Your search found 9 records
1 Li, F.; Cohen, S.; Naor, A.; Shaozong, K.; Erez, A. 2002. Studies of canopy structure and water use of apple trees on three rootstocks. Agricultural Water Management, 55(1):1-14.
Water use ; Drip irrigation ; Horticulture ; Crop production ; Water stress ; Water potential ; Climate ; Measurement ; Models / Israel / Golan / Upper Galilee
(Location: IWMI-HQ Call no: PER Record No: H029779)
2 Li, F.; Cook, S.; Geballe, G. T.; Burch , W. R. 2000. Rainwater harvesting agriculture: An integrated system for water management on rainfed land in China’s semiarid areas. Ambio, 29(8):477-483.
Rain ; Water harvesting ; Water management ; Rain-fed farming ; Groundwater ; Supplementary irrigation ; Water requirements ; Water availability ; Irrigation programs ; Water shortage ; Agricultural production / China
(Location: IWMI-HQ Call no: P 6161 Record No: H031150)
3 Li, F.; Kang, S.; Zhang, J. 2004. Interactive effects of elevated CO2, nitrogen and drought on leaf area, stomatal conductance, and evapotranspiration of wheat. Agricultural Water Management, 67(3):221-233.
(Location: IWMI-HQ Call no: PER Record No: H035181)
4 Wang, Y.; Xie, Z. K.; Li, F.; Zhang, Z. 2004. The effect of supplemental irrigation on watermelon (Citrullus lanatus) production in gravel and sand mulched fields in the Loess Plateau of northwest China. Agricultural Water Management, 69(1):29-41.
Supplementary irrigation ; Water use efficiency ; Evapotranspiration ; Water harvesting ; Soil water ; Yields ; Cost benefit analysis / China / Loess Plateau
(Location: IWMI-HQ Call no: PER Record No: H035686)
5 Xie, Z.; Wang, Y.; Li, F.. 2005. Effect o plastic mulching on soil water use and spring wheat yield in arid region of northwest China. Agricultural Water Management, 75(1):71-83.
(Location: IWMI-HQ Call no: PER Record No: H036922)
6 Mafuta, C.; Formo, R. K.; Nellemann, C.; Li, F.. (Eds.) 2011. Green hills, blue cities: an ecosystems approach to water resources management for African cities. A rapid response assessment. Arendal, Norway: United Nations Environment Programme (UNEP), GRID-Arendal. 68p.
Water resources ; Water management ; Ecosystems ; Towns ; Highlands ; Urbanization ; Water supply ; Sanitation ; History ; Water policy ; Water pollution ; Wastewater treatment ; Water demand ; Water quality ; Environmental effects ; Case studies ; Discharges / Africa / Kenya / Cameroon / Uganda / Senegal / Ethiopia / Nairobi / Yaounde / Kampala / Dakar / Addis Ababa
(Location: IWMI HQ Call no: e-copy only Record No: H046033)
http://www.preventionweb.net/files/19775_rraghbcscreen1.pdf
https://vlibrary.iwmi.org/pdf/H046033.pdf
https://vlibrary.iwmi.org/pdf/H046033.pdf
(6.43 MB) (6.43MB)
Africa is currently the least urbanised region in the world, but this is changing fast. Of the billion people living on the African continent, about 40 per cent lives in urban areas. The urban population in Africa doubled from 205 million in 1990 to 400 million in 2010, and by 2050, it is expected that this would have tripled to 1.23 billion. Of this urban population, 60 per cent is living in slum conditions. In a time of such urban growth, Africa is likely to experience some of the most severe impacts of climate change, particularly when it comes to water and food security. This places huge pressures on the growing urban populations.
7 Schneider, M. Y.; Quaghebeur, W.; Borzooei, S.; Froemelt, A.; Li, F.; Saagi, R.; Wade, M. J.; Zhu, J.-J.; Torfs, E. 2022. Hybrid modelling of water resource recovery facilities: status and opportunities. Water Science and Technology, 85(9):2503-2524. [doi: https://doi.org/10.2166/wst.2022.115]
Water resources ; Resource recovery ; Modelling ; Wastewater treatment ; Water management ; Mathematical models ; Frameworks ; Process control ; Forecasting ; Neural networks ; Urban areas ; Uncertainty
(Location: IWMI HQ Call no: e-copy only Record No: H051105)
https://iwaponline.com/wst/article-pdf/85/9/2503/1064960/wst085092503.pdf
https://vlibrary.iwmi.org/pdf/H051105.pdf
https://vlibrary.iwmi.org/pdf/H051105.pdf
(0.85 MB) (875 KB)
Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.
8 Yan, W.; Li, F.; Zhao, Y. 2022. Determination of irrigation water quantity and its impact on crop yield and groundwater. Agricultural Water Management, 273:107900. (Online first) [doi: https://doi.org/10.1016/j.agwat.2022.107900]
Irrigation water ; Crop yield ; Groundwater table ; Irrigation schemes ; Water use ; Water demand ; Crop modelling ; Maize ; Water reservoirs ; Surface water ; Soil water content ; Precipitation / China / Shijin Irrigation District / Huangbizhuang Reservoir
(Location: IWMI HQ Call no: e-copy only Record No: H051353)
(2.12 MB)
The objective of reservoir water allocation based on considering the ecological water demand of the downstream river has an important impact on the water allocation of each water user and the downstream ecology. Agricultural irrigation with large water consumption will directly affect crop yield. Irrigation water, as the recharge of groundwater in irrigation districts, also plays an important role in the restoration of groundwater. This research used the range of variability approach (RVA), a method of flow management considering the water demand of river ecosystems, to provide the recommended outflow range (RVA target) of the Huangbizhuang Reservoir upstream of the Shijin irrigation district. First, we used the RVA target to determine the water allocation of each water user, set 27 irrigation schemes with the water allocation of agriculture as a constraint, and used the validated AquaCrop model (seven years of field experiment data were used to calibrate AquaCrop model parameters) to evaluate the most appropriate irrigation schemes and their impact on groundwater restoration in the irrigation district. The results showed that the amount of water available for agricultural irrigation in the flood season under the RVA target was 84 mm, which was 36 mm less than the current irrigation quota (120 mm). Three irrigation schemes better than the current scheme (scheme 0) were selected, i.e., scheme 1 (irrigate 42 mm at the seedling and jointing stages), scheme 16 (irrigate 42 mm at the seedling stage), and scheme 22 (irrigate 84 mm at the seedling stage). Scheme 22 increased the yield of corn and WP (water productivity), which were 17 kg/ha and 1 kg/ha/mm higher than scheme 0, respectively, which provided the greatest increase in yield. Scheme 22 can restore 0.727 m of the groundwater table. The yield increased in scheme 16 was 4 kg/ha higher than that in scheme 0. Scheme 16 can restore 0.982 m of the groundwater table, which was the most conducive scheme for groundwater recharge. The WP of scheme 16 and scheme 1 was slightly different from that of scheme 0. There was a slight difference in biomass among the four schemes. The yield increased in scheme 1 was 12 kg/ha higher than that in scheme 0, which could restore 0.727 m of the groundwater table simultaneously, which was for a normal year. This research can provide a reference for the formulation of a local irrigation scheme to stabilize and increase summer maize yield on the premise of satisfying the ecological water demand of the river and restoring the groundwater table.
9 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.
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