Your search found 10 records
1 Yamamoto, T.; Naruoka, M.; Ito, S.; Yang, Z; Zhang, J. 1993. Irrigation schedules and conservation management for a pilot farm in the Mu Us Shamo Desert: Control of desertification and development of agriculture in arid land areas in China. Journal of Irrigation Engineering and Rural Planning, 25:4-15.
(Location: IWMI-HQ Call no: PER Record No: H013428)
As part of the joint research conducted by Japan and China on the agricultural development of the Mu Us Shamo Desert, surveys on soil physical properties, moisture consumption, and the irrigation effect have been carried out on several plants in the fields of the Mu US Shamo Research Center since 1985. Using these results, schedules were established for the irrigation of a pilot farm which was constructed at the Research Center in 1991. The irrigation schedules were mainly based on the design guidelines of the Ministry of Agriculture, Forestry and Fisheries of Japan. As a result, the dimensions for the irrigation interval and water quantity per irrigation unit were estimated under the various plants in the pilot farm. From these design dimensions and the daily rainfall measured during the past 27 years, the net water requirement was estimated considering the effective rainfall. Also, the characteristics of the irrigation and rainfall could be explained by discussing the ratio of the total net water requirement to the total evapotranspiration, which subsequently indicates the factors in the pilot farm's future water management. Finally, in order to prevent the salinization of soils and groundwater and to select suitable irrigation methods, some recommendations are given for better use of technology in conservation management for the pilot farm.
2 Liu, S.; Cai, J.; Yang, Z.. 2003. Migrants’ access to land in Periurban Beijing. Urban Agriculture Magazine, 11:6-8.
Land ownership ; Farming ; Land use ; Households ; Social aspects ; Migrant labor ; Villages / China / Beijing
(Location: IWMI-HQ Call no: P 6724 Record No: H033975)
3 Xia, X.; Yang, Z.; Huang, G. H.; Maqsood, I. 2004. Integrated evaluation of water quality and quantity of the Yellow River. Water International, 29(4):423-431.
(Location: IWMI-HQ Call no: PER Record No: H036709)
4 Li, C.; Yang, Z.; Wang, X. 2004. Trends of annual natural runoff in the Yellow River Basin. Water International, 29(4):447-454.
(Location: IWMI-HQ Call no: PER Record No: H036712)
5 Khangaonkar, T.; Yang, Z.; DeGasperi, C.; Marshall, K. 2005. Modeling hydrothermal response of a reservoir to modifications at a high-head dam. Water International, 30(3):378-388.
(Location: IWMI-HQ Call no: PER Record No: H038410)
6 Cohen, W. B.; Maiersperger, T. K.; Yang, Z.; Gower, S. T.; Turner, D. P.; Ritts, W. D.; Berterretche, M.; Running, S. W. 2003. Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: A quality assessment of 2000/2001 provisional MODIS products. Remote Sensing of Environment, 88:233-255.
(Location: IWMI-HQ Call no: P 7625 Record No: H039332)
7 Yang, Z.; Bai, J.; Zhang, W. 2021. Mapping and assessment of wetland conditions by using remote sensing images and POI data. Ecological Indicators, 127:107485. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2021.107485]
Wetlands ; Mapping ; Assessment ; Remote sensing ; Water resources ; Water quality ; Vegetation ; Ecological indicators ; Landsat / China / Suzhou
(Location: IWMI HQ Call no: e-copy only Record No: H050366)
https://www.sciencedirect.com/science/article/pii/S1470160X21001503/pdfft?md5=57aabe38ec6376b9d2daeb9e7191bd00&pid=1-s2.0-S1470160X21001503-main.pdf
https://vlibrary.iwmi.org/pdf/H050366.pdf
https://vlibrary.iwmi.org/pdf/H050366.pdf
(9.82 MB) (9.82 MB)
Wetlands are one of the most valuable natural resources on earth and play an important role in preserving biodiversity. However, due to economic development and human disturbances, many wetlands across the world have deteriorated and disappeared over the past several decades. By using remote sensing images and point of interest (POI) data, we proposed a knowledge-based raster mapping (KBRM)-based framework and implemented it in the assessment of wetland ecological conditions in Suzhou, China. Density maps of waterbodies, vegetation covers, imperviousness, roads, and POI values were derived and used as five ecological indicators that can represent the ecological conditions of wetlands. The KBRM approach was used to integrate these indicators into an overall rating and map wetland ecological conditions efficiently. Thus, spatial variations in wetland ecological conditions can be distinguished and represented in detail. Cross validation was conducted with water quality data at 15 field sampling sites. The validation results demonstrated that the overall wetland condition scores generated by our approach and the water quality index (WQI) values calculated from water quality data were strongly correlated. These findings confirm that our framework could be used to effectively map and evaluate spatial variations in wetland ecological conditions and provide more support for policy-making in wetland protection and management
8 Sun, J.; Hu, L.; Li, D.; Sun, K.; Yang, Z.. 2022. Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management. Journal of Hydrology, 608: 127630. [doi: https://doi.org/10.1016/j.jhydrol.2022.127630]
Groundwater management ; Groundwater table ; Forecasting ; Water supply ; Rivers ; Aquifers ; Precipitation ; Neural networks ; Models / China / Beijing
(Location: IWMI HQ Call no: e-copy only Record No: H051102)
(5.75 MB)
The overexploitation of groundwater resource and its delicacy management has gained increasing attentions in recent years worldwide because of causing a series of serious environmental and geological problems. Currently, accurately predicting the groundwater level (GWL) is an important issue in effective groundwater management across scales. In the present study, three popularly-used data-driven models, which are an autoregressive integrated moving average (ARIMA), a back-propagation artificial neural network (BP-ANN) and long short-term memory (LSTM), were established in five zones with different hydrogeological properties to explore the model’s accuracy in predicting the GWL at monthly and daily scales in a Northern Plain in China. The developed models were evaluated by both the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results indicate that the performance of the LSTM model is best at monthly time scales with the NSEs greater than 0.76 and RMSEs smaller than 1.15 m in each zone during the training period and demonstrate a good performance at daily time scales with the NSEs greater than 0.9 and the RMSEs smaller than 0.55 m at a local area. Meanwhile, the tempo-spatial distribution of the probability of drawdowns from the LSTM model was estimated by using the object-oriented spatial statistical (O2S2) method. The results show that cumulative drawdowns greater than 10 m are mainly concentrated in water source areas, with probabilities over 0.7 from 2003 to 2010 and declining to less than 0.3 from 2011 to 2014. The GWL rose generally in the study area from 2015 to 2018, but the probability of a drawdown with more than 5 m exceeded 0.8 in Zone V because of continuing groundwater exploitation. This study formulates a framework on developing effective data-driven models for predicting the GWL across scales which have the potential to aid groundwater management.
9 Meng, F.; Yuan, Q.; Bellezoni, R. A.; de Oliveira, J. A. P.; Hu, Y.; Jing, R.; Liu, G.; Yang, Z.; Seto, K. C. 2023. The food-water-energy nexus and green roofs in Sao Jose Dos Campos, Brazil, and Johannesburg, South Africa. npj Urban Sustainability, 3:12. [doi: https://doi.org/10.1038/s42949-023-00091-3]
Energy consumption ; Energy demand ; Water conservation ; Food security ; Food production ; Nexus approaches ; Sustainability ; Rainwater harvesting ; Environmental impact ; Ecological footprint ; Urban areas ; Carbon footprint ; Water footprint ; Transboundary waters ; Infrastructure / Brazil / South Africa / Sao Jose dos Campos / Johannesburg
(Location: IWMI HQ Call no: e-copy only Record No: H051940)
https://www.nature.com/articles/s42949-023-00091-3.pdf?pdf=button%20sticky
https://vlibrary.iwmi.org/pdf/H051940.pdf
https://vlibrary.iwmi.org/pdf/H051940.pdf
(2.52 MB) (2.52 MB)
Green roofs affect the urban food-water-energy nexus and have the potential to contribute to sustainability. Here we developed a generalizable methodology and framework for data-sparse cities to analyze the food-water-energy nexus of green roofs. Our framework integrates the environmental costs and benefits of green roofs with food-water-energy systems and makes it possible to trace energy-water-carbon footprints across city boundaries. Testing the framework in São José dos Campos (SJC), Brazil and Johannesburg, South Africa, we found that green roofs are essentially carbon neutral and net energy consumers from a life cycle perspective. SJC is a net water beneficiary while Johannesburg is a net water consumer. Rainwater utilization could save irrigated water, but requires 1.2 times more energy consumption. Our results show that SJC and Johannesburg could direct their green roof development from local food production and energy saving, respectively and highlight opportunities for green roof practices in cities.
10 Lin, J.; Bryan, B. A.; Zhou, X.; Lin, P.; Do, H. X.; Gao, L.; Gu, X.; Liu, Z.; Wan, L.; Tong, S.; Huang, J.; Wang, Q.; Zhang, Y.; Gao, H.; Yin, J.; Chen, Z.; Duan, W.; Xie, Z.; Cui, T.; Liu, J.; Li, M.; Li, X.; Xu, Z.; Guo, F.; Shu, L.; Li, B.; Zhang, J.; Zhang, P.; Fan, B.; Wang, Y.; Zhang, Y.; Huang, J.; Li, X.; Cai, Y.; Yang, Z.. 2023. Making China’s water data accessible, usable and shareable. Nature Water, 1:328-335. [doi: https://doi.org/10.1038/s44221-023-00039-y]
Water resources ; Data collection ; Databases ; Monitoring ; Modelling ; Water quality ; Wastewater treatment ; Stream flow ; Transboundary waters ; Water demand ; Infrastructure ; Policies / China
(Location: IWMI HQ Call no: e-copy only Record No: H052133)
(1.42 MB)
Water data are essential for monitoring, managing, modelling and projecting water resources. Yet despite such data—including water quantity, quality, demand and ecology—being extensively collected in China, it remains difficult to access, use and share them. These challenges have led to poor data quality, duplication of effort and wasting of resources, limiting their utility for supporting decision-making in water resources policy and management. In this Perspective we discuss the current state of China’s water data collection, governance and sharing, the barriers to open-access water data and its impacts, and outline a path to establishing a national water data infrastructure to reform water resource management in China and support global water-data sharing initiatives.
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