Exploring China's water scarcity incorporating surface water quality and multiple existing solutions.

Water scarcity has threatened the sustainability of human life, ecosystem evolution, and socio-economic development. However, previous studies have often lacked a comprehensive consideration of the impact of water quality and existing solutions, such as inter-basin water transfer and unconventional water resources, on water scarcity. In this paper, an improved approach was proposed to quantify water scarcity levels by comprehensively considering surface water quality and multiple solutions. China's water scarcity was first assessed at a high spatial resolution on a monthly basis over the 5-year period from 2014 to 2018. Then, the driving factors including water quality and solutions were identified by a geographic detector model. Finally, an in-depth investigation was conducted to unravel the effects of water quantity solutions (i.e., inter-basin water transfer and unconventional water use), and water quality solutions (i.e., improving surface water quality) on alleviating water scarcity. Based on monthly assessments considering water quality and multiple existing solutions, the results showed that over half of the national population (∼777 million) faced water scarcity for at least one month of the year. Agricultural water use and inadequate water quality were the main driving factors responsible for China's water scarcity. Over four-fifths of the national population (∼1.10 billion) could benefit from alleviated water scarcity through a combination of water quantity and quality solutions. However, the existing solutions considered were insufficient to completely resolve water scarcity in China, especially in Northern China, persisting as a challenging issue. The results obtained from this study provided a better understanding of China's water scarcity, which could contribute to guiding future efforts aimed at alleviating water scarcity and ensuring water security in China.

Investigation of controls on the regional soil moisture spatiotemporal patterns across different climate zones.

Understanding soil moisture spatiotemporal variability at regional scales is of great importance for studying various ecohydrological and land surface processes. In this study, long-term soil moisture data (5 years) were obtained from three regional monitoring networks across the continental United States with contrasting climatic conditions, including the Enviro-weather Automated Weather Station Network in Michigan, the Nebraska Mesonet, and the Soil Climate Analysis Network in Utah. Both soil moisture spatial variance and temporal variance were decomposed into time-invariant and time-variant components. To evaluate the impacts of different environmental factors on soil moisture spatiotemporal variability and its contribution components, static (e.g., soil properties) and non-static (e.g., climatic variables) environmental factors were also compiled for the stations of each network. The results showed that the time-invariant component was the leading factor for controlling the soil moisture spatial variance in all study regions with marked seasonal variations due to changes in soil moisture wetness conditions. More importantly, the soil moisture spatial variance and its contribution components (in absolute values and relative contributions) were shown to be affected by both soil properties (e.g., soil texture) and climatic conditions (e.g., precipitation) with varying degrees of impacts among the study regions. Meanwhile, the results further revealed that depending on the region under consideration, static and non-static environmental factors could play important roles in determining soil moisture temporal dynamics and its contribution components at regional scales. Overall, this study provided additional observational evidence, which underscored the importance of local factors (e.g., soil properties) in determining soil moisture spatiotemporal variability at regional scales.

Nitrate sources and formation of rainwater constrained by dual isotopes in Southeast Asia: Example from Singapore.

Emission of reactive nitrogen species has a major impact on atmospheric chemistry, ecosystem and human health. The origin and formation mechanisms of wet-deposited nitrate are not well understood in Southeast Asia (SEA). In this study, we measured stable isotopes of nitrate (δ15N and δ18O) and chemical compositions of daily rainwater from May 2015 to July 2017 in Singapore. Our results showed that δ15N-NO3- and δ18O-NO3- varied seasonally with higher values during the Inter-monsoon period (April-May and October-November) than during Northeast (December-March) and Southwest monsoon (June-September). Bayesian mixing modeling, which took account of the isotope fractionation, indicated that traffic emission (47 ±â€¯32%) and lightning (19 ±â€¯20%) contributed the most to NO3- with increased traffic contribution (55 ±â€¯37%) in the Northeast monsoon and lightning (24 ±â€¯23%) during the Inter-monsoon period. Biomass burning and coal combustion, likely from transboundary transport, contributed ∼25% of nitrate in the rainwater. Monte Carlo simulation of δ18O-NO3- indicated that oxidation process by hydroxyl radical contributed 65 ±â€¯14% of NO3-, with the rest from hydrolysis of N2O5. Wind speed had large effect on δ18O-NO3- variations in the atmosphere with more involvement of hydroxyl radical reactions when wind speed increased. Our study highlights the key role of isotopic fractionation in nitrate source apportionment, and the influence of meteorological conditions on nitrate formation processes in SEA.

A four-stage hybrid model for hydrological time series forecasting.

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.