, NPP simulated by CASA model and MODIS NPP products consider not only meteorological factors, but also different vegetation types and land surface information, so the results are more realistic. Table 2 .…”

Section: Resultssupporting

confidence: 89%

“…These data come from the National Forest Resources Inventory conducted by the Chinese Forestry Department during the period 1989–1993. Besides, we also used in-situ survey datasets from published kinds of literature with well-documented field sites [ 3 , 36 – 38 ]. These data provided some valuable information such as site names, latitude, longitude, elevation, biomass and NPP estimations for most of the plant components.…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of NPP using three models compared with MODIS-NPP data over China

2021

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Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.

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Section: Resultssupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Evaluation of NPP using three models compared with MODIS-NPP data over China

2021

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“…e trend of global terrestrial net primary productivity (NPP) is still uncertain [4]. In the context of climate change, the threat of rapid global urbanization to terrestrial ecosystem productivity, environment, livelihoods, and food security has gradually become one of the most critical issues in the world [5,6]. e net primary productivity refers to the amount of organic matter accumulated by green plants in unit time per unit area [7,8].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Assessment of the Effect of Urban Built-Up Land Expansion and Climate Change on Net Primary Productivity

Zhang

Jing

et al. 2020

Complexity

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Urbanization is causing profound changes in ecosystem functions at local and regional scales. The net primary productivity (NPP) is an important indicator of global change, rapid urbanization and climate change will have a significant impact on NPP, and urban expansion and climate change in different regions have different impacts on NPP, especially in densely populated areas. However, to date, efforts to quantify urban expansion and climate change have been limited, and the impact of long-term continuous changes in NPP has not been well understood. Based on land use data, night light data, NPP data, climate data, and a series of social and economic data, we performed a comprehensive analysis of land use change in terms of type and intensity and explored the pattern of urban expansion and its relationship with NPP and climate change for the period of 2000–2015, taking Zhengzhou, China, as an example. The results show that the major form of land use change was cropland to built-up land during the 2000–2015 period, with a total area of 367.51 km2 converted. The NPP exhibited a generally increasing trend in the study area except for built-up land and water area. The average correlation coefficients between temperature and NPP and precipitation and NPP were 0.267 and 0.020, respectively, indicating that an increase in temperature and precipitation can promote NPP despite significant spatial differences. During the examined period, most expansion areas exhibited an increasing NPP trend, indicating that the influence of urban expansion on NPP is mainly characterized by an evident influence of the expansion area. The study can provide a reference for Zhengzhou and even the world's practical research to improve land use efficiency, increase agricultural productivity and natural carbon sinks, and maintain low-carbon development.

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“…Therefore, it is necessary to conduct an extensive study across a large region including multiple climate zones and diverse vegetation types to have a comprehensive understanding of how climate asymmetric warming can impact vegetation dynamics. The Yellow River Basin, which spans the arid, semi-arid and arid climate zones in the north of China, is sensitive to global climate change because it faces serious water deficit problems and is thus one of the ideal regions for examing the asymmetric warming’s effects on vegetation [22,23,24]. Although some studies pointed out that the differences in hydrothermal climate distribution across the Yellow River Basin could lead to different sensitivities of vegetation to diurnal asymmetric warming in different regions [25,26,27,28,29], there is limited understanding about the effect and influence of asymmetric warming on the vegetation dynamics across the Yellow River Basin.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015

Xia

Meng

2019

Sensors

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Temperatures from 1982 to 2015 have exhibited an asymmetric warming pattern between day and night throughout the Yellow River Basin. The response to this asymmetric warming can be linked to vegetation growth as quantified by the NDVI (Normalized Difference Vegetation Index). In this study, the time series trends of the maximum temperature (Tmax) and the minimum temperature (Tmin) and their spatial patterns in the growing season (April–October) of the Yellow River Basin from 1982 to 2015 were analyzed. We evaluated how vegetation NDVI had responded to daytime and night-time warming, based on NDVI and meteorological parameters (precipitation and temperature) over the period 1982–2015. We found: (1) a persistent increase in the growing season Tmax and Tmin in 1982–2015 as confirmed by using the Mann–Kendall (M–K) non-parametric test method (p < 0.01), where the rate of increase of Tmin was 1.25 times that of Tmax, and thus the diurnal warming was asymmetric during 1982–2015; (2) the partial correlation between Tmax and NDVI was significantly positive only for cultivated plants, shrubs, and desert, which means daytime warming may increase arid and semi-arid vegetation’s growth and coverage, and cultivated plants’ growth and yield. The partial correlation between Tmin and NDVI of all vegetation types except broadleaf forest is very significant (p < 0.01) and, therefore, it has more impacts vegetation across the whole basin. This study demonstrates a methodogy for studying regional responses of vegetation to climate extremes under global climate change.

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Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

Cited by 16 publications

References 66 publications

Evaluation of NPP using three models compared with MODIS-NPP data over China

Evaluation of NPP using three models compared with MODIS-NPP data over China

Comprehensive Assessment of the Effect of Urban Built-Up Land Expansion and Climate Change on Net Primary Productivity

Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015

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