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Detecting and assessing changes in the hydrologic cycle and its response to a changing environment is essential for maintaining regional ecological security and restoring degraded ecosystems. There is no clear scientific evidence on the effects of human activities and climate variability on runoff and its components in typical arid areas. Therefore, in this study, a heuristic segmentation algorithm, a variable infiltration capacity model (VIC), and remote sensing data to quantify the effects of human activities and climate variability on runoff in the catchment of Lake Ebinur, Xinjiang, China. The results found: (1) The heuristic segmentation algorithm divided the study period into reference period (1964–1985) and two impact periods: I (1986–2000) and II (2001–2017). (2) Cropland and forest land showed an increasing trend, with grassland and barren land accounting for most of the increase. At the same time, the leaf area index (LAI) increased by 0.002 per year during the growing season. (3) Compared with the reference period, runoff depth decreased by 108.80 mm in impact period I due to human activities, but increased by 110.5 mm due to climate variability, resulting in an overall increase in runoff depth of 1.72 mm. Runoff depth increased by 11.10 mm in the impact period II compared to the reference period, with climate variability resulting in an increase of 154.40 mm, but human activities resulted in a decrease of 143.30 mm. Our results shed light on decision-making related to water stress in changing circumstances in arid regions. [ABSTRACT FROM AUTHOR]

The combination of chemical fertilizer and biochar is regarded as a useful soil supplement for improving the properties of soil and crop yields, and this study describes how the biochar of maize straw can be used to improve the quality of the degraded black soil. This has been achieved by examining the effects of combining different amounts of biochar with chemical fertilizer on the porosities and aggregate formation of soil and exploring how these changes positively impact on crop yields. A field trial design combining different amounts of maize straw biochar [0 (NPK), 15.75 (BC1), 31.5 (BC2), and 47.25 t ha−1 (BC3)] with a chemical fertilizer (NPK) has been used to investigate changes in the formation of soil aggregate, clay content, soil organic carbon (SOC), and crop yields in Chinese black soil over a three year period from 2013 to 2015. The results of this study show that the addition of fertilizer and biochar in 2013 to black soil results in an increased soybean and maize yields from 2013 to 2015 for all the treatments, with BC1/BC2 affording improved crop yields in 2015, while BC3 gave a lower soybean yield in 2015. Total porosities and pore volumes were increased for BC1 and BC2 treatments but relatively decreased for BC3, which could be attributed to increased soil capillary caused by the presence of higher numbers of fine soil particles. The addition of biochar had a positive influence on the numbers and mean weight diameters (MWD) of soil macroaggregates (>0.25 mm) that were present, with the ratio of SOC to TN in soil macroaggregates found to be greater than in the microaggregates. The most significant amount of carbon present in macroaggregates (>2 mm and 0.25–2 mm) was observed when BC2 was applied as a soil additive. Increasing the levels of maze straw biochar to 47.25 t ha−1 led to an increase in the total organic carbon of soil, however, the overall amount of macroaggregates and MWD were decreased, which is possibly due to localized changes in microbial habitat. The supplementation of biochar increased in the amount of aromatic C present (most significant effect observed for BC2), with the ratio of aliphatic C to aromatic C found to be enhanced due to a relative reduction in the aliphatic C content with >2 mm particle fraction. These changes in organic carbon content and soil stability were analyzed using univariate quadratic equations to explain the relationship between the type of functional groups (polysaccharide C, aliphatic C, aromatic C, aliphatic C/aromatic C) present in the soil aggregates and their MWDs, which were found to vary significantly. Overall, the results of this study indicate that the use of controlled amounts of maize-straw biochar in black soil is beneficial for improving crop yields and levels of soil aggregation, however, the use of excessive amounts of biochar results in unfavorable aggregate formation which negatively impacts the yields of crop growth. The data produced suggest that aromatic C content can be used as a single independent variable to characterize the stability of soil aggregate when biochar/fertilizer mixtures are used as soil additives to boost growth yields. Analysis of soil and crop performance in black soil revealed that the application of maize-straw biochar at a rate of 15.75 and 31.5 t ha−1 had positive effects on crop yields, soil aggregation and accumulation of aromatic C in the aggregate fractions when a soybean-maize rotation system was followed over three years. [ABSTRACT FROM AUTHOR]

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. [ABSTRACT FROM AUTHOR]