Your search found 6 records
1 Bates, B. C.; Kundzewicz, z. w.; Wu, S.; Palutikof, J. P. (Eds.) 2008. Climate change and water. Geneva, Switzerland: Intergovernmental Panel on Climate Change (IPCC) Secretariat. 210p. (IPCC Technical Paper VI)
Climate change ; River basins ; Lakes ; Groundwater ; Hydrology ; Environmental effects ; Precipitation ; Evapotranspiration ; Soil moisture ; Runoff ; Drought ; Water stress ; Ecosystems ; Forests ; Biodiversity ; Wetlands ; Fisheries ; Pastoralism ; Water quality ; Public health ; Water supply ; Sanitation ; Water policy ; Wastewater treatment ; Water resource management / Australia / New Zealand / Europe / Latin America / North America / Africa / Asia / Nepal / Kilimanjaro / Colorado River Basin / Columbia River basin
(Location: IWMI HQ Call no: e-copy only Record No: H041430)
http://www.ipcc.ch/pdf/technical-papers/climate-change-water-en.pdf
https://vlibrary.iwmi.org/pdf/H041430.pdf
https://vlibrary.iwmi.org/pdf/H041430.pdf
2 Bates, B.; Kundzewicz, Z. W.; Wu, S.; Palutikof, J. (Eds.) 2008. Climate change and water. Geneva, Switzerland: Intergovernmental Panel on Climate Change (IPCC) Secretariat. 200p. (IPCC Technical paper VI)
Climate change ; Water resources ; Environmental effects ; Hydrology ; Ecosystems ; Public health ; Water resource management ; Policy
(Location: IWMI HQ Call no: 551.6 G000 BAT Record No: H041636)
http://www.ipcc.ch/pdf/technical-papers/climate-change-water-en.pdf
https://vlibrary.iwmi.org/pdf/H041636.pdf
https://vlibrary.iwmi.org/pdf/H041636.pdf
3 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.
4 An, Q.; Wu, S.; Li, L.; Li, S. 2021. Inequality of virtual water consumption and economic benefits embodied in trade: a case study of the Yellow River Basin, China. Water Policy, 23p. (Online first) [doi: https://doi.org/10.2166/wp.2021.144]
Virtual water ; Water use efficiency ; Economic benefits ; River basins ; Water resources ; Water stress ; Water flow ; Transfer of waters ; Strategies ; Economic development ; Models ; Case studies / China / Yellow River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050703)
https://iwaponline.com/wp/article-pdf/doi/10.2166/wp.2021.144/955457/wp2021144.pdf
https://vlibrary.iwmi.org/pdf/H050703.pdf
https://vlibrary.iwmi.org/pdf/H050703.pdf
(1.09 MB) (1.09 MB)
The Yellow River Basin (YRB) is facing a serious water shortage. How to effectively alleviate the water crisis and achieve sustainable development in the YRB has become a widespread concern. By using the interregional input–output tables of China in 2002, 2007, 2012 and 2017, we analysed the transfer of virtual water and value-added and the inequality embodied in trade between the YRB and other regions. Results demonstrated that: (1) for the YRB, the pressure on water resources was alleviated through the net inflow of virtual water after 2007. However, the economic situation deteriorated due to the net outflow of value-added in interregional trade after 2012. (2) There existed a serious inequality in virtual water consumption and economic benefits embodied in trade between the YRB and Beijing, Shanghai, etc., with regional inequality (RI) index exceeding 1. Meanwhile, agriculture faced the most serious inequality among all sectors in the YRB. Accordingly, the YRB should aim to optimise its industrial structure and improve water use efficiency to achieve a win-win situation for both economic development and net virtual water inflow. In addition, policymakers should take measures to flexibly adjust the trade scale between the YRB and other regions based on the RI index.
5 Lu, Z.; Cai, F.; Liu, J.; Yang, J.; Zhang, S.; Wu, S.. 2022. Evolution of water resource allocation in the river basin between administrators and managers. Hydrology Research, 53(5):716-732. [doi: https://doi.org/10.2166/nh.2022.128]
Water resources ; Water allocation ; River basins ; Decision making ; Regulations ; Managers ; Strategies ; Water security ; Water intake ; Water rights ; Models ; Game theory
(Location: IWMI HQ Call no: e-copy only Record No: H051131)
https://iwaponline.com/hr/article-pdf/53/5/716/1059207/nh0530716.pdf
https://vlibrary.iwmi.org/pdf/H051131.pdf
https://vlibrary.iwmi.org/pdf/H051131.pdf
(0.64 MB) (652 KB)
The reasonable allocation of water resources runs through the main links of regional water resource planning and management, which is a complex decision-making issue, ensures the sustainable development and utilization of water resources, and makes a greater contribution to the sustainable development of social economy. In this paper, evolutionary game theory is applied to the allocation of watershed water resources in a river basin. Also, the analysis of the replication dynamics and evolutionary stability strategies of water resource allocation among water resource manufacturers was done. It was found that the evolutionary game among the water resource manufacturers has only an evolutionary stability strategy. Considering the evolutionary game between water resource managers and water resource manufacturers, the evolutionary stability strategy is analyzed. This study suggests that there are two evolutionary stability strategies ( normal water intake, high level of regulation) and ( excess water intake, low level of regulation) between the water resource manufacturers and the administrative water resource regulators, where the strategy ( normal water intake, high level of regulation) is the expected direction. The evolution factors of the strategy ( normal water intake, high level of regulation) were analyzed. Furthermore, it also suggested that an effective reward and punishment mechanism will help to draw up excessive water, dismantle the conflicts between the water resource manufacturers and the administrative water resource regulators, and increase the benefits of both sides.
6 Wu, S.; Deng, L.; Guo, L.; Wu, Y. 2022. Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery. Plant Methods, 18:68. [doi: https://doi.org/10.1186/s13007-022-00899-7]
Leaf area index ; Forecasting ; Unmanned aerial vehicles ; Thermal infrared imagery ; Data fusion ; Machine learning ; Estimation ; Wheat ; Vegetation index ; Remote sensing ; Satellites ; Biomass ; Models / China / Henan
(Location: IWMI HQ Call no: e-copy only Record No: H051401)
https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-022-00899-7.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
(7.53 MB) (7.53 MB)
Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
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