北亚热带落叶阔叶林土壤呼吸时间变化特征

利用LI-8100开路式土壤碳通量系统测定龙王山森林土壤呼吸速率,研究北亚热带落叶阔叶林土壤呼吸速率的日变化和季节性变化规律.结果表明:北亚热带落叶阔叶林土壤呼吸速率在12—14时达到最大,与土壤温度变化基本一致;森林土壤呼吸速率随土壤温度的季节性变化而变化,在夏季土壤呼吸速率较高,在冬季土壤呼吸速率较低;土壤呼吸速率与土壤温度间存在着明显的指数关系,土壤呼吸温度敏感系数Q₁₀为2.81.     

基于组网观测的那曲土壤湿度不同时间尺度的变化特征

利用第三次青藏高原大气科学试验的土壤湿度观测数据,分析了那曲多空间尺度组网观测的28个站2、5、10、20和30 cm 5个不同深度土壤湿度的季节变化和日变化特征,并对比讨论了土壤湿度站点间的差异。分析表明,各层土壤湿度均存在显著的季节变化。冬春季节,20 cm以上土壤湿度随深度变浅而减小。夏秋季节土壤湿度随深度增加而减小,并分别在7月上、中旬和9月出现两个峰值。10月以后进入土壤湿度衰减期。土壤温度和土壤湿度存在协同变化关系。在一定的温度范围内,土壤发生冻结-融化过程,引起土壤湿度变化。在太阳辐射加热下,土壤表层水分蒸发,进而影响土壤温度。不同观测站间土壤湿度差异较大,夏秋季离散性大于冬春季。不同季节土壤湿度的日变化存在差异。春季10 cm以上土壤湿度日变化明显,08-10时（北京时）达到最低,19-20时达到最高。夏季土壤湿度日变化较为平缓。秋季2 cm深度土壤湿度日变化明显。线性拟合结果表明,1、4、10月土壤湿度和土壤温度为正相关关系。但是在夏季,土壤湿度与土壤温度为负相关。站点间土壤湿度变化的离散性表明,多测站才能全面体现青藏高原某区域的陆面状态。文中结果为青藏高原地区土壤湿度卫星参数验证和数值模式参数化提供了多角度的观测依据。      

Response of carbon dioxide exchange to grazing intensity over typical steppes in a semi-arid area of Inner Mongolia

The eddy covariance technique was used to measure the CO₂ flux over four differently grazed Leymus chinensis steppe ecosystems (ungrazed since 1979 (UG79), winter grazed (WG), continuously grazed (CG), and heavily grazed (HG) sites) during four growing seasons (May to September) from 2005 to 2008, to investigate the response of the net ecosystem exchange (NEE) over grassland ecosystems to meteorological factors and grazing intensity. At UG79, the optimal air temperature for the half-hourly NEE occurred between 17 and 20 °C, which was relatively low for semi-arid grasslands. The saturated NEE (NEE_sat) and temperature sensitivity coefficient (Q ₁₀) of ecosystem respiration (RE) exhibited clear seasonal and interannual variations, which increased with canopy development and the soil water content (SWC, at 5 cm). The total NEE values for the growing seasons from 2005 to 2008 were ?32.0, ?41.5, ?66.1, and ?89.8 g C m^?2, respectively. Both the amounts and distribution of precipitation during the growing season affected the NEE. The effects of grazing on the CO₂ flux increased with the grazing intensity. During the peak growth stage, heavy grazing and winter grazing decreased NEE_sat and gross primary production (45 % for HG and 34 % for WG) due to leaf area removal. Both RE and Q ₁₀ were clearly reduced by heavy grazing. Heavy grazing changed the ecosystem from a CO₂ sink into a CO₂ source, and winter grazing reduced the total CO₂ uptake by 79 %. In the early growing season, there was no difference in the NEE between CG and UG79. In addition to the grazing intensity, the effects of grazing on the CO₂ flux also varied with the vegetation growth stages and SWC.     

表层覆盐对新疆巴里坤盐湖土壤呼吸特征的影响

本文以新疆巴里坤盐湖周边硫酸钠型盐渍土壤为研究对象,通过土柱异位培养的方法,使用开路式土壤碳通量测量系统Li-8100,研究了不同覆盐量（CK、1倍覆盐、2倍覆盐、3倍覆盐和4倍覆盐处理）对土壤呼吸特征的影响。结果表明：(1) 土壤呼吸日变化呈单峰曲线,其峰值表现出随覆盐量增加而增加的趋势;4倍覆盐处理下土壤呼吸速率的峰值出现时间（15: 00）比其他处理（17: 00）有所提前;凌晨0: 00-6: 00,部分土壤呼吸速率呈现负值。(2) 覆盐后土壤CO2日排放量随时间呈先增加后降低的趋势,与气温变化一致;培养期间土壤CO2日均排放量表现出随覆盐量增加而增加的趋势,4倍覆盐处理下土壤CO2日均排放量显著高于CK处理（P<0.05）。(3) 土壤温度敏感系数Q10表现出随覆盐量增加而增加的趋势。综上可见,覆盐处理显著影响了盐湖周边盐渍化土壤CO2排放通量、特征和土壤温度敏感性,因此,在研究气候变暖对盐渍化土壤呼吸影响时,不仅要考虑增温对土壤呼吸的直接影响,也要考虑土壤盐层厚度与土壤温度敏感性的变化。     

Seasonal patterns of soil respiration in three types of communities along grass-desert shrub transition in Inner Mongolia,China

The seasonal dynamics of soil respiration in steppe (S. bungeana), desert shrub (A. ordosica), and shrub-perennial (A. ordosica + C. komarovii) communities were investigated during the growth season (May to October) in 2006; their environmental driving factors were also analyzed. In the three communities, soil respiration showed similar characteristics in their growth seasons, with peak respiration values in July and August owing to suitable temperature and soil moisture conditions during this period. Meanwhile, changes in soil respiration were greatly influenced by temperatures and surface soil moistures. Soil water content at a depth of 0 to 10 cm was identified as the key environmental factor affecting the variation in soil respiration in the steppe. In contrast, in desert shrub and shrub-perennial communities, the dynamics of soil respiration was significantly influenced by air temperature. Similarly, the various responses of soil respiration to environmental factors may be attributed to the different soil textures and distribution patterns of plant roots. In desert ecosystems, precipitation results in soil respiration pulses. Soil carbon dioxide (CO₂) effluxes greatly increased after rainfall rewetting in all of the ecosystems under study. However, the precipitation pulse effect differed across the ecosystem. We propose that this may be a result of a reverse effect from the soil texture.     

盘锦湿地芦苇群落土壤呼吸作用动态及其影响因子分析

采用LI-6400-09土壤呼吸室对盘锦湿地芦苇群落土壤呼吸作用,于2004年7月—2005年12月进行连续野外观测。结果表明:非淹水状态下,湿地芦苇群落土壤呼吸作用具有明显的日变化和季节变化特征;淹水状态下,湿地芦苇群落土壤呼吸作用接近于0。2005年潮汐造成的洪水减少了2/3的土壤呼吸作用。2004年和2005年芦苇群落土壤呼吸作用最大值都出现于洪水退去后。影响湿地芦苇群落土壤呼吸作用空间异质性的主导因子是生物因子,而在同一时间影响湿地芦苇群落土壤呼吸作用的主导因子是温度和水分。     

Soil moisture influence on summertime surface air temperature over East Asia

Soil moisture influence on surface air temperature in summer is statistically quantified across East Asia using the Global Land Data Assimilation System soil moisture and observational temperature. The analysis uses a soil moisture feedback parameter computed based on lagged covariance ratios. It is found that significant negative soil moisture feedbacks on temperature mainly appear over the transition zones between dry and wet climates of northern China and Mongolia. Over these areas, the feedbacks account for typically 5–20% of the total temperature variance, with the feedback parameter of ?0.2°C to ?0.5°C (standardized soil moisture)^?1. Meanwhile, positive feedbacks may exist over some areas of Northeast Asia but are much less significant. These findings emphasize the importance of soil moisture-temperature feedbacks in influencing summer climate variability and have implications for seasonal temperature forecasting.     

锡林浩特草原CO2通量特征及其影响因素分析

利用锡林浩特国家气候观象台开路涡度相关系统、辐射土壤观测系统,测得的长期连续通量观测数据,对锡林浩特草原2009—2011年期间的CO₂通量观测特征进行了分析。结果表明:CO₂通量存在明显的年际、季节和日变化特征。3 a中NEE年际变率达到200 g·m^-2,季节变率最大达到460 g·m^-2,日变化幅度生长季最大达到0.25 mg·m^-2·s^-1。通过不同时间尺度碳通量与温度、水分、辐射等环境因子的分析,认为CO₂通量日变化主要受温度和光合有效辐射影响,而季节变化和年变化主要受降水和土壤含水量的影响。降水强度及时间分布是制约牧草CO₂吸收的关键因素,大于15%的土壤含水量有利于促进牧草生长。     

Calibration of a land-surface model using data from primary forest sites in Amazonia

Summary A land-surface model (MOSES) was tested against observed fluxes of heat, water vapour and carbon dioxide for two primary forest sites near Manaus, Brazil. Flux data from one site (called C14) were used to calibrate the model, and data from the other site (called K34) were used to validate the calibrated model. Long-term fluxes of water vapour at C14 and K34 simulated by the uncalibrated model were good, whereas modelled net ecosystem exchange (NEE) was poor. The uncalibrated model persistently underpredicted canopy conductance (g _c ) from mid-morning to mid-afternoon due to saturation of the response to solar radiation at low light levels. This in turn caused a poor simulation of the diurnal cycles of water vapour and carbon fluxes. Calibration of the stomatal conductance/photosynthesis sub-model of MOSES improved the simulated diurnal cycle of g _c and increased the diurnal maximum NEE, but at the expense of degrading long-term water vapour fluxes. Seasonality in observed canopy conductance due to soil moisture change was not captured by the model. Introducing realistic depth-dependent soil parameters decreased the amount of moisture available for transpiration at each depth and led to the model experiencing soil moisture limitation on canopy conductance during the dry season. However, this limitation had only a limited effect on the seasonality in modelled NEE.     

Coupling a Terrestrial Biogeochemical Model to the Common Land Model

A terrestrial biogeochemical model (CASACNP) was coupled to a land surface model (the Common Land Model,CoLM) to simulate the dynamics of carbon substrate in soil and its limitation on soil respiration.The combined model,CoLM CASACNP,was able to predict long-term carbon sources and sinks that CoLM alone could not.The coupled model was tested using measurements of belowground respiration and surface fluxes from two forest ecosystems.The combined model simulated reasonably well the diurnal and seasonal variations of net ecosystem carbon exchange,as well as seasonal variation in the soil respiration rate of both the forest sites chosen for this study.However,the agreement between model simulations and actual measurements was poorer under dry conditions.The model should be tested against more measurements before being applied globally to investigate the feedbacks between the carbon cycle and climate change.     

Modeling soil respiration and variations in source components using a multi-factor global climate change experiment

Soil respiration is an important component of the global carbon cycle and is highly responsive to changes in soil temperature and moisture. Accurate prediction of soil respiration and its changes under future climatic conditions requires a clear understanding of the processes involved. Most current empirical soil respiration models incorporate just few of the underlying mechanisms that may influence its response. In this study, a new partially process-based component model that separately treated several source components of soil respiration was tested with data from a climate change experiment that manipulated atmospheric [CO₂], air temperature and soil moisture. Results from this model were compared to results from other widely used models with the parameters fitted using experimental data. Using the component model, we were able to estimate the relative proportions of heterotrophic and autotrophic respiration in total soil respiration for each of the different treatments. The value of the Q ₁₀ parameters for temperature response component of all of the models showed sensitivity to soil moisture. Estimated Q ₁₀ parameters were higher for wet treatments and lower for dry treatments compared to the values estimated using either the data from all treatments or from only the control treatments. Our results suggest that process-based models provide a better understanding of soil respiration dynamics under changing environmental conditions, but the extent and contribution of different source components need to be included in mechanistic and process-based soil respiration models at corresponding scales.     

Precipitation drives interannual variation in summer soil respiration in a Mediterranean-climate, mixed-conifer forest

Predictions of future climate change rely on models of how both environmental conditions and disturbance impact carbon cycling at various temporal and spatial scales. Few multi-year studies, however, have examined how carbon efflux is affected by the interaction of disturbance and interannual climate variation. We measured daytime soil respiration (R _s) over five summers (June–September) in a Sierra Nevada mixed-conifer forest on undisturbed plots and plots manipulated with thinning, burning and their combination. We compared mean summer R _s by year with seasonal precipitation. On undisturbed plots we found that winter precipitation (PPT_w) explained between 77–96% of interannual variability in summer R _s. In contrast, spring and summer precipitation had no significant effect on summer R _s. PPT_w is an important influence on summer R _s in the Sierra Nevada because over 80% of annual precipitation falls as snow between October and April, thus greatly influencing the soil water conditions during the following growing season. Thinning and burning disrupted the relationship between PPT_w and R_s, possibly because of significant increases in soil moisture and temperature as tree density and canopy cover decreased. Our findings suggest that R _s in some moisture-limited ecosystems may be significantly influenced by annual snowpack and that management practices which reduce tree densities and soil moisture stress may offset, at least temporarily, the effect of predicted decreases in Sierran snowpack on R _s.     

塔克拉玛干沙漠腹地流沙下垫面土壤呼吸特征及影响因素分析

通过LI-COR8100A土壤碳通量观测系统分别于2013年1月、5月、10月和11月进行了塔克拉玛干沙漠腹地塔中流沙下垫面土壤呼吸速率测定试验,并分析了相应的土壤水热因子对呼吸速率的影响。结果表明:塔克拉玛干沙漠腹地土壤呼吸速率整体偏低,但具有明显的昼夜波动性和季节变化特征。研究区流沙土壤中可能存在的无机碳过程是导致夜间及凌晨的土壤呼吸速率为负值,白天为正值的主要原因。不同时段的土壤呼吸速率(Rs)分别与土壤表层0~5 cm平均土壤温度(T)和湿度(W)间存在较为同步的昼夜变化趋势且具有良好的回归关系。相对于单因素影响的回归分析,土壤温、湿度的协同作用能够从整体角度更好地解释土壤呼吸速率的变化情况。回归方程Rs=a+bT+cW和Rs=a+bT+cW+dTW可解释不同时段土壤呼吸速率76.0%以上的变化情况。这说明土壤温、湿度是控制土壤呼吸速率的主要环境因子。沙漠腹地土壤极低的水分条件成为土壤呼吸的限制性因子,呼吸速率对于作为限制性因子的土壤湿度的变化响应则更加直接,而对于土壤温度变化的敏感性就有所下降,导致土壤呼吸速率与土壤温度回归关系出现明显的时滞环现象。     

基于神经网络算法的Sentinel-1和Sentinel-2遥感数据联合反演土壤湿度研究

以西班牙萨拉曼卡地区为研究区域,联合Sentinel-1后向散射系数和入射角信息、Sentinel-2光学数据提取的植被指数以及地面实测数据,构建了BP神经网络土壤湿度反演模型,并将该模型应用于试验区土壤湿度反演.结果 表明:1)基于Sentinel-1卫星VV和VH极化雷达后向散射系数、雷达入射角和Sentinel-...     

Phenology shift from 1989 to 2008 on the Tibetan Plateau: an analysis with a process-based soil physical model and remote sensing data

Phenology is critical to ecosystem carbon quantification, and yet has not been well modeled considering both aboveground and belowground environmental variables. This is especially true for alpine and pan-arctic regions where soil physical conditions play a significant role in determining the timing of phenology. Here we examine how the spatiotemporal pattern of satellite-derived phenology is related to soil physical conditions simulated with a soil physical model on the Tibetan Plateau for the period 1989–2008. Our results show that spatial patterns and temporal trends of phenology are parallel with the corresponding soil physical conditions for different study periods. On average, 1 °C increase in soil temperature advances the start of growing season (SOS) by 4.6 to 9.9 days among different vegetation types, and postpones the end of growing season (EOS) by 7.3 to 10.5 days. Soil wetting meditates such trends, especially in areas where warming effect is significant. Soil thermal thresholds for SOS and EOS, defined as the daily mean soil temperatures corresponding to the phenological metrics, are spatially clustered, and are closely correlated with mean seasonal temperatures in Spring and Autumn, respectively. This study highlights the importance and feasibility of incorporating spatially explicit soil temperature and moisture information, instead of air temperature and precipitation, into phenology models so as to improve carbon modeling. The method proposed and empirical relations established between phenology and soil physical conditions for Alpine ecosystems on the Tibetan plateau could also be applicable for other cold regions.     

黄河源高寒湿地-大气间暖季水热交换特征及关键影响参数研究

湿地是由陆地和水体形成的自然综合体,具有重要的生态、水文和生物地球化学功能,黄河源高寒湿地作为黄河重要的水源涵养区,对其下垫面水热交换特征及关键影响参数的研究具有非常重要的意义。本文利用中国科学院西北生态环境资源研究院麻多黄河源气候与环境变化观测站2014年6~8月观测资料,分析了黄河源区高寒湿地-大气间暖季水热交换特征,并利用公用陆面模式（Community Land Model,简称CLM）模拟了热通量变化,提出针对高寒湿地的粗糙度优化方案。主要结果如下:（1）暖季向上、向下短波与净辐射的平均日变化规律一致,向上、向下长波平均日变化平缓,地表温度升高相对于向下短波具有滞后性,潜热通量始终为正值并大于感热通量;（2）温度变化显著层结为20 cm以上土壤浅层,存在明显的日循环规律,土壤中热量09:00（北京时,下同）下传至5 cm深度,温度升高,11:00至10 cm深度,13:00至20 cm深度,18:00后开始上传,温度降低,40 cm及以下深度受此影响较小,热量在土壤中整体由浅层向深层输送;（3）土壤湿度平均日变化小,5 cm深度为土壤湿度最小层,10 cm深度为最大层;（4）麻多高寒湿地动力学粗糙度Z_0m在暖季变化稳定,可作为常数,Z_0m=0.0143 m;（5）提出更加适合高寒湿地下垫面暖季附加阻尼kB-1参数化方案,使得热通量模拟效果较CLM原始方案有所提高。以上结果对于研究湿地下垫面陆面过程具有重要意义。     

Evaluation of a Photosynthesis-Based Canopy Resistance Formulation in the Noah Land-Surface Model

Accurately representing complex land-surface processes balancing complexity and realism remains one challenge that the weather modelling community is facing nowadays. In this study, a photosynthesis-based Gas-exchange Evapotranspiration Model (GEM) is integrated into the Noah land-surface model replacing the traditional Jarvis scheme for estimating the canopy resistance and transpiration. Using 18-month simulations from the High Resolution Land Data Assimilation System (HRLDAS), the impact of the photosynthesis-based approach on the simulated canopy resistance, surface heat fluxes, soil moisture, and soil temperature over different vegetation types is evaluated using data from the Atmospheric Radiation Measurement (ARM) site, Oklahoma Mesonet, 2002 International H₂O Project (IHOP_2002), and three Ameriflux sites. Incorporation of GEM into Noah improves the surface energy fluxes as well as the associated diurnal cycle of soil moisture and soil temperature during both wet and dry periods. An analysis of midday, average canopy resistance shows similar day-to-day trends in the model fields as seen in observed patterns. Bias and standard deviation analyses for soil temperature and surface fluxes show that GEM responds somewhat better than the Jarvis scheme, mainly because the Jarvis approach relies on a parametrised minimum canopy resistance and meteorological variables such as air temperature and incident radiation. The analyses suggest that adding a photosynthesis-based transpiration scheme such as GEM improves the ability of the land-data assimilation system to simulate evaporation and transpiration under a range of soil and vegetation conditions.     

Relevance of soil moisture for seasonal climate predictions: a preliminary study

In the framework of the Global Soil Wetness Project (GSWP), the ISBA land-surface scheme of the ARPEGE atmospheric general circulation model has been forced with meteorological observations and analyses in order to produce a two-year (1987–1988) soil moisture climatology at a 1^°×1^° horizontal resolution. This climatology is model dependent, but it is the climatology that the ARPEGE model would produce if its precipitation and radiative fluxes were perfectly simulated. In the present study, ensembles of seasonal simulations (March to September) have been performed for 1987 and 1988, in which the total soil water content simulated by ARPEGE is relaxed towards the GSWP climatology. The results indicate that the relaxation has a positive impact on both the model's climatology and the simulated interannual variability, thereby confirming the utility of the GSWP soil moisture data for prescribing initial or boundary conditions in comprehensive climate and numerical weather prediction models. They also demonstrate the relevance of soil moisture for achieving realistic simulations of the Northern Hemisphere summer climate. In order to get closer to the framework of seasonal predictions, additional experiments have been performed in which GSWP is only used for initialising soil moisture at the beginning of the summer season (the relaxation towards GSWP is removed on 1st June). The results show a limited improvement of the interannual variability, compared to the simulations initialised from the ARPEGE climatology. However, some regional patterns of the precipitation differences between 1987 and 1988 are better captured, suggesting that seasonal predictions can benefit from a better initialisation of soil moisture.     

Seasonal and diurnal dynamics of CO<Subscript>2</Subscript> emission from oligotrophic peat soil surface

The results of research of diurnal and seasonal dynamics of CO₂ emission from the oligotrophic swamp surface in the southern taiga subzone of Western Siberia in 2005–2007 are under consideration. During the summertime, the intensity of CO₂ emission increases from spring to the midsummer and then decreases by the fall. A mean CO₂ emission value was 118 mg CO₂/(m² hour). The analysis of diurnal dynamics of CO₂ emission showed that the maximum CO₂ flux is observed at 16:00, while the minimum, at 07:00. Mean amplitude of diurnal variations of the CO₂ emission is 74 mg CO₂/(m² hour). The relations established between air temperature and CO₂ flux allowed calculating carbon dioxide emission for the periods between measurements. It was found that in the summertime, the period between 10:00 and 13:00 was optimal for measuring CO₂ emission with a chamber method.     

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient (R ²)) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R ² 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R ² 0.93, 0.91) in estimating soil temperature in Turkey.