Your search found 4 records
1 Ren, H.; Fu, G.; He, X.; Ouyang, Z.; Yu, J.; Yuan, S. 2000. Water states and stress in China. In Wang, R.; Ren, H.; Ouyang, Z. (Eds.), China water vision: The eco-sphere of water, life, environment and development. Beijing, China: China Meteorological Press. pp.3-36.
Water resources ; Siltation ; Water demand ; Water use efficiency ; Irrigated farming ; Industrialization ; Water pollution ; Urbanization ; Ecology ; Natural disasters ; Flood water ; Drought / China
(Location: IWMI-HQ Call no: 333.91 G592 WAN Record No: H026833)
2 Ren, H.; Fu, G.; Yu, J.; Strzepek, K. 2000. China water vision in regions. In Wang, R.; Ren, H.; Ouyang, Z. (Eds.), China water vision: The eco-sphere of water, life, environment and development. Beijing, China: China Meteorological Press. pp.83-124.
Water resources ; Water use ; Water deficit ; Water demand ; Water supply ; Water conservation ; Watershed management ; Water quality ; River basins ; Water pollution ; Silt ; Drought ; Models ; Crop production ; Case studies / China / Yangtze Basin / Yellow River Basin
(Location: IWMI-HQ Call no: 333.91 G592 WAN Record No: H026836)
3 Hao, L.; Wang, P.; Gojenko, B.; Yu, J.; Lv, A.; Li, F.; Kenjabaev, Shavkat; Kulmatov, R.; Khikmatov, F. 2023. Five decades of freshwater salinization in the Amu Darya River Basin. Journal of Hydrology: Regional Studies, 47:101375. [doi: https://doi.org/10.1016/j.ejrh.2023.101375]
Freshwater ; Salinization ; River basins ; Salinity ; Climate change ; Agriculture ; Discharges ; Hydrology ; Spatial variations ; Seasonal variation ; Models / Central Asia / Amu Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051880)
https://www.sciencedirect.com/science/article/pii/S2214581823000629/pdfft?md5=4bff9fdaedc0eeba19a4acb52ba0321f&pid=1-s2.0-S2214581823000629-main.pdf
https://vlibrary.iwmi.org/pdf/H051880.pdf
https://vlibrary.iwmi.org/pdf/H051880.pdf
(8.41 MB) (8.41 MB)
Study region: The Amu Darya River (ADR) basin in Central Asia.
Study focus: To understand the spatiotemporal patterns and underlying driving mechanisms of river salinization in arid environments, this study gathered 50 years (1970–2019) of water chemistry data from 12 locations along the ADR. The variations in discharge and salinity were assessed by a linear regression model and violin plot. The salinity-discharge relationships were evaluated by a general hyperbolic model and Spearman’s rank correlation coefficient. Random forest models were also constructed to identify the predominant drivers of river water salinization. Finally, a conceptual model of river water salinization was constructed.
New hydrological insights for the region: The water salinity (S) in the upper stream of the ADR was 541–635 mg/L. Salinity showed an increasing trend along the river course, reaching 751–1560 mg/L downstream. In the downstream, the river salinity before the 1990 s (751–1128 mg/L) was slightly lower than that after the 1990 s (983–1560 mg/L). Generally, water salinity was notably correlated with river discharge (Q) in upstream, exhibiting a relationship of S= 17,497Q- 0.62, p < 0.05, before the 1990 s. Interannual variation in river salinity is mainly controlled by secondary salinization, and intra-annual variation is controlled by river flow. From upstream to downstream, the controlling salinization process changes from primary salinization to secondary salinization. Specifically, secondary salinization has accelerated due to intensified agricultural activities in recent years.
Study focus: To understand the spatiotemporal patterns and underlying driving mechanisms of river salinization in arid environments, this study gathered 50 years (1970–2019) of water chemistry data from 12 locations along the ADR. The variations in discharge and salinity were assessed by a linear regression model and violin plot. The salinity-discharge relationships were evaluated by a general hyperbolic model and Spearman’s rank correlation coefficient. Random forest models were also constructed to identify the predominant drivers of river water salinization. Finally, a conceptual model of river water salinization was constructed.
New hydrological insights for the region: The water salinity (S) in the upper stream of the ADR was 541–635 mg/L. Salinity showed an increasing trend along the river course, reaching 751–1560 mg/L downstream. In the downstream, the river salinity before the 1990 s (751–1128 mg/L) was slightly lower than that after the 1990 s (983–1560 mg/L). Generally, water salinity was notably correlated with river discharge (Q) in upstream, exhibiting a relationship of S= 17,497Q- 0.62, p < 0.05, before the 1990 s. Interannual variation in river salinity is mainly controlled by secondary salinization, and intra-annual variation is controlled by river flow. From upstream to downstream, the controlling salinization process changes from primary salinization to secondary salinization. Specifically, secondary salinization has accelerated due to intensified agricultural activities in recent years.
4 Zhou, G.; Li, Z.; Wang, W.; Wang, Q.; Yu, J.. 2024. Understanding the impact of population dynamics on water use utilizing multi-source big data. Journal of Hydroinformatics, jh2024179. (Online first) [doi: https://doi.org/10.2166/hydro.2024.179]
Water use ; Big data ; Water supply ; Water demand ; Sewage ; Towns ; Villages ; Water resources ; Water levels ; Wastewater treatment / China / Beijing / Haidian / Shangzhuang Town / Xibeiwang Town / Wenquan Town / Sujiatuo Town
(Location: IWMI HQ Call no: e-copy only Record No: H052622)
https://iwaponline.com/jh/article-pdf/doi/10.2166/hydro.2024.179/1356725/jh2024179.pdf
https://vlibrary.iwmi.org/pdf/H052622.pdf
https://vlibrary.iwmi.org/pdf/H052622.pdf
(1.42 MB) (1.42 MB)
Population movement, such as commuting, can affect water supply pressure and efficiency in modern cities. However, there is a gap in the research concerning the relationship between water use and population mobility, which is of great significance for urban water supply planning and supporting urban sustainable development. In this study, we analyzed the spatial–temporal dynamics of the population and its underlying mechanisms, using multi-source geospatial big data, including Baidu heat maps (BHMs), land use parcels, and point of interest. Combined with water consumption, sewage volume, and river depth data, the impact of population dynamics on water use was investigated. The results showed that there were obvious differences in population dynamics between weekdays and weekends with a ratio of 1.11 for the total population. Spatially, the population concentration was mainly observed in areas associated with enterprises, industries, shopping, and leisure activities during the daytime, while at nighttime, it primarily centered around residential areas. Moreover, the population showed a significant impact on water use, resulting in co-periods of 24 h and 7 days, and the water consumption as well as the wastewater production were observed to be proportional to the population density. This study can offer valuable implications for urban water resource allocation strategies.
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