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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.

With increased global warming, cyanobacteria are blooming more frequently in lakes and reservoirs, severely damaging the health and stability of aquatic ecosystems and threatening drinking water safety and human health. There is an urgent demand for the effective prediction and prevention of cyanobacterial blooms. However, it is difficult to effectively reduce the risks and loss caused by cyanobacterial blooms because most methods are unable to successfully predict cyanobacteria blooms. Therefore, in this study, we proposed a new cyanobacterial bloom occurrence prediction method to analyze the probability and driving factors of the blooms for effective prevention and control. Dominant cyanobacterial species with bloom capabilities were initially determined using a dominant species identification model, and the principal driving factors of the dominant species were then analyzed using canonical correspondence analysis (CCA). Cyanobacterial bloom probability was calculated using a newly-developed model, after which, the probable mutation points were identified and thresholds for the principal driving factors of cyanobacterial blooms were predicted. A total of 141 phytoplankton data sets from 90 stations were collected from six large-scale hydrology, water-quality ecology, integrated field surveys in Jinan City, China in 2014–2015 and used for model application and verification. The results showed that there were six dominant cyanobacterial species in the study area, and that the principal driving factors were water temperature, pH, total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen. The cyanobacterial blooms corresponded to a threshold water temperature range, pH, total phosphorus (TP), ammonium nitrogen level, chemical oxygen demand, and dissolved oxygen levels of 19.5–32.5 °C, 7.0–9.38, 0.13–0.22 mg L−1, 0.38–0.63 mg L−1, 10.5–17.5 mg L−1, and 4.97–8.28 mg L−1, respectively. Comparison with research results from other global regions further supported the use of these thresholds, indicating that this method could be used in habitats beyond China. We found that the probability of cyanobacterial bloom was 0.75, a critical point for prevention and control. When this critical point was exceeded, cyanobacteria could proliferate rapidly, increasing the risk of cyanobacterial blooms. Changes in driving factors need to be rapidly controlled, based on these thresholds, to prevent cyanobacterial blooms. Temporal and spatial scales were critical factors potentially affecting the selection of driving factors. This method is versatile and can help determine the risk of cyanobacterial blooms and the thresholds of the principal driving factors. It can effectively predict and help prevent cyanobacterial blooms to reduce the global probability of occurrence, protect the health and stability of water ecosystems, ensure drinking water safety, and protect human health.

Assessing and comparing global river ecosystem health in an objective and quantitative way remains a major challenge. In this study the widely-used semi-quantitative methods Rapid Biological assessment Protocols (RBPs) was used to determine the health of rivers. The findings were then compared to the results derived from our new UAV (Unmanned aerial vehicles) orthophotographic imagery method. This method quantitatively and objectively assesses river ecosystem health. As a comparison, our method was used to quantitatively measure distance and areas of a range of hydrological and biological attributes thus improving the accuracy of distance- and area-related indices, consequently avoiding subjective errors in these estimations that is fraught in methods like the RBPs. To strengthen the objectivity of the assessment the weights of these indices were objectively determined using the entropy weighting method. This new method was then tested using 9551 UAV orthophotographs taken over six field campaigns. It performed satisfactorily, showing that in our study area the health status of mountain rivers was the best with the highest score of 0.94 out of 1.0. Temporally, the health of the river was better in summer (0.65) compared with that in autumn (0.40). Changes in river ecosystem health were driven by variations in biology and water quality. In contrast the outputs of RBPs, especially in relation to distance and area indices, had ~ 20% uncertainty due to visual errors and subjectivity in estimations by observers. The UAV orthophotographic imaging method proposed in this study can improve the ability to compare the health of rivers across different periods and regions throughout the globe.

Healthy river ecosystems can provide fundamental ecological services for human survival and social development. However, previous studies that have sought to assess river ecosystem health have primarily focused on individual biological communities rather than on all aquatic communities and have been based on a single-index assessment method, thereby leading to large uncertainties in the results. In this study, we developed a new framework for the integrated assessment of aquatic ecosystem health based on the principal communities of fish, zoobenthos, phytoplankton, and zooplankton in rivers. An index of biotic integrity was used to evaluate the health of fish and zoobenthos communities, and a diversity index was used to evaluate the health of zooplankton and phytoplankton communities. To integrate the health assessment results from these four communities, a quantile normalization method was developed, where uncertainties in assessments obtained using the diversity index were well compensated for by the assessments obtained using the biotic integrity index. The framework was then applied to a pilot city, which is being constructed as a civilized freshwater ecological city in China. The results were then compared with those previously obtained based on the single-community method. Using this new framework, we found that the aquatic ecosystem health changed regularly in space and over time. Large differences were detected among the assessments of the four individual communities based on the single-community method, with the health score determined using phytoplankton being the highest, followed by that of zoobenthos, zooplankton, and fish, which made it difficult to reach a definitive conclusion regarding aquatic health status. The integrated assessment framework presented in this study successfully overcame the narrow perspective of the single-community method, thereby reducing uncertainties in the assessments based only on a single diversity index, and instead provides a comprehensive view of the status of aquatic ecosystem health. Thus, this integrated framework could assist river managers and stakeholders in developing comprehensive strategies for ecological restoration and water resource management and could become a key research tool for the health assessment and rehabilitation of aquatic ecosystems globally.

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.

Quantitatively and accurately analyzing nonpoint-source (NPS) pollution is essential for efficiently preventing the input of NPS loads into inland waters. However, the accuracy of previous NPS pollution models is limited by the accuracy of ground parameter data. In addition, there are few effective methods that thoroughly verify modeling results at large scales. This paper presents a framework for accurate NPS pollution estimation by coupling satellite and unmanned aerial vehicle (UAV) monitoring data, and the results are verified by both field sampling and a newly developed inlet NPS pollution "observation" simulation method. Fractional vegetation coverage (FVC) data obtained by satellite were used to improve the accuracy of the runoff module of the framework. Satellite and UAV data were coupled to acquire livestock data, determine inlets, and identify reservoir buffer zones and vegetation types. These new data were then used to improve the accuracy of the livestock and runoff modules in the framework. The results show that the estimation accuracy of total nitrogen, total phosphorus, ammonia nitrogen, and chemical oxygen demand with FVC were improved by 39.96%, 69.29%, 54.05% and 47.22% (in relative error), respectively. The high-resolution livestock data acquisition improved the estimation accuracy of the NPS pollution load by 7-53%. The high-resolution inlet extraction improved the accuracy by 3-24%. The high-resolution buffer zone identification improved the accuracy with the estimated NPS pollutant concentration into reservoir decreasing by 60-99%. Finally, the high-resolution vegetation type identification improved the accuracy by 10-72%. The framework performs satisfactorily, which was verified based on the simulated NPS observations with an average relative error of 11.54-24.31%. We found that the FVC, livestock number, and inlet number are key parameters for NPS pollution modeling; the introduction of monthly variation in the FVC makes the modeled NPS pollution load much higher in areas with mature complex forested ecosystems or densely distributed vegetation but much lower in areas with sparsely distributed vegetation. The above methods provide a scientific reference for high-efficiency NPS pollution prevention in inland waters, laying a solid basis for decision-making regarding water quality management in data-scarce regions around the world.

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.

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.

Application of complementary relationship (CR) approaches using only routine meteorological data is a very convenient method of estimating actual evapotranspiration (ETa). Reanalysis datasets and remote sensing data provide good tools to overcome the difficulties in obtaining observation data. This study of the Hotan River Basin (HRB) in northwest China serves as a prime example for estimation of ETa during 2006–2014 by using the modified generalized CR. Based on comparison and analysis, the maximum potential evaporation calculated by the Penman-based equation was adopted. The estimated ETa rates were verified using a regional water balance method at annual time scales because of the limited available data. The calibration parameter was calibrated based on the elevation and underlying surface types. The mean annual ETa ranged from 2.3 mm to 800 mm during 2006–2014. ETa rates in the plains regions were higher than those in the mountainous regions. Most of ETa was concentrated in the months of May to September. A water deficit occurred in the middle and lower regions, while a water surplus occurred in the upper regions. This study not only provided a new concept for calibration, but also a potential solution for different underlying surfaces and time scales.

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.

Environmental flow is the quantity, timing, and quality of water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and well-being that depend on these ecosystems. Environmental flows (e-flows) are crucial parameters for ecosystem restoration. Understanding the effects of spatial variation in the hydrological and water quality factors on e-flows aids the determination of recovery prior areas and helps to improve the success rate of ecosystem restoration projects. However, few studies have investigated the effects, which severely hinder the restoration of aquatic ecosystems and the sustainable use of water resources in inland waters. This paper therefore presents a framework for studying such effects. Spatial autocorrelation, a geostatistical method, is used to analyze the spatial variation in the hydrological and water quality factors and to further analyze the effects of various factors on the spatial heterogeneity of e-flows. Four different methods including the Tennant method, wetted perimeter method, AEHRA, and integrated water quality method are integrated to comprehensively evaluate e-flows. The former three methods consider the demands of biota on the streamflow, whereas the latter considers the demands on both the streamflow and the water quality. The results show that the Tennant and wetted perimeter methods, which focus on the statistics of only streamflow, result in similar spatial distribution of e-flows; the AEHRA and integrated water quality method, which consider the effects of water quality and other hydrological factors such as flow velocity and water depth on fish, also result in a similar spatial variation. Consideration of both demands on the hydrological factors and the water quality environmental factors makes the integrated water quality method more practical, particularly in developing regions with excessive pollutant discharge into rivers. In addition, spatial variation in the hydrological and water quality factors influenced the presence of principal fish species and consequently affected the e-flows. Of the 37 water quality factors identified, water transparency had a negative impact on e-flow because the increase in transparency could reduce the number of principal fish species. Of the four hydrological factors, flow velocity and river width had positive impacts on fish because the increase in flow velocity can provide breeding sites and habitats for more fish, respectively, both of which result in increases in the numbers of principal fish species. We found that spatial variation in the hydrology and water quality factors had a profound impact on the living environments of aquatic organisms; negative changes in these factors lowered the survival probability of principal species, which changed the hierarchy and structure of the ecosystems and thus led to variation in e-flows. The results can provide priori knowledge for e-flow methods selection and a reference for ecosystem restoration helping improve the success rate of project elsewhere in the world.

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&ndash, 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&ndash, area method, namely the Manning&ndash, Strickler formula (M&ndash, S), Saint-Venant system of equivalence (which has two definitions, S-V-1 and S-V-2), and the Darcy&ndash, Weisbach equivalence (D&ndash, 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&ndash, Sutcliffe efficiency coefficient is 0.97, indicating strong practical application in the study area. When the discharge is less than 10 m3&frasl, s, D&ndash, W is the most appropriate method, M&ndash, S and S-V-1 are better than other methods when the discharge is between 10 m3&frasl, s and 50 m3&frasl, s. However, if the discharge is greater than 50 m3&frasl, s, S-V-2 provides the most accurate results. Furthermore, we found that hydraulic radius is an important parameter in the slope&ndash, area method. This study offers a quick and convenient solution to extract hydrological information in ungauged catchments.

Background Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. New method This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naive Bayesian Classifier (WNBC) for classification. Results Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. Comparison with existing methods The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. Conclusions The proposed approach is a promising candidate for future BCI systems.

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.

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.

© 2019 Elsevier Ltd Global aquatic ecosystems are essential to human existence and have deteriorated seriously in recent years. Understanding the influence mechanism of habitat variation on the structure of the food-web allows the effective recovery of the health of degraded ecosystems. Whereas most previous studies focused on the selection of driving habitat factors, the impact of habitat variation on the food-web structure was rarely studied, resulting in the low success rate of ecosystem restoration projects globally. This paper presents a framework for exploring the effects of spatial variations in water quality and hydrological habitat factors on the food-web structure in city waters. Indices for the evaluation of the food-web structure are first determined by integrating model-parameter extraction via literature refinement. The key water quality and hydrological factors are then determined by coupling canonical correspondence analysis with partial least squares regression. Their spatial variation is investigated using spatial autocorrelation. Finally, fuzzy clustering is applied to analyze the influence of the spatial variations in water quality and hydrological factors on the food-web structure. The results obtained in Ji'nan, the pilot city of water ecological civilization in China, show that the Shannon diversity index, connectance index, omnivory index, and the ratio of total primary production to the total respiration are important indicators of food-web structural change. They show that the driving factors affecting the aquatic food-web structure in Ji'nan are hydrological factors (e.g., river width, water depth, and stream flow), physical aspects of water quality (e.g., air temperature, water temperature, electrical conductivity, and transparency), and chemical aspects (e.g., potassium, dissolved oxygen, calcium, and total hardness). They also show that the stability of the food-web is more prone to spatial variations in water quality than in hydrological factors. Higher electrical conductivity, potassium, total hardness, and air temperature lead to deteriorated food-web structures, whereas better transparency improves structure and stability. We found that water and air temperature are the most important factors in the spatial variation of the food-web structure in the study area, followed by total hardness. Transparency is the least important factor. Large disparities and varied spatial distributions exist in the driving effects of water quality and hydrological factors across regions attributable to differences in geographical environments, water salinity (fresh vs. sea water), and environmental factors (e.g., water pollution). The above methods and results serve as a theoretical and scientific basis for a high success rate of aquatic ecosystem restoration projects in the study area and other cities worldwide.

Global algal blooms have been severely threatening safety of drinking water and development of socio-economy. Effective prevention and accurate control of algal blooms require a quantitative assessment of the influence of human activities and identification of priority areas. However, previous studies on the quantitative assessment of the effects of human activities on algal communities are lacking, severely hindering the effective and precise control of algal blooms. This paper proposes a quantitative assessment model to evaluate the impact intensity of human activities on phytoplankton. Applications showed that the proliferation of phytoplankton were more limited by nutrients such as total phosphorus and ammonia where waters are less influenced by human activities, yet were less limited by these nutrients where there are highly intensive human activities. The density of phytoplankton in waters increased with an increase in human activity intensity, particularly in concentrated agricultural areas, which are priority areas for the prevention and control of algal blooms. The methodologies can clearly identify key areas for algal bloom prevention and control and can provide scientific evidence for water and nutrient management throughout the world, reducing the risk of algal blooms and ensuring aquatic ecosystem health and potable water safety.

It has been shown that climate change impacts the overall health of a river's ecosystem. Although predicting river health under climate change would be useful for stakeholders to adapt to the change and better conserve river health, little research on this topic exists. This paper presents a methodology predicting river health under different climate change scenarios. First, a multi-source, distributed, time-variant gain hydrological model (MS-DTVGM) was used to predict the runoff from a mountainous river in eastern China using the data from three existing IPCC5 climate change models (RCP2.6, RCP4.5, and RCP8.4). Next, a model was developed to predict the river's water quality under these scenarios. Finally, a multidimensional response model utilizing hydrology, water quality, and biology was used to predict the river's biological status and ascertain the impact of climate change on its overall health. The river is in a mountainous area near Jinan City, one of China's first “pilot” cities recognized as a “healthy water ecological community.” Our results predict that the overall health of the Yufu River, which is minimally influenced by human activities, will improve by 2030 due to the increased river flow due to an increase in rainfall frequency and subsequent peak runoff. However, the total nitrogen concentration is predicted to increase, which is a potential eutrophication risk. Therefore, effective control of nitrogen pollutants entering the river will be necessary. The increase in flow velocity (the annual average increase is ~0.5 m/s) is favorable for fish reproduction. Our methods and results will provide scientific guidance for policy makers and river managers and will help people to better understand how global climate change impacts river health.

© 2018 The survival of aquatic biota in stream ecosystems depends on both water quantity and quality, and is particularly susceptible to degraded water quality in regulated rivers. Maintenance of environmental flows (e-flows) for aquatic biota with optimum water quantity and quality is essential for sustainable ecosystem services, especially in developing regions with insufficient stream monitoring of hydrology, water quality and aquatic biota. Few e-flow methods are available that closely link aquatic biota tolerances to pollutant concentrations in a simple and practical manner. In this paper a new method was proposed to assess e-flows that aimed to satisfy the requirements of aquatic biota for both the quantity and quality of the streamflow by linking fish tolerances to water quality criteria, or the allowable concentration of pollutants. For better operation of water projects and control of pollutants discharged into streams, this paper presented two coefficients for streamflow adjustment and pollutant control. Assessment of e-flows in the Wei River, the largest tributary of the Yellow River, shows that streamflow in dry seasons failed to meet e-flow requirements. Pollutant influx exerted a large pressure on the aquatic ecosystem, with pollutant concentrations much higher than that of the fish tolerance thresholds. We found that both flow velocity and water temperature exerted great influences on the pollutant degradation rate. Flow velocity had a much greater influence on pollutant degradation than did the standard deviation of flow velocity. This study provides new methods to closely link the tolerance of aquatic biota to water quality criteria for e-flow assessment. The recommended coefficients for streamflow adjustment and pollutant control, to dynamically regulate streamflow and control pollutant discharge, are helpful for river management and ecosystems rehabilitation. The relatively low data requirement also makes the method easy to use efficiently in developing regions, and thus this study has significant implications for managing flows in polluted and regulated rivers worldwide.

© 2018 Elsevier B.V. Globally, water quality degradation severely threatens the security of water resources. Understanding a river's capacity to accommodate pollutants (or water environmental capacity: WEC) can help efficiently protect rivers. However, the requirement for comprehensive ground-observed hydrological and water quality data in previous methods makes it difficult to estimate WEC in areas with limited ground observations. This paper proposes a new framework for WEC estimation in data-scarce areas based on remotely sensed skin water temperature and limited ground observations. Two new models were developed to calculate the two critical parameters for WEC estimation: water temperature, and integrated pollutant degradation coefficients (k). Images of ASTER Surface Kinetic Temperature (AST_08) 90 m grid product were used to retrieve water temperatures. The above results were subsequently used to calculate a river's capacity to accommodate pollutants, or WEC, in agriculturally dominated areas. The use of remote sensing techniques enables the methods to be applied over large spatial scales and to areas with limited ground observations. The application and testing of the framework in four rivers, including two Chinese rivers (the Huai and the Wei Rivers) and two Australian rivers (the Ovens and the Gwydir Rivers), suggest that the models performed well to calculate the real-time water temperature and the coefficient k based on limited ground-observations. Uncertainty analysis on water temperature calculated from remotely sensed land surface temperature and ground-observed meteorological air temperature suggests that remotely sensed water temperature had high concurrence with ground observations (RMSE = 3.08 °C with R2= 0.88), while the sparse-spatially distributed meteorological stations reduced the accuracy in estimating water temperature (RMSE = 4.39 °C with R2= 0.91). We found that the coefficient (k) increased with water temperature over different seasons in an exponential form but in a logarithmical form with streamflow velocity. Comparison with previous research and other models with abundant data revealed the practicability and effectiveness of our models, which can be easily applied to rivers with insufficient ground observations across the globe.

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.

To control soil erosion and restore the degraded environment in the Loess Plateau, a large number of measures related to soil and water conservation have been employed that have profoundly affected catchment properties. This study constructed three indicators to characterize changes to the catchment slope, proposed both a method for a regression analysis of adjacent images and a sequence model, and applied multisource remotely sensed images and GIS spatial clustering analysis technologies to extract thematic information and comprehensively analyze the catchment change characteristics. The results indicate that the catchment slope properties changed significantly. At catchment scale, the average values of ARC, DVC and ART were 6.43%, 25.57% and 4.30%, respectively. There were six clustering types of catchment slope property changes. The maximum and minimum of the average similarities of the clustering types were 0.992 and 0.935. Each slope control measures had a distinct effect on catchment slope; the dominating factor of each clustering type was identified as: Type 1: D-VC, Type 2: D-VCLU, Type 3: D-LUVC, Type 4: D-TAVC, Type 5: D-TAC and Type 6: D-MFC. Type 5 and Type 1 covered the largest areas, respectively occupying 37.28% and 31.01%. Catchment slope property changes also had distinct types that depended on their geomorphological conditions. These findings provide a useful basis from which to further study catchment slope hydrological and soil erosion processes.

A phosphorus resource crisis threatens the security of global crop production, especially in developing countries like China and Brazil. Legacy phosphorus (legacy-P), which is left behind in agricultural soil by over-fertilization, can help address this issue as a new resource in the soil phosphorus pool. However, issues involved with calculating and defining the spatial distribution of legacy-P hinder its future utilization. To resolve these issues, this study applied remote sensing and ecohydrological modeling to precisely quantify legacy-P and define its spatial distribution in China's Sanjiang Plain from 2000 to 2014. The total legacy-P in the study area was calculated as 579,090 t with an annual average of 38,600 t; this comprises 51.83% of the phosphorus fertilizer applied annually. From 2000 to 2014, the annual amount of legacy-P increased by more than 3.42-fold, equivalent to a 2460-ton increase each year. The spatial distribution of legacy-P showed heterogeneity and agglomeration in this area, with peaks in cultivated land experiencing long-term agricultural development. This study supplies a new approach to finding legacy-P in soil as a precondition for future utilization. Once its spatial distribution is known, legacy-P can be better utilized in agriculture to help alleviate the phosphorus resource crisis.

Cyanobacterial blooms are a serious issue and frequently occur in lakes and reservoirs. Understanding how topography and human activities affect cyanobacterial dominance and blooms can greatly enhance the success of restoration projects to reduce blooms. Therefore, in this study the dominant species of cyanobacteria were determined by calculating the break point of the cumulative dominance curve for multiple sites in Jinan, China. In addition, the key drivers affecting the dominant cyanobacteria species were identified by canonical correspondence analysis and correlations between topography, human activities and the key drivers of the dominant cyanobacteria were investigated using random forests analysis. Merismopedia glauca, Merismopedia tenuissima, Microcystis aeruginosa, Oscillatoria tenuis, Phormidium tenus and Raphidiopsis sinensia were determined to be the dominant species. The key drivers of cyanobacterial bloom development were total phosphorus (TP), ammonium nitrogen (NH3-N), water temperature and total hardness. Topography and human activities were highly correlated with the key driving factors. The higher the altitude, the greater the effect of water temperature on the cyanobacteria community. In the area where sewage irrigation was present and a large amount of chemical fertiliser was used, total hardness had a greater effect on the cyanobacteria community. In areas where the population was more concentrated, TP and NH3-N had greater effects on the cyanobacteria community.

A practical approach to separate the impact of the processes of climate change (CC) and human activities (HA) on streamflow is presented. A non-parametric Mann-Kendall-Sneyers test, combined with moving t test and Yamamoto methods, was used to recognize abrupt change points in the runoff time series to determine a baseline period. A new algorithm to separate CC and HA influence on streamflow was deduced based on the climate elasticity concept. Application to the Chao River, China, shows that CC imposed a positive impact on streamflow in this region (25%, on average), while HA exerted a continuous negative impact of –75% in the period after the 1950s. These results are of great use in understanding the variation of CC and HA impacts under different human development patterns.Editor Z.W. Kundzewicz; Associate editor H. Aksoy

The hindered stepwise aggregation (HSA) model was used to elucidate the molecular aggregation in heavy petroleum fractions which were derived from supercritical fluid extraction fractionation (SFEF) of Venezuela Orinoco vacuum residue (VR). The SFEF fractions consisted of multiple extractable narrow fractions and a nonextractable end-cut. The SFEF fractions were diluted with toluene, and their number-average molecular weights (MWs) were determined using vapor pressure osmometry (VPO). The initial molecular association constants (K1) and aggregation hindrance factors (H) of the HSA model for each SFEF fraction were calculated from the VPO MWs at various SFEF solution concentrations. The results showed that the HSA model fit well with VPO MW data and the parameters of the HSA model are physically significant. The values of MW and K1 increased as the SFEF fraction became heavier. The SFEF end-cut had the highest K1 and lowest H value, in which the aggregates were 2 to 8 monomers. Except for the initial fract...

Understanding the effects of climate change on runoff is important for the sustainable management of water resources. However, the mechanism of such effects in the Asian monsoon region remains unclear. This study revisits Fu's two-parameter climate elasticity index and enhances it by using the Gardner function to strengthen the former's prediction reliability when the future climate condition is beyond the historical range. Then the improved method was applied to study the elasticity change with temperature and precipitation in the eastern monsoon basins of China, whereas to explore the mechanism of climate change on runoff. Furthermore, the runoff change and the elasticity of the study area from 2020 to 2050 under representative concentration pathways (RCPs) were predicted. Results show that the trend of elasticity change assumes a centrosymmetric picture with the symmetric point (0,0). Different catchments respond differently to the same climate change scenario: the sensitivity of the Haihe Basin is the highest; those of Yellow, Huaihe, Liaohe, Songhua, Pearl, Yangtze, and Southeast Rivers are lower, in descending order. The changing mode of precipitation and temperature differs greatly to keep the runoff unchanged. For semi-humid regions in which the mean annual temperature ranges from 0.71 degrees C to 9.0 degrees C, such as the basins of Songhua, Liaohe, Haihe, and Yellow, a 1 degrees C increase in temperature requires a corresponding 3.2-4.0% increase in precipitation to keep the runoff unchanged. However, in wet regions, such as the basins of Yangtze, Southeast Rivers, and Pearl, the same change in temperature requires a less than 2.8% increase in precipitation to keep the runoff unchanged. In the future, the runoff in most basins may decrease in different degrees. The decreasing velocity of the runoff is the fastest in the RCP8.5 scenario and the decreasing trend of the runoff slows down under the RCP4.5 and RCP2.6 scenarios. The proposed method can be applied to other basins to assess potential climate change effects on annual runoff. The results of the basins studies can inform planning of long-term basin water management strategies taking into account global change scenarios. (C) 2014 Elsevier B.V. All rights reserved.

In areas with topographic heterogeneity, land use change is spatially variable and influenced by climate, soil properties, and topography. To better understand this variability in the high-sediment region of the Loess Plateau in which soil loss is most severe and sediment diameter is larger than in other regions of the plateau, this study builds some indicators to identify the characteristics of land use change and then analyze the spatial variability as it is affected by climate, soil property, and topography. We build two indicators, a land use change intensity index and a vegetation change index, to characterize the intensity of land use change, and the degree of vegetation restoration, respectively. Based on a subsection mean method, the two indicators are then used to assess the spatial variability of land use change affected by climatic, edaphic, and topographic elements. The results indicate that: 1) Land use changed significantly in the period 1998-2010. The total area experiencing land use change was 42,302 km 2 , accounting for 22.57%of the study area. High-coverage grassland, other woodland, and forest increased significantly, while low-coverage grassland and farm- land decreased in 2010 compared with 1998. 2) Land use change occurred primarily west of the Yellow River, between 35 and 38 degrees north latitude. The four transformation types, including (a) low-coverage grassland to medium-coverage grassland, (b) medium-coverage grassland to high-coverage grassland, (c) farmland to other woodland, and (d) farmland to medium-coverage grassland, were the primary types of land use change, together constitut- ing 60% of the area experiencing land use change. 3) The spatial variability of land use change was significantly affected by properties of dryness/wetness, soil conditions and slope gradient. In general, land use changed dramatically in semi-arid regions, remained relatively

Hydrological simulation in ungauged regions is a popular topic in water resource and environmental research, and is also an important part of the international research initiative Predictions in Ungauged Basins (PUB). In this study, a multi-spatial data-based DTVGM (MS-DTVGM), combining multi-source spatial data (MS-spatial data) with the Distributed Time-Variant Gain Model (DTVGM), was built in order to reduce dependence on conventional observation, and was applied to the Ili River basin where traditional data sets are scarce. Because it utilizes MS-spatial data to measure precipitation, potential evapotranspiration, air temperature, vegetation parameters, and soil parameters, the model is driven purely by data from common platforms, thus overcoming the disadvantage of the large amounts of data typically required for distributed hydrological models. The inputs and simulation results were calibrated and validated using station or field observations. The results indicate that: 1) the MS-DTVGM is feasible in the Ili River basin; all model inputs can be acquired from multi-source spatial data and the key parameters are accurate; 2) the MS-DTVGM has good performance on a monthly time scale, and its simulation results can be used for a longer time-scale water resource analysis; and (3) daily runoff generation correlated strongly with snowmelt, the R2 is about 0.69 indicating that the latter is an important contributor to water resources and suggesting that a snowmelt module is indispensable this area. The potential of distributed models for hydrological simulation in data-scarce regions using MS-spatial data was clearly demonstrated.

Evapotranspiration (ET) products were evaluated over the conterminous United States (CONUS). These products include the following: 1 product from machine learning model (GFET), 3 products based on remotely sensed observations (SSEBop, MOD16 and GLEAM) and 8 products from land surface models in NLDAS-2 (Mosaic, Noah28, SAC and VIC403) and NLDAS-3 (CLSM25, Noah36, NoahMP36 and VIC412). The AmeriFlux observations and water balance derived ET (WBET) were used to validate these products at point and basin scales. The three-corned hat (TCH) method was employed to quantify ET uncertainties over the basin scale and in each grid over the whole CONUS. The ET interannual variability and the impacts of drought on ET were analyzed over the basin scale and Texas. The results indicate that all models are able to capture ET seasonal variations compared to AmeriFlux observations. Over basin scale, all ET products are closely related to WBET with high correlation coefficient values (larger than 0.83). Noah28 (VIC412) has smallest root-mean-square difference (RMSD) of 27.32 mm/year (42.78 mm/year). The uncertainties calculated from TCH method indicate that NLDAS-3 monthly ET products have lower uncertainties (4–7 mm/month) than those from NLDAS-2 ET (7–8 mm/month) and two MODIS ET (MOD16 and SSEBop) (10–15 mm/month). Specifically, ET uncertainty is reduced 47% (19%) for Noah36 (NoahMP36) compared to Noah28; it is reduced 19% for VIC412 compared to VIC403; it is reduced 19% for CLSM25 compared to Mosaic. The GFET (4.37 mm/month) and GLEAM (6.44 mm/month) have comparable low uncertainties with NLDAS-3 ET products. This study provides an important basis for the selection of proper ET data sets for the hydrological analysis over CONUS.

Inland streamflow estimation is essential in global water supply and environment protection. In data-scarce areas a highly efficient way of estimating streamflow is through remote sensing methods. However, high requirement of most previous methods on ground-measured data hinder their wide use in data-scarce areas. Therefore, this paper presented a new framework for estimation of streamflow in medium-to-small rivers with few ground measurements by using high-resolution unmanned aerial vehicle (UAV) imagery. A new Virtual Hydraulic Radius (VHR) method was proposed to complement AMHG (at-many-stations hydraulic geometry), a method not requiring any ground measurements when global parameters are used (global–AMHG) in large-scaled rivers but yielding great uncertainties in smaller scaled rivers, thus creating a VHR-AMHG method for medium-to-small rivers. The accuracy verification of the proposed method was performed by comparing it to field measurement data and the global parameters of the original AMHG (global–AMHG). Results showed that the root mean square error calculated from VHR-AMHG was 32.15 m3/s, while that from global–AMHG was 305.65 m3/s, indicating that the VHR-AHRG method yields a significantly higher accuracy for streamflow estimation for medium-to-small rivers. We found that regardless of the size of the river, AMHG is not applicable for rivers having excessively small b values in the equation w = aQb (low-b rivers). For medium-to-small rivers with b

This study estimated average annual soil loss and clarified its spatial distribution and impact on reservoirs in the upper Mekong River basin in Yunnan Province, China. A quantitative grid-based estimation was made using a Universal Soil Loss Equation model in a geographic information system framework, along with remote sensing and other source data. The results suggest that the average annual soil loss in most of the area ranged from 0 to 2853 t/ha/y, with a mean value of 19.8 t/ha/y. We estimated that a little more than half (61.0%) of the study area undergoes minimal erosion; this was primarily observed in the south, and more particularly in the southeast portion of the study area. Almost one fifth (19.2%) of the study area was estimated to undergo low erosion; this was primarily found in the central and southwest portions of the study area. Moderate soil erosion was observed in 8.5% of the study area. We estimated 11.3% of the study area to undergo high or extreme erosion; these locations were concentrated in the northern part of the study area. Soil erosion appeared most frequently at the mean elevation and mean slope zone. Dams on the upper reaches were found to be threatened by the presence of sediment.

Venezuela Orinoco heavy crude oil was fractionated into diesel, vacuum gas oil (VGO), vacuum residue (VR), and asphaltene fractions, which were subjected to molecular weight (MW) measurement by vapor pressure osmometry (VPO). The VPO is known to overestimate the average molecular weight (MW) of heavy hydrocarbons, because of molecular aggregation. This paper proposes a hindered stepwise aggregation (HSA) model to simulate the molecular aggregation and used the model to estimate the true MW of heavy petroleum fractions. A data regression procedure was developed to determine the model parameters, aggregation equilibrium constant, and aggregate distribution, using a fast simulated annealing (FSA) algorithm based on the VPO data. This data analysis method is self-tuned to fit the VPO data to the HSA models of various petroleum fractions using the optimized solution of the FSA algorithm. The results showed that the VPO data of heavy petroleum fractions at various solution concentrations were in good agreement ...