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Water depletion and pollution caused by industrialization have become major environmental concerns worldwide, which are further complicated by the urbanization and globalization. China has become an industrial powerhouse for the world, and its industrial activities-induced water pressure is no longer negligible. This study investigates China's industrial water pressure from final consumption and international export based on water footprint (WF) and dynamic structure decomposition analysis (SDA). The industrial blue and gray WFs with respect to rural consumption, urban consumption, and export are accounted for 2002–2015, and an advanced dynamic SDA model is developed and applied to examining the evolving WFs with 9 socio-economic drivers quantified. Results show that China's water resources pressure is intensified due to the increase of total industrial blue WF (by 47.4%) for 2002–2015, whereas a mitigation of water environment pressure is suggested by the decreasing gray virtual water outflow and total internal gray WF (by 31.9% and 36.4%, respectively). The SDA demonstrates that export scale and urban consumption level are two major drivers of water situation deterioration, which accumulatively contribute 38.3% and 33.0% to the evolving blue and gray WFs, respectively. In contrast, technology development is the dominant contributor alleviating China's water pressure, with absolute contribution proportions of 38.9% and 56.5%, correspondingly. Based on these, export regulation, consumption pattern adjustment and technological innovation are recommended for water policy making. The methodology used in this study can be extended to diagnosing the water issues in other nations/regions as well.

Urban flooding is increasingly pervasive, with the decreasing water area in cities and the dreadful impact on people. Therefore, it is necessary to carry out a quantitative evaluation of the area of urban water and analyze the threshold of the reasonable water ratio under different drainage scenarios. In this research, simulations were performed by using the Personal Computer Storm Water Management Model (PCSWMM), and the model parameter calibration method was based on the runoff coefficient as a contribution to meeting the data-poor urban areas. Then, urban flood control standards were divided into four classes, and the rainfall scenario once in 200 years and historical maximum were selected to build future extreme inundation scenarios in the case study. Simultaneously, methods for the reasonable water area ratio estimation were decided. The cost-effectiveness of all possible solutions was examined across the two scenarios based on the assessment method. The results show that the range of reasonable ratios of the water area under traditional planning is 5.78–6.02% to meet the standard of urban drainage. Similarly, the range of reasonable ratios of water area under the LID (low impact development) plan combined with the sponge city concept is 5.63–5.88%.

River algal blooms have become a challenging environmental problem worldwide due to strong interference of human activities and megaprojects (e.g., big dams and large-scale water transfer projects). Previous studies on algal blooms were mainly focused on relatively static water bodies (i.e., lakes and reservoirs), but less on the large rivers. As the largest tributary of the Yangtze River of China and the main freshwater source of the South-to-North Water Diversion Project (SNWDP), the Han River has experienced frequent algal blooms in recent decades. Here we investigated the algal blooms during a decade (2003–2014) in the Han River by two gradient boosting machine (GBM) models with k-fold cross validation, which used explanatory variables from current 10-day (GBMc model) or previous 10-day period (GBMp model). Our results advocate the use of GBMp due to its higher accuracy (median Kappa = 0.9) and practical predictability (using antecedent observations) compared to GBMc. We also revealed that the algal blooms in the Han River were significantly modulated by antecedent water levels in the Han River and the Yangtze River and water level variation in the Han River, whereas the nutrient concentrations in the Han River were usually above thresholds and not limiting algal blooms. This machine-learning-based study potentially provides scientific guidance for preemptive warning and risk management of river algal blooms through comprehensive regulation of water levels during the dry season by making use of water conservancy measures in large rivers.

River algal blooms have become a complex and challenging environmental issue due to the impacts of human activities and inter-basin water transfer project. As the water source of the Middle Route Project (MRP) of South-to-North Water Diversion Project (SNWDP), the Hanjiang River (HR) has experienced frequent algal blooms and this caused potential risk to the drinking water safety of residents along the river. Here, an integrated river algal bloom model was developed to simulate river algal blooms and analyze the effects of MRP of SNWDP on algal bloom occurrence patterns in the middle-lower HR. The integrated river algal bloom model considered multiple physical-chemical-biological processes, which include the hydrological cycle of basin, the interactive mechanisms of river hydrodynamics and the improved algal dynamics process by adding a hydrology factor. The results show that the relative error between simulated and observed algae density at the Qin Duankou (QDK) and Zongguan (ZG) sections are 17.1% and 18.5% during the calibration period and are 18.3% and 19.7% during the validation period, indicating that the proposed integrated algal bloom model met the basic requirements for simulating river algal blooms. Additionally, our analysis indicates that the occurrence of algal bloom events in the middle-lower HR will increase by about two-fold under the water transfer scenario of 9.5 billion m3 year–1 and 2.5-fold under the scenario of 13 billion m3 year–1 at the QDK section. This study potentially provides scientific tool for local governments to carry out optimal scheduling of water conservancy projects for the protection of water ecological environment.

Crop production with intensive water use is burdening global water resources system. From the perspective of production, this study focuses on investigating world's water challenge from crop cultivation based on a 26-yr multi-scale analysis. Blue water footprint (BWF) dynamics covering 146 crops over 180 countries are examined during 1992–2017, applying a fast-track based BWF assessment and a modified dynamic decomposition analysis (DDA). Results show that global crop BWF has increased by 10.8% during 1992–2017 with increased growth rate after 2002, indicating rising water pressure from crop farming. About three-fifths of world's regions/nations have grown crop BWFs, especially in Southeast Asia, Oceania, and North-Central-West Africa (grown by over 30% each). Absolute changing rates of national crop BWFs trend to increase along with the decrease of population and BWF scales, with developing countries presenting large crop BWF increases, especially India, Pakistan, and Egypt (> 9 billion m3 each). The crop BWF growths come mainly from the farming of fruits, cereals, and sugar crops, with contribution proportions of 32.4%, 19.6%, and 12.8%, respectively. Decomposition results show that world's growing crop BWF is dominantly driven by population (27.5%) and yield-improving technology (24.8%) while water-saving technology (-24.8%) and proportion of rural population (-18.9%) contribute to offsetting the BWF growth. Efforts towards water sustainable cropping should be made in technological development and planting structure adjustment, especially in those countries with high or largely grown BWFs. Based on these, we expect to provide useful and informative findings for building a better water conservation system on earth.

Author(s): Yang, Z; Hsu, K; Sorooshian, S; Xu, X; Braithwaite, D; Zhang, Y; Verbist, KMJ | Abstract: With high spatial-temporal resolution, Satellite-based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground-based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high-quality precipitation estimates. An inverse-root-mean-square-error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) estimates (daily, 0.04° × 0.04°) over Chile, for the 6 year period of 2009-2014. Daily observations from about 90% of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground “truth” for validation. Evaluation results indicate high effectiveness of the model in producing high-resolution-precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root-mean-square errors, and absolute mean biases that were consistently improved from the original PERSIANN-CCS estimates. The cross-validation evidences that the framework is effective in providing high-quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates.

With rapid economic development and population growth, pressure on the urban ecological environment is becoming intense, and the ecological environment carrying capacity is becoming an important issue. In this study, an index system combining the system dynamics (SD) and analytic hierarchy process (AHP) methods was constructed to assess the water ecological carrying capacity (WECC) of Changzhou city. In particular, the construction of the aquatic habitat and aquatic organism indexes were constructed and showed that water ecology health should be monitored along with the economy and population, which were focused in the past. Scientific research has neglected the internal spatial heterogeneity in administrative regions. Therefore, the SD and AHP models were applied to simulate the WECC of administrative regions and control units from 2010 to 2016. The simulation results show that the WECC of Liyang and Jintan counties reached a safe carrying capacity state, while the urban districts were in a critically overloaded state. In addition, the MIKE11 model was adopted to simulate the WECC under different inflow and the river connectivity index scenarios, and the optimal parameters for improving the WECC were obtained when the inflow recharge was 10 m3/s and the river connectivity index exceeded 60%. The prediction results from the optimized WECC showed that the WECC of all administrative areas and control units in Changzhou city reached the safe carrying capacity state. The results provide insights for improving the WECC of Changzhou city.

Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide.

China has experienced rapid economic development during the recent years. In the meantime, the total water use in China has greatly increased, causing considerably severe water shortage. To quantify how the social-economic development drives the change of water use is of great importance for Chinese government. This study aims to quantitatively investigate the determinants of the growing water use in China during 1997–2007. China's water usages in 1997–2007 are indicated by the Internal Water Footprint (IWF), External Water Footprint (EWF), and Exported Virtual Water (ExVW) using an input–output based WF analysis. A dynamic Structural Decomposition Analysis (SDA) model is employed to decompose the changes in the total WFs (TWF) into five social-economic determinants. The dynamic SDA model, which uses a nonlinear path function to simulate the real paths followed by determinants, is a further development of a path-based SDA algorithm. To validate the dynamic SDA, a comparison study is carried out against the commonly used mean decomposition model. The results from the WF analysis show that, during 1997–2007, the increase of China's water use is dominated by the ExVW, which increases significantly due to the expansion of exports. While the changes in the IWF and EWF tend to help mitigating the water pressure. The dynamic decomposition results indicate that the consumption level is the dominant factor of China's water use growth, and the changes in water-saving technology and final demand pattern contribute largely to offsetting the water use growth. The model comparison shows that the dynamic SDA model is more advanced in obtaining reliable results and addressing the static issue of traditional SDA models. This study provides a robust framework for understanding the water use situation from the social-economic perspectives, which benefits sustainable water management.

Recently, the skilful prediction of climate change has drawn high attention from the scientific community. Evidence has been reported the skill of prediction is not satisfactory for the magnitude of inter-annual precipitation and extreme precipitation, and at a smaller spatial scale as well. Based on observational data sets and outputs from the Global Climate Models (GCMs), this study aims at achieving a mathematical model, named multi-GCM divide-integration model (MGDI). The MGDI model is developed by hybridizing finer spatial scale and multi-linear regression model (MLRM) on five state-of-art of GCMs to improve the skills of five GCMs, which is applied to the second level of water resources regionalization in China. It is found that the performance after MGDI model correction has been improved significantly over that of individual GCMs. The errors between observation and simulation after correction (1.6 % ~ 4.4%) are within the margin of error (smaller than 5 %) and all of the varying trends in each second level of water resources regionalization were same. Furthermore, this study also used the MGDI model to predict the variation of precipitation and droughts at different spatial scale, including second level of water resources regionalization of China and the whole HHB, for the next 40 years. Predictions indicate the climate will gradually change from drying to wetting over the HHB wherein the trend of annual rainfall is 9.3 mm/10 a. The frequency of drought events will be decreasing as time goes on. The occurrence of mild and severe drought in the Luan River and Jidong Coastal, Tuhai majia River are higher than that in other regions, 9 and 8 respectively. These findings would provide scientific support for current water resources management and future drought-resisting planning of districts in China.

Secondary salinisation has become a hot spot internationally due to its adverse effects on freshwater ecosystems. Although its effects on ecosystem patterns has been broadly studied, its potential effect on ecosystem functions, in particular on the functional traits of freshwater organisms, and functional trait recovery are largely unknown. In this study, we conducted a field investigation at 405 sample sites from May 2009 to July 2016 in surface mining-contaminated streams, in order to evaluate the influence of secondary salinisation on macroinvertebrate functional traits and the recovery potential of dominant functional traits. Results of univariate models showed that sensitive, very tolerant, gill-breathers, cutaneous-breathers, shredders, predators and gatherers were the most responsive indicators to enhanced specific conductivity and sulfate loadings with sensitive, gill-breathers, shredders and predators demonstrating a reduction in abundance, whereas cutaneous-breathers and gatherers exhibiting an increase. Complicated relationships among different species indicated that co-exclusions would not occur because all macroinvertebrate taxa exhibited positive correlations. Results of relative recovery potential showed that omnivores and gatherers recovered quickly following improvements in water quality, whereas gill-breathers, pneumostome-breathers, filterers and scrapers would be expected to recover slowly due to their sensitivity to both specific conductivity and sulfate and low drift propensity. Overall, secondary salinisation has posed severely ecological risks to macroinvertebrate functional attributes in surface mining-contaminated streams, and their effects should be considered in future conservation plans.

With the effects of human activities and climate change, river algal blooms have frequently occurred worldwide. Compared with eutrophication issues in lakes and reservoirs, the characteristic identification and cause analysis of river algal blooms have been rarely studied, and the cause of river algal bloom remains an academic difficulty. This study aimed to analyze the characteristics and causes of algal blooms in a large river system in China, the Hanjiang River, from 2004 to 2014 by applying various statistical approaches. Statistical analyses included seasonal trend decomposition procedure based on Loess, cross wavelet analysis, Spearman analysis, and redundancy analysis. Results showed the following. (1) The high risk period of algal bloom in Hanjiang River occurred from mid-February to early-March, and the peak value tended to move forward from early-February to mid-February after 2009. (2) The water level of Yangtze River, total P, and water temperature were main contribution factors for river algal blooms downstream of Hanjiang River in China, contributing 44.5%, 39.6%, and 11.4%, respectively. (3) Hydrologic factors played important roles for the occurrences of river algal blooms when a suitable hydrodynamic condition existed between water flow of Hanjiang River and water level of Yangtze River. Finally, the key factors leading to river algal blooms are discussed, and further studies are suggested to ascertain the influence of complex hydrodynamic conditions in order to reveal the cause of river algal blooms.

River algal blooms have become a newly emerging global environmental issue in recent decades. Compared with water eutrophication in lakes and reservoirs, algal blooms in large river systems can cause more severe consequences to watershed ecosystems at the watershed scale. However, reveal the causes of river algal blooms remains challenging in the interdisciplinary of hydrological-ecological-environmental research, due to its complex interaction mechanisms impacted by multiple factors. In addition, there were still considerable uncertainties on the characteristics, impacts, driving factors, as well as the applicable water system models for river algal blooms. In this paper, we reviewed existing literature to elaborate the definition and negative effects of river algal blooms. We analyzed sensitive factors including nutrient, hydrological and climatic elements. We also discussed the application of ecohydrological models under complicated hydrological conditions. Finally, we explored the essence of the river algal bloom by the interaction effects of physical and biogeochemical process impacted by of climate change and human activities. The model-data integration accounting for multi-factor effects was expected to provide scientific guidance for the prevent and control of algal blooms in large river systems.