Your search found 9 records
1 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)
2 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)
3 Wu, J.; Hu, B. X.; Zhang, D.; Shirley, C. 2003. A three-dimensional numerical method of moments for groundwater flow and solute transport in a nonstationary conductivity field. Advances in Water Resources, 26(11):1149-1169.
(Location: IWMI-HQ Call no: PER Record No: H033126)
4 Zhang, W. J.; Feng, J. X.; Wu, J.; Parker, K. 2004. Differences in soil microbial biomass and activity for six agroecosystems with a management disturbance gradient. Pedosphere, 14(4):441-447.
(Location: IWMI-HQ Call no: PER Record No: H036122)
5 Ali, R.; Wu, J.. 2006. Impacts of improved irrigation and drainage systems of the Yinchuan Plain, northern China. In Willett, I. R.; Gao, Z. (Eds.) Agricultural water management in China: Proceedings of a workshop held in Beijing, China, 14 September 2005. Canberra, Australia: ACIAR. pp.39-51.
Furrow irrigation ; Flood irrigation ; Drainage ; Seepage ; Open channels ; Water pollution ; Soil salinity ; Groundwater ; Surface water ; Conjunctive use ; Irrigation practices / China / Yinchuan Plain / Yellow River
(Location: IWMI-HQ Call no: 631.7 G592 WIL Record No: H039220)
6 Packett, E.; Grigg, N. J.; Wu, J.; Cuddy, S. M.; Wallbrink, P. J.; Jakeman, A. J. 2020. Mainstreaming gender into water management modelling processes. Environmental Modelling and Software, 127:104683 (Online first). [doi: https://doi.org/10.1016/j.envsoft.2020.104683]
Water management ; Modelling ; Gender mainstreaming ; Integrated management ; Water resources ; Sustainable Development Goals ; Gender equality ; Equity ; Decision making ; Stakeholders ; Policies
(Location: IWMI HQ Call no: e-copy only Record No: H049569)
https://www.sciencedirect.com/science/article/pii/S1364815219306966/pdfft?md5=9bbd07f9dad094b7d69d4f78e41cc5ec&pid=1-s2.0-S1364815219306966-main.pdf
https://vlibrary.iwmi.org/pdf/H049569.pdf
https://vlibrary.iwmi.org/pdf/H049569.pdf
(0.51 MB) (524 KB)
Although the Dublin principles of Integrated Water Resource Management (IWRM) are well-established, the third principle on gender is commonly missing in practice. We use gender mainstreaming to identify examples where gender-specific perspectives might influence water resource management modelling choices. We show how gender considerations could lead to different choices in all modelling phases, providing examples from three familiar components of modelling practice: (a) problem framing and conceptualization, (b) model construction, documentation and evaluation and (c) model interpretation and decision support. We suggest a future approach for integrating gender perspectives in modelling. Including gender dimensions could strengthen modelling results by engaging with a range of stakeholders and highlighting questions, knowledge, values and choices that may otherwise be overlooked. Such an approach won't always result in a different model and results. At the very least it's a mechanism to explore and reveal gendered assumptions knowingly, or unknowingly, embedded into the model.
7 Wu, J.; Wang, X.; Zhong, B.; Yang, A.; Jue, K.; Wu, J.; Zhang, L.; Xu, W.; Wu, S.; Zhang, N.; Liu, Q. 2020. Ecological environment assessment for greater Mekong Subregion based on pressure-state-response framework by remote sensing. Ecological Indicators, 117:106521. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2020.106521]
Environmental Impact Assessment ; Ecological indicators ; Remote sensing ; Landsat ; Biodiversity ; Vegetation ; Land use ; Land cover ; Spatial distribution ; Farmland ; Ecosystems ; Anthropogenic factors ; Evapotranspiration ; Sustainable development / China / Myanmar / Lao People's Democratic Republic / Greater Mekong Subregion / Yunnan / Sipsongpanna
(Location: IWMI HQ Call no: e-copy only Record No: H049753)
(6.71 MB)
The environment project in the greater Mekong sub-region was the largest multi-field environmental cooperation launched by six countries (China, Vietnam, Laos, Myanmar, Thailand and Cambodia) in 2006, since the cooperation mechanism was established by Asian Development Bank (ADB) in 1992. How to establish the indicators to assess the achievements of the biological corridor construction and the status of ecological environment quantitatively is one of the prerequisites for the future project ongoing phase. The popular Pressure-State-Response (PSR) framework was employed in this study to assess the natural and human pressure, the healthy state of regional natural environment, and the subsequent response of ecosystem dynamic change in the Greater Mekong Subregion. Instead of using surveying based data as driving parameters, large amount of driving factors were retrieved from multi-source remote sensing data from 2000 to 2017, which provides access to larger updated and real-time databases, more tangible data allowing more objective goal management, and better spatially covered. The driving factors for pressure analysis included digital elevation, land surface temperature, evapotranspiration, light index, road network map, land cover dynamic change and land use degree, which were derived directly and indirectly from remote sensing. The indicators for state evaluation were composed of vegetation index, leaf area index, and fractional vegetation cover from remote sensing directly. The comprehensive response index was mainly determined by the pressure and state indicators. Through the analysis based on an overlay technique, it showed that the ecological environment deteriorated firstly from 2000 to 2010 and then started to improve from 2010 to 2017. The proofs indicated that the natural forest and wetland ecosystems were improved and the farmland area was decreased between 2000 and 2017. This study explored effective indicators from remote sensing for the ecological and environmental assessment, which can provide a strong decision-making basis for promoting the sustainable development of the ecological environment in the greater Mekong subregion, as well as the technological support for the construction of the biodiversity corridor.
8 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.
9 Zhang, R.; Wu, J.; Yang, Y.; Peng, X.; Li, C.; Zhao, Q. 2022. A method to determine optimum ecological groundwater table depth in semi-arid areas. Ecological Indicators, 139:108915. [doi: https://doi.org/10.1016/j.ecolind.2022.108915]
Groundwater table ; Water depth ; Indicators ; Ecological factors ; Semiarid zones ; Models ; Normalized difference vegetation index ; Uncertainty ; Remote sensing ; Soil water content ; Populus / China / Inner Mongolia / Hetao Irrigation District
(Location: IWMI HQ Call no: e-copy only Record No: H051128)
https://www.sciencedirect.com/science/article/pii/S1470160X22003867/pdfft?md5=99831de53fd285ba271967a2781724db&pid=1-s2.0-S1470160X22003867-main.pdf
https://vlibrary.iwmi.org/pdf/H051128.pdf
https://vlibrary.iwmi.org/pdf/H051128.pdf
(9.24 MB) (9.24 MB)
Groundwater depth (GWD) is an important factor to sustain the ecological integrity of some ecosystems and is often used as an indicator of environmental quality in dry areas. Single-scale data gained from quadrat surveys is always used to establish a relationship with GWD to determine the optimum GWD. However, the randomness and uncertainty in single-scale data may result in insufficient reliability of results. To overcome this shortage, multiple growth indicators of poplar trees (Populus euphratica) in Hetao Irrigation District, including average crown width (ACW), tree height, diameter at breast height (DBH), mean ring spacing (MRC), and normalized difference vegetation index (NDVI), were acquired by field sampling and remote sensing. These indicators were used to establish relationships with the GWD by considering spatial and temporal variation to identify the optimum GWD. The cloud model was introduced and its three digital features derived from optimum groundwater depth data (expectation: Ex, entropy: En, and super-entropy: He) were calculated to construct the reverse cloud models W (Ex, En, He) for describing ecological GWD to determine the optimum ecological GWD in semi-arid areas. The results show that the optimum GWD range was 1.60–2.20 m. The cloud models obtained on spatial and temporal scales were WS (2.01, 0.07, 0.04) and WT (1.78, 0.10, 0.02), respectively. The resulting comprehensive cloud model WC (1.87, 0.14, 0.03) exhibited better variability, so 1.87 m was taken as the optimum GWD for poplars. This method can determine the regional ecological groundwater level more accurately and effectively, and provide evaluation indicators for the management of regional groundwater.
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