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Lack of surface water is a key factor leading to agricultural drought in karst regions and exacerbates the karst rocky desertification, which is characterized by high rate of exposed bedrock. With the characteristics of impermeability, runoff collection, and wide distribution, stone bud and karren (SBK), a unique karst landscape type composed of exposed bedrock and gullies, has a great potential to solve water shortage in agricultural irrigation, especially in the rain-rich karst regions of Guizhou Province, China. Using unmanned aerial vehicle images to identify SBK and measured rainfall–runoff data for validation, we applied SBK as a basic unit to harvest rainfall water on rocky desertification slopes and analyzed the benefits of rainfall water collection and irrigation via SBK. The results showed the following: (1) Karrens on rocky desertification slopes could be identified using a method coupling terrain opening difference and depression filling, and the simulated runoff on SBK could achieved the average NSE and R2 of 0.82 and 0.86, with RMSE and RSR less than 0.03 m3 and 0.51, respectively. (2) The exposed bedrock utilization rate and rainfall water collection coefficient of SBK were 0.62 and 0.55, respectively, such that the area ratio of exposed bedrock to the cultivated land meeting irrigation water demand reached 1:3.1, 1:3.7 and 1:7.0 under 90 %, 80 % and 50 % possibilities of irrigation. (3) Implementing SBK for rainfall water harvesting and irrigation could efficiently avoid crop failure in the karst regions where crops faced drought within only one week after rainfall, and was at an affordable cost to local farmers with limited environmental damage. The use of SBK to harvest rainfall water could also alleviate surface water shortages in karst regions caused by problems such as uneven temporal distribution of rainfall and engineering water shortages. Consequently, SBK demonstrate a strong application potential to alleviate agricultural drought in karst rocky desertification regions.

The water delivery capacity of Karez is gradually declining, and it is possible that such a traditional water supply systems might be completely replaced by modern water conservancy projects. To determine whether these ancient water conservancy projects have a future, we conducted a study in a typical research area of Xinjiang Province, China. Using remote sensing technology, in situ surveying, and the analytic hierarchy process, we evaluated Karez in terms of water delivery capacity, water intake convenience, and capability to maintaining the ecological stability of the surrounding area. The derived results are as follows. (1) During 2005–2020, the length of 14 Karez systems in the study area decreased; however, the calculated water access convenience degree (C) indicated that Karez are more convenient for water intake compared with modern transmission channels, i.e., Ckarez = 0.68 and Cchannel = 0.51. (2) The mean annual runoff of the Karez system was 69.38 × 106 m3, similar to that of the modern water transmission channels in the area (73.49 × 106 m3). (3) Change in ecological sensitivity occurred mostly in regions where Karez systems have disappeared, with increase of 70.69 % and 14.51 % in high and medium sensitivity areas, respectively, and decline of 21.53 % and 9.74 % in low-sensitivity and non-sensitivity areas. Our research shows that Karez have considerable water delivery capacity, and that their existence is beneficial to maintaining the stability of the surrounding ecology. The Karez system can be considered a template for harmonious coexistence between humans and nature.

The anomalies of cadmium (Cd) in karst region pose a severe threat to plant growth and development. In this study, the responses of Rhododendron decorum to Cd stress were investigated at physiological, molecular, and endophytic microbial levels, and the potential correlation among these responses was assessed. The Cd stress impeded R. decorum growth and led to an increase in malondialdehyde (MDA) and hydrogen peroxide (H2O2) levels, as well as enhanced superoxide dismutase (SOD) and catalase (CAT) activities. Meanwhile, Cd stress increased the Cd (up to 80 times compared to the control), sodium (Na), aluminum (Al), and zinc (Zn) contents, while decreased the magnesium (Mg) and manganese (Mn) contents in R. decorum leaves. Transcriptome suggested that Cd significantly regulated the pathways including “protein repair”, “hormone-mediated signaling pathway”, and “ATP-binding cassette (ABC) transporters”. Additionally, q-PCR analysis showed that Cd stress significantly up-regulated the expressions of ABCB19-like and pleiotropic drug resistance, while down-regulated the expressions of indole-3-acetic acid-amido synthetase and cytokinin dehydrogenase. The Cd stress influenced the composition of endophytic microbial communities in R. decorum leaves and enhanced the interspecific bacterial associations. Furthermore, the bacterial genera Achromobacter, Aureimonas and fungal genus Vishniacozyma exhibited a high degree of connectivity with other nodes in networks constructed by the metal element contents, differentially expressed genes (DEGs), and microbial communities, respectively. These findings provide a comprehensive insight into the response of R. decorum to Cd-induced stress, which might facilitate the breeding of the Cd-tolerant R. decorum.

Ecological connectivity in landscapes is crucial for plant diversity conservation. The barrier risk to ecological connectivity represents the risk to ecological connectivity loss or weakening, resulting from the barrier to biological information exchange among habitats. Therefore, clarifying the barrier risk to the ecological connectivity of plant diversity in space can reveal the spatial impacts of reduced ecological connectivity on plant diversity. This study analyzed effects of karst peak, river network, arable land, and impervious surface on plant diversity in karst natural, countryside, urban, and island landscapes in Guizhou Province with fragile environment. Then, we calculated the barrier distance of ecological connectivity to reveal the barrier risk to the ecological connectivity of plant diversity in space. The results showed that karst peak was the source of high plant diversity, and plant diversity could diffuse about 400 m around karst peaks. River network and arable land enhanced the connectivity among karst peaks to maintain plant diversity, and the effect on enhancing the connectivity was about 300 m and 450 m, respectively, while the weakening effect of impervious surface on connectivity was about 350 m. Based on the distance for plant diversity diffusing around karst peaks, the barrier distance of ecological connectivity was determined by the combination type of river network, arable land and impervious surface in landscapes. From low to high, the barrier risk to the ecological connectivity of plant diversity was about 1,110 m in the combination of river network and arable land, about 790 m in the combination of river network, arable land and impervious surface, about 520 min the combination of arable land and impervious surface, about 400 m in the combination of river network and impervious surface. Our findings clarify the barrier risk to the ecological connectivity of plant diversity in space, and provide a scientific basis for plant diversity conservation from the perspective of ecological connectivity.

China’s commitment to achieving the goal of carbon peak and carbon neutrality (CPCN) has attracted worldwide attention, and the efforts made by China are critical to realization of the 1.5 °C temperature control objective of the 2015 Paris Agreement. However, it is unclear whether long-term spatiotemporal changes in China’s carbon emissions and carbon sinks exhibit specific spatial patterns, such as the Hu Line, which might affect China’s future policymaking. Based on the emission factor, inventory, and eddy covariance methods, this study calculated China’s carbon emissions (2003–2018) and terrestrial carbon sinks (2003–2020). Results showed that the increase in carbon sinks is limited in comparison with the increase in carbon emissions, and that the carbon sequestration ratio remains deficient and generally maintained at around 10 %. The spatial pattern of carbon emissions and carbon sinks showed an unbalanced state across the Hu Line, mainly reflected in accelerated increase in both the carbon emission rankings and the proportion of emissions to China’s total carbon emissions of provinces on the northwestern side of the Hu Line. Despite this, the gross domestic product (GDP) rankings of those provinces have not improved substantially, whereas provinces on the southeastern side of the Hu Line have contributed most to China’s GDP and terrestrial carbon sinks. The findings of this study improve understanding of the spatiotemporal relationship between carbon emissions and terrestrial carbon sinks in China, and represent alternative insights that could support adjustment of carbon-related policies and promote realization of CPCN in China.

Frequent surface water–groundwater interactions and prevalent anthropogenic inputs make karst water systems vulnerable to human disturbance. As a typical karst region in North China, the Jinan Spring Catchment has become increasingly threatened due to rapid population growth and urban expansion. In this study, the local river–spring interaction and its interference with the hydrogeochemical evolution of groundwater are evaluated based on water stable isotopes and hydrochemistry. Twenty-two karst groundwater, eleven Quaternary pore water, sixteen spring water, and thirty-two surface water samples were collected during low- and high-flow conditions over the course of a year. The isotopic signatures of four different water types display significant differences, reflecting the recharge–discharge relationship of the karst water system. Mountainous springs feature lighter isotopes, whereas urban springs have significantly heavier isotopes. The result of end-member mixing analysis shows that the surface–groundwater interaction varies spatially and temporally within the spring catchment. Urban springs receive considerable replenishment from the surface water, especially after rainy episodes (up to 50%), while mountainous springs show little hydraulic dependence on surface water leakage (4~6%). Local mineral dissolution (including calcite, dolomite, gypsum, and halite), CO2 dissolution/exsolution, and cation exchange are the main hydrogeochemical processes constraining water chemistry in the spring catchment. The deterioration of water quality can be attributed to anthropogenic influences involving the discharge of domestic effluents, agricultural activities, and irrigation return flow. The findings of this work can improve our understanding of the complex karst water system and serve as a reference for sustainable groundwater management in other karst areas of northern China.

Ebinur Lake is a shallow lake and vulnerable to strong winds, which can lead to drastic changes in suspended particulate matter (SPM). High spatial and temporal resolution images are therefore urgently needed for SPM monitoring over the Ebinur Lake. Hence, a high-efficiency inversion model of estimating SPM from high-resolution images using machine learning is essential to increase the amount of extracted information through band combinations quadratic optimization. This article aims to evaluate the capability of the PlanetScope images and four machine learning approaches for estimating SPM of the Ebinur Lake. The specific objectives include: to obtain the sensitive bands and band combinations for SPM using correlation analysis; to quadratically optimize the combination pattern of sensitive bands using a linear model; and to compare the accuracy of traditional linear model and machine learning models in estimating SPM. The results of the study confirm that after linear model quadratic optimization, the band combinations of B3*B4, (B2+B3)/ (B2-B3), (B3+B4)*(B3+B4), and (B3-B2)/(B2/B3) have higher accuracy than that of the single band model. By inputting the preferred four-band combinations into the partial least squares, random forest, extreme gradient boosting, gradient boosting decision tree, and categorical boosting (CatBoost) models, the performance of the SPM inversion based on PlanetScope images is better than the traditional linear model. Validation of the inversion maps with observations further indicates that the CatBoost model performed the best.

In recent decades, China has experienced substantial climate change and significant vegetation greenness due to the extensive implementation of artificial ecological restoration programs. However, the quantitative contributions of climatic and anthropogenic drivers to the national variations in associated evapotranspiration (ET) and gross primary productivity (GPP) over China at different climate zoning sub-regions remain unclear. Based on the analysis of climate factor and vegetation disturbance trends created by anthropogenic activities, this study constructed a remote sensing-based ecological model consisting of Penman–Monteith–Leuning (PML) and light use efficiency (LUE) components. The proposed model simulated the spatiotemporal changes in ET and GPP between 1999 and 2018 over China. The contributions of climatic factors and anthropogenic activities to ET and GPP variations were quantitatively calculated by ridge regression. The results show that (1) both interannual ET and GPP markedly increased, by 1.32 mm yr−1 and 8.01 g C m−2 yr−1, respectively; (2) vegetation changes due to anthropogenic disturbance made the dominant contribution to GPP variations over China, while the dominant factor influencing ET changes differed by sub-region due to the joint effects of vegetation and climate; (3) temperature and precipitation positively affected ET, while wind speed, humidity, and solar radiation negatively contributed to ET in most parts of Mainland China. These findings may provide a workable, scientific reference for further ecological restoration decision-making processes in China.

With the development of human society, the balance between the minimum ecological instream flow requirement (MEIFR), which is an essential part of the ecological water demand in arid areas, and anthropogenic water depletion has received increasing attention. However, due to the lack of hydrological station data and river information on arid basins, previous researchers usually considered only the individual ecological water demand of rivers, lakes, or oases. To address this issue, a new method that combines river hydraulic parameters and the wet circumference obtained by an unmanned aerial vehicle (UAV) and remote sensing hydrological station (RSHS) technologies was applied to obtain the MEIFR and, then, systematically and quantitatively explore the balance from the perspective of the entire basin of Aiding Lake from 1990 to 2022, which is the lowest point of Chinese terrestrial territory. The results showed the following: (1) since 1990, the discharge of the seven rivers in the study area increased by 1–6%, and the MEIFR of these rivers increased by 15–100%; both quantities decreased by 3–5% from the upper to the lower reaches of the basin; (2) the surface area and water level of Aiding Lake decreased by 5% and 14%, respectively, but the MEIFR first decreased by 25% from 1990 to 2013 and, then, increased by 66.7% from 2013 to 2022; and (3) from 2011 to 2022, the MEIFR and anthropogenic water depletion exhibited a balance. Against the background of climate change, this research revealed that the MEIFR of the rivers in the Aiding Lake Basin have shown an upward trend over the past 30 years and quantitatively determined the above balance relationship and the period of its occurrence. This study supplied a method that could provide guidance for water resource management by decision-makers at a global level, thus helping achieve the sustainable development goals (SDGs).

Current digital soil mapping of soil properties (soil organic carbon, SOC; electrical conductivity, EC; and pH) is mainly based on transfer learning, which is inadequate in terms of accuracy for the northern plain area of Xinjiang. To address this issue, establishing a new model is urgently required that can improve our understanding of the soil properties in this region. To this end, based on the global bioclimatic variables and surface dry–wet and wet–dry transitions, The study developed a spectral–water–heat database (SWHD). The study then incorporated this database and background data into machine learning algorithms (XGBoost, LightGBM, and random forest) to establish models applicable to the study area and draw spatial changes in the key soil properties. Our findings revealed that the organic carbon content was the highest in grasslands, whereas shrublands had high soil salinity. The pH value indicated overall alkalinity in the study area. Additionally, the SWHD-based predictions outperformed the mean or maximum value datasets, with LightGBM showing superior performance among all models. Furthermore, the validation accuracy obtained through our optimal algorithm was significantly higher than that obtained by other products, such as Harmonized World Soil Database (HWSD) and SoilGrid250, likely because of the limitations of these datasets, which may represent historical soil properties rather than current variations in the soil properties in the region. The study also observed that the mean SOC and EC values significantly decreased compared to the historical data, while the decrease in pH was smaller but not significant. Structural equation modeling and variable importance analysis revealed that the variables with the greatest influence on modeling SOC, EC, and pH were BIO10, DTW2021_406-426_B3 (Surface reflectance acquired in spring), and land use type. Our improved model developed based on the SWHD dataset offers important scientific evidence and decision support for land use management and provides a solid foundation for future research in this field.

The widespread distribution of karst landforms has led to a shortage of water resources in Southwest China. Understanding the spatiotemporal variations in and driving factors of evapotranspiration (ET) in this area is crucial for understanding and predicting severe water resource shortage. This study conducted trend analysis using meteorological data from 2003 to 2020 as well as remote sensing products such as Penman–Monteith–Leuning equation version 2 (PML-V2) ET. The factors influencing the spatial distribution pattern of average ET were identified using a geographical detector. Partial correlation analysis was performed to characterize the relationships between ET and the factors governing its variations, determined using the random forest model. The results demonstrated the following: (1) The average ET decreased with increasing latitude and altitude, primarily affected by the landform type in terms of longitude and displaying “W”-shaped fluctuations. Overall, the annual ET exhibited a significant (p < 0.05) increasing trend, with 72.63% of its area under the increasing trend. (2) The results of the geographic detector indicated sunshine duration as the strongest explanatory factor of the spatial distribution of ET, followed by enhanced vegetation index (EVI), landform type, precipitation, elevation, slope, and aspect. Instead of an individual factor, the interplay between multiple factors more considerably influenced the spatial distribution pattern of ET. (3) The EVI exhibited an overall increasing trend, with a significant increase over 73.59% of the study area and a positive correlation with ET. Thus, the increase in EVI had the strongest impact on ET in the study area, which was further confirmed by the results of the random forest model for 42.92% of the study area. Thus, the present findings clarify the spatiotemporal variations in and driving factors of ET in Southwest China and can serve as a benchmark for policies aiming to develop and manage water resources in this region.

Plant diversity is essential to maintain terrestrial ecosystem stability and balance, but it is highly susceptible to environmental changes that are generally dominated by human activities. In the natural habitat, plant growth and distribution patterns are known to be regulated by abiotic environments such as water, temperature, and nutrient. However, how growing human dominance impacts plant diversity at the watershed scale remains unclear. From the view of the meta-watershed ecosystem, this study constructed indicators to examine the plant diversity responses in karst watersheds of different urban development intensities. The results revealed that the growing urban development intensity deteriorated plant diversity and riparian plant diversity in the meta-watershed ecosystem. Both socioeconomic and water conservancy development had noticeable adverse effects on plant diversity. The riparian plants were more susceptible to artificial development than forest plants. The predicted results of various development scenarios suggest that, by the middle of the 21st century, plant diversity and food production in the watershed will meet a crisis unless forest protection policies are applied, and large-scale urban expansion is curbed. In the meta-watershed ecosystem, growing human dominance increasingly destroys plant diversity and the ecosystem supply. Policies and measures are urgently needed to reverse the potential deteriorating trend of the ecosystem, maintain the stability of the ecosystem, and promote the sustainable development of human society.

Calcium is an essential macronutrient in soils and plays an important role in the structure and function of an ecosystem. In this study, we selected the Huajiang dry-hot valley region in southwest China as our research object, aiming to comprehend the soil calcium distribution characteristics of different altitudes and vegetation types in this karst dry-hot valley region. The results showed that the mean value of total soil calcium content in the karst dry-hot valley was 13.00 ± 3.28 g·kg−1, and the mean value of the proportion of exchangeable calcium content to total calcium was 50.31%. In the vertical profile, total soil and exchangeable calcium contents decreased with increasing soil depth. With increasing altitude, total soil and exchangeable calcium contents increased. Among the different vegetation types, the total and exchangeable calcium contents of crops were higher than the three natural vegetation types of forest, scrub, and grassland, and the soil calcium content of forest was the lowest. Total soil and exchangeable calcium content in the karst dry-hot valley were affected by rock exposure rate, vegetation coverage, soil thickness, soil organic matter and soil pH. In addition, the unique environmental gradient characteristics at different elevations in the dry-hot valley area may redistribute soil calcium, and the positive vegetation succession may cause a gradual decrease of soil calcium content in the area.

Accurate assessment of the spatial distribution and severity of soil salinization has long plagued local governments and researchers in the arid parts of Xinjiang Uygur Autonomous Region (XJUAR). The emergence of machine learning has brought hope to this research field, such as Random Forest (RF) and Stochastic Gradient Treeboost (SGT),however, which are few applications to the quantitative assessment of soil salinization. Therefore, in order to evaluate the accuracy level of the two algorithms for predicting soil salinity, twenty-seven environmental subsets were designed. Each data set is calculated using both RF and SGT to produce an optimal set of variables. The simulation results from 70.37% (19/27) of the subsets showed that the predicted value of soil salinity from SGT is closer to the observed value than is that from RF. The statistics of all datasets showed that the average values of R2 value for RF and SGT were 0.38 and 0.40, the average Root Mean Squared Error (RMSE) value were 28.59 and 27.46, and the Ratio of Prediction to Deviation (RPD) averages were 1.20 and 1.24, respectively. The important dominant factor were topographic variables with coarse resolution, temperature and vegetation indices, land use and landform.

Urbanization is a comprehensive process of mutual influence among the population, economy, society and living environment, and it depends on the synergy of a series of factors. This paper uses the statistical data of 76 counties in Xinjiang from 1996 to 2018 to construct a comprehensive urbanization evaluation system. Based on the entropy method, comprehensive evaluation model and coupling coordination model, from the scales of time and space, this paper discusses the current situation of the coordinated development of population, economy, society and living environment factors in counties in Xinjiang in the process of urbanization. Local spatial autocorrelation analysis is used to further study the spatial agglomeration effect of the coupling and coordination of urbanization development in the counties. The results show the following: (1) The comprehensive urbanization level of 76 counties in Xinjiang has the characteristics of "center-periphery" development, and high-level counties are clustered on the northern slopes of the Tian Mountains. (2) Most counties are in a serious state of imbalance; notably, the degree of population-economy-society-living environment coupling and coordination in the border counties and towns is in an unsatisfactory state. (3) The county-level cities in Northern Xinjiang belong to the diffusion and spillover areas, the county-level cities in southern Xinjiang belong to the polarization benefit areas, and most other counties are in the state of no spillover effect.

Water use efficiency (WUE) has garnered considerable attention at global and regional levels. However, spatio-temporal variations of WUE and related influencing factors in the complex karst landforms of southwest China require further elucidation. Herein, the ratio of gross primary productivity (GPP) to evapotranspiration (ET) obtained through the PML-V2 product was used to characterize ecosystem WUE, the spatio-temporal variations to ecosystem WUE, and responses to temperature, precipitation, and the enhanced vegetation index (EVI) in southwest China. The results showed that: (1) The ecosystem WUE in southwest China decreased with increasing latitude and altitude. Spatially, the ecosystem WUE fluctuates in a “W” pattern with increasing longitude because of the karst landforms’ distribution patterns. (2) The non-significant trend in increased ecosystem WUE during 2003–2017 may be associated with significant increases in the ET offsetting part of the GPP contribution to ecosystem WUE. Spatial distribution of changes in WUE is similar to GPP owing to the dominant role of GPP in changes to ecosystem WUE. (3) The multi-year average ecosystem WUE was lower in karst than in non-karst landforms; however, vegetation restoration projects have contributed in significantly increasing variation rate of ecosystem WUE in karst than that in non-karst landforms. (4) Temperature, precipitation, and EVI were generally positively correlated with ecosystem WUE and were important factors for the increase in ecosystem WUE. EVI characterized vegetation restoration indicators showed that the ecological engineering construction in the study area was effective and was the dominant factor of ecosystem WUE change in 59.59% of the study area. The results of this study are important for further understanding carbon and water cycling processes in karst regions.

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.

The question of whether a balance exists between vegetation recovery and long-term human development remains unclear worldwide, especially in the midst of climate change. To answer this question, two groups of spatially isolated islands in Hongfeng Lake, Guizhou Province, China that have experienced periods of human development, a mix of development and protection (interim period) and strict protection were chosen and monitored from 1988 to 2019. We applied multisource remote sensing to obtain NDVI data in the growth season and atmospheric data. We used an attribution analysis method to identify vegetation growth, climate factors, including temperature and precipitation in the region, and land use and land cover change on the islands over the 32-year study period. In the human development period, population size was the main influencing factor, with a weight exceeding 0.3. In the interim period, precipitation was the key factor that influenced vegetation growth and recovery (average weight of 0.27). In the protected period, human influences from the population size and the area of cultivated land were the most prominent factors (average weight of 0.26). Although there was considerable human activity across the islands, the vegetation has continued to recover over the past 20 years. We identified a recovery phase that occurred approximately 10 years after protected areas were created, which indicates that human development can coexist with protected areas. Moreover, variations in temperature and precipitation did not prevent vegetation recovery or growth.

Many different machine learning approaches have been applied for various purposes. However, there has been limited guidance regarding which, if any, machine learning models and covariate sets might be optimal for predicting soil salinity across different oases in the Xinjiang Uyghur Autonomous Region (XJUAR) of China. This study aimed to compare five machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Multiple Adaptive Regression Splines (MARS), Classification and Regression Trees (CART), Random Forest tree ensembles (RF), and Stochastic Gradient Treeboost (SGT), to predict soil salinity in three geographically distinct areas (the Qitai, Kuqa, and Yutian oases). A total of 21 data sets from three oases were used to evaluate the performance of the algorithm and to screen the optimal variables. The results show the following indices are considered to be important indicators for quantitative assessment of soil salinity: EEVI, CSRI, EVI2, GDVI, SAIO, and SIT. Comparison results show that SGT is the most suitable algorithm for predicting soil salinity in arid areas. This study provides a comprehensive comparison of machine learning techniques for soil salinity prediction and may assist in the modeling and variable selection of digital soil mapping in the XJUAR of China.

Eutrophication caused by excessive total nitrogen (TN) and total phosphorus (TP) emissions is of wide concern for society at large. Studies have revealed certain relationships among land use, TN, and TP. However, the relationships among land use compound topographic position, TP, and TN have seldom been studied. Therefore, the objectives of this paper are to construct optimal zoning of land use and reduce the nutrient load of lakes. Spearman correlation and redundancy analyses were used to reveal the relationship between land use comprehensive topographic position and TN and TP in the lakes of Guizhou Plateau. The results show that the nutritional state of the research area is medium. The trophic level index (TLI) value and TN concentration were high during flood periods, while TP concentration was high in dry periods. The TN concentration in the tributaries was higher than that in the reservoir area. Construction land and valley were the sources of the pollution, whereas forest land and gentle slope were the sink. According to the ”source–sink” effect, once the optimal zoning of land use is completed, the governance of urban land pollution governed areas should be strengthened next. This paper can provide decision support for water environment management and sustainable development decision-making.

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.

River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization.

Climate change has a global impact on the water cycle and its spatial patterns, and these impacts are more pronounced in eco-fragile regions. Arid regions are significantly affected by human activities like farming, and climate change, which influences lake water volumes, especially in different latitudes. This study integrates radar altimetry data from 2002 to 2018 with optical remote sensing images to analyze changes in the lake areas, levels, and volumes at different altitudes in Xinjiang, China. We analyzed changes in lake volumes in March, June, and October and studied their causes. The results showed large changes in the surface areas, levels, and volumes of lakes at different altitudes. During 2002–2010, the lakes in low- and medium-altitude areas were shrinking but lakes in high altitude areas were expanding. Monthly analysis revealed more diversified results: the lake water levels and volumes tended to decrease in March (−0.10 m/year, 37.55×108 m3) and increase in June (0.03 m/year, 3.48×108 m3) and October (0.04 m/year, 26.90×108 m3). The time series lake water volume data was reconstructed for 2011 to 2018 based on the empirical model and the total lake water volume showed a slightly increasing trend during this period (71.35×108 m3). We hypothesized that changes in lake water at high altitudes were influenced by temperature-induced glacial snow melt and lake water in low- to medium-altitude areas was most influenced by human activities like agricultural irrigation practices.

Hydraulic lift (HL) of deep-rooted plants is a water adaptation phenomenon to extreme drought conditions which would subsequently improve the survival of shallow-rooted plants in an arid desert area. There is an ongoing debate on whether the difference in water potential between plant roots and soils determine the presence of HL, thus considerable research efforts are needed to improve our understanding. In this study, we used the Ryel model and comparative analysis to determine the changes in soil water potential (SWP), the soil layer of obtaining water from plant roots (SLOW), the amount water released from plant roots into soils, and the total amount of release water of HL (HT) of five stratified soil layers at different depths (i.e. 0-10, 10-40, 40-70, 70-100 and 100-150 cm) across plant growing season (i.e. June, August and October). The results showed that SLOW always appeared in the lowest SWP soil layer, and that lowest SWP differed among soil layers. The lowest SWP soil layer and SLOW shifted from shallow to deep soil layers across the growing seasons. Additionally, HT decreased across the growing seasons. Fine root biomass decreased in shallow whereas increased in deep soil layers across growing seasons. Our results proved the water potential difference among soil layers determined the presence of HL in an arid desert region. The changes in water potential difference among soil layers might shift the lowest SWP soil layer from shallow to deep soil layers, and as a consequent decrease HT across plant growing seasons.

The Pamir Plateau is an extremely important water resource area for over 60 million people in Central Asia. With the increasingly significant response of water resources to climate change, timely hydrological predictions for the future supply are necessary. In the plateau, accessing and monitoring the glaciers and their melt outflow are challenging due to the harsh geographic environments. Unmanned aerial vehicles (UAVs) combined with remote sensing technologies offer great potential for providing information to improve water resources management and decision-making. In this study, we integrated UAV and satellite remote sensing data, and applied a water balance model to estimate monthly and annual river discharges for the ten river sections in the Eastern Pamir Plateau, China from 1999 to 2020. We found that the glacier area in the controlled basins of these sections has decreased by approximately 63% from 1999 to 2020. Basins with smaller glacier areas are more sensitive to climate change. The ten river sections are characterized by decreasing trends in monthly river discharge, with an average reduction of −21.05%. The annual variation of total runoff and glacial meltwater discharge is consistent with the monthly variation of discharge, and the average discharge from glacier meltwater accounts for 83% of the total runoff. We conclude that the overall decreasing trend of discharge is closely related to the recession of glaciers. Under the background of climate warming in the region, glaciers are no longer sufficient to support the increase in river discharge, which has passed its peak value and shows a decreasing trend.

The Tianshan Mountains, known as the “water tower” of Central Asia, are the major source of water for the most part of Xinjiang and oasis region of Central Asia. However, climate warming has amplified the discharges of glacial meltwater in the Tianshan Mountains. In this study, we calculated river discharge by integrating cross-sections mapped using unmanned aerial vehicles (UAV) and water velocity data collected in the field. Multiple remote sensing images, such as Landsat and Sentinel-2 imagery, were applied to estimate the long-term discharge of 19 river sections in ungauged regions of the Tianshan Mountains. River discharge variations under climate change were also examined. Using our in-situ measured discharges as reference, the UAV derived discharge results have an NSE (Nash–Sutcliffe efficiency) of 0.98, an RMSE (root mean square error) of 8.49 m3/s, and an average qualification rate of 80%. The monthly discharge of glacial meltwater-dominated river sections showed an average decrease of 2.46% during 1989–2019. The shrinking and even disappearance of mountain glaciers (approximately −4.98 km2/year) was the main reasons for the decrease trend. However, the precipitation-dominated river sections showed an average increase of 2.27% for the same period. The increase in precipitation (approximately 1.93 mm/year) was the key cause for the increase tendency. This study highlights remote sensing hydrological station technology and its application in the long-term prediction of river discharge, which is critical for decision-making regarding integrated water resource management in alpine regions.

Changes in atmospheric water vapor mainly occur in the atmospheric boundary layer. However, due to many factors, such as orography and ground thermal dynamic conditions, the change trends and transformation law of atmospheric water vapor contents above different surfaces are still unclear. In this work, a Doppler weather radar with high spatial-temporal resolution was used to monitor the variations and transformations of water vapor contents over different land surfaces for two years. The results show that the atmospheric water vapor content shows a very good positive correlation with elevation at altitudes between 600 m and 1200 m, while different land surfaces have delicate impacts on atmospheric water vapor contents, such as extreme values appearing above impervious urban surfaces, uniform distributions appearing over water body and vegetated surfaces being wet but avoiding extreme conditions. Compared with previous studies, the results and conclusions of this study are mainly derived from accurate direct observations based on high-resolution radar. Identifying the distribution and transformation of water vapor over different surfaces can enhance our understanding of the movement and variation of atmospheric water vapor over complex terrain and different land surfaces, and improve the planning and construction capacity of different surfaces, such that humankind can mitigate the severe disasters caused by drastic changes in atmospheric water vapor.

Some of the rivers in northern China are dried, and reclaimed water (RW) is used to restore these degraded river ecosystems, during which the RW could recharge the aquifer by river bank infiltration. From 2007 to 2018, 2.78 × 108 m3 of RW has been replenished to the dried Chaobai River (Shunyi reach), Beijing, China, which is located on the edge of one depression cone in groundwater caused by groundwater over-pumping. The groundwater hydrodynamic variations and the flow path of the RW were identified by eight-year hydrological, hydrochemical, and stable isotopic data, together with multivariate statistical analysis. The RW infiltration drastically impacts the groundwater dynamics with a spatiotemporal variation. The 30-m depth groundwater levels at Perennial intake reach increased quickly around 3 m after 2007, which indicated that they were dominated by RW infiltration. Other 30-m depth groundwater levels were controlled by precipitation recharge from 2007 to 2011, showing significant seasonal variations. In 2012, with more RW transferred to the river, the hydrodynamic impact of the RW on 30-m depth aquifer expanded downstream. However, the 50-m and 80-m depth groundwater levels showed decreasing trend with seasonal variations, due to groundwater pumping. The 30-m depth aquifer was mainly recharged by RW, being evidenced by the enriched δ2H and δ18O. The depleted δ2H and δ18O of the 50-m and 80-m depth groundwater indicated that they were dominated by regional groundwater with meteoric origin. The heterogenous properties of the multi-layer alluvial aquifer offer the preferential flow path for RW transport in the aquifers. The proportion of the RW in the aquifers decreases with depth that was calculated by the chloride conservative mixing model. The increased lateral hydraulic gradient (0.43%) contributes to the RW transport in the 30-m depth aquifer. RW usage changed 30-m depth groundwater type from Ca·Mg-HCO3 to Na·Ca·Mg-HCO3·Cl. RW preferentially recharged the 50-m and 80-m depth aquifers by vertical leakage.

Obtaining the water volume of small- and medium-sized lakes in enclosed watersheds with scarce data is a global focus of research. River flow into a lake is an important factor affecting the water volume. However, most river flow measurement methods involve long cycles, low efficiency, and transdisciplinary expertise, making rapid assessments in ungauged basins impossible. This paper proposes a remote sensing flow estimation method based on multi-source remote sensing data, which quickly assesses river flow and provides important input data for lake water volume simulation. The cross-section flow was estimated by extracting the river width. The calculated results were consistent with the measured data, with accuracy greater than 90%. The results compared with daily data measured at hydrological stations, and the Nash coefficient was greater than 0.9. Additionally, the simulation method for lake area, water volume, and water level was constructed using river inflow input data, greatly reducing the parameters required by the conventional lake water volume simulation method. Based on the remote sensing discharge estimation method, we quickly and conveniently obtained changes in river flow into the lake, simulated lake water volume, and provided the basis for water resource management in terminal lake basins with scarce data.

Global climate change, especially the snow drought events, is causing extreme weather events influencing regional vegetation growth and terrestrial ecosystem stability in a long-term and persistent way. In this study, the Sanjiang Plain was selected, as this area has been experiencing snow drought in the past two decades. Logistic models, combined with multisource remote sensing and unmanned aerial vehicle (UAV) data, as well as the meteorological data over the past 20 years, were used to calculate sixteen phenological periods and biomass. The results show that (1) over the past two decades, snow drought has been based on the snow accumulation and has been occurring more frequently, wider-ranging and more severely; (2) snow drought has advanced the forest start of season (SOS)/end of season (EOS) by 6/5 days, respectively; (3) if the snowfall is greater than 80% of a normal year, the SOS/EOS of grass is postponed by 8/6 days; conversely, if it is less than 80%, the SOS/EOS are advanced by 7/5 days; and (4) biomass decreased approximately 0.61%, compared with an abundant snowfall year. Overall, this study is the first to explore how snow drought impacts the phenological period in a mid-high latitude area, and more attention should be paid to these unknown risks to the ecosystem.

Soil salinization is a major soil health issue globally. Over the past 40 years, extreme weather and increasing human activity have profoundly changed the spatial distribution of land use and water resources across seven oases in southern Xinjiang, China. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since a land survey in the 1970s to 1980s (the harmonized world soil database, HWSD) due to scarce observational data. Now, given the uncertainty raised by near future climate change, it is important to develop quick, reliable and accurate estimates of soil salinity at larger scales for a better manage strategy to the local fragile ecosystem that with limited land and water resources. This study collected electromagnetic induction (EMI) readings near surface soil to update on the spatial distribution and changes of water and salt in the region and to map apparent electrical conductivity (ECa, mS·m−1), in four coil configurations: vertical dipole in 1.50 m (ECav01) and 0.75 m (ECav05), so as the horizontal dipole in 0.75 m (ECah01) and 0.37 m (ECah05), then all the ECa coil configurations were modeled with random forest algorithm. The validation results showed an R2 range of 0.77–0.84 and an RMSE range of 115.17–142.76 mS·m−1. The validation accuracy of deep ECa dipole (ECah01, ECav05, and ECav01) was greater than that of shallow ECa (ECah05), as the former integrated a thicker portion of the subsurface. The range of EC spatial variability that can be explained by ECa is 0.19–0.36 (farmland, mean value is 0.28), grassland is 0.16–0.49 (shrub/grassland, mean value is 0.34), and bare land is 0.28–0.70 (bare land, mean value is 0.56). Among them, ECav01 has the best predictive ability. As the depth increased, the influence of soil-related variables decreased, and the contribution of climate-related variables increased. The main factor affecting ECa variation was climate-related variables, followed by vegetation-related variables and soil-related variables. Scatter plot show ECa was significantly correlated with ECe_HWSD_030 (0–30 cm, r = 0.482, p < 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p < 0.01). The predicted spatial ECa maps were similar to the ECe values from HWSD, but also implies that the distribution of soil water and salt has undergone tremendous changes since 1980s. The study demonstrates that EMI data provide a reliable and cost-effective tool for obtaining high-resolution soil maps that can be used for better land evaluation and soil improvement at larger scales.

Research into global water resources is challenged by the lack of ground-based hydrometric stations and limited data sharing. It is difficult to collect good quality, long-term information about river discharges in ungauged regions. Herein, an approach was developed to determine the river discharges of 24 rivers in ungauged regions on the Tibetan Plateau on a long-term scale. This method involved coupling the Manning–Strickler formula, and data from an unmanned aerial vehicle (UAV) and the Gaofen-2, SPOT-5, and Sentinel-2 satellites. We also compared the discharges calculated by using the three satellites’ data. Fundamental information about the rivers was extracted from the UAV data. Comparison of the discharges calculated from the in-situ measurements and the UAV data gave an R2 value of 0.84, an average NSE of 0.79, and an RMSE of 0.11 m3/s. The river discharges calculated with the GF-2 remote sensing data and the in-situ experiments for the same months were compared and the R2, RMSE, and the NSE were 0.80, 1.8 m3/s, and 0.78, respectively. Comparing the discharges calculated over the long term from the measured in-situ data and the SPOT-5 and Sentinel-2 data gave R2 values of 0.93 and 0.92, and RMSE values of 2.56 m3/s and 3.16 m3/s, respectively. The results showed that the GF-2 and UAV were useful for calculating the discharges for low-flow rivers, while the SPOT-5 or the Sentinel-2 satellite gave good results for high-flow river discharges in the long-term. Our results demonstrate that the discharges in ungauged tributaries can be reliably estimated in the long-term with this method. This method extended the previous research, which described river discharge only in one period and provided more support to the monitoring and management of the tributaries in ungauged regions.

Ephemeral rivers are vital to ecosystem balance and human activities as essential surface runoff, while convenient and effective ways of calculating the peak discharge of ephemeral rivers are scarce, especially in ungauged areas. In this study, a new method was proposed using an unmanned aerial vehicle (UAV) combined with the incipient motion of stones to calculate the peak discharge of ephemeral rivers in northwestern China, a typical arid ungauged region. Two field surveys were conducted in dry seasons of 2017 and 2018. Both the logarithmic and the exponential velocity distribution methods were examined when estimating critical initial velocities of moving stones. Results reveal that centimeter-level orthoimages derived from UAV data can demonstrate the movement of stones in the ephemeral river channel throughout one year. Validations with peak discharge through downstream culverts confirmed the effectiveness of the method. The exponential velocity distribution method performs better than the logarithmic method regardless of the amount of water through the two channels. The proposed method performs best in the combination of the exponential method and the river channel with evident flooding (>20 m3/s), with the relative accuracy within 10%. In contrast, in the river channel with a little flow (around 1 m3/s), the accuracies are weak because of the limited number of small moving stones found due to the current resolution of UAV data. The poor performance in the river channel with a little flow could further be improved by identifying smaller moving stones, especially using UAV data with better spatial resolution. The presented method is easy and flexible to apply with appropriate accuracy. It also has great potential for extensive applications in obtaining runoff information of ephemeral rivers in ungauged regions, especially with the quick advance of UAV technology.

Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

Regional hydrological modeling in ungauged regions has attracted growing attention in water resources research. The southern Tibetan Plateau often suffers from data scarcity in watershed hydrological simulation and water resources assessment. This hinders further research characterizing the water cycle and solving international water resource issues in the area. In this study, a multi-spatial data based Distributed Time-Variant Gain Model (MS-DTVGM) is applied to the Yarlung Zangbo River basin, an important international river basin in the southern Tibetan Plateau with limited meteorological data. This model is driven purely by spatial data from multiple sources and is independent of traditional meteorological data. Based on the methods presented in this study, daily snow cover and potential evapotranspiration data in the Yarlung Zangbo River basin in 2050 are obtained. Future (2050) climatic data (precipitation and air temperature) from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR5) are used to study the hydrological response to climate change. The result shows that river runoff will increase due to precipitation and air temperature changes by 2050. Few differences are found between daily runoff simulations from different Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5 and RCP8.5) for 2050. Historical station observations (1960-2000) at Nuxia and model simulations for two periods (2006-2009 and 2050) are combined to study inter-annual and intra-annual runoff distribution and variability. The inter-annual runoff variation is stable and the coefficient of variation (CV) varies from 0.21 to 0.27. In contrast, the intra-annual runoff varies significantly with runoff in summer and autumn accounting for more than 80% of the total amount. Compared to the historical period (1960-2000), the present period (2006-2009) has a slightly uneven intra-annual runoff temporal distribution, and becomes more balanced in the future (2050).

River discharge is of great significance in the development of water resources and ecological protection. There are several large ungauged catchments around the word still lacking sufficient hydrological data. Obtaining accurate hydrological information from these areas is an important scientific issue. New data and methods must be used to address this issue. In this study, a new method that couples unmanned aerial vehicle (UAV) data with the classical slope−area method is developed to calculate river discharges in typical ungauged catchments. UAV data is used to obtain topographic information of the river channels. In situ experiments are carried out to validate the river data. Based on slope−area method, namely the Manning−Strickler formula (M−S), Saint-Venant system of equivalence (which has two definitions, S-V-1 and S-V-2), and the Darcy−Weisbach equivalence (D−W) are used to estimate river discharge in ten sections of the Tibet Plateau and Dzungaria Basin. Results show that the overall qualification rate of the calculated discharge is 70% and the average Nash−Sutcliffe efficiency coefficient is 0.97, indicating strong practical application in the study area. When the discharge is less than 10 m3⁄s, D−W is the most appropriate method; M−S and S-V-1 are better than other methods when the discharge is between 10 m3⁄s and 50 m3⁄s. However, if the discharge is greater than 50 m3⁄s, S-V-2 provides the most accurate results. Furthermore, we found that hydraulic radius is an important parameter in the slope−area method. This study offers a quick and convenient solution to extract hydrological information in ungauged catchments.

Terracing and vegetation are an effective practice for soil and water conservation on sloped terrain. They can significantly reduce the sediment yield from the surface area, as well as intercept the sediment yield from upstream. However, most hydrological models mainly simulate the effect of the terraces and vegetation on water and sediment reduction from themselves, without considering their roles in the routing process, and thus likely underestimate their runoff and sediment reduction effect. This study added the impact of terraces and vegetation practice on water and sediment routing using the time-area method. The outflow in each travel time zone was revised in each time step by extracting the watershed of the terrace units and the vegetation units and calculating the water or sediment stored by the terraces or held by the vegetation. The revised time-area method was integrated into the Land change Model-Modified Universal Soil Loss Equation (LCM-MUSLE) model. Pianguanhe Basin, in the Chinese Loess Plateau, was chosen as the study area and eight storms in the 1980s and 2010s were selected to calibrate and verify the original LCM-MUSLE model and its revised version. The results showed that the original model was not applicable in more recent years, since the surface was changed significantly as a result of revegetation and slope terracing, while the accuracy improved significantly when using the revised version. For the three events in the 2010s, the average runoff reduction rate in routing process was 51.02% for vegetation, 26.65% for terraces, and 71.86% for both terraces and vegetation. The average sediment reduction rate in routing process was 32.22% for vegetation, 24.52% for terraces, and 53.85% for both terraces and vegetation. This study provides a generalized method to quantitatively assess the impact of terraces and vegetation practice on runoff and sediment reduction at the catchment scale.

Evaluating the impacts of warming on water balance components in the groundwater⁻soil⁻plant⁻atmosphere continuum (GSPAC) and crop growth are crucial for assessing the risk of water resources and food security under future global warming. A water transformation dynamical processes experimental device (WTDPED) was developed using a chamber coupled with a weighing lysimeter and groundwater supply system, which could simultaneously control both climatic and ground-water level conditions and accurately monitor water fluxes in the GSPAC. Two experiments with maize under increased temperature by 2 °C (T-warm) and ambient temperature (T-control) scenarios were conducted via the WTDPED. The duration of growing season decreased from 125 days under T-control to 117 days under 2 °C warming. There was little difference of total evapotranspiration (ET) (332.6 mm vs. 332.5 mm), soil water storage change (∆W) (−119.0 mm vs. −119.0 mm), drainage (D) (−13.6 mm vs. −13.5 mm) between T-control and T-warm experiments. The average daily ET for maize significantly increased by approximately 6.7% (p < 0.05) in the T-warm experiment, especially during the sixth leaf to tasseling—silking stage with an increase of 0.36 mm with respect to the T-control experiment. There were evident decreases in LAI (leaf area index), whereas non-significant decreases in mean stem diameter, crop height and leaf chlorophyll content under T-warm compared to T-control experiment. However, the chlorophyll content increased by 12% during the sixth leaf to tasseling⁻silking stage under 2 °C warming, which accelerated the photosynthesis and transpiration rate. The grain yield and water-use efficiency (WUE) for maize increased by 11.0% and 11.1% in the T-warm experiment, respectively, especially due to enhanced growth during the sixth leaf to tasseling⁻silking stage. This study provided important references for agricultural planting and water management to adapt to a warming environment.

The foliage Clumping Index (CI) is an important vegetation structure parameter that allows for the accurate separation of sunlit and shaded leaves in a canopy. The CI and its seasonality are critical for global Leaf Area Index (LAI) estimating and ecological modelling. However, the cover of snow tends to reduce the reflectance anisotropy of the vegetation canopy and thus probably influences CI estimates. In this paper, we investigate the influence of snow on the magnitude and seasonal variation of the CI retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) products based on field-measured CI and statistics from the global MODIS CI product. We find that the backup algorithm can effectively correct abnormally large CI values and obtain more reasonable CI retrievals than the main algorithm without any constraints in snow-covered areas. Validation indicates that the time-series CI product shows the potential in investigating the trajectories of the clumping effect in snow seasons. For evergreen forests, the clumping effect is relatively stable throughout the year; however, for deciduous vegetation types, CI values tend to display significant seasonal variations. This study suggests that the latest version of the global MODIS CI product, in which the backup algorithm is used to process the snow-covered pixels, has improved accuracy for CI retrievals in snow-covered areas and thus is probably more suitable as the input parameter for ecological and meteorological models.

Inter-basin water transfer projects are designed to relieve water scarcity around the world. However, ecological problems relating to reductions in protection zone functions can occur during inter-basin transfers. This paper uses the largest inter-basin water transfer project in the world, namely, the South-to-North Water Transfer Project (SNWTP) in China, as an example to analyze the variation of Miyun Reservoir’s inner protection zone functions when water is transferred. Specifically, a riparian model (RIPAM) coupled with remote sensing data were used to calculate the nitrogen (N) and phosphorus (P) losses due to plant uptake, and these results were validated by in situ survey data. Then, correlations between water levels and N and P removal were analyzed. The results show that water table disturbances resulting from elevated water levels strongly influence the growth of plants and have obvious negative impacts on N and P removal in the inner protection zone. With the implementation of the middle route of the SNWTP, the water level of Miyun will rise to 150 m in 2020, and subsequently, the total net primary productivity (NPP) could decline by more than 40.90% from the level in 2015, while the N and P uptake could decline by more than 53.03% and 43.49%, respectively, from the levels in 2015, according to the modeling results. This will lead to declines in the inner protection zone’s defense effectiveness for N and P interception and increases in risks to the security of water resources. The results of this study provide useful knowledge for managing the defense function of the terminal reservoir’s inner protection zone and for ensuring that water quality is maintained during the diversion process.

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to −7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of −60.91% (−58.19%, −65.30%, and −63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (−72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (−2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region.

Vegetation deterioration and soil loss are the main causes of more precipitation leakages and surface water shortages in degraded karst areas. In order to improve the utilization of water resources in such regions, water storage engineering has been considered; however, site selection and cost associated with the special karstic geological structure have made this difficult. According to the principle of the Soil Plant Atmosphere Continuum, increasing both vegetation cover and soil thickness would change water cycle process, resulting in a transformation from leaked blue water (liquid form) into green water (gas or saturated water form) for terrestrial plant ecosystems, thereby improving the utilization of water resources. Using the Soil Vegetation Atmosphere Transfer model and the geographical distributed approach, this study simulated the conversion from leaked blue water (leakage) into green water in the environs of Guiyang, a typical degraded karst area. The primary results were as follows: (1) Green water in the area accounted for normal year > rainy year. Thus, increased soil thickness was shown effective in improving the utilization ratio of water resources and in raising the amount of plant ecological water use; (4) The transformation of blue water into green water, which avoids constructions of hydraulic engineering, could provide an alternative solution for the improvement of the utilization of water resources in degraded karst area. Although there are inevitable uncertainties in simulation process, it has important significance for overcoming similar problems.

The transport characteristics of phosphorus in soil and the assessment of its environmental risk have become hot topics in the environmental and agricultural fields. The Sanjiang Plain is an important grain production base in China, and it is characterised by serious land use change caused by large-scale agricultural exploitation. Agricultural inputs and tillage management have destroyed the soil nutrient balance formed over long-term conditions. There are few studies on non-point source phosphorus pollution in the Sanjiang Plain, which is the largest swampy low plain in a mid-high-latitude region in China. Most studies have focused on the water quality of rivers in marsh areas, or the export mechanism of phosphorus from specific land uses. They were conducted using experimental methods or empirical models, and need further development towards mechanism models and the macro-scale. The question is how to find a way to couple processes in phosphorus cycling and a distributed hydrological model considering local hydrological features. In this study, we report an attempt to use a distributed phosphorus transport model to analyse non-point source total phosphorus pollution from different land uses and soil types on the Sanjiang Plain. The total phosphorus concentration generally shows an annually increasing trend in the study area. The total phosphorus load intensity is heterogeneous in different land use types and different soil types. The average total phosphorus load intensity of different land use types can be ranked in descending order from paddy field, dry land, wetlands, grassland, and forestland. The average total phosphorus load intensity of different soil types can be ranked in descending order: paddy soil, bog soil, planosol, meadow soil, black soil, and dark brown earth. The dry land and paddy fields account for the majority of total phosphorus load in the study area. This is mainly caused by extensive use of phosphate fertilizer on the cultivated land. This has important implications for future agricultural management and non-point source control in this agricultural area of the mid-high latitude region.