Your search found 10 records
1 He, C.; Riggs, J. F.; Kang, Y. T. 1993. Integration of geographic information systems and a computer model to evaluate impacts of agricultural runoff on water quality. Water Resources Bulletin, 29(6):891-900.
(Location: IWMI-HQ Call no: PER Record No: H014501)
2 He, C.. 1997. Modeling hydrologic impact of withdrawing the Great Lakes water for agricultural irrigation. Journal of the American Water Resources Association, 33(5):1055-1068.
Irrigated farming ; Hydrology ; Water use ; Simulation models ; Evapotranspiration / USA / Great Lakes / Michigan / Mud Creek Irrigation District / Saginaw Bay Basin
(Location: IWMI-HQ Call no: PER Record No: H021914)
3 He, C.. 1999. Use of hydrologic budget and chemical data for ground-water assessment. Journal of Water Resources Planning and Management, 125(4):234-238.
Groundwater ; Hydrology ; Recharge ; Discharges ; Assessment ; Supplementary irrigation ; Evapotranspiration ; Watersheds ; Salinity ; Aquifers ; Case studies / USA / Saginaw Bay Basin / Michigan
(Location: IWMI-HQ Call no: PER Record No: H024563)
4 He, C.; Shi, C.; Yang, S. C.; Agosti, B. P. 2001. A windows-based GIS-AGNPS interface. Journal of the American Water Resources Association, 37(2):395-406.
GIS ; Computer models ; Watersheds ; Analysis ; Water pollution ; Land use ; Simulation ; Climate ; Environmental effects ; Databases / USA / Michigan / Dowagiac River Watershed
(Location: IWMI-HQ Call no: PER Record No: H028523)
5 Hu, B. X.; Wu, J.; Panorska, A. K.; Zhang, D.; He, C.. 2003. Stochastic study on groundwater flow and solute transport in a porus medium with multi-scale heterogeneity. Advances in Water Resources, 26(5):541-560.
(Location: IWMI-HQ Call no: PER Record No: H031774)
6 He, C.; Malcolm, S. B.; Dahlberg, K. A.; Fu, B. 2000. A conceptual framework for integrating hydrological and biological indicators into watershed management. Landscape and Urban Planning, 49:24p.
Watershed management ; Planning ; Ecosystems ; Hydrology ; Ecology ; Indicators ; Simulation models ; Land use ; Water quality ; Evapotranspiration ; Runoff ; Social participation ; Decision making
(Location: IWMI-HQ Call no: P 7046 Record No: H035621)
7 He, C.; Cheng, S. K.; Luo, Y. 2005. Desiccation of the Yellow River and the South Water Northward Transfer Project. Water International, 30(2):261-268.
Rivers ; Water shortage ; Water demand ; Water resource management ; Water storage ; Water delivery / China / Yellow River
(Location: IWMI-HQ Call no: PER Record No: H037856)
8 Huang, Q.; Yin, D.; He, C.; Yan, J.; Liu, Z.; Meng. S.; Ren, Q.; Zhao, R.; Inostroza, L. 2020. Linking ecosystem services and subjective well-being in rapidly urbanizing watersheds: insights from a multilevel linear model. Ecosystem Services, 43:101106. (Online first) [doi: https://doi.org/10.1016/j.ecoser.2020.101106]
Ecosystem services ; Assessment ; Watersheds ; Socioeconomic environment ; Urbanization ; Rural communities ; Sustainability ; Regional planning ; Hygroscopicity ; Carbon sequestration ; Ecological factors ; Linear models / China / Hebei / Baiyangdian Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H049673)
(0.84 MB)
In rapidly urbanizing watersheds with conflicts between socioeconomic development and ecological protection, understanding the relationship between ecosystem services (ESs) and human well-being is important for regional sustainability. However, quantifying their relationship over multiple scales remains challenging. We selected a typical rapidly urbanizing watershed, the Baiyangdian watershed in China, and used surveys and a multilevel linear model to analyze the influence of regional ESs and individual characteristics on subjective well-being (SWB). Our results showed that the multilevel linear model could effectively capture the influences of regional ESs on the residents’ SWB. For the watershed, 95.9% of the total variance in the residents’ SWB was attributed to variation between individuals, and the remaining 4.1% was attributed to variation between regions. The SWB of rural residents was more likely to be affected by regional ESs than urban residents. In the Baiyangdian watershed, which has a water supply shortage, the SWB of low-income and elderly residents in the rural areas was more sensitive to water retention services, and the association was significant. The results suggest that in rapidly urbanizing watersheds, government should pay attention to maintaining and improving key regulating services to effectively maintain and promote the SWB of rural residents and regional sustainability.
9 He, C.; Liu, Z.; Wu, J.; Pan, X.; Fang, Z.; Li, J.; Bryan, B. A. 2021. Future global urban water scarcity and potential solutions. Nature Communications, 12:4667. [doi: https://doi.org/10.1038/s41467-021-25026-3]
Water scarcity ; Urbanization ; Urban population ; Towns ; Climate change mitigation ; Water demand ; Water availability ; Water use efficiency ; Water stress ; Transfer of waters ; Virtual water ; Infrastructure ; Sustainability ; Socioeconomic development
(Location: IWMI HQ Call no: e-copy only Record No: H050694)
(1.64 MB) (1.64 MB)
Urbanization and climate change are together exacerbating water scarcity—where water demand exceeds availability—for the world’s cities. We quantify global urban water scarcity in 2016 and 2050 under four socioeconomic and climate change scenarios, and explored potential solutions. Here we show the global urban population facing water scarcity is projected to increase from 933 million (one third of global urban population) in 2016 to 1.693–2.373 billion people (one third to nearly half of global urban population) in 2050, with India projected to be most severely affected in terms of growth in water-scarce urban population (increase of 153–422 million people). The number of large cities exposed to water scarcity is projected to increase from 193 to 193–284, including 10–20 megacities. More than two thirds of water-scarce cities can relieve water scarcity by infrastructure investment, but the potentially significant environmental trade-offs associated with large-scale water scarcity solutions must be guarded against.
10 Qian, Y.; Chakraborty, T. C.; Li, J.; Li, D.; He, C.; Sarangi, C.; Chen, F.; Yang, X.; Leung, L. R. 2022. Urbanization impact on regional climate and extreme weather: current understanding, uncertainties, and future research directions. Advances in Atmospheric Sciences, 39(6):819-860. [doi: https://doi.org/10.1007/s00376-021-1371-9]
Climate change ; Extreme weather events ; Urbanization ; Uncertainty ; Precipitation ; Air temperature ; Air pollution ; Air quality ; Towns ; Satellite observation ; Meteorological stations ; Heat stress ; Surface temperature ; Vegetation ; Land cover ; Land use ; Boundary layers ; Turbulence ; Models / China
(Location: IWMI HQ Call no: e-copy only Record No: H051076)
https://link.springer.com/content/pdf/10.1007/s00376-021-1371-9.pdf
https://vlibrary.iwmi.org/pdf/H051076.pdf
https://vlibrary.iwmi.org/pdf/H051076.pdf
(3.73 MB) (3.73 MB)
Urban environments lie at the confluence of social, cultural, and economic activities and have unique biophysical characteristics due to continued infrastructure development that generally replaces natural landscapes with built-up structures. The vast majority of studies on urban perturbation of local weather and climate have been centered on the urban heat island (UHI) effect, referring to the higher temperature in cities compared to their natural surroundings. Besides the UHI effect and heat waves, urbanization also impacts atmospheric moisture, wind, boundary layer structure, cloud formation, dispersion of air pollutants, precipitation, and storms. In this review article, we first introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies. We also highlight the major research gaps and challenges in our understanding of the impacts of urbanization and provide our perspective and recommendations for future research priorities and directions.
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