A national-scale assessment of land subsidence in China's major cities.

China's massive wave of urbanization may be threatened by land subsidence. Using a spaceborne synthetic aperture radar interferometry technique, we provided a systematic assessment of land subsidence in all of China's major cities from 2015 to 2022. Of the examined urban lands, 45% are subsiding faster than 3 millimeters per year, and 16% are subsiding faster than 10 millimeters per year, affecting 29 and 7% of the urban population, respectively. The subsidence appears to be associated with a range of factors such as groundwater withdrawal and the weight of buildings. By 2120, 22 to 26% of China's coastal lands will have a relative elevation lower than sea level, hosting 9 to 11% of the coastal population, because of the combined effect of city subsidence and sea-level rise. Our results underscore the necessity of enhancing protective measures to mitigate potential damages from subsidence.

Improvement of evapotranspiration simulation study in the Hailar River basin under the influence of vegetation dynamics.

In arid and semi-arid areas with <400 mm of precipitation, evapotranspiration (ET) accounts for about 80% of precipitation and is the main water consumer in the watershed. However, vegetation greening in recent years will increase ET and exacerbate the aridity of the area by affecting soil moisture in the root system. Vegetation changes are regional and spatially heterogeneous, therefore, in order to characterize ET changes under vegetation dynamics, it is necessary to expand the spatial scale of ET simulation. However, widely used evapotranspiration simulation models, such as the Shuttleworth-Wallace model (SW model), are deficient in reflecting the direct and indirect effects of vertical (i.e., soil depths) and horizontal (i.e., vegetation dynamics) directions. Based on field sampling and constructed structural equation model (SEM), we found that vegetation dynamics affect evapotranspiration not only directly, but also indirectly by affecting soil moisture at different depths. On this basis, we defined the weighting coefficients of 0.85 and 0.15 for grassland vegetation zones, 0.3, 0.15, 0.20, 0.25, 0.10 for forest-grass interspersed zones, and 0.20, 0.55, 0.25 for forested zones, respectively, based on the SEM results. Different soil moisture weighting coefficients were defined within different vegetation type zones and the improved SW model is called S-W-α. Comparing the simulation results with the measured data, S-W-α improved the ET simulation accuracy in this region by 33.92% and the improved ET spatial trend can respond to the dynamic changes of vegetation. Replacing the ET module in the Block-wise use of TOPMODEL and Muskingum-Cunge method mode (BTOP model) with the modified S-W-α, the results show that the simulation accuracy of the improved model is increased by 25%, and the Nash is higher than 75% for both the rate period and the validation period, which realizes the extension of the model from the point scale to the basin scale. The modified model may provide technical support for simulation of evapotranspiration and management of ecosystem health in ecologically fragile areas.

Identification of pollution source and prediction of water quality based on deep learning techniques.

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.

Exploring the spatiotemporal variations in regional rainwater harvesting potential resilience and actual available rainwater using a proposed method framework.

Rainwater harvesting potential provides a basis for alleviating regional drought and water shortages. The resilience of rainwater harvesting potential is directly related to the sustainable level of actual available rainwater. Thus, SWAT model was combined with the proposed rainwater harvesting potential evaluation model to quantify rainwater harvesting potential, its resilience and actual available rainwater in the study area. The results showed that: (1) restoration of forest and grass increased the rainwater resource potential in the study area by 12.41 %, especially in the northeast, central and southwest of the study area. Although the surface runoff increased slightly in the past 20 years, it remained stable at 28.62 % of rainwater harvesting potential, which was benefited from the rainwater harvesting potential resilience to maintain the component stability; (2) rainwater harvesting potential resilience in the study area increased from class II to class III, which was closely related to the 17.93 % increase in the resilience intensity of the study area to resist external interference; and (3) surface runoff and net soil moisture content were the main components affecting the spatiotemporal variation of actual available rainwater, and lateral flow was also the main component affecting the spatial variation of actual available rainwater. In the past 20 years, the actual available rainwater has been increasing, and its conversion rate exceeded 89 %. The high level of actual available rainwater has been expanding to the western region with dense grassland coverage. This study provides a scientific basis for clarifying the sustainable utilization level of rainwater.

Changes in vegetation cover and its influencing factors in the inner Mongolia reach of the yellow river basin from 2001 to 2018.

Vegetation cover is one of the primary indicators of changes in ecosystems. China has implemented a few large-scale afforestation programs in the arid and semi-arid areas, including the Inner Mongolia Reach of the Yellow River Basin to prevent and control soil erosion. Although these programs have alleviated the environment problems in the region to a certain extent, the effects of the increasing vegetation greenness on the environments under climate change remain controversial for the argued large water consumption. In this study, the spatio-temporal characteristics of Normalized Difference Vegetation Index (NDVI) in the vegetation coverage area of the study area based on remote sensing data from 2001 to 2018. Meanwhile, using the Extreme Gradient Boosting (XGBoost) method - an excellent algorithm for ensemble learning methods - to forecast vegetation growth in the following ten years. The results indicated that, despite of the spatial heterogeneity, the vegetation NDVI exhibited a significant increase across the study area. Based on the NDVI trend, the area of improved vegetation in this region was much larger than the degraded area from 2001 to 2018, accounting for 85.9% and 8.6% of the total vegetation coverage area, respectively. However, the forecasting result by the Hurst index shows the future growth and carbon sequestration capacity in most areas showed a declining trend. Further, based on the Coupled Model Inter comparison Project - Phase 6 (CMIP6) data, the XGBoost method is used to predict the growth status and carbon sequestration capacity of vegetation in this area under different climate scenarios. The results showed that different climate scenarios had little effect on vegetation growth from 2019 to 2030. Results from this study may provide basis for the protection of ecological environment in the Inner Mongolia Reach of the Yellow River Basin.

Contribution of nonpoint source pollution from baseflow of a typical agriculture-intensive basin in northern China.

Baseflow is an essential component of total surface runoff that is widely considered one of the most influential factors regarding water quality via nonpoint source (NPS) pollution. Previously, many researchers and policy makers have directed their efforts toward surface runoff pollution, largely ignoring nutrient delivery via baseflow. Taking a typical agriculture-intensive basin of northern China as an example, this study explored the spatiotemporal characteristics of baseflow and pollution load in relation to NPS pollution. Baseflow was quantified using digital filtering techniques, and the results together with observed pollution data were used to validate a physically based hydrological model, i.e., the Soil and Water Assessment Tool. Then, the spatial and temporal distribution characteristics of NPS and baseflow pollution were investigated using the modeling results. Results indicated that baseflow contribution to total runoff accounted for more than 70% during the studied years (2016-2018), and 84.15% of the basin area showed non-point source pollution dominated by baseflow pollution; both baseflow and its pollution load were greater in the nonflood seasons (spring, autumn, and winter) than in the flood season (summer); the spatial distribution of baseflow total nitrogen and total phosphorus pollution intensity showed higher values in the east and lower values in the west; the scaling effects of baseflow and its pollution load was found with increasing basin area. The results of our study highlighted the necessity for management of pollution load via baseflow in the river basin and provided reference information for improvement of NPS pollution management in other similar basins.

Phytoplankton community variation and ecological health assessment for impounded lakes along the eastern route of China's South-to-North Water Diversion Project.

Interbasin water diversion projects have been proven to effectively alleviate water resource shortages in areas along water diversion lines, but few studies have focused on ecological health in impounded lakes compared with research on water quality and pollutants. Herein, monitoring data were collected during the nonwater diversion period (NWDP) and the water diversion period (WDP) from 2018 to 2019, and the index of biological integrity (IBI) method based on phytoplankton communities was used to evaluate the ecological health of the impounded lakes (Nansi Lake and Dongping Lake) along the eastern route of the South-to-North Water Diversion Project. The results demonstrated that water diversion improved the water quality of the impounded lakes during the WDP, especially total nitrogen and ammonia nitrogen. Meanwhile, the water diversion affected the phytoplankton community structure and diversity, and network analysis further revealed water diversion could be beneficial to the ecological health of impounded lakes. Furthermore, the P-IBI showed that the overall ecological health assessment was "good" during the WDP. Water diversion substantially improved the ecological health status and stability of the impounded lakes during the dry season. Finally, the direct correlations between the water quality parameters and the P-IBI were weak, and water quality parameters could indirectly affect the P-IBI by changing the phytoplankton community structure. These findings will enhance our understanding of the ecological health of the impounded lakes of the South-to-North Water Diversion Project. Furthermore, this study will provide a reference to support the ecosystem security of impounded lakes in other large water diversion projects.

Assessing alterations of water level due to environmental water allocation at multiple temporal scales and its impact on water quality in Baiyangdian Lake, China.

Lakes in arid/semiarid regions face problems of insufficient inflow and degradation of water quality, which threaten the health of the lake ecosystem. Baiyangdian Lake (BYDL), the largest lake in the North China Plain, is confronted with such challenges. The objective of this study was to improve understanding of how changes in water level influence water quality in the BYDL at different temporal scales, especially related to implementations of intermittent environmental water allocation activities in the past two decades, by using data on monthly lake water level, climate factors of precipitation and temperature, and lake water quality. The Mann-Kendall method and continuous wavelet analysis revealed that the lake water level shows a significant decreasing trend after 1967, and the period of 16-year was identified as the principal period for 1950-2018. Based on cross-wavelet transform and wavelet coherence analysis, the periodic agreement and coherence between water level and climatic factors decreased after 1997, when environmental water allocations started, indicating that the influences of climatic factors, i.e., precipitation and temperature, became weak. By utilizing the cross-wavelet transform and wavelet coherence analysis methods, the relationships between lake water level and water quality parameters of chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus were investigated. We found that the change in source and amount of environmental water allocation is one possible reason for the temporal evolution in joint variability between lake water level and water quality. Meanwhile, a dilution effect of freshwater allocated to BYDL was detected in the time-frequency domain. However, the result also indicates that the driving mechanism of water quality is complex due to the combined impacts of water allocation, nonpoint source pollution in the rainy season, and nutrient release from lake sediment. Our findings improve the general understanding of changes in water level in lakes located in arid and semiarid regions under climate change and intensive human activities, and also provide valuable knowledge for decision making in aquatic ecosystem restoration of BYDL and other similar lakes.

Evaluating the influence of different environmental water allocation schemes on the water level of a typical shallow lake in semiarid regions: From the perspective of an integrated modeling approach.

Many lakes in semiarid regions around the world rely on environmental water allocation to maintain the health of the lake ecosystem. However, under changing environments, the competition for water resources between human society and natural ecosystems has intensified. How to manage environmental water allocation more reasonably and precisely has become an important issue. The largest lake on the North China Plain, Baiyangdian Lake (BYDL), is a typical lake facing such challenges. To provide feasible strategies for sustainable water allocation to BYDL, with the proper parameterization of hydrological processes, this study developed a 10-day temporal scale lake water level prediction model to quantify how environmental water allocation regulates the BYDL water level under different hydroclimatic conditions. Evaluation of model performance revealed that environmental water allocation rather than natural climatic periodicity dominates the variation in the BYDL water level. The model structure could be further improved with consideration of more detailed observations of both the surface runoff entering BYDL and the water area beneath the canopy of the reeds in BYDL. Analysis of 72 model simulation scenarios indicated that water allocations from multiple sources are indispensable and that the water resources that guarantee maintaining the BYDL water level within the ecologically suitable range vary substantially under different hydroclimatic conditions. More elaborate allocation plans are required both to improve the water quality and health of the aquatic ecosystem of BYDL and to reduce the risk of flooding. The findings from this study are valuable for guiding the implementation of environmental water allocations to lakes in semiarid regions worldwide.

Spatiotemporal variations in evapotranspiration and its influencing factors in the semiarid Hailar river basin, Northern China.

Evapotranspiration (ET) is a critical variable in the world's water cycle, and plays a significant role in estimating the impact of environmental change on the regional hydrothermal cycle. Moreover, as an essential of eco-hydrological processes, changes in ET may exceptionally impact the local climate and provide indicative information on the eco-system's functioning. The Hailar River Basin (HRB), located in northern China, is one of the most sensitive areas to climate warming. Under the influence of climate change in recent years, the vegetation dynamics of the basin have been significant and have had profound effects on the regional water cycle conditions and hydrological processes. The HRB is located in a semiarid region and ET is the main mode of water consumption. The ET response to climate change and vegetation dynamics is the focus of research on ecohydrological processes in this basin. In this study, a distributed hydrological model, the BTOPMC model, is used to evaluate the actual ET in the HRB from 1981 to 2020, based on in situ meteorological data as well as LAI data obtained by satellite remote sensing. The seasonal, interannual and spatial dynamics of ET were characterized. The contribution of meteorological factors to ET was calculated by sensitivity analysis and multiple linear regression analysis, and the predominant elements influencing the difference in ET in the HRB were also discussed. The results show that: (1) estimated ET values can clarify over 85% of the seasonal variation in the observed values (R2= 0.79, P < 0.001; R2= 0.84, P < 0.001), which demonstrates that the model has a high precision. (2) Over the past 40 years, the annual ET has shown a clear increasing trend and a large spatial heterogeneity in its spatial distribution, which is consistent with the trend of vegetation. It mainly shows that the eastern forest area is larger than the central forest-grass transition area and the western meadow steppe area. (3) Sensitivity and influential factor contribution analyses show that the main factor driving interannual variability in ET is climate warming, followed by precipitation. At the same time, vegetation dynamics also play a crucial role in ET, especially in areas with different vegetation types and high coverage, while climatic factors also have a strong influence on ET indirectly through vegetation. Due to its special geographic location, the HRB is more sensitive to global climate change and is a typical ecologically fragile area. Therefore, this study has important scientific value and social significance for maintaining ecological security and the sustainable use of water resources.

Root-zone soil moisture estimation based on remote sensing data and deep learning.

Soil moisture in the root zone is the most important factor in eco-hydrological processes. Even though soil moisture can be obtained by remote sensing, limited to the top few centimeters (<5 cm). Researchers have attempted to estimate root-zone soil moisture using multiple regression, data assimilation, and data-driven methods. However, correlations between root-zone soil moisture and its related variables, including surface soil moisture, always appear nonlinear, which is difficult to extract and express using typical statistical methods. The artificial intelligence (AI) method, which is advantageous for nonlinear relationship analysis and extraction is applied for root-zone soil moisture estimation, but by only considering its separate temporal or spatial correlations. The convolutional long short-term memory (ConvLSTM) model, known to capture spatiotemporal patterns of large-scale sequential datasets with the advantage of dealing with spatiotemporal sequence-forecasting problem, was used in this study to estimate root-zone soil moisture based on remote sensing-based variables. Owing to limitation of regional soil moisture observation data, the physical model Hydrus-1D was used to generate large and spatiotemporal vertical soil moisture dataset for the ConvLSTM model training and verification. Then, normalized difference vegetation index (NDVI) etc. remote sensing-based factors were selected as predictive variables. Results of the ConvLSTM model showed that the fitting coefficients (R2) of the root-zone soil moisture estimation significantly increased compared to those achieved by Global Land Data Assimilation System products, especially for deep layers. For example, R2 increased from 0.02 to 0.60 at depth of 40 cm. This study suggests that a combination of the physical model and AI is a flexible tool capable of predicting spatiotemporally continuous root-zone soil moisture with good accuracy on a large scale.

A novel ecohydrological model by capturing variations in climate change and vegetation coverage in a semi-arid region of China.

Variations in vegetation are influenced by regional climate regimes and, in turn, control the water balance behavior in water-limited regions. Owing to its role in ecohydrological processes, vegetation is an essential link in modeling the relationships among climate conditions, vegetation patterns, and dynamic water balance behavior. However, previous ecohydrological models have been empirical and complex, without physically significant parameters. Here, we propose a novel ecohydrological model (a Budyko model-coupled vegetation model) that combines the impacts of climate change and vegetation variations, featuring simple and deterministic parameters. In addition to accounting for the fundamental water balance model and its factors, mean precipitation, potential evapotranspiration, runoff, and variations in water storage (δS), the model showed better performance when incorporating δS (RMSE = 2.72 mm yr-1) and its parameter ε -, which is mechanically and quantitively subject to the vegetation coverage (R2 = 0.95, p < 0.01). This was estimated as a function of vegetation potential canopy conductance, mean rainstorm depth, mean time between storms, and potential rate of evapotranspiration in a semi-arid watershed with impulsive precipitation in China (R2 = 0.80, p < 0.01). The model also found that vegetation growth was mainly controlled by soil water content and decoupled the impact of the total amount of precipitation on vegetation in the northeastern area of the watershed. Hence, our method presents a new tool for building an ecohydrological model that includes deterministic parameters of mechanical significance.

Modification and upscaling of S-W model based on vertical distributions of soil moisture and vegetation root biomass.

Soil water is the dominant factor controlling evapotranspiration (ET) in arid and semi-arid regions. However, the widely used ET simulation models, such as the Shuttleworth-Wallace model (S-W model), are insufficient in simulating the direct influence of soil moisture (SM), especially in the root zone. Based on SM and ET field data, we found that the influence of SM on ET increased with soil depth in the grassland. Evaporation in the S-W model was optimized by SM at 0-5 cm as the root mean square error (RMSE) decreased from 1.4 to 0.17, while transpiration was optimized by SM at 10-20 cm as the RMSE decreased from 0.26 to 0.07. The modified S-W model incorporating SM was called the S-W-Ï´ model. To up-scale application and to verify the accuracy of the S-W-Ï´ model under watershed water balance, we replaced the ET simulation module based on the S-W model with our S-W-Ï´ model and the Block-wise TOPMODEL with Muskingum-Cunge routing method (BTOPMC) model that we used as the basis of our simulation. The influence of SM was determined by the proportion of root biomass of different vegetation types at different depths, and each depth interval was assigned a weighting reflecting its degree of influence. The results showed that the S-W-Ï´ model improved accuracy with all the modification schemes tested. The modification scheme determined by the vegetation root distribution pattern had the greatest effect, providing a 4% accuracy improvement. The modified ET and hydrological models have the potential to support water basin management to a greater extent.

Evaluating the risks of spatial and temporal changes in nonpoint source pollution in a Chinese river basin.

In watershed management, it is of great importance to evaluate the risks of nonpoint source (NPS) pollution. In this study, the Nonpoint Source Pollution Risk Index (NSPRI), a multi-factor NPS risk assessment model that was based on the source-sink landscape theory, was proposed and applied in Muzhuhe River Basin, Shandong, China to (1) highlight spatial and temporal variations in the risks from nitrogen and phosphorus losses, and (2) identify how the basin characteristics influenced the risk of nutrient loss. According to the analysis on land use change, the study area is featured with high proportions of forest and agricultural land uses; the area of urban and industrial land had increased considerably from 2000 and 2018. Based on the division of the calculated risk indices on subbasin scale, the area with extremely high risks has decreased from 56,442 ha to 43,922 ha. The average and coefficient of variation (CV) values of NSPRI in the river basin have dropped from 1.3 to 1.1, and from 78.2% to 48.9%, respectively. The distribution of NSPRI suggested an increase in spatial clustering and improvements in the ecological balance. Correlation analysis of the Soil and Water Assessment Tool (SWAT) model (R2 > 0.68, ENS > 0.59) and NSPRI indicated the applicability of the method used (r > 0.84, p < 0.01). Analysis on the impact of metrics of land use composition, landscape, and environmental settings on NSPRI indicated that the water quality was more significantly correlated with land use composition, landscape pattern and vegetation cover than with flow path distance, soil erodibility, and rainfall erosivity. Moreover, results of redundancy analysis revealed that nutrient loss risk was better explained by land use compositions than by landscape configuration. The assessment method provided scientific support for NPS pollution control from the perspective of source-sink landscape theory.

Modeling water quantity and quality for a typical agricultural plain basin of northern China by a coupled model.

Water crisis across the globe has placed high pressure on social development due to the need to balance the water consumption between sustainable economy and functioning ecosystem. Integrated process-based modeling has been reported as an effective tool to better understand the complex mechanisms of water issues on a basin scale. Considering that it is still relatively difficult to simulate the water quantity-quality processes simultaneously, this study proposed an integrated modeling framework by coupling a hydrological model with a water quality model. Taking the Xiaoqing River Basin in the Shandong Province of northern China as an example, this study coupled a distributed hydrological model, SWAT, with a one-dimensional hydrodynamic-water quality model, HEC-RAS, to investigate its ability to simulate water quality and quality at the basin scale. The coupling of the two models adopted the "output-input" scheme, where the runoff modeling results from SWAT are input into HEC-RAS for hydrodynamic and water quality simulations of the river channel. The results show that the SWAT model can adequately reproduce runoff with accepted accuracy for the calibration and validation periods with acceptable R2 and Nash-Sutcliffe coefficients for the two hydrological stations. Further analysis also shows that the coupled model can simulate the concentration of ammonia nitrogen (NH4-N) and the chemical oxygen demand (COD) in the middle and upper stream of the river for both low and high flow periods. The coupling of the hydrological and hydraulic models in this study provides a good tool for identifying the spatial patterns of the water pollutants over the basin and, thus, helps simplify precision water management.

Dynamic landscapes and the driving forces in the Yellow River Delta wetland region in the past four decades.

Wetlands provide a broad range of ecosystem services, such as flood control, groundwater replenishment, and water purification. These services are particularly important in the Yellow River Delta, one of four estuarine deltas in China. The aim of this study was to examine the patterns and drivers forces of wetland landscape in the Yellow River Delta. We analyzed the spatio-temporal characteristics of land use change and dynamic landscape patterns changes between 1980 and 2018, divided into eight periods, from land use and remote sensing data. We also analyzed data for annual precipitation, annual temperature, annual evapotranspiration, digital elevation, slope, distance from the main river, built-up land, GDP, and population with principal component analysis, to identify the main drivers behind the changes in the wetland landscape. The results showed that, from 1980 to 2018, the total area of wetland decreased first and then increased the total area of wetland increased and was 1172.73 km2 greater in 2018 than it was in 1980, and the types of wetland in the Yellow River Delta changed frequently. From 1980 to 2005, the area of wetland decreased and the landscape dominance and degree of fragmentation were relatively high. From 2006 to 2018, as environmental policies were implemented and wetland protection became more important, the rate of development of wetland areas increased in a north-south direction, the proportion of landscape types became more balanced, and the spatial distribution homogenized. The main drivers of change in the wetland landscapes were GDP, population, precipitation, and temperature which were included 81.852% of the original information. The findings from this study provide us with an improved understanding of how land use and wetland landscapes changed from 1980 and 2018 and may have implications for the protection of wetland ecosystems, species diversity, and sustainable development.

Unraveling the sensitivity and nonlinear response of water use efficiency to the water-energy balance and underlying surface condition in a semiarid basin.

Ecological water use efficiency (WUE) is a crucial indicator of hydrological and vegetation carbon cycle coupling and can drastically affect water and energy exchanges. However, little is known about the sensitivity of WUE to water-energy exchange in semiarid regions. Moreover, few studies have considered the link between WUE and water-energy exchange models, i.e., the Budyko-type framework. Here, we investigated the relationships between WUE and a Budyko-type model in a semiarid basin. Seven primary indicators were selected to represent the water, energy, and vegetation variations in the ecosystem: gross primary productivity (GPP), air temperature (T), potential evapotranspiration (PET), evapotranspiration (ET), precipitation (P), soil water content (SWC), and snow water equivalent (SWE). Three similar ecological WUEs were defined as GPP/ET, GPP/P, and GPP/SWC to analyze the factors of the water-energy exchange model (i.e., ET/P, PET/P, SWC/P, and SWE/P). Based on the results, four linear Budyko-type models were proposed for the basin (GPP/P, ET/P, SWC/P, and SWE/P as a function of PET/P). The results suggested that both SWC and P influenced the trend of GPP variation, whereas P influenced the lower limit of GPP or GPP/P within the Budyko model for grassland areas. The results indicated that the rate of increase of SWC was lower than that of P in forest areas because of differences in canopy structure. The results also revealed a nonlinear (s-type) relationship between the WUEs and the underlying surface parameter m within the Budyko framework, suggesting that unit plant productivity consumes less water when the water-energy supply condition is invariant, if the variation of the underlying surface characteristics promotes the increase of m. Our research provides new insight regarding quantification of the sensitivity of WUE to the water-energy balance in a semiarid region.

Establishing a time series trend structure model to mine potential hydrological information from hydrometeorological time series data.

This paper addresses the problem of missing latent time series information caused by the differences in the analysis of time series data and non-time series data. A time series trend structure model (TSTM) was established using the analysis of time series patterns and rules, the trends of patterns and rules, and trends in confidence and support. Shandong Province was selected as the study area. Rainfall and evaporation time series data from this area were input into the TSTM. The results show that: (1) the structure of multi-year precipitation and evaporation trends of the meteorological stations in the study area have continuously increasing or decreasing characteristics. The TSTM can excavate the different trend structure characteristics of different meteorological elements and enables diversity in time series data analysis; (2) the evaporation trend structure tends to change synchronously with increases and decreases in precipitation and evaporation. The synchronous change frequency is essentially the same as that of the rainfall trend structure. This indicates that the TSTM has spatial and temporal characteristics for time series data analysis; and (3) from the maximal non-descending and non-ascending subsequence in the TSTM, it can be concluded that there exists continuity in the years when the trend structure of precipitation and evaporation increases and decreases synchronously. In addition, the degree of similarity in the model is well reflected in the spatial distribution characteristics of time series data, and the model provides clustering characteristics for time series data analysis. The TSTM proposed in this paper can effectively obtain the potential hydrological information contained in time series data, and provides a scientific and reliable basis for rules for the spatial optimization of watershed data and for the calibration of hydrological models.

Integrating satellite observations and human water use data to estimate changes in key components of terrestrial water storage in a semi-arid region of North China.

Sustainable management strategies for water resources rely on accurate knowledge about the dynamics of hydrological processes, especially in drylands, where freshwater is the limiting factor for the development of human society and ecosystems. The populated Loess Plateau (LP) in North China is a typical semi-arid region where competition for water between people and nature is worth noting because of afforestation promoted by the Grain to Green Program. In this study, changes in key components of terrestrial water storage (TWS) in the LP were explored using a multi-satellite approach, including Gravity Recovery and Climate Experiment (GRACE) observations and Earth observations of precipitation, evapotranspiration and soil moisture. By integrating data on human water use from different sources with satellite observations, we were able to examine the mechanisms driving these changes. The results demonstrated that, according to an evaluation based on reproducing TWS computed from the regional water balance in the LP, the mascon solution of the Center for Space Research (CSR) at University of Texas at Austin performed best out of the commonly used GRACE products. Regional TWS derived from the CSR mascon solution in the LP decreased significantly for the period 2003-2015. Significant decreases were also detected for regional ground water storage (GWS) estimated by decomposing the GRACE TWS using multi-sources remote sensing data. GWS made the greatest contribution to the changes in TWS. Increased plant transpiration was one reason for the decreasing trend of GWS. Because changes in precipitation, soil moisture and water consumed by irrigation were minor at regional scales, we concluded that the increase of transpiration is driven by deep-rooted trees planted, which use the part of precipitation that previously recharged groundwater. The findings from this study are valuable for water resource management and ecological restoration in semi-arid regions with high populations.

Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants.

Point sources are important routes through which pollutants enter rivers. It is important to identify the characteristics of and trace the origins of water pollutants. In this study, an artificial intelligence system called the integrated long short-term memory network (LSTM), using cross-correlation and association rules (Apriori), was used to identify the characteristics of water pollutants and trace industrial point sources of pollutants. Water quality monitoring data from Shandong Province, China, were used to verify the applicability of the artificial intelligence system using a cross-correlation method to develop a water quality cross-correlation map. The map was used to identify highly correlated pollutants affecting water quality, then the association rules (Apriori) were used to track the pollutants to industries common in the study area. The highly correlated water pollutants and relevant industries were used as inputs for the LSTM to determine how well the LSTM traced sources of water pollutants. The results showed that (1) changes in water quality were affected in different ways by different industries and different distributions and production cycles of the pollutant point sources; (2) water quality correlation maps can be used to identify regular and abnormal fluctuations in point source pollutant emissions by identifying changes in water quality characteristics and frequent itemsets in water quality indices can be used to trace the industries that most strongly affect water quality; and (3) the LSTM accurately traced point sources of future changes in water quality. In conclusion, the artificial intelligence scheme described here can be applied to aquatic systems.