Streamflow forecasting is a crucial component in the management and control of water resources. Decomposition-based approaches have particularly demonstrated improved forecasting performance. However, direct decomposition of entire streamflow data with calibration and validation subsets is not practical for signal component prediction. This impracticality is due to the fact that the calibration process uses some validation information that is not available in practical streamflow forecasting. Unfortunately, independent decomposition of calibration and validation sets leads to undesirable boundary effects and less accurate forecasting. To alleviate such boundary effects and improve the forecasting performance in basins lacking meteorological observations, we propose a two-stage decomposition prediction (TSDP) framework. We realize this framework using variational mode decomposition (VMD) and support vector regression (SVR) and refer to this realization as VMD-SVR. We demonstrate experimentally the effectiveness, efficiency and accuracy of the TSDP framework and its VMD-SVR realization in terms of the boundary effect reduction, computational cost, and overfitting, in addition to decomposition and forecasting outcomes for different lead times. Specifically, four comparative experiments were conducted based on the ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA), discrete wavelet transform (DWT), boundary-corrected maximal overlap discrete wavelet transform (BCMODWT), autoregressive integrated moving average (ARIMA), SVR, backpropagation neural network (BPNN) and long short-term memory (LSTM). The TSDP framework was also compared with the wavelet data-driven forecasting framework (WDDFF). Results of experiments on monthly runoff data collected from three stations at the Wei River show the superiority of the VMD-SVR model compared to benchmark models.

In the practice of watershed ecological compensation, due to the inconsistency between the interests of economic development and the objectives of ecological protection, there are typical game characteristics among the relevant stakeholders. Taking the ecological compensation between Henan and Shaanxi in the Yellow River Basin as an example, this paper constructs an evolutionary game model, obtains the external conditions for various stakeholders to achieve stable cooperation from the perspective of the government, and demonstrates the necessity of combining vertical and horizontal ecological compensation. The sensitivity of each party’s decision making to key elements is analyzed through a simulation. The results show that: (1) the optimal strategy is mainly affected by the initial willingness of the upstream government and the central government; the strong regulatory power of the central government can eliminate the influence of the initial will; (2) development opportunity costs and vertical fiscal transfer payments have the most obvious influence on upstream government decision-making; (3) the effect on optimal decision state of downstream paying upstream ecological compensation is higher than that of upstream paying downstream ecological compensation; (4) the punishment of the central government should ensure the binding force on the lower governments, and the revenue and expenditure under its supervision strategy should ensure the effectiveness of the supervision public power. The above conclusions provide support for improving the ecological compensation mechanism of transboundary basins.

A complex interwoven relationship exists between water resources and urbanization, which is of much interest in international water science research. To study the urban development in large cities facing water deficiency problems, it is important to identify rational thresholds of urbanization to achieve optimal utilization of limited water resources, and to promote sustainable economic and population growth and social development. A multi-objective optimization model is proposed to search for the optimal scale of urbanization of large cities with limited water resources. To solve a large-dimensional multi-objective optimization problem, the non-dominated sorting genetic algorithm (NSGA-II) is improved to the OENSGA-II based on the orthogonal generation method and the E-optimality method and applied to a typical arid city, Xi’an of China, which underwent rapid urbanization in recent years. For Xi’an, a statistically significant positive correlation is found between urbanization rate (U_r) and gross domestic product (GDP), domestic water, tertiary industry water, and ecological water. However, U_r is negatively correlated with the primary industry water. If the current urbanization trend continues, the water resources available are far from sufficient to support the future city of Xi’an. In this work, it was found that, by implementing restrictive water resources management and water saving measures, the economic threshold of Xi’an could be raised to 1890.3 and 2403.3 billion yuan, while the population threshold could be raised to 11.0 and 13.9 million, and U_r to 79.9% and 85.9% in 2020 and 2030, respectively. The corresponding maximum urban area to be constructed based on the projected population will be 964.81 and 1197.6 km² in those years. It will be prudent for Xi’an to practice strict water resource management, and to allocate its water resources among various water sectors effectively and equitably to avoid major water shortage problems in the future.