Your search found 6 records
1 Shen, D.; Wu, J. 2003. Mountain-River-Lake Integrated Water Resources Development Program, Jiangxi, China. In ADB, Water and poverty – A collection of case studies: Experiences from the Field. Manila, Philippines: ADB. pp.128-137.
Water resources development ; Rivers ; Watersheds ; Mountains ; Development projects ; Credit ; Poverty ; Farmers ; Financial institutions ; Aid / China / Jiangxi
(Location: IWMI-HQ Call no: 333.91 G000 ADB Record No: H032552)
2 Shen, D.. 2004. The 2002 Water Law: Its impacts on river basin management in China. Water Policy, 6(4):345-364.
(Location: IWMI-HQ Call no: PER Record No: H035981)
3 Shen, D.. 2005. Water-related risk management in China: A legal, institutional, and regulatory overview. Water International, 30(3):329-338.
Water resource management ; Risks ; Irrigation management ; History ; Rivers ; Flooding ; Water law ; Institutions / China
(Location: IWMI-HQ Call no: PER Record No: H038404)
4 Liang, R.; Guowei, Y.; Shen, D.. (Comps.) 2002. International Symposium on Integrated Water Resources Management: Methodology and Practices for Northwest China, Global Water Partnership China Secretariat, Beijing, China, 5 May 2002. In Chinese and English. Beijing, China: Global Water Partnership (GWP). China Technical Advisory Committee. 202p.
Water management ; Participatory management ; Stakeholders ; Water users ; Poverty ; Water rights ; Water market ; Legal aspects ; Water policy / China
(Location: IWMI HQ Call no: 333.91 G592 LIA Record No: H044352)
(0.29 MB)
5 Shen, D.; Yu, X.; Shi, J. 2015. Introducing new mechanisms into water pricing reforms in China. In Dinar, A.; Pochat, V.; Albiac-Murillo, J. (Eds.). Water pricing experiences and innovations. Cham, Switzerland: Springer International Publishing. pp.343-358. (Global Issues in Water Policy Volume 9)
Water rates ; Pricing ; Reforms ; Economic value ; Water resources ; Water supply ; Water use ; Household consumption ; Urban areas ; Wastewater treatment ; Hydraulic engineering ; Development projects ; Environmental protection ; Case studies / China / Beijing / Shanxi
(Location: IWMI HQ Call no: e-copy SF Record No: H047130)
This chapter analyzes the water pricing structure, reform process, and case studies in China and presents a overall picture of pricing water resources and its services during the past 60 years, particularly after 1980. China now implements a comprehensive water pricing framework and develops it step by step. The water resources fee was introduced in the 1980s, and the wastewater treatment and collection fee was developed in the late 1990s. By the 2000s, a comprehensive system was developed. Two case studies, involving Beijing and Shanxi Province, are discussed, which demonstrate increasing tariff standards in both regions. In the future, China will continue struggling with its water sector’s increasing tariff levels in order to meet its multi-objective water pricing.
6 Zheng, Y.; Jing, X.; Lin, Y.; Shen, D.; Zhang, Y.; Yu, M.; Zhou, Y. 2024. Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring. Water Science and Technology, 89 (11):2894-2906. [doi: https://doi.org/10.2166/wst.2024.174]
(Location: IWMI HQ Call no: e-copy only Record No: H052879)
https://iwaponline.com/wst/article-pdf/89/11/2894/1436009/wst089112894.pdf
https://vlibrary.iwmi.org/pdf/H052879.pdf
https://vlibrary.iwmi.org/pdf/H052879.pdf
(1.00 MB) (1.00 MB)
With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.
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