Schematic illustration of the spatial downscaling process. The downscaled precipitation field is constructed from CRU data in August 2014.
ABSTRACT
The Loess Plateau (LP) in China is sensitive to ...climate change because of its fragile ecological environment and geographic features. This study presents a detailed assessment of the climate change trends over the LP from 1901 to 2100 based on the 1‐km spatial resolution climate data generated through delta downscaling. The following results are drawn: (1) Delta downscaling performs well in detecting the monthly precipitation and temperature over the LP based on the mean absolute error between downscaled data and independent surface observations. Among the 28 general circulation models, the GFDL‐ESM2M and NorESM1‐M models show the best performance in reproducing the monthly precipitation and temperature in the future period, respectively. (2) The annual precipitation over the entire LP shows no significant trends in the historical and future periods. By contrast, the annual temperature shows a significantly increasing trend with 0.097 °C/10 year in the historical period (1901–2014) and with 0.113, 0.24, 0.355, and 0.558 °C/10 year in the future period (2015–2100) under the RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. (3) The significantly increasing trends in precipitation and temperature at each grid of the LP present various spatial distributions in terms of their magnitude. The significant trend magnitude calculated by the downscaled data has a larger range and a higher percentage of area – and is even observed at a small area – compared with that calculated by the raw data. (4) The spatial results calculated by the downscaled data provide more detailed information about the locations and percentages of area, both of which are valuable in assessing the change trends in precipitation and temperature. These spatio‐temporal results present the climate change trends over the LP in detail and provide valuable insights for developing flexible adaptation and mitigation strategies to address the climate change challenges in this region.
Grassland degradation received considerable concern because of its adverse impact on agronomic productivity and its capacity to provide goods and service. Climate change and human activities are ...commonly recognized as the two broad underlying drivers that lead to grassland degradation. In this study, a comprehensive method based on net primary productivity (NPP) was introduced to assess quantitatively the relative roles of climate change and human perturbations on worldwide grassland degradation from 2000 to 2010. The results revealed that at a global scale, 49.25 % of grassland ecosystems experienced degradation. Nearly 5 % of these grasslands experienced strong to extreme significant degradation. Climate change was the dominant cause that resulted in 45.51 % of degradation compared with 32.53 % caused by human activities. On the contrary, 39.40 % of grassland restoration was induced by human interferences, and 30.6 % was driven by climate change. The largest area of degradation and restoration both occurred in Asia. NPP losses ranged between 1.40 Tg C year⁻¹ (in North America) and 13.61 Tg C year⁻¹ (in Oceania) because of grassland degradation. Maximum NPP increase caused by restoration was 17.57 Tg C year⁻¹ (in North America). Minimum NPP was estimated at 1.59 Tg C year⁻¹ (in Europe). The roles of climate change and human activities on degradation and restoration were not consistent at continental level. Grassland ecosystems in the southern hemisphere were more vulnerable and sensitive to climate change. Therefore, climate change issues should be gradually integrated into future policies and plans for domestic grassland management and administration.
Grasslands, one of the most widespread land cover types in China, are of great importance to natural environmental protection and socioeconomic development. An accurate quantitative assessment of the ...effects of inter-annual climate change and human activities on grassland productivity has great theoretical significance to understanding the driving mechanisms of grassland degradation. Net primary productivity (NPP) was selected as an indicator for analyzing grassland vegetation dynamics from 2001 to 2010. Potential NPP and the difference between potential NPP and actual NPP were used to represent the effects of climate and human factors, respectively, on grassland degradation. The results showed that 61.49% of grassland areas underwent degradation, whereas only 38.51% exhibited restoration. In addition, 65.75% of grassland degradation was caused by human activities whereas 19.94% was caused by inter-annual climate change. By contrast, 32.32% of grassland restoration was caused by human activities, whereas 56.56% was caused by climatic factors. Therefore, inter-annual climate change is the primary cause of grassland restoration, whereas human activities are the primary cause of grassland degradation. Grassland dynamics and the relative roles of climate and human factors in grassland degradation and restoration varied greatly across the five provinces studied. The contribution of human activities to grassland degradation was greater than that of climate change in all five provinces. Three outcomes were observed in grassland restoration: First, the contribution of climate to grassland restoration was greater than that of human activities, particularly in Qinghai, Inner Mongolia, and Xinjiang. Second, the contribution of human activities to grassland restoration was greater than that of climate in Gansu. Third, the two factors almost equally contributed to grassland restoration in Tibet. Therefore, the effectiveness of ecological restoration programs should be enhanced whenever climate change promotes grassland restoration.
•NPP was used to assess the relative roles of climate and human land use in degradation.•Climate change benefits grassland restoration, human land use drives its degradation.•Effectiveness of China government environment projects should be enhanced.
Vegetation and climate are important aspects of ecology and environmental research. To explore the relationship between vegetation and climatic factors in the Loess Plateau, we analyzed the trends ...and responses of vegetation to climate changes in the whole zone as well as in different types of vegetation cover zones using linear trend analysis, Pearson′s correlation analysis, multiple linear regression models, and path analysis methods published between 2000 and 2015. The vegetation cover classification data were obtained from maps published by European Space Agency Climate Change Initiative Land Cover（ESA CCI-LC） and the Normalized Difference Vegetation Index（NDVI） data were derived from MODND1 T/NDVI vegetation index data. The results indicated that（1） from 2000 to 2015, the regions with significant increase in NDVImax in the Loess Plateau accounted for 74.25% of the total area, and among all the different vegetation cover types, the evergreen broad leaf forests and crop showed the greatest increase of 0.012/a. The decreasing order of the means of NDVImax of the different vegetation cover types is as follows: evergreen broad leaf forests > evergreen needle leaf forests > deciduous broad leaf forests > deciduous needle leaf forests > mosaic grassland > crop > mosaic tree > grassland > shrub;（2） there was no significant correlation between NDVI and climatic factors such as temperature, sunshine hours, precipitation, and relative humidity in the whole zone. However, in different vegetation cover type zones, climatic factors had different significant effects on NDVI;（3） NDVI in the whole zone and in different vegetation cover type zones changed consistently with change in precipitation, whereas change in temperature did not have a significant effect;（4） vegetation cover types that were dominated by trees, such as forests with evergreen broad leaves, deciduous broad leaves, evergreen needle leaves and mosaic trees, were significantly affected by the mean annual relative humidity and mean annual sunshine hours. However, grass-dominated vegetation types, such as grasslands and mosaic grasslands, were significantly affected by the total annual precipitation. These results imply that the distinction between vegetation types is more important in studying the impact of climate change on vegetation.
Given the context of global warming and the increasing frequency of extreme climate events, concerns have been raised by scientists, government, and the public regarding drought occurrence and its ...impacts, particularly in arid and semi-arid regions. In this paper, the drought conditions for the forest and grassland areas in the northern region of China were identified based on 12 years of satellite-based Drought Severity Index (DSI) data. The impact of drought on dryland vegetation in terms of carbon use efficiency (CUE) and water use efficiency (WUE) were also investigated by exploring their correlations with DSI. Results indicated that 49.90% of forest and grassland experienced a dry trend over this period. The most severe drought occurred in 2001. In general, most forests in the study regions experienced near normal and wet conditions during the 12 year period. However, grasslands experienced a widespread drought after 2006. The forest CUE values showed a fluctuation increase from 2000 to 2011, whereas the grassland CUE remained steady over this period. In contrast, WUE increased in both forest and grassland areas due to the increasing net primary productivity (NPP) and descending evapotranspiration (ET). The CUE and WUE values of forest areas were more sensitive to droughts when compared to the values for grassland areas. The correlation analysis demonstrated that areas of DSI that showed significant correlations with CUE and WUE were 17.24 and 10.37% of the vegetated areas, respectively. Overall, the carbon and water use of dryland forests was more affected by drought than that of dryland grasslands.
To assess the variation in distribution, extent, and NPP of global natural vegetation in response to climate change in the period 1911-2000 and to provide a feasible method for climate change ...research in regions where historical data is difficult to obtain. In this research, variations in spatiotemporal distributions of global potential natural vegetation (PNV) from 1911 to 2000 were analyzed with the comprehensive sequential classification system (CSCS) and net primary production (NPP) of different ecosystems was evaluated with the synthetic model to determine the effect of climate change on the terrestrial ecosystems. The results showed that consistently rising global temperature and altered precipitation patterns had exerted strong influence on spatiotemporal distribution and productivities of terrestrial ecosystems, especially in the mid/high latitudes. Ecosystems in temperate zones expanded and desert area decreased as a consequence of climate variations. The vegetation that decreased the most was cold desert (18.79%), while the maximum increase (10.31%) was recorded in savanna. Additionally, the area of tundra and alpine steppe reduced significantly (5.43%) and were forced northward due to significant ascending temperature in the northern hemisphere. The global terrestrial ecosystems productivities increased by 2.09%, most of which was attributed to savanna (6.04%), tropical forest (0.99%), and temperate forest (5.49%). Most NPP losses were found in cold desert (27.33%). NPP increases displayed a latitudinal distribution. The NPP of tropical zones amounted to more than a half of total NPP, with an estimated increase of 1.32%. The increase in northern temperate zone was the second highest with 3.55%. Global NPP showed a significant positive correlation with mean annual precipitation in comparison with mean annual temperature and biological temperature. In general, effects of climate change on terrestrial ecosystems were deep and profound in 1911-2000, especially in the latter half of the period.
As a key factor that determines carbon storage capacity, residence time (τE) is not well constrained in terrestrial biosphere models. This factor is recognized as an important source of model ...uncertainty. In this study, to understand how τE influences terrestrial carbon storage prediction in diagnostic models, we introduced a model decomposition scheme in the Boreal Ecosystem Productivity Simulator (BEPS) and then compared it with a prognostic model. The result showed that τE ranged from 32.7 to 158.2 years. The baseline residence time (τ'E) was stable for each biome, ranging from 12 to 53.7 years for forest biomes and 4.2 to 5.3 years for non-forest biomes. The spatiotemporal variations in τE were mainly determined by the environmental scalar (ξ). By comparing models, we found that the BEPS uses a more detailed pool construction but rougher parameterization for carbon allocation and decomposition. With respect to ξ comparison, the global difference in the temperature scalar (ξt) averaged 0.045, whereas the moisture scalar (ξw) had a much larger variation, with an average of 0.312. We propose that further evaluations and improvements in τ'E and ξw predictions are essential to reduce the uncertainties in predicting carbon storage by the BEPS and similar diagnostic models.
Relative roles of climate change and human activities in desertification are the hotspot of research on desertification dynamic and its driving mechanism.To overcome the shortcomings of existing ...studies,this paper selected net primary productivity （NPP） as an indicator to analyze desertification dynamic and its impact factors.In addition,the change trends of actual NPP,potential NPP and HNPP （human appropriation of NPP,the difference between potential NPP and actual NPP） were used to analyze the desertification dynamic and calculate the relative roles of climate change,human activities and a combination of the two factors in desertification.In this study,the Moderate Resolution Imaging Spectroradiometer （MODIS）-Normalised Difference Vegetation Index （NDVI） and meteorological data were utilized to drive the Carnegie-Ames-Stanford Approach （CASA） model to calculate the actual NPP from 2001 to 2010 in the Heihe River Basin.Potential NPP was estimated using the Thornthwaite Memorial model.Results showed that 61％ of the whole basin area underwent land degradation,of which 90.5％ was caused by human activities,8.6％ by climate change,and 0.9％ by a combination of the two factors.On the contrary,1.5％ of desertification reversion area was caused by human activities and 90.7％ by climate change,the rest 7.8％ by a combination of the two factors.Moreover,it was demonstrated that 95.9％ of the total actual NPP decrease was induced by human activities,while 69.3％ of the total actual NPP increase was caused by climate change.The results revealed that climate change dominated desertification reversion,while human activities dominated desertification expansion.Moreover,the relative roles of both climate change and human activities in desertification possessed great spatial heterogeneity.Additionally,ecological protection policies should be enhanced in the Heihe River Basin to prevent desertification expansion under the condition of climate change.
The vegetation coverage dynamics and its relationship with climate factors on different spatial and temporal scales in Inner Mongolia during 2001–2010 were analyzed based on MODIS-NDVI data and ...climate data. The results indicated that vegetation coverage in Inner Mongolia showed obvious longitudinal zonality, increasing from west to east across the region with a change rate of 0.2/10°N. During 2001–2010, the mean vegetation coverage was 0.57, 0.4 and 0.16 in forest, grassland and desert biome, respectively, exhibiting evident spatial heterogeneities. Totally, vegetation coverage had a slight increasing trend during the study period. Across Inner Mongolia, the area of which the vegetation coverage showed extremely significant and significant increase accounted for 11.25% and 29.13% of the area of whole region, respectively, while the area of which the vegetation coverage showed extremely significant and significant decrease accounted for 7.65% and 26.61%, respectively. On inter-annual time scale, precipitation was the dominant driving force of vegetation coverage for the whole region. On inter-monthly scale, the change of vegetation coverage was consistent with both the change of temperature and precipitation, implying that the vegetation growth within a year is more sensitive to the combined effects of water and heat rather than either single climate factor. The vegetation coverage in forest biome was mainly driven by temperature on both inter-annual and inter-monthly scales, while that in desert biome was mainly influenced by precipitation on both the two temporal scales. In grassland biome, the yearly vegetation coverage had a better correlation with precipitation, while the monthly vegetation coverage was influenced by both temperature and precipitation. In grassland biome, the impacts of precipitation on monthly vegetation coverage showed time-delay effects.
GIMMS （Global Inventory Modeling and Mapping Studies） NDVI （Normalised Difference Vegetation Index） from 1982 to 2006 and MODIS （Moderate Resolution Imaging Spectroradiometer） NDVI from 2001 to 2010 ...were blended to extract the, grass coverage and analyze its spatial pattern. The response of grass coverage to climatic variations at annual and monthly time scales was analyzed. Grass coverage distribution had increased from northwest to southeast across China. During 1982-2010, the mean nationwide grass coverage was 34% but exhibited apparent spatial heterogeneity, being the highest （61.4%） in slope grasslands and the lowest （17.1%） in desert grasslands. There was a slight increase of the grass coverage with a rate of 0.17% per year. Increase in slope grasslands coverage was as high as 0.27% per year, while in the plain grasslands and meadows the grass coverage in- crease was the lowest （being 0.11% per year and 0.1% per year, respectively）. Across China, the grass coverage with extremely significant increase （P〈0.01） and significant increase （P〈0.05） accounted for 46.03% and 11% of the total grassland area, respectively, while those with extremely significant and significant decrease accounted for only 4.1% and 3.24%, respectively. At the annual time scale, there are no significant correlations between grass coverage and annual mean temperature and precipitation. However, the grass coverage was somewhat affected by temperature in alpine and sub-alpine grassland, alpine and sub-alpine meadow, slope grassland and meadow, while grass coverage in desert grassland and plain grassland was more affected by precipitation. At the monthly time-scale, there are significant correlations between grass coverage with both temperature and precipitation, indicating that the grass coverage is more affected by seasonal fluctuations of hydrothermal conditions. Additionally, there is one-month time lag-effect between grass coverage and climate factors for each grassland types.
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