Sangong River Research Articles

Dryland agricultural expansion leads to lower content and higher variability of soil inorganic carbon in topsoil

Soil inorganic carbon (SIC) is of key importance to the global carbon cycle and mitigating climate change. However, decades of agricultural expansion in the drylands have resulted in vast and fragmented agricultural landscapes and have potentially affected SIC. Previous studies investigating the impact of agricultural expansion on SIC have primarily focused on cultivation impacts, while ignoring edge effects from agricultural land boundaries. Here, we combined cultivation impacts with edge effects and fully evaluated the impacts of agricultural expansion on SIC. We conducted spatial statistical and buffer analyses using a SIC distribution map, which was developed using 388 sample points in 0 – 20 cm topsoil over the Sangong River watershed in drylands of China. We found that decades-long agricultural expansion resulted in lower content and higher variability of SIC in topsoil. Specifically, cultivation decreased SIC content by 28.57% (1.64 g kg−1) and increased SIC variability by 95.71% (0.67 g kg−1) in agricultural lands. Edge effects altered SIC content by − 1.95–11.51% (−0.11 to 0.54 g kg−1) and increased SIC variability by 39.14% (0.34 g kg−1) at edges. In addition, we found that fewer sampling points in areas with high SIC variability (e.g., agricultural lands and edges) decreased the estimation accuracy. Our study highlights the need for SIC conservation during dryland agricultural expansion. Also, it suggests that as many sampling points as possible should be set in high variability areas, which is especially useful for accurately estimating SIC distribution in drylands.

Assessing the effects of agricultural management practices and land-use changes on soil organic carbon stocks

Soil organic carbon (SOC) stocks have profound effects on climate change, sustainable agricultural development, and environmental management. Our objectives were to propose a conceptual framework and quantify the impact of land use change (LUC) and agricultural management practices (AMPs) on SOC stocks. By comparison, we choose the Kriging-based spatial prediction model to estimate SOC stocks based on the field sampled soil data (depth of 0–30 cm) in 2005 and 2019. Film mulching, drip irrigation, and fertilizer application were selected to represent the regional AMPs. Our results indicate that SOC stocks increased by 12.7% in the Sangong river basin from 2005 to 2019. From the proposed conceptual framework, we notice that the transition between different land-use types may cause both losses (e.g., −9.49 Gg C caused by expansion of construction land) and gains (e.g., +3 Gg C caused by the conversion of cultivated land to grassland) of SOC storage. Benefiting from improved AMPs (e.g., film mulching, drip irrigation, and fertilizer application), the “stable cultivated land” category contributes the most (+36.0 Gg C) to the growth of SOC stocks.

Contribution of Fine Roots to Soil Organic Carbon Accumulation in Different Desert Communities in the Sangong River Basin.

This study explored the relationship between soil organic carbon (SOC) and root distribution, with the aim of evaluating the carbon stocks and sequestration potential under five plant communities (Alhagi sparsifolia, Tamarix ramosissima, Reaumuria soongorica, Haloxylon ammodendron, and Phragmites communis) in an arid region, the Sangong River watershed desert ecosystem. Root biomass, ecological factors, and SOC in different layers of a 0–100 cm soil profile were investigated. The results demonstrated that almost all living fine root biomass (11.78–34.41 g/m2) and dead fine root biomass (5.64–15.45 g/m2) levels were highest in the 10–20 cm layer, except for the P. communis community, which showed the highest living and dead fine root biomass at a depth of 60–70 cm. Fine root biomass showed strong seasonal dynamics in the five communities from June to October. The biomass levels of the A. sparsifolia (138.31 g/m2) and H. ammodendron (229.73 g/m2) communities were highest in August, whereas those of the T. ramosissima (87.76 g/m2), R. soongorica (66.29 g/m2), and P. communis (148.31 g/m2) communities were highest in September. The SOC of the five communities displayed strong changes with increasing soil depth. The mean SOC value across all five communities was 77.36% at 0–30 cm. The highest SOC values of the A. sparsifolia (3.08 g/kg), T. ramosissima (2.35 g/kg), and R. soongorica (2.34 g/kg) communities were found in June, and the highest value of the H. ammodendron (2.25 and 2.31 g/kg, p > 0.05) community was found in June and September. The highest SOC values of the P. communis (1.88 g/kg) community were found in July. Fine root production and turnover rate were 50.67–486.92 g/m2/year and 1.25–1.98 times per year. The relationships among SOC, fine root biomass, and ecological factors (soil water content and soil bulk density) were significant for all five communities. Based on the results, higher soil water content and soil conductivity favored the decomposition of root litter and increased fine root turnover, thereby facilitating SOC formation. Higher pH and bulk density levels are not conducive to soil biological activity and SOC mineralization, leading to increased SOC levels in desert regions.

Intermediate human activities maximize dryland ecosystem services in the long-term land-use change: Evidence from the Sangong River watershed, northwest China

Human activities cause widespread changes in landscape composition, which can affect ecosystem services produced by these landscapes. It is usually believed that ecosystem services can be maximized only when we eliminate all human activities. However, this belief is not the case, at least in dryland ecosystems. Here, a gradient of human activity intensity was used to investigate changes in the value of ecosystem services over 30-years of land-use change between 1990 and 2020 in the arid Sangong River watershed of northwest China. Spatial analyses were performed to determine how the value of dryland ecosystem services changed with human activity intensity. Stepwise regressions and linear programming models were also performed to examine how to optimize the value of ecosystem services (i.e., regulating services, provisioning services, supporting services, and cultural services). We found that landscapes of the Sangong River watershed became increasingly fragmented and that human activities gradually intensified, but the value of ecosystem services fluctuated rather than linearly decreasing over the past 30 years. Specifically, a unimodal relationship was observed between human activities and ecosystem services. The peak value of ecosystem services was 5799 USD ha−1 yr−1 under intermediate human activity intensity (i.e., human footprint index ranged from 0.2 to 0.4 at a scale of one). Gross domestic product (GDP) per capita, population, and water consumption were the three most important driving factors of human activities and ecosystem services. Our results suggest that intermediate human activities may maximize dryland ecosystem services in long-term land-use change at the watershed scale, and highlight the importance of regulating economic development, population, and water consumption for the management of dryland ecosystem services.

Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization

ABSTRACT Soil salinization endangers the development of ecological agriculture. As soil salinization is often heavily affected by regional environments, difficulties arise when constructing an adaptive multi-regional soil salinity estimation model. In this study, we proposed an extreme gradient boosting (XGBoost) model based on the Tree-structure Parzen Estimator (TPE) optimization algorithm to apply to four study areas with different environments (TPE-XGBoost). The four areas are the Weigan-Kuqa Oasis (Weiku), the Sangong River Basin (Sgr) and the Qitai Oasis in Xinjiang, China, and the middle and lower reaches of the Syr Darya Basin in Kazakhstan. Most previous soil salinity studies did not pay much attention to the impact of feature selection and hyper-parameter tuning on the performance of machine learning models, and the complex dependence and interaction between input features and hyper-parameters. In order to improve the performance of XGBoost model in estimating soil salinity, we proposed for the first time to use TPE algorithm to jointly optimize feature selection and hyper-parameter tuning, and verified it in four areas. Coefficient of determination (R2) and Root Mean Square Error (RMSE) were used to evaluate the model performance. First, we calculated 55 environmental features from Landsat and terrain data. Then, in order to reduce the computational complexity of the TPE-XGBoost model, we used Pearson correlation analysis between surface soil salinity content (SSC) and features to initially filter out the features that were not significantly related (P > 0.05). Finally, the TPE algorithm was used to jointly optimize the parameter space composed of features and hyper-parameters. The results showed that (1) TPE joint optimization algorithm significantly improved the performance of the XGBoost model, achieving high accuracy in the four areas, and had powerful generalization. R2 values of test sets for Weiku Oasis, Qitai Oasis, Sgr Basin, and the Syr Basin were 0.95, 0.95, 0.80, and 0.81, respectively. (2) There is no universal feature can be applied to soil salinity inversion in different environments. TPE algorithm adaptively selected different types and numbers of features for four areas, 19, 11, 25, and 15 features were selected in Weiku Oasis, Qitai Oasis, Sgr Basin, and the Syr Basin, respectively. This showed that the optimal model parameters should not be fixed parameters, but should be re-determined locally according to different environmental conditions. The TPE algorithm can capture the features that reflect environmental differences. (3) The XGBoost model can provide feature importance ranking, which improves the interpretability of machine learning model. The importance analysis results showed that the features had different contributions in different areas. The TPE-XGBoost model proposed in this study has great potential in multi-regional soil salt estimation research.

Induced soil degradation risks and plant responses by salinity and sodicity in intensive irrigated agro-ecosystems of seasonally-frozen arid regions

Salinity and sodicity posing large risks to soil hydroecological functioning and agricultural production. However, a comprehensive investigating of the long-term salinity and sodicity dynamics and how these contribute to soil degradation risk and plant response in intensive irrigated agro-ecosystems is still scarce, especially in cold/arid agricultural regions. To quantify long-term soil degradation risk and plant response as driven by rainfall, irrigation and groundwater in seasonally-frozen arid regions, taking into account the influence of capillary flux from shallow brackish groundwater and annual soil freezing-thawing cycles and snowmelt infiltration to soil moisture (s), salinity (Cs) and sodicity (Ex), we coupled the Salt of the Earth (SOTE) model and the Wavelet Neural Network (WNN) model (i.e. SOTE-WNN model) as well as improved the Crop Response to the Soil Environment (ANSWER) model. The long-term dynamics of root zone s, Cs and Ex and therefore soil degradation risks and plant responses in irrigated agro-ecosystems in three different hydrological regions (i.e. ADFO: alluvial-diluvial-fan oasis; APOU: alluvial-plain oasis in the upper part; and APOL: alluvial-plain oasis in the lower part) in the Sangong River watershed (SRW) of northwestern China were investigated. The SOTE-WNN model was calibrated and validated using field comprehensive observations during April 2018–April 2021. The simulated results of root zone s, Cs and Ex resulted in a good agreement with the observed values in the SRW. Results showed that the s in the three hydrological regions has a similar periodic steady state with sawtooth pattern; the Cs is the largest in the APOL and corresponding the crop relative yield is the lowest; in the APOU the Ex is the largest and corresponding the soil degradation risk is the highest. Carried out sensitivity analysis revealed that the increase in rainfall and snowfall can clearly alleviate soil degradation by reducing soil alkalization in the root zone. The irrigation water quality is one of the primary controls on long-term dynamics of root zone s, Cs and Ex, and imposing adverse impact on soil hydraulic conductivity and agricultural crop productivity. The shallow groundwater table and quality have non-monotonic effects on the root zone s, Cs and Ex dynamics, the 2–3 m is the critical water table interval that limits soil salinization and alkalization hazards in the SRW. The results highlight that the trade-offs between irrigation water quality and water table need to be emphasized at appropriate spatial and temporal scales when promoting intensive irrigation practices in arid/cold climate regions.

Spatio-temporal dynamics and eco-hydrological controls of water and salt migration within and among different land uses in an oasis-desert system

Identifying the eco-hydrological processes associated with water-salt dynamics is important for the sustainable management of water resources and eco-environmental systems in groundwater-dependent ecosystems, especially across different land use types in salt-affected oasis-desert ecosystems. In this study, a typical cropland-shelterbelt-desert site at the oasis-desert system in the Sangong River watershed of northwestern China was selected to investigate the spatio-temporal variations of water-salt dynamics using the Spearman rank correlation analysis and water/mass balance analysis, and to identify the response of vegetation dynamics to water-salt variations based on a model framework for vegetation–salinity–groundwater interactions, within and among these land uses during crop growth period (CGP: April 1–June 28, 2018) and non-crop-growth period (Non-CGP: June 29–October 31, 2018). Results showed that the soil water content (SWC) and soil electrical conductivity (SEC) had clear vertical stratification, horizontal transition and seasonal fluctuation characteristics during both CGP and Non-CGP. Significant differences in groundwater depth and salinity were exhibited between both study periods. The water exchange flux (WEF) and salt exchange flux (SEF) in both the cropland and shelterbelt were closely related to irrigation events and evidently higher than that in desert. The cropland maintained a salt accumulation state (especially at the >60–80 cm soil layer) during CGP. Hydrological links and salt transport processes among adjacent land uses have been weakened owing to the application of water-saving irrigation in cropland and the significantly declined of regional groundwater tables. Groundwater pumping and lateral groundwater flow (LGWF) were the most important media for water-salt exchange in the site. The interactions of vegetation with both the soil water-salt balance and groundwater dynamics may cause a discontinuous and irreversible ecosystem response to changes in land use or environmental conditions. Anthropogenic processes, especially the development of modern water-saving irrigation agriculture with groundwater-fed, are dominating the vegetation-salinity-groundwater interactions and its ecohydrological consequences in this ecosystem. Adaptive management of water and salt migration in soil and groundwater is essential for maintaining the coexistence of oasis-desert ecosystems in arid areas.

AGA-SVR-based selection of feature subsets and optimization of parameter in regional soil salinization monitoring

ABSTRACTSalinization is a major eco-environmental threat in arid and semi-arid regions. Machine learning combined with salinization-related factors extracted from satellite images and Digital Elevation Model (DEM) data to monitor salinization is popular in recent years. The hyper-parameters referring to the parameters prior to fitting the model to the data and the features referring to the factors used for model establishment are vital to modelling accuracy, while the optimization of the above two was not given enough attention. In this study, we proposed a novel approach for simultaneously identifying the input features and hyper-parameters of Support Vector Regression (SVR) based on Adaptive Genetic Algorithm (AGA) for quantitative assessment of salinization in Weigan-Kuqa river delta oasis (Wei-Ku oasis), Sangong River Basin, and Qitai oasis of Xinjiang. First, a total of 41 salinization-related factors of 7 categories were extracted from Landsat 5 TM and DEM data. In each sub-region, the Pearson’s correlation analysis was developed between Soil Salt Content (SSC) and salinization-related factors, and the factors significantly correlated with SSC were arranged in descending order of absolute correlation coefficient to form the Candidate Feature Variables (CFVs). The ration of Coefficient of Determination (R2) and Root Mean Square Error (RMSE) multiplied by 1000 was considered as the fitness function of Genetic Algorithm (GA) and AGA. The CFVs and hyper-parameters were combined together and binary coded, then brought into the AGA and GA for simultaneous feature selection and hyper-parameters optimization of SVR, and established salinization monitoring models (AGA-SVR, GA-SVR). In order to highlight the importance of identifying the input features and hyper-parameter to modelling accuracy, the GS-SVR with all the CFVs as input was established using Grid Search (GS) algorithm to optimize the hyper-parameters. Finally, the salinization maps predicted by three models were compared. The results showed that the sensitivity of salinization-related factors to SSC varied with regions, and 25, 16, 24 salinization-related factors were selected as CFVs in Wei-Ku oasis, Sangong River Basin, and Qitai oasis, respectively. Compared with GS-SVR, the GA-SVR and AGA-SVR got more accurate salinization monitoring with fewer features as input, and fitness generated by AGA-SVR increased by 25.968% in Wei-Ku oasis, 25.159% in Sangong River Basin, 27.568% in Qitai oasis, respectively. Both nature and human factors lead to salinization. The difference of Land Surface Temperature (LST) was the main contributor of different salinization between Wei-Ku oasis of southern Xinjiang and the other two sub-regions of northern Xinjiang. The differences in soil texture, irrigation methods, and livestock carrying capacity were the main factors resulting in differences of salinization in Qitai oasis and Sangong River Basin. Our study shows that the proposed approach can provide technical support for accurate salinization monitoring.

Influence of mulched drip irrigation on landscape scale evapotranspiration from farmland in an arid area

Water-saving irrigation measures in arid areas affect evapotranspiration (ET) processes while conserving water. Mulched drip irrigation is considered the most efficient irrigation method because it distributes water uniformly in the soil, restricts deep percolation, and minimizes unproductive evaporation from soil. The effect of mulched drip irrigation on farmland ET during the growing season at a landscape scale remains unclear, despite being vital for developing optimal water resource management strategies in arid areas. To compare the effects of mulched drip irrigation and mulched border irrigation on ET, based on Landsat satellite imagery (2007–2009 with mulched border irrigation, and 2014–2016 with mulched drip irrigation), an improved Surface Energy Balance Algorithm for Land (SEBAL) model was used to evaluate ET for the two periods of an oasis at Sangong River Basin in the arid region of Northwest China. The results show that daily ET rates from farmland managed under mulched drip irrigation were on average 0.2–1 mm.d−1 higher than under mulched border irrigation between June and August. Correspondingly, the net radiation flux (R) increased 73.32 W·m-2 on average, and R was found to be the main determinant of the ET differences. Meanwhile, the average land surface albedo decreased by 20%, and negatively correlated with R (P < 0.05), indicating that the land surface albedo was the main factor affecting R. Furthermore, the Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with land surface albedo. More importantly, the Temperature–Vegetation Dryness Index (TVDI) under mulched drip irrigation was found to be approximately 21% lower than that under mulched border irrigation, indicating that the soil moisture conditions of the farmland under mulched drip irrigation was significantly improved compared to mulched border irrigation. Namely, less water stress resulted in better developed canopy of the crops, which in turn captured more radiation and thus increased ET. In the end, the observed increases in landscape-scale ET under mulched drip irrigation in arid area resulted from enhanced productivity of the crops due to lessened drought stress.

Extraction and Analysis of Natural Vegetation Cover and Soil Moisture Using Landsat TM Data in the Arid Oasis of Xinjiang, China

The natural vegetation cover and soil moisture have significant impacts on the stability of the Arid oasis. In this paper, the 75 Landsat TM images were processed to extract the natural vegetation cover, obtained by the Normalized Difference Vegetation Index (NDVI), and soil moisture observed by Tasseled Cap Wetness (TCW) during the year 1990–2011 in the Sangong River Basin, Xinjiang, China. Four plots of natural vegetation were selected to measure the average values of NDVI and TCW. The results reflected that the NDVI and TCW kept stable during the past 20 years with a strong correlation between each other. The influences of different geographical locations and different cover types on the change of NDVI mean in four plots were different. There was a close relationship between NDVI (or TCW) and precipitation in a short time. On the other hand, the data of groundwater depth measured from 16 groundwater observation wells year from 1998 to 2002 were used to perform spatial interpolation in the study area. Two shrub land plots in northern oasis were selected to extract the values of groundwater depth, vegetation index and soil moisture at each grid pixel (30 m) for carrying out the correlation analysis. The results demonstrated that there were obvious correlations among them when the groundwater depth varied in a range of 2.5–4 m; otherwise, the growth of vegetation would be inhibited in this area. The remote sensing as an effective way of achieving time series information could provide important data for natural vegetation development and evolution, which could be in favor of oasis’ stability and sustainable development.

地表反照率不同计算方法对干旱区流域蒸散反演结果的影响——以新疆三工河流域为例

地表反照率不同计算方法对干旱区流域蒸散反演结果的影响——以新疆三工河流域为例

三工河流域琵琶柴群落凋落物对土壤有机碳固定的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 三工河流域琵琶柴群落凋落物对土壤有机碳固定的影响 DOI: 10.5846/stxb201806301443 作者: 作者单位: 贵州大学动物科学学院,中国科学院植物研究所,贵州大学动物科学学院,贵州大学动物科学学院,贵州省草地技术试验推广站,贵州大学动物科学学院,贵州大学 作者简介: 通讯作者: 中图分类号: 基金项目: 贵州省科技厅-贵州大学联合基金项目（黔科合LH字[2017]7288号）；国家自然科学基金青年项目（31700390） Effects of litter on soil organic carbon fixation in Reaumuria soongorica communities in the Sangong River basin Author: Affiliation: College of Animal Science, Guizhou University,,,,,,College of Animal Science, Guizhou University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:土壤有机碳是土壤碳库的重要组成部分，对生态系统生产力和全球碳循环有着重要作用。采用凋落物收集器和DIRT法（添加和去除凋落物法）研究三工河流域两处不同琵琶柴群落凋落物的产量、现存量、凋落物处理对土壤有机碳的影响。结果表明：群落1和群落2的凋落物产量季节变化趋势相同，均呈"N "型变化，在10月份达到最大值，7月或8月份达到次大值。凋落物现存量随季节均呈现" W"型变化，在10月份达到最大值，最大值分别为30.65 g/m2和57.87 g/m2。土壤有机碳随土壤深度的增加均逐渐降低，群落1和群落2分别下降了61.73%-62.39%和18.24%-25.84%。与对照处理相比，去除凋落物处理（NL）的群落1和群落2土壤有机碳分别降低了6.97%和18.38%；添加凋落物处理（DL）的土壤有机碳分别增加了19.64%和13.66%；去除凋落物处理（NL）的群落1和群落2土壤有机碳储量分别为1007.36 kg/hm2和709.30 kg/hm2，添加凋落物处理（DL）的土壤有机碳储量分别为1197.88 kg/hm2和1010.78 kg/hm2。双因素方差分析表明群落1的土层深度和三种处理对土壤有机碳的交互作用不显著，群落2的交互作用显著。回归分析显示：土壤水分、电导率、pH、容重和温度是导致两琵琶柴群落土壤有机碳不同的主要生态因子。相对较高的土壤pH和盐分含量抑制了凋落物的分解，导致凋落物现存量较高、土壤有机碳含量低；相对较高的土壤含水量和较小的容重，有利于土壤生物的活性和土壤有机碳的矿化，导致土壤有机碳含量降低。 Abstract:Soil organic carbon is an important component of the soil carbon pool,and plays a key role in ecosystem productivity and the global carbon cycle. In this research,litter removal and detritus input treatment (DIRT) methods were used to study the litter productivity,litter amount,and effects of different litter treatments on the soil organic carbon in two Reaumuria soongarica communities in the Sangong River Basin. The results showed that the litter productivities of two communities had almost identical seasonal changes;they reached their second-maximum and maximum values in July (August) and October,and both showed "N" distributions. The litter amount also presented similar seasonal change trends;both showed "W" distributions,and reached their maximum values of 30.65 g/m2 and 57.87 g/m2 in October,respectively. The soil organic carbon content gradually reduced with soil depth;Community 1 and Community 2 decreased by 61.73%-62.39% and 18.24%-25.84%,respectively. The average soil organic carbon content of Community 1 and Community 2 was reduced by 6.97% and 18.38% by the litter removal treatment (no litter,NL),and increased by 19.64% and 13.66% by the litter treatment (double litter,DL),respectively. The soil organic carbon storage values of Community 1 and Community 2 were 1007.36 kg/hm2 and 709.30 kg/hm2 after NL treatment,and 1197.88 kg/hm2 and 1010.78 kg/hm2 after DL treatment,respectively. Two-way analysis of variance showed that the interaction of soil organic carbon between the soil depths and three litter treatments was significant in Community 2,but not Community 1. Regression analysis showed that soil properties were determinants of the differences in soil organic carbon between the two communities. The relatively high litter amount and low soil organic carbon content were attributed to the high soil pH and salt content, which inhibited litter decomposition. Otherwise,the relatively high soil water content and low soil bulk density promoted both soil organism activity and soil organic carbon mineralization,which led to the decrease in soil organic carbon content. 参考文献 相似文献 引证文献

Identifying change in spatial accumulation of soil salinity in an inland river watershed, China

Soil salinity accumulation is strong in arid areas and it has become a serious environmental problem. Knowledge of the process and spatial changes of accumulated salinity in soil can provide an insight into the spatial patterns of soil salinity accumulation. This is especially useful for estimating the spatial transport of soil salinity at the watershed scale. This study aimed to identify spatial patterns of salt accumulation in the top 20cm soils in a typical inland watershed, the Sangong River watershed in arid northwest China, using geostatistics, spatial analysis technology and the Lorenz curve. The results showed that: (1) soil salt content had great spatial variability (coefficient variation >1.0) in both in 1982 and 2015, and about 56% of the studied area experienced transition the degree of soil salt content from one class to another during 1982–2015. (2) Lorenz curves describing the proportions of soil salinity accumulation (SSA) identified that the boundary between soil salinity migration and accumulation regions was 24.3m lower in 2015 than in 1982, suggesting a spatio-temporal inequality in loading of the soil salinity transport region, indicating significant migration of soil salinity from the upstream to the downstream watershed. (3) Regardless of migration or accumulation region, the mean value of SSA per unit area was 0.17kg/m2 higher in 2015 than 1982 (p<0.01) and the increasing SSA per unit area in irrigated land significantly increased by 0.19kg/m2 compared with the migration region. Dramatic accumulation of soil salinity in all land use types was clearly increased by 0.29kg/m2 in this agricultural watershed during the studied period in the arid northwest of China. This study demonstrates the spatial patterns of soil salinity accumulation, which is particularly useful for estimating the spatial transport of soil salinity at the watershed scale.

An analysis of oasis evolution based on land use and land cover change: A case study in the Sangong River Basin on the northern slope of the Tianshan Mountains

This study investigated oasis evolution and the changes of peripheral desert in the Sangong River Basin since the 1950s by rebuilding seven land cover maps derived from black-and-white aerial photographs (1958, 1968, and 1978), a color-infrared aerial photograph (1987), Landsat Thematic Mapper (TM) imagery (1998), Satellite Pour l’Observation de la Terre (SPOT) imagery (2004), and Landsat Operational Land Imager (OLI) imagery (2014). The results showed that: (1) Since 1950, the oasis consecutively expanded more than four times from an alluvial fan to an alluvial plain, causing the shrinkage of desert landscapes that were dominated by a Haloxylon ammodendron Bunge community (HBC) and a Tamarix chinensis Lour community (TLC). Furthermore, the primary (1958–1968) and final (2004–2014) stages were the most important periods, during which agricultural land experienced the most rapid expansion during the period 1958–1968, and the built-up area showed the most rapid expansion after the 2000s. (2) Two basic management modes, a “local mode” formed by the local governments and a “farm management mode” developed by Xinjiang Production and Construction Corps, together promoted oasis evolution under various land-use and landcover (LULC) stages. (3) The evolution of the modern oasis during the 1950s–2004 showed the general features of an arid oasis, while during the period of 2004–2014 it was characterized by a large-scale inter-basin water diversion or the import of new water sources. (4) The oasis expanded at the expense of desert vegetation, resulting in distinct variation in the structure of the desert plant community, which will make it more difficult to protect the desert ecosystem.

Major ion chemistry of groundwater in the Sangong River Watershed, Northwestern China

Hydrochemical investigations were conducted to determine the chemical composition of groundwater in the Sangong River Watershed (SRW), Northwestern China. Thirty-two groundwater samples were collected from different wells to monitor the water chemistry of various ions. The results of the chemical analysis indicate that the groundwater in the area is generally neutral to slightly alkaline and predominantly contains Ca2+ and Mg2+ cations as well as HCO3 − and SO4 2+ anions. The Na++K+ concentration increases from the alluvial–diluvial fan to alluvial plain. TDS values are becoming larger along the direction of the groundwater flow in general because long residence time and evaporation. The contribution of Na++K+ to the major cations which have been affected by water–rock interactions has great uncertainty. High positive correlations were obtained among the following ions: SO4 2−–Mg2+, SO4 2−–Na++K+, Cl−–Mg2+, Cl–Na++K+, Mg2+–Na++K+ and SO4 2−–Cl−. The dominant hydrochemical facies is Ca2+–Mg2+–HCO3 −, which is in relation with their interaction with the geological formations of the basin, cation exchange between groundwater and clay minerals and anthropogenic activities. The saturation index (SI) indicates that the gypsum-halite dissolution reactions occur during a certain degree of rock weathering. Almost all SI values of anhydrite are less than zero which shows that it remains unsaturated with respect to these minerals. That is to say, those carbonate mineral phases may have influenced the chemical composition of the study area. According to the Wilcox, US Salinity Laboratory and residual sodium carbonate (RSC) classifications, most of the groundwater samples in the study area are suitable for irrigation.

The relationship between soil, climate and forest development in the mid-mountain zone of the Sangong River watershed in the northern Tianshan Mountains, China

The mountainous forests in arid regions, being sensitive to climate change, are one of the key research topics related to the mechanism of interaction between climate and the terrestrial ecosystem. In this study, the spatial distribution of a mid-mountain forest and its environmental factors were investigated by using a combination of remote sensing technology, field survey, climate indices and soil nutrient analysis in the Sangong River watershed of the northern Tianshan Mountains. The forest (Picea schrenkiana) was distributed between 1,510 and 2,720 m asl. Tree height and diameter at breast height (DBH) exhibited a bi-modal pattern with increasing elevation, and rested at 2,450 and 2,250 m asl, respectively. The two maxima of DBH appeared at 2,000 and 2,550 m asl, and the taller trees were observed at 2,100 and 2,600 m asl. For the annual mean temperature, the difference was approximately 5.8°C between the lowest and the highest limits of the forest, and the average decreasing rates per hundred meters were 0.49°C and 0.55°C with increasing altitude between 1,500 and 2,000 m asl and above 2,000 m asl, respectively. The annual precipitation in the forest zone first increased and then decreased with the increase of altitude, and the maximum value was at 2,000 m asl. For per hundred meters, the annual precipitation increased with the rate of 31 mm between 1,500 and 2,000 m asl and decreased by 7.8 mm above 2,000 m asl. The SOM, TN and TP were high between 2,000 and 2,700 m asl and low at the lower and upper forest limits. The minimum CaCO3 concentration, pH value and EC coincided with the maximum precipitation belt at 2,000 m asl. The SOM, TN and TP were high in the topsoil (0–10 cm) and differed significantly from the values observed in the deep soil layers (>10 cm). The soil nutrients exhibited spatial heterogeneity and higher aggregation in the topsoil. In conclusion, soil and climate are closely related to each other, working synergistically to determine the development and spatial distribution of the mid-mountain forest in the study area. The order of the importance of environmental factors to forest development in this study is as follows: soil nutrients>precipitation>elevation>temperature.

三工河流域两种琵琶柴群落细根生物量、分解与周转

三工河流域两种琵琶柴群落细根生物量、分解与周转

三工河流域琵琶柴群落特征与土壤因子的相关分析

PDF HTML阅读 XML下载 导出引用 引用提醒 三工河流域琵琶柴群落特征与土壤因子的相关分析 DOI: 10.5846/stxb201210071379 作者: 作者单位: 中国科学院植物研究所,中国科学院植物研究所,中国科学院植物研究所,中国科学院植物研究所,中国科学院植物研究所,中国科学院植物研究所,中国科学院植物研究所,青城山-都江堰旅游景区管理局,青城山-都江堰旅游景区管理局,中国科学院植物研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展计划资助项目（2009CB825103） Correlation between characteristics of Reaumuria soongarica communities and soil factors in the Sangong River basin Author: Affiliation: Institute of Botany,Chinese Academy of Sciences,,Institute of Botany,Chinese Academy of Sciences,Institute of Botany,Chinese Academy of Sciences,Institute of Botany,Chinese Academy of Sciences,Institute of Botany,Chinese Academy of Sciences,Institute of Botany,Chinese Academy of Sciences,,,Institute of Botany,Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:琵琶柴（Reaumuria soongorica）是我国荒漠地区分布最广的地带性植被类型之一，对维系荒漠地区生态系统的稳定性具有重要作用。以三工河流域两个琵琶柴群落为对象，在2010年主要生长季节（6-10月），通过群落和土壤调查，采用土钻法、土柱法、地上收割法对两个琵琶柴群落的土壤性质、生物多样性、细根生物量、地上生物量、生物多样性与土壤性质的关系进行研究，结果表明：两个琵琶柴群落在冠幅、盖度、多度和物种多样性等方面均存在显著差异。在0-100 cm土壤层内，两个群落土壤电导率、pH值、容重、含水量存在显著差异。除土壤容重外，群落2各个土壤因子的值均大于群落1，并随土壤深度的增加表现出类似的趋势。两个群落物种多样性指数、地上生物量、细根生物量存在显著差异，从6月到10月呈现先下降再上升的趋势。由于7、8月群落1有大量夏雨型短命植物和类短命草本植物的出现，Shannon-Wiener多样性指数、Pielou均匀度指数急剧降低，Simpson指数表现出相反的变化趋势。群落2土壤电导率和pH值较高，草本植物鲜有出现，多样性指数和均匀度指数变化均较为平缓。两个群落的Sorenson相似性系数较低，群落差异明显。相关和回归分析表明土壤环境因子是导致两个琵琶柴群落特征、生物多样性和生物量不同的主要因素。较高的土壤含水量可以增加琵琶群落的生物多样性，较高的土壤容重抑制琵琶柴群落细根的生长，轻度的干旱胁迫促进地上生物量的积累，一定浓度的土壤pH值和土壤盐分可以促进琵琶柴群落细根的生长。 Abstract:Reaumuria soongarica is one of the most widely distributed plant species in arid regions of China and has an important role in maintaining the stability of the desert ecosystem. On the basis of field data, including the species composition, number of species, plant height, coverage and abundance, and selected soil properties (electrical conductivity, pH, bulk density and moisture content), the fine root biomass and aboveground biomass of two R. soongarica communities in the Sangong River basin were investigated over the course of the main growing season (from June to October) of 2010. In addition, by calculation of derivative biodiversity indices comprising Pielou's evenness index, Simpson's dominance index, the Shannon-Wiener diversity index, and Sorenson's similarity index, the relationships between fine root biomass, biodiversity and soil properties of the two communities were analyzed. The results showed that the crown area, coverage, abundance, species number, Pielou's evenness index, Simpson's dominance index, Shannon-Wiener diversity index, aboveground biomass, fine root biomass, and the soil electrical conductivity, pH, bulk density and moisture content in the 0-100 cm soil layer of two R. soongarica communities were significantly different. Except for soil bulk density, the values of all soil properties of Community 2 were higher than those of Community 1, and showed similar trends with increment in soil depth. Except for species number and Simpson's dominance index, the other biodiversity indices decreased initially and thereafter increased from June to October, whereas the aboveground biomass and fine root biomass increased initially and thereafter decreased from June to October. Owing to the appearance of a large number of summer rain-dependent herbs in July and August in Community 1, the Shannon-Wiener diversity index and Pielou's evenness index showed sharp reductions over the growing season, whereas Simpson's dominance index showed the opposite trend. Compared with Community 1, Community 2 had higher soil electrical conductivity and soil pH, and few summer rain-dependent herbs, and therefore the Shannon-Wiener diversity index, Pielou's evenness index and Simpson's dominance index changed only moderately. In addition, Sorenson's similarity coefficient was small and the difference between the two communities was significant. Regression analysis showed that soil properties were determinants of the differences in biodiversity and biomass of the two communities. High soil moisture content increased biodiversity, high soil bulk density inhibited fine root growth, and certain soil pH and soil salt contents promoted fine root growth in R. soongarica communities. We concluded that soil salt content and soil pH are the main factors that restrict biodiversity, community structure, and growth of R. soongarica in the Sangong River basin. Differences in microenvironments, especially in soil characteristics, could induce strong differences in two communities with identical dominant species in the same climatic zone. These findings provide a scientific base for management of natural R. soongarica communities and rehabilitation of degraded communities, as well as for improvement of soil salinization in arid regions of China. 参考文献 相似文献 引证文献

Saline soil enzyme activities of four plant communities in Sangong River basin of Xinjiang, China

Soil enzyme activity plays an important role in the conversion of soil organic carbon into inorganic carbon, which is significant for the global carbon cycle. In this study, we investigated the soil enzyme activities of two ligninolytic enzymes (peroxidase and polyphenol oxidase) and five non-ligninolytic enzymes (α-1,4-glucosidase (AG); β-1,4-glucosidase (BG); N-acetyl-β-glucosaminidase (NAG); β-D-cellobiosidase (CBH); and β-xylosidase (BXYL)) in four plant communities of the Sangong River basin in Fukang, North Xinjiang, China. The four typical plant communities were dominated by Haloxylon ammodendron, Reaumuria soongonica, Salsola passerina, and Tamarix rarmosissima, respectively, with saline soils of varied alkalinity. The results showed that the soil peroxidase activity decreased seasonally. The activities of the five non-ligninolytic enzymes decreased with increasing soil depths, while those of the two ligninolytic enzymes did not show such a trend. In the four plant communities, BG had the highest activity among the five non-ligninolytic enzymes, and the activities of the two ligninolytic enzymes were higher than those of the four non-ligninolytic ones (AG, NAG, CBH, and BXYL). The community of H. ammodendron displayed the highest activity with respect to the two ligninolytic enzymes in most cases, but no significant differences were found among the four plant communities. The geometric mean of soil enzyme activities of the four plant communities was validated through an independently performed principal component analysis (PCA), which indicated that different plant communities had different soil enzyme activities. The correlation analysis showed that soil polyphenol oxidase activity was significantly positively correlated with the activities of the five non-ligninolytic enzymes. The soil pH value was positively correlated with the activities of all soil enzymes except peroxidase. Soil microbial carbon content also showed a significant positive correlation (P<0.01) with the activities of all soil enzymes except polyphenol oxidase. The results suggested that the H. ammodendron community has the highest ability to utilize soil organic carbon, and glucoside could be the most extensively utilized non-ligninolytic carbon source in the saline soil of arid areas in Xinjiang.

Detection of alpine tree line change with high spatial resolution remotely sensed data

This study examined vegetation distribution and tree line change in the Sangong River watershed in the Tianshan Mountains, Xinjiang, China, using aerial photographs from 1962 and 1981 and a QuickBird image from 2006. An object-oriented classification method was used to map vegetation distribution, and the postclassification comparison approach was used to detect vegetation change. The results were linked to digital elevation model data for exploring the relationships between vegetation distribution/expansion and topographic factors. It shows that Schrenkiana is mainly distributed in the region with elevations less than 2,600 m, but its tree line can reach 2,880 m. Sabina is mainly distributed in the region with elevations within 2,600 to 2,900 m, and its highest elevation can be 3,270 m. Approximately 73% of Schrenkiana areas are distributed in aspects of 0 to 45 deg and 270 to 360 deg and 52% of Sabina areas are in aspects of 45 to 90 deg and 225 to 270 deg. The Schrenkiana expansion is mainly distributed in slopes of 20 to 40 deg (accounting for 82%) and aspects of 0 to 90 deg and 270 to 315 deg (accounting for 77%). The elevation of Schrenkiana tree lines increased by 2 to 3 m from 1962 to 2006 and of Sabina increased by 7 to 12 m from 1981 to 2006. Both Schrenkiana and Sabina expansion is mainly located in vegetation frontiers.