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.

Rapid population growth, urbanization, and diverse production materials have led to a surge in municipal solid waste (MSW), harming the environment, climate, and sanitation. Despite China’s efforts to implement MSW segregation policies, results have been unsatisfactory. Research often emphasizes public participation, with less focus on the entire policy implementation process. This paper employed Smith’s theoretical model to comprehensively analyze the challenges of implementing a MSW segregation policy, using qualitative methods and secondary data from literature and policy documents. The findings revealed challenges at four levels: policy formulation, implementing agencies, target groups, and the external environment. Issues include policy ambiguity, unclear authority, government-enterprise cross-functionality, casual attitudes, implementer shortages, poor public participation, and economic disparities. To address these, the study recommends enhancing public engagement, clarifying responsibilities among agencies, and increasing financial support for disadvantaged areas to improve policy implementation.

HIGHLIGHTS. This study contrasts the impacts from multiyear with daily regulation hydropower stations on hydrological regimes.The supplementary daily regul