Manasi River Research Articles

Duration of continuous cropping with straw return affects soil organic carbon

AbstractUnderstanding the impact of continuous cropping with straw return on soil organic matter functional groups is of great importance for maintaining chemical stabilization of SOC in arid regions. Infrared spectral characteristics of cotton field soil with different continuous cropping durations (0, 5, 10, 15 and 20 years) were determined with Fourier‐transform infrared spectroscopy for fields in Manasi River Basin, Xinjiang province, China. The effects of continuous cropping duration on the stability and chemical composition of soil organic carbon (SOC) were analysed. The results indicate an initial increase in the SOC, soil particulate organic carbon (POC), and mineral‐associated organic carbon (MOC) content of soil under continuous cropping with straw return. However, as continuous cropping duration increased, the levels of SOC, POC, and MOC in soil began to decrease. The soil POC content and POC/MOC were highest after 5 years of straw return, and started to decrease as the continuous cropping duration increased. The SOC and MOC contents were highest after 10 years of continuous cropping, and were 3.30 and 1.84 times higher than the control, respectively. As continuous cropping duration increased, the relative peak intensities for polysaccharides and aromatic groups in soil organic matter decreased, while the relative peak intensities for aliphatic compounds and hydroxyketone increased. In conclusion, continuous cropping with straw return in cotton fields promotes SOC but only for about 10 years. An increase in straw return duration boosts the degree of esterification in soil organic matter. Moreover, enhanced protection by mineral binding was observed for soil organic matter, increasing organic matter stabilization. This study aimed to provide an empirical foundation for the management of the SOC pool and the establishment of rational straw return practices for cotton fields in arid areas.

Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images

Soil salinization is a major environmental risk caused by natural or human activities especially in arid and semi-arid regions. Machine learning for rapidly monitoring large-scale spatial soil salinization becomes possible. However, machine learning often needs large training samples and obtaining extensive soil salinization information by field investigation is laborious and difficult. In practice, the field soil sampling datasets are often sparse and non-normally distributed. The intricacy of features extracted from remote sensing images increases the model complexity and often leads to degradation in the prediction performance. To solve this problem, an integrative framework is proposed to predict soil salt content (SSC) based on light gradient boosting machine (LGBM). In this model, we first introduce the data augmentation method (Mixup) to improve sample diversity and alleviate model overfitting by the sparsity of samples. To improve the generalization and robustness of the model in different spatial heterogeneity of soil salinization, the Mixup-LGBM model is adaptively and jointly optimized by combining hyperparameters and feature selection in a Bayesian optimization framework. Furthermore, model interpretability is improved using shapley additive explanations (SHAP) value based on the combination of the confidence of the synthetic data through model visualization and feature importance assessment. In addition, different cases are simulated to test the model performance. In Case I, the raw sample-sparsity model using the data augmentation algorithm has higher prediction accuracy than other unused models. In Case Ⅱ, the extreme sample-sparsity model still achieves satisfactory results while the other models can’t learn any effective information after multiple iterations. The experimental results reveal that the proposed model can automatically find representative features in heterogeneous environments and has strong adaptability in different study areas. This finding indicates that digital elevation model (DEM) has a high influence on SSC in both study areas. Besides the DEM, soil salinization in the Manasi River Basin is more sensitive to human activities, while that in the Werigan–Kuqa River Delta Oasis is more sensitive to natural factors. The Mixup-LGBM model is suitable for predicting SSC in different sample sparsity scenarios while ensuring the high accuracy. The model has considerable potential for dealing with other complex sample sparsity regression tasks.

Altitudinal and Temporal Variations of Near-Surface Air Temperature Lapse Rate on the Northern Slope of the Tianshan Mountains

Abstract Near-surface air temperature lapse rate (NSATLR) is vital for hydrological simulation and mountain climate research in snowmelt-dominated regions. In this study, NSATLRs of two vertical zones (i.e., mountain grassland–coniferous forest belt and alpine meadow belt) of the Manasi River basin on the northern slope of the Tianshan Mountains were calculated using the near-surface air temperature data from 18 observation stations. Furthermore, temporal variations of NSATLRs of these two vertical zones at seasonal, monthly, and daily scales were analyzed, combined with altitudinal differences of local environments. The results show that the temporal variations of NSATLRs are different between these two vertical zones. The steepest monthly NSATLR occurs in July in the mountain grassland–coniferous forest belt and in April in the alpine meadow belt. In spring, summer, and autumn, the hourly NSATLRs in the mountain grassland–coniferous forest belt generally steepen with increasing solar radiation and vice versa, contrary to those in the alpine meadow belt. During winter, the hourly NSATLRs on sunny days are overall positive at night but negative during the day in the mountain grassland–coniferous forest belt. The findings of this study indicate that it is necessary to divide mountains with similar local environments to the study area into different vertical zones to accurately estimate NSATLR, and the use of a fixed NSATLR for different months and vertical zones is not suitable for snowmelt runoff modeling in snow-dominated regions such as the northern slope of the Tianshan Mountains. Significance Statement This study aims to investigate the altitudinal and temporal variations of near-surface air temperature lapse rate (NSATLR) on the northern slope of the Tianshan Mountains and how mountain environments affect NSATLR. This is important because altitudinal differences of mountain environments lead to different NSATLRs, and these altitudinal variations on the northern slope of the Tianshan Mountains are different from those on the Alps at the same latitude. Our results explain how altitudinal differences of mountain environments affect NSATLRs; hence, using a fixed NSATLR for different months and vertical zones is inappropriate, and estimating NSATLRs for different vertical zones is necessary.

An improved approach of dry snow density estimation using C-band synthetic aperture radar data

Snow density is one of the important indicators of snow cover hydrological potential. The application of existing algorithms for retrieving dry snow density using synthetic aperture radar (SAR) data is limited by single scattering mechanism, small terrain fluctuation or narrow incidence angle range. In the study, an improved approach was proposed to retrieve dry snow density from C-band SAR data with a wide range of roughness and local incidence angles. Both the snow-ground interface scattering and volume scattering were considered in the approach. First, the relationship between the backscattering at the snow-ground interface and relative permittivity was obtained based on simulation using the Advanced Integral Equation Model (AIEM) and regression analysis. Then the classical relationship between the volume backscattering and relative permittivity obtained by the first-order volume scattering model was incorporated into the approach. For comparison, the coefficients of the Shi algorithm were redefined by the AIEM model and regression analysis, and the Shi algorithm initially developed for L-band was modified for C-band. In experiments, the RADARSAT-2 data obtained in the Manasi River Basin on December 12–17, 2013 and the C-band GaoFen-3 data obtained in the Kelan River Basin on January 17, 2018 were selected to validate the applicability of the proposed approach under different conditions. The inversion results in the Manasi River Basin using the proposed approach, Singh algorithm, and modified Shi algorithm were compared. The results in the Manasi River Basin show that the correlation coefficients (Rs) between the measured and estimated dry snow density are 0.868, 0.694, and 0.653 for the three methods, respectively. The root mean square errors (RMSEs) are 31.1 kg m−3, 59.1 kg m−3, and 64.7 kg m−3, respectively, and the mean relative errors (MREs) are 12.9%, 21.9%, and 25.5%, respectively. The corresponding R, RMSE, and MRE in the Kelan River Basin using the proposed approach are 0.717, 57.2 kg m−3, and 27.1%, respectively. The results prove that the dry snow density under different C-band SAR data and different areas can be effectively retrieved using the proposed approach superior to the other two algorithms.

Runoff forecasting model based on CEEMD and combination model: a case study in the Manasi River, China

Abstract Accurate forecasting of runoff is necessary for water resources management. However, the runoff time series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved forecasting accuracy, a novel combination model based on complementary ensemble empirical mode decomposition (CEEMD) for runoff forecasting is proposed and applied in this paper. Firstly, the original runoff series is decomposed into a limited number of intrinsic mode functions (IMFs) and one residual based on CEEMD, which makes the runoff time series stationary. Then, approximate entropy is introduced to judge the complexity of each IMF and residual. According to the calculation results of approximate entropy, the high complexity components are predicted by Gaussian process regression (GPR), the medium complexity components are predicted by support vector machine (SVM), and the low complexity components are predicted by autoregressive integrated moving average model (ARIMA). The advantages of each forecasting model are used to forecast the appropriate components. In order to solve the problem that the forecasting performance of GPR and SVM is affected by their parameters, an improved fireworks algorithm (IFWA) is proposed to optimize the parameters of two models. Finally, the final forecasting result is obtained by adding the forecasted values of each component. The runoff data collected from the Manasi River, China is chosen as the research object. Compared with some state-of-the-art forecasting models, the comparison result curve between the forecasted value and actual value of runoff, the forecasting error, the histogram of the forecasting error distribution, the performance indicators and related statistical indicators show that the developed forecasting model has higher prediction accuracy and is able to reflect the change laws of runoff correctly.

Assessment of the effect of water-table depth on riparian vegetation along the middle and lower reaches of the Manasi River, Northwest China

The Manasi riparian zone in northwestern China has become a survival habitat for numerous native plant species and requires urgent protection owning to rapid expansion of farmland. The critical factor affecting the growth of desert riparian vegetation in arid regions is recognized to be groundwater, but in this region the threshold of water-table depth for riparian species has been rarely studied. To determine the association between species and their major growth factors and to ascertain the water-table depth threshold, this study employed field investigation, a logarithm distribution model and canonical correspondence analysis. According to the findings, (1) the water-table depth largely regulates the species distribution; (2) from the results of the logarithm distribution model, the water-table depth appropriate for herbs is 1–1.5 m and for shrubs it is 2–4 m, and a water-table depth of less than 6 m could satisfy the growth requirement of major species; (3) species diversity peaks at the water-table depths of 2–3, 3–5, and 2–4 m for herbs, shrubs and all species, respectively; (4) the frequency of appearance of Phragmites communis (grass herb) and Tamarix chinensis (deciduous shrub) was not as sensitive to depth to water table. To reconstruct a riparian zone, Phragmites communis and Tamarix chinensis could be planted in areas with water-table depth of less than 3 m and 2–5 m, respectively. These results may contribute to suitable policy regarding vegetation restoration.

Effect of sub‐cloud evaporation on precipitation in the Tianshan Mountains (Central Asia) under the influence of global warming

AbstractIt is crucial for accurately describing the precipitation patterns and their underlying mechanisms to optimise the hydro‐climatic model parameters and improve the accuracy of precipitation forecasting. Based on 212 precipitation samples collected during August 2015 to July 2016 in the mid‐mountain region of the Manasi River Basin in the northern slope of the Tianshan Mountains, we estimated the effect of sub‐cloud evaporation on precipitation, analysed the factors that influence the sub‐cloud evaporation, and modelled the response of sub‐cloud evaporation to global warming. The mean remaining raindrop mass fraction after evaporation (f) in this region is 94.39%. The mean deviation between d‐excess (Δd) of ground precipitation and raindrops under cloud is −4.22‰. The intensity of sub‐cloud evaporation is the highest in summer. There is a significant positive correlation between f and Δd (0.72‰/%). The relative humidity and diameter of raindrops were observed to have a direct influence on the intensity of sub‐cloud evaporation. The temperature was observed to influence the intensity of sub‐cloud evaporation indirectly by influencing the relative humidity and diameter of raindrops. Global warming will increase the intensity of sub‐cloud evaporation in the Tianshan Mountains, especially for small precipitation events.

Retrieving snow wetness based on surface and volume scattering simulation

Wetness is one of the important physical parameters of snowpack. Its spatial and temporal changes play a key role in snowmelt runoff forecast, regional climate change, and agricultural irrigation. In this study, we proposed a new method to retrieve snow wetness from full-polarimetric synthetic aperture radar (SAR) data. First, the dominant scattering components, i.e. surface and volume scattering, in wet-snow conditions were obtained by polarimetric decomposition. The random rough surface scattering models and the dense media radiative transfer (DMRT) model were then used to establish the surface and volume scattering models, through which the surface and volume scattering lookup tables were created, respectively. Based on the lookup tables and the polarimetric decomposition results, the snow surface and volume wetness were retrieved, respectively. Finally, the effective snow wetness was derived from the weighted summation of surface and volume wetness. The advantage of this method mainly comes from the full consideration the snow surface roughness, the local incidence angle on complex mountain terrain, and the polarimetric information. In experiments, the GaoFen-3 data obtained on January 17, 2018 in the Kelan River Basin and the Radarsat-2 data obtained on March 19, 2014 in the Manasi River Basin were selected to verify the applicability of the proposed method at different conditions. From the analysis of experiment results, the correlation coefficient between the estimated and the ground measured snow wetness in the Kelan River Basin is 0.72. The mean absolute error (MAE) and the root mean square error (RMSE) are 3.35% and 3.89%, respectively. The correlation coefficient between the snow wetness estimated by Radarsat-2 and the measured values in the Manasi River Basin is 0.62. MAE and RMSE are 1.32% and 1.62%, respectively. These results proved that the proposed method can effectively retrieve snow wetness under different SAR data, different areas, and different snow periods.

The Influence of Hydrogeology to Generation of Hydrogen Sulfide of Low-Rank Coal in the Southeast Margin of Junggar Basin, China

The salinity, chemical properties, and migration characteristics of groundwater in coal measures are the key factors that affect the generation, migration, and reservoir of hydrogen sulfide (H2S) in low-rank coal seams. Taking the Jurassic coal and rock strata in the southeastern margin of the Junggar basin as the research object, according to the hydrogeological characteristics of the coal measures, the region is divided into 4 hydrogeological units. The coalbed methane contains a large number of secondary biogas. Along the direction of groundwater runoff, the salinity and the pH value increase gradually. The salinity in the hydrogeological units is low; it is not conducive to the propagation of sulfate-reducing bacteria and the formation of hydrogen sulfide of the Houxia, the south of Manasi River, and Hutubi and Liuhuangou area, the western region of the Miquan. The high salinity center and depressions of low water level (hydrodynamic stagnation zone) in the hydrogeological unit of the Liuhuanggou and the Miquan are the main areas for the production and enrichment of H2S in the low-rank coal. The high salinity in water is formed by infiltration, runoff, and drought evaporation. At the same time, the deep confined water environment closed well; in conditions of hydrocarbon-rich, under the action of sulfate-reducing bacteria, bacterial sulfate reduction will occur and hydrogen sulfide formed. According to the circulation characteristics of water bearing H2S in the region, imbricate and single bevel two kind generation and enrichment mode of hydrogen sulfide under the action of hydrodynamic control. The solubility of hydrogen sulfide in pure water and solutions of NaCl and Na2SO4 with different molar concentrations was calculated. The H2S solubility of groundwater in coal measures of 4 hydrogeological units was estimated.

Regional groundwater cycle patterns and renewal capacity assessment at the south edge of the Junggar Basin, China

The groundwater cycle pattern in the arid and semi-arid regions is drastically affected by both natural condition changes and human activities, resulting in serious eco-geological environmental problems. This paper selected the south edge of the Junggar basin as a pilot area for the basin-scale assessment of groundwater renewal capacity. Assessment indicator system was first established, assessment units were then constructed, and the Analytic Hierarchy Process method was used to identify the weights of the assessment indicators. The first three indicators are natural recharge ratio, groundwater residence time, and balance ratio between groundwater recharge and exploration, respectively. Two types of environmental assessment models, including the comprehensive index model and the fuzzy comprehensive model, were used and the results determine that low-lying intermountain areas and alluvial–proluvial piedmont plains have stronger groundwater renewal capacity, whereas that of the alluvial plain is much weaker. Second, at the typical watershed scale, the Manasi watershed was adopted as the study area, and numerical simulation method was used to assess the groundwater renewal capacities of intermountain low-lying land and the pediment alluvial–proluvial plain. They are found to have renewal periods of about 66.95 years and 25.05 years, respectively. These values are favorably compared to the results of isotopic analysis, which is corresponding to 46 years and 30 years, respectively. At last, four schemes were put forward to improve the regulation and storage capacity in different segment of Manasi River watershed. The results not only provide key guidance for reasonable groundwater resource exploitation and eco-environmental protection, but also offer a model for sustainable groundwater utilization for similar northwestern districts in China or other world similar districts.

Occurrence and distribution of perfluorooctane sulfonate and perfluorooctanoic acid in three major rivers of Xinjiang, China.

Drinking water is a main pathway of human exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA). These two compounds have been identified in environmental waters worldwide, but little is known about their occurrence in Xinjiang. In this study, 155 water samples were obtained from 37 locations across Ulungur River, Manasi River, and Tarim River in Xinjiang, and were assessed by using liquid chromatography tandem-mass spectrometry. PFOS and PFOA were detected in over 50% of the samples with mean concentrations of 3.194 ng/L for PFOS and 3.460 ng/L for PFOA. Spatial and regional distribution differences do exist among the three analyzed rivers. PFOS and PFOA in Manasi River were observed at the highest levels (especially in M10 and M11), but no aggravation occurred from 2014 to 2017. Seasonal variations of PFOS and PFOA concentrations showed that water samples collected during summer were higher than those in other three seasons. The occurrence, levels, and distribution patterns of PFOS and PFOA were investigated in the present study, which provides useful theory and data support for human health risk assessment. The findings of the present study can be considered for controlling these water pollutants in environmental waters.

Forgiveness as an essential component of suicidality reduction

To capture the temporal variability of parameters of hydrological models, the segmented optimization algorithm (SOA) is usually used which subdivides the calibration period into a number of sub-periods and seeks optimal parameters for each sub-period by optimizing the objective function based on the measured and estimated data in the same sub-period. In this paper, we developed a new method that is called a progressive segmented optimization algorithm (PSOA), which seeks optimal parameters by optimizing the objective function based on both the current and all the prior sub-periods.We applied and compared the SOA and PSOA algorithms to the Snowmelt Runoff Model (SRM) in simulating snow-melt streamflow for the Manasi River basin, northwest of China, during snowmelt seasons of 2001–2012. The study showed: (1) PSOA can effectively calibrate the time-variant model parameters while avoiding too much computational time caused by a significant increase of parameter dimensionality. (2) PSOA outperforms SOA for both single-snowmelt-season and multi-snowmelt-season simulations. (3) For single-snowmelt-season simulation, the length of the sub-period has an apparent effect on model performance, the shorter the sub-period is, the better the model performance will be, when the model is calibrated using the PSOA method. (4) For multi-snowmelt-season simulation, an over-short sub-period may cause overfitting problems in some cases such as the situation of taking Nash-Sutcliffe efficiency (NSE) as the objective function. A compromised length of sub-period and objective function may have to be chosen as a trade-off among evaluation criteria and between the importance of calibration and validation.

A progressive segmented optimization algorithm for calibrating time-variant parameters of the snowmelt runoff model (SRM)

To capture the temporal variability of parameters of hydrological models, the segmented optimization algorithm (SOA) is usually used which subdivides the calibration period into a number of sub-periods and seeks optimal parameters for each sub-period by optimizing the objective function based on the measured and estimated data in the same sub-period. In this paper, we developed a new method that is called a progressive segmented optimization algorithm (PSOA), which seeks optimal parameters by optimizing the objective function based on both the current and all the prior sub-periods.We applied and compared the SOA and PSOA algorithms to the Snowmelt Runoff Model (SRM) in simulating snow-melt streamflow for the Manasi River basin, northwest of China, during snowmelt seasons of 2001–2012. The study showed: (1) PSOA can effectively calibrate the time-variant model parameters while avoiding too much computational time caused by a significant increase of parameter dimensionality. (2) PSOA outperforms SOA for both single-snowmelt-season and multi-snowmelt-season simulations. (3) For single-snowmelt-season simulation, the length of the sub-period has an apparent effect on model performance, the shorter the sub-period is, the better the model performance will be, when the model is calibrated using the PSOA method. (4) For multi-snowmelt-season simulation, an over-short sub-period may cause overfitting problems in some cases such as the situation of taking Nash-Sutcliffe efficiency (NSE) as the objective function. A compromised length of sub-period and objective function may have to be chosen as a trade-off among evaluation criteria and between the importance of calibration and validation.

Comparison of Radiometric Quality of GF-1 Satellite Image in Snow-covered Area of Manasi River Basin

Comparison of Radiometric Quality of GF-1 Satellite Image in Snow-covered Area of Manasi River Basin

Snow Grain-Size Estimation Over Mountainous Areas From MODIS Imagery

Snow in mountainous areas is a major source of surface water and groundwater recharge in the world. The water balance in mountainous regions is controlled by the interactions between the climate, cryospheric, and hydrological systems. Surface snow grain is a sensitive thermodynamic indicator of snowpack and plays an important role in the snow albedo. In mountainous regions, the complicated terrain conditions may introduce errors in the snow grain size estimated from satellite imagery. In this letter, an effective method based on the Snow Grain-Size and Pollution (SGSP) amount algorithm is proposed to estimate the surface snow grain size with careful topographic correction, using spectral reflectance data in Channel 5 (1.24 $\mu \text{m}$ ) of the Moderate Resolution Imaging Spectrometer. The SGSP-estimated snow grain size was validated with in situ measurements collected from field campaigns in mountainous areas of Manasi River Basin, China, during the periods of snow accumulation and ablation from 2011 to 2015. The $R^{2}$ value of 0.90 and root mean squared error of $80.42~\mu \text{m}$ were obtained.

Soil aggregation and aggregate-associated organic carbon under typical natural halophyte communities in arid saline areas of Northwest China

Information on the effects of halophyte communities on soil organic carbon (SOC) is useful for sequestrating C in arid regions. In this study, we identified four typical natural halophyte communities in the Manasi River Basin in Xinjiang Province, Northeast China, namely, Karelinia caspia (Pall.) Less., Bassia dasyphylla (Fisch. et C. A. Mey.) Kuntze, Haloxylon ammodendron (C. A. Mey.) Bunge, and Tamarix ramosissima Lour. We compared soil aggregation and aggregated-associated SOC under these communities. The aggregate fraction of 0.053–0.25 mm accounted for 47%–75% of the total soil mass, significantly more than the > 0.25 and < 0.053 mm fractions, under all the halophyte communities. Significant differences in soil aggregate size distribution were observed among the plant communities, with the H. ammodendron and B. dasyphylla communities showing the highest proportions of > 0.25 mm aggregates (13.3%–43.8%) and T. ramosissima community having more < 0.053 mm aggregates (14.1%–27.2%). Aggregate-associated SOC concentrations were generally the highest in the > 0.25 mm fraction, followed by the < 0.053 mm fraction, and were the lowest in the 0.053–0.25 mm fraction; however, because of their large mass, 0.25–0.053 mm aggregates contributed significantly more to the total SOC. Total SOC concentrations (0–60 cm depth) decreased in the order of H. ammodendron (5.7 g kg−1) > T. ramosissima (4.9 g kg−1) > K. caspia (4.2 g kg−1) > B. dasyphylla (3.4 g kg−1). The H. ammodendron community had the highest total SOC and aggregate-associated SOC, which was primarily because aggregate-associated SOC content at the 0–10 and 10–20 cm depths under this community were higher than those under other plant communities. The H. ammodendron community could be beneficial for increasing SOC in saline soils in the arid region.

Vertical distribution of snow cover and its relation to temperature over the Manasi River Basin of Tianshan Mountains, Northwest China

How snow cover changes in response to climate change at different elevations within a mountainous basin is a less investigated question. In this study we focused on the vertical distribution of snow cover and its relation to elevation and temperature within different elevation zones of distinct climatology, taking the mountainous Manasi River Basin of Xinjiang, Northwest China as a case study. Data sources include MODIS 8-day snow product, MODIS land surface temperature (LST) data from 2001 to 2014, and in situ temperature data observed at three hydrological stations from 2001 to 2012. The results show that: (1) the vertical distribution of snow areal extent (SAE) is sensitive to elevation in low (<2100 m) and high altitude (>3200 m) regions and shows four different seasonal patterns, each pattern is well correspondent to the variation of temperature. (2) The correlation between vertical changes of the SAE and temperature is significant in all seasons except for winter. (3) The correlation between annual changes of the SAE and temperature decreases with increasing elevation, the negative correlation is significant in area below 4000 m. (4) The snow cover days (SCDs) and its long-term change show visible differences in different altitude range. (5) The long-term increasing trend of SCDs and decreasing trend of winter temperature have a strong vertical relation with elevation below 3600 m. The decreasing trend of SCDs is attributed to the increasing trend of summer temperature in the area above 3600 m.

Assessment of Reclamation Treatments of Abandoned Farmland in an Arid Region of China

Reclamation of abandoned farmland is crucial to a sustainable agriculture in arid regions. This study aims to evaluate the impact of different reclamation treatments on abandoned salinized farmland. We investigated four artificial reclamation treatments, continuous cotton (CC), continuous alfalfa (CA), tree-wheat intercropping (TW) and trees (TS), which were conducted in 2011–2012 in the Manasi River Basin of Xinjiang Province, China. Soil nutrient, microorganism and enzyme activity were examined in comparison with natural succession (CK) in an integrated analysis on soil fertility improvement and soil salinization control with these reclamations. Results indicate that the four artificial reclamation treatments are more effective approaches than natural restoration to reclaim abandoned farmland. TW and CA significantly increased soil nutrient content compared to CK. CC reduced soil salinity to the lowest level among all treatments. TW significantly enhanced soil enzyme activity. All four artificial reclamations increased soil microbial populations and soil microbial biomass carbon. TW and CA had the greatest overall optimal effects among the four treatments in terms of the ecological outcomes. If both economic benefits and ecological effects are considered, TW would be the best reclamation mode. The findings from this study will assist in selecting a feasible method for reclamation of abandoned farmland for sustainable agriculture in arid regions.

Soil aggregation and aggregate-associated carbon under four typical halophyte communities in an arid area.

The soil microbial biomass carbon (MBC) is considered as a sensitive index of soil carbon ecosystem. The distribution of aggregate-associated MBC determines the capacity of the soil to store soil organic carbon (SOC). We compared soil aggregate-associated SOC and aggregate-associated MBC under four halophyte communities: Karelinia caspia (Pall.) Less. (Abbr. K. caspia), Bassia dasyphylla (Fisch. et C. A. Mey.) Kuntze. (Abbr. B. dasyphylla), Haloxylon ammodendron (C. A. Mey.) Bunge. (Abbr. H. ammodendron), and Tamarix ramosissima Lour (Abbr. T. ramosissima) on an alluvial fan in the Manasi River Basin, Xinjiang, China. The specific objectives of the study were to determine which aggregate size fraction was the most important for MBC and SOC retention in these soils of four halophyte communities. The results showed that the 0.053-0.25mm fraction contained 47 to 75% of the total soil mass. The amount of soil in the 0.053-0.25mm fraction was significantly greater than that in the >0.25 and the <0.053mm fractions. The >0.25 and the <0.053mm fractions contained 7.8 to 43.0% of the soil mass. Aggregate-associated SOC concentrations ranged from 1.70 to 13.68gkg-1, and the aggregate-associated SOC were the highest under the H. ammodendron and T. ramosissima communities. The aggregate-associated MBC ranged from 55.26 to 217.11gkg-1, and the aggregate-associated MBC were higher under the K. caspia and B. dasyphylla communities. The aggregate-associated SOC concentrations were significantly higher in the >0.25 and the <0.053mm fractions than in the 0.053-0.25mm fraction. The aggregate-associated MBC in the 20-40cm depth was consistent with its law. However, in the 0-20cm depth, the aggregate-associated MBC concentrations were significantly higher in the >0.25mm fraction than the other two aggregate fractions, and there were no significant differences in 0.25-0.053 or <0.053mm fraction. Correlation analyses showed that the aggregate-associated MBC positively correlated with aggregate-associated SOC in >0.25mm fraction (P<0.01). The microbial entropies ranged from 1.12 to 4.17%, and the microbial entropy generally was higher in >0.25mm fraction. Overall, the H. ammodendron community had the higher aggregate-associated SOC and aggregate-associated MBC, but the microbial entropy was low. This suggested that among the four halophyte communities in this study, the H. ammodendron community could be beneficial for soil carbon storage in arid regions.

Temporal changes in soil organic carbon and aggregate-associated organic carbon after reclamation of abandoned, salinized farmland

SUMMARYA field experiment was conducted to quantify changes in soil aggregation and aggregate-associated soil organic carbon (SOC) concentration 1, 3, 5 and 10 years after abandoned, salinized land in the Manasi River Basin was reclaimed for cotton (Gossypium hirsutum L.). Results showed that reclamation significantly increased SOC concentrations and SOC stocks. Specifically, 10 years of cotton production increased SOC concentrations by 45% in the 0–60 cm depth and SOC stocks by 35%. The SOC concentrations and stocks decreased as soil depth increased. Reclamation time, season and soil depth had significant interaction effects on SOC. The SOC concentrations were significantly and positively correlated with available soil nitrogen and available soil phosphorus. Compared with abandoned farmland, macro-aggregate-associated (&gt;250 µm) SOC concentrations in the 0–60 cm depth increased by 47% after 5 years of cotton production and by 53% after 10 years of cotton production. The contribution of macro-aggregate-associated SOC to total SOC in the 0–60 cm depth increased by 87% after 5 years of cotton production and by 69% after 10 years of cotton production. The findings indicate that soil aggregates were more stable after abandoned, salinized farmland was reclaimed for cotton production. Furthermore, cotton production can increase SOC concentrations and sequester C in this arid area.