Deyong Yu

Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model

By using a land cover map, normalized difference vegetation index (NDVI) data sets, monthly meteorological data and observed net primary productivity (NPP) data, we have improved the method of estimating light use efficiency (LUE) for different biomes and soil moisture coefficients in the Carnegie–Ames–Stanford Approach (CASA) ecosystem model. Based on this improved model we produced an annual NPP map (in 1999) for the East Asia region located at 10–70° N, 70–170° E (about 19.66% of the terrestrial surface of the Earth). The results show that the mean NPP for the study area in 1999 was 374.12 g carbon (C) m−2 year−1 and the total NPP was 1.096 × 1014 kg C year−1, making up 17.51–18.39% of the global NPP. Comparison between the estimated NPP obtained from this improved CASA ecosystem model and the observed NPP obtained from two NPP databases indicates that the estimated NPP is close to the observed NPP, with an average error of 5.15% for the study region. We used two different land cover maps of China to drive the improved CASA model by keeping other inputs unchanged to determine how the classification accuracy of the land cover map affects the estimated NPP, and the results indicate that an accurate land cover map is important for obtaining an accurate and reliable estimate of NPP for some regions, especially for a particular biome.

Impacts of changes in climate and landscape pattern on ecosystem services.

The restoration of degraded vegetation can effectively improve ecosystem services, increase human well-being, and promote regional sustainable development. Understanding the changing trends in ecosystem services and their drivers is an important step in informing decision makers for the development of reasonable landscape management measures. From 2001 to 2014, we analyzed the changing trends in five critical ecosystem services in the Xilingol Grassland, which is typical of grasslands in North China, including net primary productivity (NPP), soil conservation (SC), soil loss due to wind (SL), water yield (WY) and water retention (WR). Additionally, we quantified how climatic factors and landscape patterns affect the five ecosystem services on both annual and seasonal time scales. Overall, the results indicated that vegetation restoration can effectively improve the five grassland ecosystem services, and precipitation (PPT) is the most critical climatic factor. The impact of changes in the normalized difference vegetation index (NDVI) was most readily detectable on the annual time scale, whereas the impact of changes in landscape pattern was most readily detectable on the seasonal time scale. A win-win situation in terms of grassland ecosystem services (e.g., vegetation productivity, SC, WR and reduced SL) can be achieved by increasing grassland aggregation, partitioning the largest grasslands, dividing larger areas of farmland into smaller patches, and increasing the area of appropriate forest stands. Our work may aid policymakers in developing regional landscape management schemes.

Integrating Multiple Source Data to Enhance Variation and Weaken the Blooming Effect of DMSP-OLS Light

Defense Meteorological Satellite Program/Operational Linescan System (DMSP-OLS) nighttime light has proved to be an effective tool to monitor human activities, especially in mapping urban areas. However, the inherent defects of DMSP-OLS light including saturation and blooming effects remain to be tackled. In this study, the Normalized Difference Vegetation Index (NDVI) product of the Moderate-resolution Imaging Spectroradiometer/Normalized Difference Vegetation Index 1-Month (MODND1M), the temperature product of Moderate-resolution Imaging Spectroradiometer/Land Surface Temperature 1-Month (MODLT1M) and DMSP-OLS light were integrated to establish the Vegetation Temperature Light Index (VTLI), aiming at weakening the saturation and blooming effects of DMSP-OLS light. In comparison with DMSP-OLS nighttime light, this new methodology achieved the following improvements: (1) the high value (30%–100%) range of VTLI was concentrated in the urban areas; (2) VTLI could effectively enhance the variation of DMSP-OLS light, especially in the urban center; and (3) VTLI reached convergence faster than Vegetation Adjusted Normalized Urban Index (VANUI). Results showed that the urban areas extracted by VTLI were closer to those from Landsat TM images with the accuracy of kappa coefficients in Beijing (0.410), Shanghai (0.718), Lanzhou (0.483), and Shenyang (0.623), respectively. Thus, it can be concluded that the proposed index is able to serve as a favorable option for urban areas mapping.

The potential effects of climate change on the distribution and productivity of Cunninghamia lanceolata in China

Climate changes may have immediate implications for forest productivity and may produce dramatic shifts in tree species distributions in the future. Quantifying these implications is significant for both scientists and managers. Cunninghamia lanceolata is an important coniferous timber species due to its fast growth and wide distribution in China. This paper proposes a methodology aiming at enhancing the distribution and productivity of C. lanceolata against a background of climate change. First, we simulated the potential distributions and establishment probabilities of C. lanceolata based on a species distribution model. Second, a process-based model, the PnET-II model, was calibrated and its parameterization of water balance improved. Finally, the improved PnET-II model was used to simulate the net primary productivity (NPP) of C. lanceolata. The simulated NPP and potential distribution were combined to produce an integrated indicator, the estimated total NPP, which serves to comprehensively characterize the productivity of the forest under climate change. The results of the analysis showed that (1) the distribution of C. lanceolata will increase in central China, but the mean probability of establishment will decrease in the 2050s; (2) the PnET-II model was improved, calibrated, and successfully validated for the simulation of the NPP of C. lanceolata in China; and (3) all scenarios predicted a reduction in total NPP in the 2050s, with a markedly lower reduction under the a2 scenario than under the b2 scenario. The changes in NPP suggested that forest productivity will show a large decrease in southern China and a mild increase in central China. All of these findings could improve our understanding of the impact of climate change on forest ecosystem structure and function and could provide a basis for policy-makers to apply adaptive measures and overcome the unfavorable influences of climate change.

Spatio-temporal distribution of net primary productivity along the northeast china transect and its response to climatic change from 1982 to 2000

An improved Carnegie Ames Stanford Approach model (CASA) model was used to estimate the net primary productivity (NPP) of the Northeast China Transect (NECT) from 1982 to 2000. The spatial-temporal distribution of NPP along NECT and its response to climatic change were also analyzed. Results showed that: (1) The spatial distribution of NPP in NECT is quite similar with that of precipitation and their spatial correlation coefficient is up to 0.93 (P<0.01). (2) The interannual variation of NPP in NECT is mainly affected by the change of the aestival NPP of each year. It accounts for 67.6% of the inter-annual increase in NPP and their spatial correlation coefficient is 0.95 (P<0.01). (3) The NPP in NECT is mainly cumulated between May and September, which accounts for 89.8% of the annual NPP. Summer (June to September) accounts for 65.9% of the annual NPP and winter has the lowest NPP. (4) The mean NPP in NECT was 392.4 gC/m/yr in these 19 years, ranging from 333.8 to 448.4 gC/m/year. NPP increased 14.3% from 1980s to 1990s. The inter-annual linear trend of NPP is 4.6 gC/m/yr/yr, and the relative trend 1.17%/yr owning mainly to the increasing temperature. Keywords-Northeast China Transect; remote sensing; NPP; climatic change; spatio-temporal distribution

Spatiotemporal patterns, relationships, and drivers of China’s agricultural ecosystem services from 1980 to 2010: a multiscale analysis

ContextDuring the past three decades, China’s agroecosystem has undergone dramatic alterations because of changes in climatic and management factors, which threatened agricultural sustainability.ObjectivesWe investigated how climatic and management factors affected agricultural ecosystem services (AES).MethodsWe adopted the GIS-based Environmental Policy Integrated Climate (EPIC) model to simulate the five critical AES: food production, soil organic carbon (SOC), nitrate leaching, water erosion, and wind erosion from 1980 to 2010 and used a partial least square regression model to quantify the contributions of the drivers of the variation in the AES on the main grain-producing area (MGPA), climatic zone, and national scales.ResultsOn the MGPA scale, SOC exhibited no obvious change and food production increased, whereas the negative environmental effects largely increased. The MGPA is important to ensure the safety of China’s food supply. At the climatic zone scale, food production and SOC increased, water erosion in the tropical-subtropical monsoonal zone and water and wind erosion in the temperate monsoonal zone decreased, whereas N leaching, water erosion, and wind erosion increased in other climate zones. At the national scale, food production, SOC, N leaching, and wind erosion increased, whereas water erosion decreased. The crop cultivated area played a major role in the effect on food production and SOC. The dominant factors for N leaching, water erosion, and wind erosion varied with crop type and study scales.ConclusionsAdjustment of agricultural management measures is vital and possible to minimize the tradeoffs, increase the synergies among agro ecosystem services, and promote adaptation to the changing climate.

Understanding the Patterns and Drivers of Air Pollution on Multiple Time Scales: The Case of Northern China

China’s rapid economic growth during the past three decades has resulted in a number of environmental problems, including the deterioration of air quality. It is necessary to better understand how the spatial pattern of air pollutants varies with time scales and what drive these changes. To address these questions, this study focused on one of the most heavily air-polluted areas in North China. We first quantified the spatial pattern of air pollution, and then systematically examined the relationships of air pollution to several socioeconomic and climatic factors using the constraint line method, correlation analysis, and stepwise regression on decadal, annual, and seasonal scales. Our results indicate that PM2.5 was the dominant air pollutant in the Beijing–Tianjin–Hebei region, while PM2.5 and PM10 were both important pollutants in the Agro-pastoral Transitional Zone (APTZ) region. Our statistical analyses suggest that energy consumption and gross domestic product (GDP) in the industry were the most important factors for air pollution on the decadal scale, but the impacts of climatic factors could also be significant. On the annual and seasonal scales, high wind speed, low relative humidity, and long sunshine duration constrained PM2.5 accumulation; low wind speed and high relative humidity constrained PM10 accumulation; and short sunshine duration and high wind speed constrained O3 accumulation. Our study showed that analyses on multiple temporal scales are not only necessary to determine key drivers of air pollution, but also insightful for understanding the spatial patterns of air pollution, which was important for urban planning and air pollution control.

Quantifying the impacts of climatic trend and fluctuation on crop yields in northern China

Climate change plays a critical role in crop yield variations, which has attracted a great deal of concern worldwide. However, the mechanisms of how climatic trend and fluctuations affect crop yields are not well understood and need to be further investigated. Thus, using the GIS-based Environmental Policy Integrated Climate (EPIC) model, we simulated the yields of major crops (i.e., wheat, maize, and rice) and evaluated the impacts of climatic factors on crop yields in the Agro-Pastoral Transitional Zone (APTZ) of northern China between 1980 and 2010. The partial least squares regression model was used to assess the contribution rates of climatic factors (i.e., precipitation, photosynthetically active radiation (PAR), minimum temperature (Tmin), maximum temperature (Tmax)) to the variation of crop yields. The Breaks for Additive Season and Trend (BFAST) model was adopted to decompose the climate factors into trend and fluctuation components, and the relative contributions of climate trend and fluctuation were then evaluated. The results indicated that the contributions of climatic factors to yield variations of wheat, maize, and rice were 31.7, 37.7, and 23.1%, respectively. That is, climate change had larger impacts on maize than wheat and rice. More cultivated areas were significantly and positively correlated with precipitation than with other climatic factors due to the limited precipitation in the APTZ. Also, climatic trend component had positive impacts on crop yields in the whole region, whereas the climate fluctuation was associated mainly with the areas where the crop yields decreased. This study helps improve our understanding of the mechanisms of climate change impacts on crop yields, and provides useful scientific information for designing regional-scale strategies of adaptation to climate change.

Intensive land-use drives regional-scale homogenization of plant communities

Intensive anthropogenic land-use causes habitat loss and landscape homogenization, which leads to the decrease of biodiversity and ecosystem degradation. Therefore, it is important to study the influence of landscape heterogeneity on biodiversity. In this study, vegetation surveys conducted at 53 sites in the Tabu River basin, located at the agro-pastoral ecotone of Inner Mongolia of China, revealed 146 species. Species diversity was evaluated at three scales: species richness within patches (alpha diversity), between patches (beta diversity) and at the landscape scale (gamma diversity). We analyzed landscape heterogeneity (LHtotal) and its driving factors including environmental variables (LHDFenv-var, such as precipitation and altitude), environmental heterogeneity (LHDFenv-het) and human activities (LHDFhum). We used structural equation modeling (SEM) to evaluate the response of species richness to landscape heterogeneity at three scales and determined the relative contribution of driving factors in explaining species diversity at these scales. The results of the study are summarized as follows: 1) Alpha diversity was the dominant component of gamma diversity in the Tabu River basin in Inner Mongolia. 2) There is no significant correlation (P = 0.512) between alpha diversity and LHtotal; with the increase of LHtotal beta and gamma diversities showed hump-shaped relationships. 3) LHDFenv-het was the primary factor in maintaining alpha diversity, with heterogeneity of mean annual precipitation (MAP), temperature (MAT) and altitude (ALT) acting as three largest contributors. LHDFhum primarily contributed to the maintenance of beta diversity. 4) LHDFhum was the primary contributor to gamma diversity, and human activity exceeded threshold values for positive effects. Based on our findings we suggest liming agricultural use along the river to prevent reductions in species diversity.

Diverse effects of crop distribution and climate change on crop production in the agro-pastoral transitional zone of China

Both crop distribution and climate change are important drivers for crop production and can affect food security, which is an important requirement for sustainable development. However, their effects on crop production are confounded and warrant detailed investigation. As a key area for food production that is sensitive to climate change, the agro-pastoral transitional zone (APTZ) plays a significant role in regional food security. To investigate the respective effects of crop distribution and climate change on crop production, the well-established GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted with different scenario designs in this study. From 1980 to 2010, the crop distribution for wheat, maize, and rice witnessed a dramatic change due to agricultural policy adjustments and ecological engineering-related construction in the APTZ. At the same time, notable climate change was observed. The simulation results indicated that the climate change had a positive impact on the crop production of wheat, maize, and rice, while the crop distribution change led to an increase in the production of maize and rice, but a decrease in the wheat production. Comparatively, crop distribution change had a larger impact on wheat (–1.71 × 106 t) and maize (8.53 × 106 t) production, whereas climate change exerted a greater effect on rice production (0.58 × 106 t), during the period from 1980 to 2010 in the APTZ. This study is helpful to understand the mechanism of the effects of crop distribution and climate change on crop production, and aid policy makers in reducing the threat of future food insecurity.