Geping Luo

Carbon stock and its responses to climate change in Central Asia

Central Asia has a land area of 5.6 × 10(6) km(2) and contains 80-90% of the worlds temperate deserts. Yet it is one of the least characterized areas in the estimation of the global carbon (C) stock/balance. This study assessed the sizes and spatiotemporal patterns of C pools in Central Asia using both inventory (based on 353 biomass and 284 soil samples) and process-based modeling approaches. The results showed that the C stock in Central Asia was 31.34-34.16 Pg in the top 1-m soil with another 10.42-11.43 Pg stored in deep soil (1-3 m) of the temperate deserts. They amounted to 18-24% of the global C stock in deserts and dry shrublands. The C stock was comparable to that of the neighboring regions in Eurasia or major drylands around the world (e.g. Australia). However, 90% of Central Asia C pool was stored in soil, and the fraction was much higher than in other regions. Compared to hot deserts of the world, the temperate deserts in Central Asia had relatively high soil organic carbon density. The C stock in Central Asia is under threat from dramatic climate change. During a decadal drought between 1998 and 2008, which was possibly related to protracted La Niña episodes, the dryland lost approximately 0.46 Pg C from 1979 to 2011. The largest C losses were found in northern Kazakhstan, where annual precipitation declined at a rate of 90 mm decade(-1) . The regional C dynamics were mainly determined by changes in the vegetation C pool, and the SOC pool was stable due to the balance between reduced plant-derived C influx and inhibited respiration.

Estimating land-surface temperature under clouds using MSG/SEVIRI observations

The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the diurnal cycle of LST by exploiting the temporal domain offered by geo-stationary satellite observations (i.e. MSG/SEVIRI), and yields LST estimates even for overcast moments when satellite sensor can only record cloud-top temperatures. Contrasting to the neighboring pixel approach as presented by Jin and Dickinson (2002), our approach naturally satisfies all sorts of spatial homogeneity assumptions and is hence more suited for earth surfaces characterized by scattered land-use practices. Validation is performed against in situ measurements of infrared land-surface temperature obtained at two validation sites in Africa. Results vary and show a bias of −3.68 K and a RMSE of 5.55 K for the validation site in Kenya, while results obtained over the site in Burkina Faso are more encouraging with a bias of 0.37 K and RMSE of 5.11 K. Error analysis reveals that uncertainty of the estimation of cloudy sky LST is attributed to errors in estimation of the underlying clear sky LST, all-sky global radiation, and inaccuracies inherent to the ‘neighboring pixel’ scheme itself. An error propagation model applied for the proposed temporal neighboring-pixel approach reveals that the absolute error of the obtained cloudy sky LST is less than 1.5 K in the best case scenario, and the uncertainty increases linearly with the absolute error of clear sky LST. Despite this uncertainty, the proposed method is practical for retrieving the LST under a cloudy sky condition, and it is promising to reconstruct diurnal LST cycles from geo-stationary satellite observations.

A spatial-explicit dynamic vegetation model that couples carbon, water, and nitrogen processes for arid and semiarid ecosystems

Arid and semiarid ecosystems, or dryland, are important to global biogeochemical cycles. Dryland’s community structure and vegetation dynamics as well as biogeochemical cycles are sensitive to changes in climate and atmospheric composition. Vegetation dynamic models has been applied in global change studies, but the complex interactions among the carbon (C), water, and nitrogen (N) cycles have not been adequately addressed in the current models. In this study, a process-based vegetation dynamic model was developed to study the responses of dryland ecosystems to environmental changes, emphasizing on the interactions among the C, water, and N processes. To address the interactions between the C and water processes, it not only considers the effects of annual precipitation on vegetation distribution and soil moisture on organic matter (SOM) decomposition, but also explicitly models root competition for water and the water compensation processes. To address the interactions between C and N processes, it models the soil inorganic mater processes, such as N mineralization/immobilization, denitrification/nitrification, and N leaching, as well as the root competition for soil N. The model was parameterized for major plant functional types and evaluated against field observations.

A spatial geostatistical analysis of impact of land use development on groundwater resources in the Sangong Oasis Region using remote sensing imagery and data

In this study, the relationship between land use and cover change (LUCC) and variation of groundwater level and quality in the SanGong Oasis Region is investigated using a spatial geostatistical approach. Specifically, interactions among groundwater, surface water, and LUCC are analyzed through the utilization of geographical information system (GIS), remote sensing (RS) Imagery processing, and geostatistics. Study outputs indicate that recharging into the groundwater does not change significantly during the period from 1978 to 1998. However, both LUCC and groundwater level have changed substantially in the SanGong Oasis Region, and their variations are closely correlated to each other spatially and temporally over the past two decades. It has confirmed that urbanization process and increased industrial activities are the direct reasons of groundwater table descending and the deterioration of water quality. The results of this research have provided a scientific basis for understanding sustainability-related problems and solution options in the oasis areas of western China. (17 refs.)

Biomass allocation patterns across China's terrestrial biomes.

Root to shoot ratio (RS) is commonly used to describe the biomass allocation between below- and aboveground parts of plants. Determining the key factors influencing RS and interpreting the relationship between RS and environmental factors is important for biological and ecological research. In this study, we compiled 2088 pairs of root and shoot biomass data across China’s terrestrial biomes to examine variations in the RS and its responses to biotic and abiotic factors including vegetation type, soil texture, climatic variables, and stand age. The median value of RS (RSm) for grasslands, shrublands, and forests was 6.0, 0.73, and 0.23, respectively. The range of RS was considerably wide for each vegetation type. RS values for all three major vegetation types were found to be significantly correlated to mean annual precipitation (MAP) and potential water deficit index (PWDI). Mean annual temperature (MAT) also significantly affect the RS for forests and grasslands. Soil texture and forest origin altered the response of RS to climatic factors as well. An allometric formula could be used to well quantify the relationship between aboveground and belowground biomass, although each vegetation type had its own inherent allometric relationship.

Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery

Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context (vegetation cover, moisture, surface roughness, and organic matter) and the weak spectral features of salinized soil. Therefore, an index such as the salinity index (SI) that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index (NDVI), as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index (AFII) was created as supported by the spectral mixture analysis (SMA), which is more appropriate for analyzing variations in vegetation cover (particularly halophytes) than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model (SDM) that combines AFII and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFII and SI using a scatterplot. The SDMs were then compared to the combined spectral response index (COSRI) from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs (R2>0.86, RMSE<6.86) compared to COSRI (R2=0.71, RMSE=16.21). These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.

Modeling grassland net primary productivity and water-use efficiency along an elevational gradient of the Northern Tianshan Mountains

Mountainous ecosystems are considered highly sensitive and vulnerable to natural disasters and climatic changes. Therefore, quantifying the effects of elevation on grassland productivity to understand ecosystem-climate interactions is vital for mountainous ecosystems. Water-use efficiency (WUE) provides a useful index for understanding the metabolism of terrestrial ecosystems as well as for evaluating the degradation of grasslands. This paper explored net primary productivity (NPP) and WUE in grasslands along an elevational gradient ranging from 400 to 3,400 m asl in the northern Tianshan Mountains-southern Junggar Basin (TMJB), Xinjiang of China, using the Biome-BGC model. The results showed that: 1) the NPP increased by 0.05 g C/(m2·a) with every increase of 1-m elevation, reached the maximum at the mid-high elevation (1,600 m asl), and then decreased by 0.06 g C/(m2·a) per 1-m increase in elevation; 2) the grassland NPP was positively correlated with temperature in alpine meadow (AM, 2,700–3,500 m asl), mid-mountain forest meadow (MMFM, 1,650–2,700 m asl) and low-mountain dry grassland (LMDG, 650–1,650 m asl), while positive correlations were found between NPP and annual precipitation in plain desert grassland (PDG, lower than 650 m asl); 3) an increase (from 0.08 to 1.09 g C/(m2·a)) in mean NPP for the grassland in TMJB under a real climate change scenario was observed from 1959 to 2009; and 4) remarkable differences in WUE were found among different elevations. In general, WUE increased with decreasing elevation, because water availability is lower at lower elevations; however, at elevations lower than 540 m asl, we did observe a decreasing trend of WUE with decreasing elevation, which may be due to the sharp changes in canopy cover over this gradient. Our research suggests that the NPP simulated by Biome-BGC is consistent with field data, and the modeling provides an opportunity to further evaluate interactions between environmental factors and ecosystem productivity.

Can soil respiration estimate neglect the contribution of abiotic exchange

This study examines the hypothesis that soil respiration can always be interpreted purely in terms of biotic processes, neglecting the contribution of abiotic exchange to CO2 fluxes in alkaline soils of arid areas that characterize 5% of the Earth’s total land surface. Analyses on flux data collected from previous studies suggested reconciling soil respiration as organic (root/microbial respiration) and inorganic (abiotic CO2 exchange) respiration, whose contributions in the total CO2 flux were determined by soil alkaline content. On the basis of utilizing meteorological and soil data collected from the Xinjiang and Central Asia Scientific Data Sharing Platform, an incorporated model indicated that inorganic respiration represents almost half of the total CO2 flux. Neglecting the abiotic module may result in overestimates of soil respiration in arid alkaline lands, which partly explains the long-sought “missing carbon sink”.

Modeling the contribution of abiotic exchange to CO2 flux in alkaline soils of arid areas

AbstractRecent studies on alkaline soils of arid areas suggest a possible contribution of abiotic exchange to soil CO2 flux (Fc). However, both the overall contribution of abiotic CO2 exchange and its drivers remain unknown. Here we analyzed the environmental variables suggested as possible drivers by previous studies and constructed a function of these variables to model the contribution of abiotic exchange to Fc in alkaline soils of arid areas. An automated flux system was employed to measure Fc in the Manas River Basin of Xinjiang Uygur autonomous region, China. Soil pH, soil temperature at 0–5 cm (Ts), soil volumetric water content at 0–5 cm (θs) and air temperature at 10 cm above the soil surface (Tas) were simultaneously analyzed. Results highlight reduced sensitivity of Fc to Ts and good prediction of Fc by the model