For areas that are urbanized rapidly, the practice of low impact development (LID) has gained an important place in stormwater management and urban planning due to its capability and beneficial effects in restoring the original hydrological cycle. The performances of LID alternatives can vary substantially due to different climate conditions. This study investigated the performances of five LID alternatives under a semi-arid climate in northern China on water balance and flood control. A numerical model, Storm Water Management Model version 5 (US Environmental Protection Agency), was employed to run 10 years' rainfall events for these objectives. Two evaluation methods were proposed in this study: the efficiency index for water balance and a performance radar chart. The investigation of the five LID alternatives revealed that these LID alternatives functioned differently in flood control and water balance, and porous pavement performed best in all indices except the lag time. The two evaluation methods, in conjunction with the long-term numerical simulation, can facilitate design and decision making by providing a clear picture of the performance and functions for these LID alternatives.

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The authors propose an improved decision-based detail-preserving variational method (DPVM) for removal of random-valued impulse noise. In the denoising scheme, adaptive centre weighted median filter (ACWMF) is first ameliorated by employing the variable window technique to improve its detection ability in highly corrupted images. Based on the improved ACWMF, a fast iteration strategy is used to classify the noise candidates and label them with different noise marks. Then, all the noise candidates are restored one-time by weight-adjustable detail-preserving variational method. The weights between the data-fidelity term and the smooth regularisation term of the convex cost-function in DPVM are decided by the noise marks. After minimisation, the restored image is obtained. Extensive simulation results show that the proposed method outperforms some existing algorithms, both in vision and quantitative measurements. Moreover, our method is faster than some decision-based DPVM. Therefore it can be ported into practical application easily.

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The pressures on water system are increasing in cities. Rapid urbanisation caused by booming population leads to more impervious area and less infiltration, with the consequence of larger runoff volume and higher flood risk. Launched in 2014, the low impact development (LID), an important part of Sponge City in China initiative, invests in projects that aim to restore the water cycle in the urban area. A comprehensive understanding of the performance of LID measures at watershed scale under different rainfall scenarios and life cycle costs is necessary. The objectives of this study are to assess the hydrological performance and to identify the optimal LID design by using SWMM model and life cycle cost (LCC) method. This study found that LID practices, including bioretention, grass swale, and permeable pavement, showed good performance on urban storm mitigation at watershed scale under different rainfall scenarios. Furthermore, the rates of surface runoff reduction were largely insusceptible to the change of rainfall volume and duration. Regarding the cost-effectiveness, the priority was grass swale > bioretention > permeable pavement in the study area. The optimal LID scenario was the combination of these three types of LID. The proposed approach can help the decision-makers to determine the preferable LID plan suitable for the local communities.

Panjiakou Reservoir is the main water supply source for Tianjin City, which has a population of over 14 million. In order to develop a watershed management strategic plan for source water protection, it is necessary to have reliable information on point source (PS) and non-point source pollution (NPS). The modelling approach has been frequently used in the study of partitioning of PS and NPS pollution on a basin scale. This study employed the Loading Simulation Program in C++ (LSPC) model to investigate the PS and NPS source pollution loadings to Panjiakou Reservoir. The hydrological model and the water quality model were developed and validated using field data from 2006 to 2010. It has been found that the PS pollution is still the major source for chemical oxygen demand (COD) loadings, accounting for about three-quarters of the total annual loadings to the reservoir; while near half of the total nitrogen (TN) and total phosphorus (TP) total annual loadings are from NPS pollution. There is a large seasonal variation for TN and TP loadings from NPS pollution. The contribution of TN and TP from NPS in the flooding seasons can reach 70%, whereas the contribution can also be as low as 4% during the dry season in the winter.

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