Search

Gully erosion can be a serious threat to ecologic and socio-economic stability. Although gully erosion intensity is highly impacted by catchment area changes, few studies have focused on the relevant mechanisms. This study presents a new approach to studying this issue by combining historic aerial photography and recent unmanned aerial vehicle (UAV) imagery to determine changes in gully morphology over 40 years on China's Loess Plateau, one of the world's most erosion-prone regions. Aerial photographs from 1976 were matched with UAV imagery taken in 2017 to compare gully and surface conditions in two study areas over time. Next, a new method was developed for calculating changes in gully volume and erosion moduli based on the UAV-derived digital surface model. Finally, the impacts of catchment area change on gully erosion moduli over the study period were analyzed. The results showed that the catchment areas decreased by 0.71–77.88%; greater decreases resulted in lower gully erosion moduli (with an exponential correlation) and reducing catchment area effectively slowed gully development. In addition, regional agricultural terrace construction has reduced the catchment areas, in turn reducing the amount of runoff entering gullies and thus reducing incision. Therefore, the management and maintenance of abandoned terraces should be strengthened by local governments and stakeholders to reduce runoff-induced gully erosion and sediment loss from upslope areas. The methodologies and results of this study have the potential to provide significant scientific references for the conservation of runoff and sediment in erosion-prone regions of China and elsewhere.

Soil moisture is a core ecohydrological element in the middle–high latitude frozen area, which occupies approximately 30% of the global land area. The Sanjiang Plain, an important commercial grain production area, is the largest swampy low plain in a middle-high latitude frozen area in China. However, no published studies on this region have clarified the accuracy of soil moisture data from in-situ measurements; the performance of ecohydrological models are also unknown. In this study, we compared the ability of the SMAP product, a RS-DTVGM model, and two self-erected soil moisture monitoring stations to capture soil moisture information in the Sanjiang Plain by using the Triple Collocation method. Soil moisture data were collected for 642 continuous days, and the data period was divided into three frozen periods (FT) and two non-frozen (NFT) periods. Valid data supply rates of the three methods were also calculated and compared. From the Triple Collocation method, the root mean square error (RMSE) of the SMAP, model and in-situ monitoring data sets were 0.03, 0.08, and 0.05. The valid data supply rates of the SMAP product in the FT and NFT periods were 27.1% and 57.5%, respectively. The Pearson correlation coefficient between the SMAP product and the in-situ monitoring dataset was 0.301 for the FT period and 0.557 for the NFT period. The RS-DTVGM model had a data supply rate of 100%. The Pearson correlation coefficient for this model was 0.101 for the FT period, but reached 0.726 for the NFT period. For the three soil moisture data sets, the accuracy ranking in this middle-high latitude area was SMAP > in-situ monitoring > model simulation. Although neither SMAP nor the RS-DTVGM model could capture the real soil moisture information for the FT period, both could accurately simulate the soil moisture in the NFT period. The results from the RS-DTVGM model demonstrated a closer relationship with in-situ soil moisture measurement data relative to the SMAP method. The in-situ method provided accurate soil moisture data, but its application is limited by high cost. In the future, the data from the three methods should be assimilated together to measure soil moisture data with high accuracy and high spatial and temporal resolution in this important area.

In seasonal freeze-thaw zones of NE China, the policy-oriented land management has caused successive land use conversions of native woodland, dry cropland and paddy field for food security. Control ...

Accurately delineating the effects of hydrological and water quality habitat factors on the aquatic biota will significantly assist the management of water resources and restoration of river ecosystems. However, current models fail to comprehensively consider the effects of multiple habitat factors on the development of fish species. In this study, a dynamic framework for river ecosystems was set up to explore the effects of multiple habitat factors in terms of hydrology and water quality on the fish community in rivers. To achieve this the biomechanical forms of the relationships between hydrology, water quality, and aquatic organisms were determined. The developing processes of the food web without external disturbance were simulated by 208 models, constructed using Ecopath With Ecosim (EWE). These models were then used to analyze changes in biomass (ΔB) of two representative fish species, Opsariichthys bidens and Carassius auratus, which are widely distributed in Asia, and thus have attracted the attention of scholars and stakeholders, due to the consequence of habitat alteration. Results showed that the relationship between the changes in fish biomass and key habitat factors can be expressed in a unified form. T-tests for the unified form revealed that the means of the two data sets of simulated and observed ΔB for these two fish species (O. bidens and C. auratus) were equal at the significance level of 5%. Compared with other ecological dynamic models, our framework includes theories that are easy to understand and has modest requirements for assembly and scientific expertise. Moreover, this framework can objectively assess the influence of hydrological and water quality variance on aquatic biota with simpler theory and little expertise. Therefore, it is easy to be put into practice and can provide a scientific support for decisions in ecological restoration made by river administrators and stakeholders across the world.

As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, new version 6 products for Global Satellite Mapping of Precipitation (GSMaP) have been released. However, few studies have systematically evaluated the GSMaP products over mainland China. This study quantitatively evaluated three GPM-based GSMaP version 6 precipitation products for China and eight subregions referring to the Chinese daily Precipitation Analysis Product (CPAP). The GSMaP products included near-real-time (GSMaP_NRT), microwave-infrared reanalyzed (GSMaP_MVK), and gauge-adjusted (GSMaP_Gau) data. Additionally, the gauge-adjusted Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (IMERG_Gau) was also assessed and compared with GSMaP_Gau. The analyses of the selected daily products were carried out at spatiotemporal resolutions of 1/4° for the period of March 2014 to December 2015 in consideration of the resolution of CPAP and the consistency of the coverage periods of the satellite products. The results indicated that GSMaP_MVK and GSMaP_NRT performed comparably and underdetected light rainfall events (

Reservoir and lake levels are susceptible to interference from inter-basin water transfer projects that are intended to alleviate increasing serious water resource crises. However, the impact of these transfers on riparian buffers as well as to water security is not clear. This paper considers the world’s largest inter-basin water transfer, the South-to-North Water Diversion (SNWD), as an example for assessing changes in riparian buffer width and biomass in order to indicate the influence of the project on the ecological system of the receiving reservoir. The riparian buffer width is determined by Landsat NDWI. Basing on in situ survey, the method that combing unmanned aircraft vehicle (UAV) and remote sensing (Landsat) was employed to calculate riparian biomass. Results show that water arriving from the SNWD directly decrease the existing buffer width due to the rising water level, and riparian biomass shows a negative response to elevated water levels. Therefore, the transferred water will impair the ecological functions of that buffer and increase risk to the security of the water resource. The vegetation cover in extant riparian buffer zones should be restored if the buffers are to be effective as usual. This study provides information useful to assess riparian impact from artificial inter-basin water projects prior to implementation.

The world’s aquatic ecosystems yield numerous vital services, which are essential to human existence but have deteriorated seriously in recent years. By studying the mechanisms of interaction between ecosystems and habitat processes, the constraining factors can be identified, and this knowledge can be used to improve the success rate of ecological restoration initiatives. At present, there is insufficient data on the link between hydrological, water quality factors and the changes in the structure of aquatic communities to allow any meaningful study of driving factors of aquatic ecosystems. In this study, the typical monitoring stations were selected by fuzzy clustering analysis based on the spatial and temporal distribution characteristics of water ecology in Jinan City, the first pilot city for the construction of civilized aquatic ecosystems in China. The dominant species identification model was used to identify the dominant species of the aquatic community. The driving effect of hydrological and water quality factors on dominant species was analyzed by Canonical Correspondence Analysis. Then, the principal factors of aquatic ecosystem dependence were selected. The results showed that there were 10 typical monitoring stations out of 59 monitoring sites, which were representative of aquatic ecosystems, 9 dominant fish species, and 20 dominant invertebrate species. The selection of factors for aquatic ecosystem dependence in Jinan were highly influenced by its regional conditions. Chemical environmental parameters influence the temporal and spatial variation of invertebrate much more than that of fish in Jinan City. However, the methodologies coupling typical monitoring stations selection, dominant species determination and driving factors identification were certified to be a cost-effective way, which can provide in-deep theoretical and technical directions for the restoration of aquatic ecosystems elsewhere.

Most of the terminal lakes in inland basins, which account for half of the world's lake reserves, have been shrinking at an alarming rate in recent years. In a Terminal Lake Basin, changes in the water level of the lake can lead to variations in the local erosion base level. From the perspective of local erosion base level, we revealed the response of river evolution to change in the water level of lake in Ebinur Lake Basin for the past 5000 years. Our results verified the three geomorphic development stages of natural decline, balance maintenance, and imbalanced decline. In modern times, the decline in the number of rivers entering the lake due to human activities has made the water level of the lake drop 15–30 times that observed from 5 kaBP–0.1 kaBP. The results showed that the average undercut erosion rate of the river entering the lake tends to increase with a rapid decrease in the water level of the lake in the past 5000 years. The instantaneous undercutting rate of the Bortala river section in the basin was 1.6–4.2 times that observed from 5 kaBP–0.1 kaBP. This result showed that a rapid decline in the water level of the lake due to human activities will accelerate the erosion of rivers. Therefore, from the perspective of geomorphology, the river erosion triggered by the rapid decline in the local erosion base level is an important reason for the continuous shrinkage of Ebinur Lake.

Harmful algal bloom is prevalent in the reclaimed-water-source (RWS) river caused by the excessive nutrient's inputs. Rainfall water may be the sole nutrient-diluted water source for the RWS river. However, the effects of storm events on the algal bloom in the RWS river are poorly understood. This study presents chlorophyll-a (Chl-a) variations before, during, and after the initial storm events (Pre-storm, In-storm, and Post-storm) at four representative sites with distinct hydraulic conditions in a dam-regulated RWS river system, Beijing. The response of Chl-a to the initial storm events mostly depends on the ecosystem status that caused by the river hydraulic properties. The upstream is more river-like and downstream is more lake-like. In the river-like system, elevated water temperature (WT, increased by 2 %) could support the dominating algae (diatom) growth (Chl-a increased by 130 %) from Pre-storm to In-storm period. In the lake-like system, the dominant algae (blue algae) declined (Chl-a sharply decreased by 96%–99 %) due to the lower WT (decreased by 3%–7%) and increased flow velocities from Pre-storm to In-storm period. During the Post-storm period, the dominant algae break out (Chl-a surged by 20%–319 %) in the lake-like system caused by the recovered WT (increased by 3%–6%) and flow velocity. The occurrence of algal bloom can be predicted by the Random Forest (RF) model based on water quality parameters such as total nitrogen (TN). The thresholds of algal bloom for the Pre-storm, In-storm, and Post-storm periods were identified as 30 μg/L, 10 μg/L, and 10 μg/L, respectively. The two driven factors were WT and nitrate (NO3-N) for the Pre-storm period and were WT and TN for the In- & Post-storm periods. A higher risk of algal bloom is highlighted during the initial storm events in the RWS river. We propose recommendations for improving water quality in the RWS river systems under the climatic change.

Soil spatial variations are scale dependent and can be controlled by many environmental factors. Numerous factors have been related to variations of soil salinity, some of them combined empirical mode decomposition (EMD) method and correlation analysis. However, environmental factors that essentially affect soil-water balance are not given enough attention. In addition, further analysis is needed in exploring how well the environmental factors can interpret the variations in soil salinity at different scales, especially in arid oasis areas and at large scales. This paper explores the potential of modeling variations in soil salinity via the EMD and Random Forest modeling of remote sensing based environmental factors. A case study is presented for Tarim basin, Xinjiang, China, using land surface temperature (LST), evapotranspiration (ET), TRMM precipitation (TRM) and digital elevation model (DEM) products. Soil salinity and its decompositions were first correlated with environmental factors for feature selection. Then, those selected environmental factors and their decompositions were correlated and coupled with their counterparts of soil salinity to evaluate their synchronization. Finally, those IMF components of environmental factors that had high correlation coefficients and were coupled well with corresponding IMF components of soil salinity were identified and divided into different feature sets for modeling. Mean absolute error and mean bias error were adopted for accuracy assessment of the models. Our results indicate that soil salinity series can be separated into eight scales ranging from 170 km to 480 km. IMF components 5–7 account for most of the variation and can be modeled using the corresponding IMF components of different combinations of DEM, ET, LST and TRM. IMF components 6–7 are well coupled with LST and ET at approximately 475 km scale. Overall, regional-scale modeling of variations in soil salinity based on remote sensing products is possible. Reasonably accurate results can be obtained in arid oasis areas where researchers and policy makers must focus on preventing the loss of agricultural productivity and ecosystem health.

Better understanding of the occurrence of water phosphorus (P) at the water-sediment interface is vital to clarify P sources of origin in freshwater shallow lake ecosystems. This study focused on w ...

Intense human activity has led to serious degradation of basin water ecosystems and severe reduction in the river flow available for aquatic biota. As an important water ecosystem index, environmental flows (e-flows) are crucial for maintaining sustainability. However, most e-flow measurement methods involve long cycles, low efficiency, and transdisciplinary expertise. This makes it impossible to rapidly assess river e-flows at basin or larger scales. This study presents a new method to rapidly assessing e-flows coupling UAV and ground monitorings. UAV was firstly used to calculate river-course cross-sections with high-resolution stereoscopic images. A dominance index was then used to identify key fish species. Afterwards a habitat suitability index, along with biodiversity and integrity indices, was used to determine an appropriate flow velocity with full consideration of the fish spawning period. The cross-sections and flow velocity values were then combined into AEHRA, an e-flow assessment method for studying e-flows and supplying-rate. To verify the results from this new method, the widely used Tennant method was employed. The root-mean-square errors of river cross-sections determined by UAV are less than 0.25 m, which constitutes 3–5% water-depth of the river cross-sections. In the study area of Jinan city, the ecological flow velocity (V E ) is equal to or greater than 0.11 m/s, and the ecological water depth (H E ) is greater than 0.8 m. The river ecosystem is healthy with the minimum e-flow requirements being always met when it is close to large rivers, which is beneficial for the sustainable development of the water ecosystem. In the south river channel of Jinan, the upstream flow mostly meets the minimum e-flow requirements, and the downstream flow always meets the minimum e-flow requirements. The north of Jinan consists predominantly of artificial river channels used for irrigation. Rainfall rarely meets the minimum e-flow and irrigation water requirements. We suggest that the water shortage problem can be partly solved by diversion of the Yellow River. These results can provide useful information for ecological operations and restoration. The method used in this study for calculating e-flow based on a combination of UAV and ground monitoring can effectively promote research progress into basin e-flow, and provide an important reference for e-flow monitoring around the world.

© 2017 Elsevier B.V. Sustaining adequate environmental flows (e-flows) is a key principle for maintaining river biodiversity and ecosystem health, and for supporting sustainable water resource management in basins under intensive human activities. But few methods could correctly relate river health to e-flows assessment at the catchment scale when they are applied to rivers highly impacted by human activities. An effective method is presented in this study to closely link river health to e-flows assessment for rivers at the catchment scale. Key fish species, as indicators of ecosystem health, were selected by using the foodweb model. A multi-species-based habitat suitability model (MHSI) was improved, and coupled with dominance of the key fish species as well as the Index of Biological Integrity (IBI) to enhance its accuracy in determining the fish-preferred key hydrologic habitat variables related to ecosystem health. Taking 5964 fish samples and concurrent hydrological habitat variables as the basis, the combination of key variables of flow-velocity and water-depth were determined and used to drive the Adapted Ecological Hydraulic Radius Approach (AEHRA) to study e-flows in a Chinese urban river impacted by intensive human activities. Results showed that upstream urbanization resulted in abnormal river-course geomorphology and consequently abnormal e-flows under intensive human activities. Selection of key species based on the foodweb and trophic levels of aquatic ecosystems can reflect a comprehensive requirement on e-flows of the whole aquatic ecosystem, which greatly increases its potential to be used as a guidance tool for rehabilitation of degraded ecosystems at large spatial scales. These findings have significant ramifications for catchment e-flows assessment under intensive human activities and for river ecohealth restoration in such rivers globally.

High phosphorus risk in intensive agricultural regions, which threatens an increasing number of water bodies within the regions, is driven by the nitrogen-phosphorus ratio (N:P ratio). The mechanism remains unclear, however. Most nutrient studies are based on local-scale field trials, which failed to identify the geographical factors that drive phosphorus accumulation at the larger scale of a watershed. To address this issue, we adopted a large-scale, remote-sensing-driven model to simulate the N:P ratio in the soil of the Sanjiang Plain, one of the most important commercial grain bases of China. A grid-random sampling method was employed to test the validity of the model. The results show that 35% of the higher N:P ratios were converted to lower ratios in old cultivated land where the concentration of soil phosphorus increased by 1–2 times over 11 years (2000–2010); however, in the new cropland cultivated from forestland, grassland and wetland, 18% of the lower N:P ratios were converted to higher ratios, with soil phosphorus concentration increased slightly. Higher N:P ratios come from greater nitrogen input into the new cropland soil, which reduces soil pH and then enhances phosphorus accumulation, thus increasing the phosphorus risk. We find that the variation in land-use types and the excessive input of anthropogenic fertilizers are the two dominant factors influencing the N:P ratio. Intensive anthropologic activities changed traditional knowledge on the relationship between the N:P ratio and phosphorus accumulation, and further necessitated large-scale research on nutrients in intensive agricultural regions. The finding can shed new light on phosphorus management and non-point source pollution control in intensive agricultural areas.

Large-scale anthropic conversion of natural ecosystems into human-dominated countryside ecosystems has led to biodiversity loss worldwide. However, how countryside landscapes affect biodiversity is still controversial, especially in karst regions lack of relevant studies. To answer this question, we analyzed the impact of countryside landscapes on biodiversity changes in the South China Karst, featured as a World Natural Heritage site of subtropical karst landscape and rich biodiversity. We presented a new framework to examine the impact of countryside landscapes on biodiversity change at a semi centennial-scale using three indicators: biodiversity cluster characteristics, the barrier of villages to biodiversity, and the barrier of arable land to biodiversity. Unmanned aerial vehicle (UAV) images, satellite remote sensing data, and field survey data were combined during the process. Results revealed that plant biodiversity was positively correlated to vegetation restoration, and vegetation took approximately 80 years to recover to a stable state. Plant biodiversity spatial points are densely distributed at karst peaks, with an average distance of only 420 m–480 m. Small villages create no apparent barriers to biodiversity, with an average barrier distance of about 450 m–530 m. Arable land intersecting with forest patches connects biodiversity spatial points. Hence, the expansion of countryside landscapes in karst depressions was restricted by karst peaks, and patches of artificial arable land and forest connect the natural reserved forest at karst peaks, which both promote local spatial heterogeneity and sustain local biodiversity. This study enriches the application of countryside biogeography theory in karst regions and provides evidence for integrating biodiversity conservation into sustainable planning in broader areas. We strongly recommend that a heterogeneous landscape pattern similar to karst peak forests, such as the distance between biodiversity spatial points and the barrier distance to villages both around 450–500 m, would be conducive to biodiversity preservation.

Tarim River Basin is experiencing heavy soil degeneration in a long term because of the extreme natural conditions, added with improper human activities such as reclamation and rejected field repeatedly, which hindered the soil health. One of the mainly form is soil salinization. Spatial distribution and variation of soil salinity is essential both for agricultural resource management and local economic development. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since 1980s while land use and climate have undergone major changed. Electromagnetic induction (EMI) has been successfully used to directly measurement the spatial distribution of targeting soil property at field- scale, and apparent electrical conductivity (ECa, mS m−1) has become a surrogate of soil salinity (EC, dS m−1) studied by many researchers at local scale. However, the effectiveness of this equipment has not been verified in the typical soil salinization areas in southern Xinjiang, especially on a large scale. This study was aimed to test the performance of ECa jointed with Random Forest (RF) for soil salinity regional–scale mapping at a typical arid area, taking Tarim River Basin as an example. The result showed that ECa together with environmental derivative variables and with RF were suited for regional–scale soil salinity mapping. Predicted accuracy of EC was higher at surface (0–20 cm, R2 = 0.65, RMSE = 5.59) and deeper soil depth (60–80 cm, R2 = 0.63, RMSE = 2.00, and 80–100 cm, R2 = 0.61, RMSE = 1.73), lower at transitional zone (20–40 cm, R2 = 0.55, RMSE = 2.66, and 40–60 cm, R2 = 0.51, RMSE = 2.49). When ECa is involved in modeling, the prediction accuracy of multiple depths of EC is improved by 13.33%–61.54%, of which the most obvious depths are 60–80 cm and 0–20 cm. The results of variable importance show that SoilGrids were also favored the power EC model. Hence, we strongly recommended to joint EMI reads with remote sensing imagery for soil salinity monitoring at large scale in southern Xinjiang. These EC and ECa map can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor water and salt dynamics, and a guide for the design of future soil surveys.

The variation in soil salinity is affected by environmental factors that occur at different scales with varying intensities. It is critical to adequately consider environmental variables under scale effects for digital soil mapping which has been minimally discussed in previous studies. The objectives of this research are to analyze the scale-dependent variability in soil salinity distribution under environmental controlling factors using discrete wavelet transform (DWT) techniques and to compare the differences between the accuracy of soil salinity predictions with and without multiple scale-specific relationships. Thirteen environmental factors related to soil salinity that included influencing environmental factors and indicative environmental factors involving climate, soil, terrain, and vegetation were extracted at 500 m intervals along four transects through farmland and salt-affected land situated at the oasis and oasis-desert ecotones of Xinjiang, China. Each spatial series of soil salinity and environmental variables along the four transects was separated into seven scale components (six details components, namely D1 through D6, and one approximation component, namely A6). A Hilbert transform was used to identify the specific spatial scales of each scale component in the DWT procedure. The results indicate that 21.77 km and >32 km were the dominant scales, which explained approximately 60–80% of the spatial variation of soil salinity throughout the oases. The prediction accuracy with wavelet reconstruction that depended on all the scale components of environmental variables is significantly improved compared with the accuracy of those with the stepwise multiple linear regression method at a single sampling scale. The generalized difference vegetation index (GDVI) was the major predictor of soil salinity on salt-affected land, while evapotranspiration and the terrain ruggedness index (TRI) were the major contributing factors in farmland inside the oases. This study demonstrated that specific scale-dependent relationships can reveal the scale control of soil salinity variation and had the potential to improve the prediction accuracy of soil properties.

Water scarcity is a serious global problem, and accurate estimations are urgently needed. The Water stress index (WSI) is one of the most commonly used methods for global or large scale water scarcity evaluation, but this method lacks of consideration of water demand and water supply positions non-overlapped spatial distribution, tends to overestimate water stress when applied to a moderate resolution grid (e.g., 1 km). In this study, we used a non-overlapping water supply-and-demand unit approach to improve the calculation scheme and constructed a spatial unit non-overlapping WSI model (Sun-WSI model). We then applied the new model to the Yarlung Tsangpo-Brahmaputra River (TBR) and estimated monthly water stress from 2006 to 2012 with a 1 km spatial resolution. The results showed that the determination coefficient (R2) between the normalized Drought Index (DI) and water stress was mainly in the range of 0.2–0.7, accounting for 77.4% of the study area. The spatial pattern of water stress estimated by the Sun-WSI model was consistent with the DI. Further analysis showed that both overall and grid water stress estimated by the Sun-WSI model were close to the results from existing studies; however the Sun-WSI model had a higher spatial resolution. With a 1 km resolution, the Sun-WSI model performed better than the conventional WSI with respect to both overall results and spatial details. This suggests that the Sun-WSI model is suitable for evaluating regional or moderate-resolution grid water scarcity.

Soil salinization is one of the most predominant processes responsible for land degradation globally. However, monitoring large areas presents significant challenges due to strong spatial and temporal variability. Environmental covariates show promise in predicting salinity over large areas provided a reasonable relationship is developed with field measured salinity at few points. While simple regression-based approaches to complex data mining methods have been used in the prediction, a comprehensive comparison of their performances has not been explored, leading to uncertainty in which algorithms to select. This study compares thirteen popularly and non-popularly used algorithms and their performances following four criteria in predicting soil salinity from environmental covariates from Kuqa Oasis from Xinjiang, China. The environmental covariates used for the prediction include principal components of Landsat satellite images at multiple spectral bands, climate factors (referring to land surface temperature), vegetation indices, salinity and soil-related indices, soil moisture indices, DEM derived indices, land use, landform and soil type and categorized them under parameter categories of the SCORPAN (S, soils; C, climate; O, organisms, biotic factor; R, relief; P, parent material; A, age; and N, space) model. The predictive relationships were developed using the algorithms including some previously used ones such as Multiple Linear regression (MLR), Multi-Layer Perceptron-Artificial Neural Network (MLP-ANN), Stochastic Gradient Treeboost (SGT), M5 Model Tree (M5), Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Regression (SVR) and some that have not previously been used in predicting salinity such as Alternating Model Tree (ATM), Gaussian Processes Regression (GPR), Gaussian Radial Basis Functions (GRBF), Least Median Squared Linear Regression (LMSLR), and Reduced Error Pruning Tree (REPTree). Here, 5-fold cross-validation and an independent dataset (30% overall samples) at three depths, 0–10 cm, 10–30 cm, 30–50 cm, were used for parameter optimization and evaluating the performance of algorithms. The performances of these algorithms were compared against multiple criteria, including the parameterization, error level/fitting accuracy (determination coefficient, R2; root mean squared error, RMSE), stability (based on the Pearson correlation coefficient, R; mean absolute percent error, MAPE; root mean squared error, RMSE; Lin’s concordance correlation coefficient, LCCC) and computational efficiency of the algorithms. Finally, the result showed that CSRI is most important parameter for the prediction of soil salinity at the 0–10 cm and 10–30 cm depths, whereas for the 30–50 cm depth interval, VD was the most important predictor. For depths of 0–10 cm, 10–30 cm and 30–50 cm across all models, the model R2 values ranged from 0.60 to 0.74, 0.15 to 0.31, and 0.30 to 0.47, and the RMSE values ranged from 18.87 to 23.49 dS m−1, 9.94 to 13.48 dS m−1 and 3.79 to 7.11 dS m−1. The optimal algorithms at three depths of 0–10 cm, 10–30 cm and 30–50 cm are RF, M5 and GRBF with considering accuracy and stability. After a comprehensive assessment of algorithm performance, we recommend RF for mapping salinity in an arid environment such as that of Xinjiang and elsewhere globally. However, there is no algorithm that can perform ideally for all datasets. Therefore, we suggest that the algorithm should be carefully chosen according to the purposes of the study.

Excessive farming has transformed large proportions of Xinjiang's terrestrial surface, which leads to widespread loss and degradation of ecosystems and biodiversity. Mapping croplands at regional scale and in a rapid and less costly approach is becoming increasingly needed. This study explored an integrated approach based on a combined use of multiple remotely sensed data to map croplands in the oasis of northwest China. The Terra MODIS land surface temperature, Enhanced vegetation index, TRMM precipitation and SRTM digital elevation model data products were examined and combined. The Random Forest models were established to estimate fractional croplands at the regional scale. The influences of basin, images and percentage of croplands were all examined on the accuracy of cropland estimation. Moreover, time series analysis of croplands from 2000 to 2017 was conducted across the basin scale. The results are: ① the integrated approach for cropland mapping at regional scale is promising with the explained variances of fifteen trial models ranging from 58%–93% and two basin scale models ranging from 77.21%–86.10%. ② The prediction accuracy of croplands in Tarim Basin was much higher than that in Junggar Basin indicated by explained variances. ③ Prediction errors increase and then decrease with the increase of percentage of croplands. ④ EVI and DEM were identified to be the most important variable in Junggar and Tarim basin. ⑤ During the past 18 years, the area of croplands has been expanding at approximately 641.3km2/year in Junggar Basin and 271.3 km2/year in Tarim Basin respectively with no sign of cease. The fastest expansion period and the most dramatic changing area were identified. This study is especially valuable for time series analysis of croplands and its correlation with land degradation in arid region.

The content and status of phosphorus (P) in agricultural soil have become critical environmental concerns because of the risk of excessive P reaching adjacent water bodies through leaching or runoff. The Sanjiang Plain, an important commercial grain base, is the largest swampy low plain in a mid-high-latitude region in China. The increasing risk of P leaching threatens the area's ecology and agricultural security, particularly because there have been more than 60 years of build-up of P in the soil as a result of intensive agricultural development. A remote sensing-driven model was used in this study to simulate the total phosphorus in the Sanjiang Plain soil from 2000 to 2010. A grid-random sampling method was employed to test the validity of the model in a typical in situ experimental area. Meanwhile, spatial analysis methods were used to analyze model outputs. The results show a significantly increasing trend in the area with higher concentrations of P in the Sanjiang Plain, phosphorus accumulation in the soil and an increasing area at risk of phosphorus leaching; among all land use types, cultivated land and water bodies were observed to have a higher phosphorus risk. We conclude that the growth in farmland converted from forest or wetland and the use of excessive fertilization to maintain high crop productivity are the two principal factors resulting in the growing concentration of soil P. The risk of higher phosphorus in the study area will pose a serious threat to the quality of both surface and ground water, which has important ramifications for future agricultural management and non-point source control in this agricultural area of the mid-high latitude region.

Microsomal prostaglandin E(2) synthase-1 (mPGES-1) is a novel therapeutic target for the treatment of inflammation and pain. In the preceding letter, we detailed the discovery of clinical candidate PF-04693627, a potent mPGES-1 inhibitor possessing a novel benzoxazole structure. While PF-04693627 was undergoing further preclinical profiling, we sought to identify a back-up mPGES-1 inhibitor that differentiated itself from PF-04693627. The design, synthesis, mPGES-1 activity and in vivo PK of a novel set of substituted benzoxazoles are described herein. Also described is a conformation-based hypothesis for mPGES-1 activity based on the preferred conformation of the cyclohexane ring within this class of inhibitors.

Agriculture has been identified as the major contributor of non-point source pollution of Hainan water resources. In this study, we coupled the Xinanjiang model and SWAT to a new model, EcoHAT, to assess the non-point source pollution in Hainan. The study site is located around the Songtao reservoir, Hainan Island, China, which is regarding as the most important water source system for Hainan. EcoHAT, including algorithms for the hydrological cycle, nutrient cycle, and plant growth cycle, simulated the non-point source pollution for the watershed in calculated grid cell units based on remote sensing data. Remote sensing data were used to interpret the spatial land surface information and derive the model parameters. Besides the remote sensing data, other essential databases such as the meteorological databases, soil chemical and physical databases, and plant nutrients databases were also used in this study. The EcoHAT model was calibrated and validated with 5 years of monitored water quantity and quality data in the Songtao reservoir watershed. The study results indicated that the EcoHAT model has simulated the hydrologic pollutant adequately. After the calibration and validation, the parameters were applied to simulate the nutrient and sediment transport in the Songtao watershed during 2003–2007. In the end, the effects of several specific scenarios of changes in the land covers or management practices on the local watershed nutrients transport were also simulated. The results revealed that: (1) the model has predicted the runoff volume within a range of acceptable accuracy which was reflected by a large coefficient of determination; (2) regression analysis between the observed and simulated values resulted in high values of coefficient of determination (R2) during the calibration and validation period. The high values of Nash–Sutcliffe simulation efficiency were achieved with a close agreement between the observed and simulated pollutants concentrations in the runoff. It also indicated that the model simulated the NO3–N, NH4+–N, and P concentrations in the runoff for the Songtao watershed with considerable accuracy; (3) the sediment loads, TN, and TP, experienced temporal and spatial variations, with strong correlations existing between the parameters and the land use as well as the precipitation; (4) the scenario analysis showed that with 40% fertilization reduction, 7.51% and 7.76% reduction on TN and TP loads respectively could be reached. Besides, the conservation measures are more effective in the study area to reduce the sediment loads than in other areas.

The ecological water requirement of grassland in the year 2009 in Ili river basin in growing season is calculated according to multi-source remote sensing data, including MODIS products (Land Surface Temperature, Leaf Area Index, and emissivity), Global Land Data Assimilation System (GLDAS)-wind speed, monthly mean air temperature, GLDAS-daily maximum and minimum air temperature, STRM-DEM and MERIS land cover product. Radiation estimation method in SEBS model, the FAO- recommended Penman-Monteith formula and single crop coefficient method are adopted to calculate net solar radiation, reference evapotranspiration and crop coefficient, respectively. The results show that ecological water requirement of grassland in April is the lowest, about 2.90 mm/day, while that in July reached its peak value, about 4.66 mm/day. Except the result in May, the ecological water requirement at east side is higher than other sides of the river basin. The analysis on obtained results reveals that multi-source remote sensing data can be effectively used instead of observed data to estimate ecological water requirement in arid and semi-arid region where meteorological and hydrological data are nearly ungauged.

Monoamine oxidase (MAO) is a flavoenzyme that catalyzes the oxidation of various biogenic and xenobiotic amines. Benzyl 1-(aminomethyl)cyclopropane-1-carboxylate (1) was designed as a diactivated cyclopropane mechanism-based inactivator of MAO (Silverman, R.B.: Ding, C.Z.; Borrillo, J.L.; Chang, J.T.J. Am. Chem. Soc. 1993, 115, 2982). [1,1-2H2]-1 exhibits a deuterium isotope effect of 4.5 on inactivation, but in D2O the isotope effect is only 2.3. [1-3H]-1 and [1-14C]-1 were synthesized; upon inactivation of MAO, 1.1 and 2.0 equiv of radioactivity, respectively, are incorporated into the enzyme. Tritium as 3H2O, is released during inactivation with [1-3H]-1. The flavin absorption spectrum changes from that of oxidized to that of reduced flavin after inactivation; denaturation of the inactivated enzyme shows a reduced flavin spectrum, suggesting the formation of a modified flavin. Tryptic digestion of the enzyme labeled with [1-3H]-1 or [1-14C]-1, followed by HPLC analysis, monitoring at 310 nm [corrected] (flavin), shows that the radioactivity comigrates with the 310 nm [corrected] absorptions. The metabolites that are generated during inactivation are benzyl 1-formylcyclopropane-1-carboxylate, benzyl alcohol, and 1-formylcyclopropane-1-carboxylic acid; no ring-cleaved products were detected. The partition ratio, as determined from the ratio of nonamines to enzyme, is 110. These results are rationalized in terms of a single-electron transfer mechanism leading to the imine of benzyl 1-formylcyclopropane-1-carboxylate, which alkylates the flavin coenzyme.