Jiangzhou Xia

Improved estimations of gross primary production using satellite-derived photosynthetically active radiation

Terrestrial vegetation gross primary production (GPP) is an important variable in determining the global carbon cycle as well as the interannual variability of the atmospheric CO2 concentration. The accuracy of GPP simulation is substantially affected by several critical model drivers, one of the most important of which is photosynthetically active radiation (PAR) which directly determines the photosynthesis processes of plants. In this study, we examined the impacts of uncertainties in radiation products on GPP estimates in China. Two satellite-based radiation products (GLASS and ISCCP), three reanalysis products (MERRA, ECMWF, and NCEP), and a blended product of reanalysis and observations (Princeton) were evaluated based on observations at hundreds of sites. The results revealed the highest accuracy for two satellite-based products over various temporal and spatial scales. The three reanalysis products and the Princeton product tended to overestimate radiation. The GPP simulation driven by the GLASS product exhibited the highest consistency with those derived from site observations. Model validation at 11 eddy covariance sites suggested the highest model performance when utilizing the GLASS product. Annual GPP in China driven by GLASS was 5.55 Pg C yr(-1), which was 68.85%-94.87% of those derived from the other products. The results implied that the high spatial resolution, satellite-derived GLASS PAR significantly decreased the uncertainty of the GPP estimates at the regional scale. Key Points To compare the performances of several major satellite-based and reanalysis PAR To investigate the uncertainty in PAR datasets at different spatial resolutions To quantify the impacts on GPP simulations due to uncertainties in PAR inputs

Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006

Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based

Multiyear precipitation reduction strongly decreases carbon uptake over northern China

Drought has been a concern in global and regional water, carbon, and energy cycles. From 1999 to 2011, northern China experienced a multiyear precipitation reduction that significantly decreased water availability as indicated by the Palmer Drought Severity Index and soil moisture measurements. In this study, a light use efficiency model (EC-LUE) and an ecosystem physiological model (IBIS) were used to characterize the impacts of long-term drought on terrestrial carbon fluxes in northern China. EC-LUE and IBIS models showed the reduction of averaged GPP of 0.09 and 0.05 Pg C yr-1 during 1999-2011 compared with 1982-1998. Based on the IBIS model, simulated ecosystem respiration experienced an insignificant decrease from 1999 to 2011. The multiyear precipitation reduction changed the regional carbon uptake of 0.011 Pg C yr-1 from 1982 to 1998 to a net source of 0.018 Pg C yr-1 from 1999 to 2011. Moreover, a pronounced decrease in maize yield in almost all provinces in the study region was found from 1999 to 2011 versus the average of yield from1978 to 2011. The largest maize yield reduction occurred in Beijing (2499kgha-1yr-1), Jilin (2180kgha-1yr-1), Tianjing (1923kgha-1yr-1), and Heilongjiang (1791kgha-1yr-1), and the maize yield anomaly was significantly correlated with the annual precipitation over the entire study area. Our results revealed that recent climate change, especially drought-induced water stress, is the dominant cause of the reduction in the terrestrial carbon sink over northern China.

A meta-analysis of the response of soil moisture to experimental warming

Soil moisture is an important variable for regulating carbon, water and energy cycles of terrestrial ecosystems. However, numerous inconsistent conclusions have been reported regarding the responses of soil moisture to warming. In this study, we conducted a meta-analysis for examination of the response of soil moisture to experimental warming across global warming sites including several ecosystem types. The results showed that soil moisture decreased in response to warming treatments when compared with control treatments in most ecosystem types. The largest reduction of soil moisture was observed in forests, while intermediate reductions were observed in grassland and cropland, and they were both larger than the reductions observed in shrubland and tundra ecosystems. Increases (or no change) in soil moisture also occurred in some ecosystems. Taken together, these results showed a trend of soil drying in most ecosystems, which may have exerted profound impacts on a variety of terrestrial ecosystem processes as well as feedbacks to the climate system.

Characterization of locations and extents of afforestation from the Grain for Green Project in China

The Chinese government started implementation of the Grain for Green Project (GGP) in 1999, aiming to convert cropland to forestland to mitigate soil erosion problems in areas across the country. Although the project has generated substantial environmental benefits, such as erosion reduction, carbon sequestration and water quality improvements, the magnitude of these benefits has not yet been well quantified due to the lack of location-specific data describing the afforestation efforts. Remote sensing is well suited to detect afforestation locations, a prerequisite for estimating the impacts of the project. In this study, we first examined the practicability of using the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product to detect afforestation locations; however, the results showed that the MODIS product failed to distinguish the afforestation areas of GGP. Then, we used a normalized difference vegetation index (NDVI) time series analysis approach for detecting afforestation locations, applying statistical data to determine the NDVI threshold of converted croplands. The technique provided the necessary information for location of afforestation implemented under GGP, explaining 85% of conversion from cropland to forestlands across all provinces. The coefficients of determination between detected afforestation and statistical areas at the most provinces were more than 0.7 which indicated the high performance. Moreover, more than 60% of GGP locations identified in all the provinces had a slope of over 25°, which was consistent with the main criterion of GGP. These results should enable wide application of the method to evaluate the impacts of the project on regional carbon budgets, water yield and soil erosion.

Satellite-Based Analysis of Evapotranspiration and Water Balance in the Grassland Ecosystems of Dryland East Asia

The regression tree method is used to upscale evapotranspiration (ET) measurements at eddy-covariance (EC) towers to the grassland ecosystems over the Dryland East Asia (DEA). The regression tree model was driven by satellite and meteorology datasets, and explained 82% and 76% of the variations of ET observations in the calibration and validation datasets, respectively. The annual ET estimates ranged from 222.6 to 269.1 mm yr−1 over the DEA region with an average of 245.8 mm yr−1 from 1982 through 2009. Ecosystem ET showed decreased trends over 61% of the DEA region during this period, especially in most regions of Mongolia and eastern Inner Mongolia due to decreased precipitation. The increased ET occurred primarily in the western and southern DEA region. Over the entire study area, water balance (the difference between precipitation and ecosystem ET) decreased substantially during the summer and growing season. Precipitation reduction was an important cause for the severe water deficits. The drying trend occurring in the grassland ecosystems of the DEA region can exert profound impacts on a variety of terrestrial ecosystem processes and functions.

Loess Plateau check dams can potentially sequester eroded soil organic carbon

Check dams are special soil and water conservation structures in the Loess Plateau, China. They play an important role in intercepting sediments and soil organic carbon (SOC). However, the decomposition of intercepted SOC and the environmental regulations at check dams have not been investigated. We conducted several paired field experiments at both check dams and slope lands in the Yanhe Watershed of the Loess Plateau to examine the characteristics of SOC decomposition at check dams. On average, the SOC mineralization rate in slope lands was approximately three times higher than in check dams. Increased soil moisture and compaction in check dams can constrain carbon mineralization by limiting the oxygen availability of SOC and can isolate substrate carbon from heterotrophic microorganisms. Our results indicate that check dams display a considerable potential for eroded SOC sequestration via reducing the soil respiration rate and highlight the important implications of lateral carbon redistribution and human engineering projects when estimating regional or global ecosystem carbon cycles.

Applications of rough set theory to river environment quality evaluation in China

Very often, inexact, uncertain, or vague hydrological data are encountered in water researches. Statistical and fuzzy set theories are applied to deal with hydrological uncertainty problems. In this paper, the basic concept of the rough set theory is introduced and its application to hydrological data is illustrated. The proposed method is applied to water environment and environment quality evaluation of the Hang Jiang River, a major branch of the Yangtze River, in China. Our numerical applications suggest that the rough set theory is a useful tool for analysis of inexact, uncertain, or vague hydrological data.

Global simulations of carbon allocation coefficients for deciduous vegetation types

The allocation of photosynthate among the plant components plays an important role in regulating plant growth, competition and other ecosystem functions. Several process-based carbon allocation models have been developed and incorporated into ecosystem models; however, these models have used arbitrary model parameters and have never been sufficiently validated on a global scale. This study uses the Integrated Biosphere Simulator (IBIS) model as a platform to integrate a carbon allocation model (resource availability model) with satellite-derived leaf area index (LAI) dataset, which allows us to inversely predict the allocation parameters for five deciduous vegetation types. Our results showed that the carbon allocation coefficients can be reliably constrained by the satellite LAI product, and the new parameters substantially improved model performance for simulating LAI and aboveground biomass globally. The spatial pattern of allocation coefficients among plant parts is supported by a number of studies. Compared with the standard version of the IBIS model using fixed allocation coefficients, the revised resource availability carbon allocation model tends to promote higher root carbon allocation. Our study provides a method for inverting the parameters of the carbon allocation model and improves the model performance in simulating the LAI and biomass.

Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations

Gross Primary Production (GPP) is the largest flux in the global carbon cycle. However, large uncertainties in current global estimations persist. In this study, we examined the performance of a process-based model (Integrated BIosphere Simulator, IBIS) at 62 eddy covariance sites around the world. Our results indicated that the IBIS model explained 60% of the observed variation in daily GPP at all validation sites. Comparison with a satellite-based vegetation model (Eddy Covariance-Light Use Efficiency, EC-LUE) revealed that the IBIS simulations yielded comparable GPP results as the EC-LUE model. Global mean GPP estimated by the IBIS model was 107.50±1.37 Pg C year−1 (mean value ± standard deviation) across the vegetated area for the period 2000–2006, consistent with the results of the EC-LUE model (109.39±1.48 Pg C year−1). To evaluate the uncertainty introduced by the parameter Vcmax, which represents the maximum photosynthetic capacity, we inversed Vcmax using Markov Chain-Monte Carlo (MCMC) procedures. Using the inversed Vcmax values, the simulated global GPP increased by 16.5 Pg C year−1, indicating that IBIS model is sensitive to Vcmax, and large uncertainty exists in model parameterization.