Your search found 10 records
1 Li, Z.; Zhang, J. 2001. Calculation of field Manning's roughness coefficient. Agricultural Water Management, 49(2):153-161.
(Location: IWMI-HQ Call no: PER Record No: H028456)
2 Li, Z.; Dxin, Y.; Yingcui, Z.; Hongye, Z.; Guangyuan, Y.; Rego, T. J.; Wani, S. P. 2005. Efficient management of water resources for improving the livelihoods through integrated watershed management approach. In Sharma, Bharat; Samra, J. S.; Scott, Christopher; Wani, S. P. (Eds.). Watershed management challenges: improving productivity, resources and livelihoods. Colombo, Sri Lanka: International Water Management Institute (IWMI); Indian Council of Agricultural Research (ICAR); International Crops Research Institute for Semi-Arid Tropics (ICRISAT) pp.327-336.
Watershed management ; Villages ; Farm income ; Crops ; Diversification ; Farmers ; Training / China / Tibet / Xiaoxincun Watershed / Lucheba Watershed
(Location: IWMI-HQ Call no: IWMI 333.91 G635 SHA Record No: H037685)
(2.65 MB)
3 Li, W.; Li, Z.; Li, W. 2004. Effect of the niche-fitness at different water supply and fertilization on yield of spring wheat in farmland of semi-arid areas. Agricultural Water Management, 67(1):1-13.
(Location: IWMI-HQ Call no: PER Record No: H035172)
4 Li, J.; Inanaga, S.; Li, Z.; Eneji, A. E. 2005. Optimizing irrigation scheduling for winter wheat in the North China Plain. Agricultural Water Management, 76(1):8-23.
Wheat ; Water use efficiency ; Irrigation efficiency ; Soil water ; Wheat ; Yields / China / North China Plain
(Location: IWMI-HQ Call no: PER Record No: H037130)
5 Wang, X.; Chen, Y.; Li, Z.; Fang, G.; Wang, Y. 2020. Development and utilization of water resources and assessment of water security in Central Asia. Agricultural Water Management, 240:106297. (Online first) [doi: https://doi.org/10.1016/j.agwat.2020.106297]
Water resources development ; Water security ; Assessment ; Agriculture ; Water use ; Water supply ; Water demand ; International waters ; River basins ; Ecological factors ; Socioeconomic environment ; Indicators ; Forecasting ; Models / Central Asia / Kazakhstan / Kyrgyzstan / Tajikistan / Turkmenistan / Uzbekistan
(Location: IWMI HQ Call no: e-copy only Record No: H049902)
(2.23 MB)
The utilization of water resources and water security in Central Asia are critical to the stability of the region. This paper assesses the water security of the five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) by using the projection pursuit model based on particle swarm optimization (PSO-PEE). The results show that the average annual water consumption in Central Asia is about 1255.57 × 108 m3, and the proportion of agricultural water consumption decreased due in large part to the changes of crop planting structure. For the ecological security, Kazakhstan, Tajikistan and Kyrgyzstan have improved their status, but Turkmenistan is getting worse. For the quantity security of water resources, Tajikistan and Kyrgyzstan are relatively safe, whereas Uzbekistan is at risk. For the socio-economic conditions, Kazakhstan scored the highest, while Tajikistan and Uzbekistan scored the lowest, water consumption per 10,000 dollars of GDP across all five countries is relatively high but shows a significant decreasing trend. For the water supply and demand security, the status of Kazakhstan, Kyrgyzstan and Tajikistan are better than that of Turkmenistan and Uzbekistan. Kazakhstan has achieved a relatively safe level (level ) and the degree of water security is high. Kyrgyzstan, Tajikistan and Turkmenistan are only in the basically safe level (level III). Uzbekistan is under significant pressure with regard to water security (level IV), which indicates that the country needs to strictly control population growth and strengthen the comprehensive management of water resources.
6 Gao, J.; Li, Z.; Chen, Z.; Zhou, Y.; Liu, W.; Wang, L.; Zhou, J. 2021. Deterioration of groundwater quality along an increasing intensive land use pattern in a small catchment. Agricultural Water Management, 253:106953. (Online first) [doi: https://doi.org/10.1016/j.agwat.2021.106953]
Groundwater ; Water quality ; Land use change ; Catchment areas ; Chemical analysis ; Nitrates ; Ions ; Stable isotopes ; Farmland ; Vegetation ; Fertilizers ; Contamination ; Wells / China / Shaanxi / Yujiahe Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H050383)
(5.25 MB)
Land use change has greatly influenced groundwater quality worldwide. Identifying the effects of different intensive land uses on the groundwater quality is the first step in taking proper action to solve the problem. In this study, we compared the effects of different intensive land uses (region A, natural vegetation; region B, cereal fields; region C, kiwifruit orchards) in the Yujiahe catchment between 2015 and 2017 in Shaanxi, China, on the major ions and stable isotopes of nitrate (d15N–NO3– and d18O–NO3–). The NO3- groundwater concentrations increased from region A to region B and region C; NO3- concentrations in shallow groundwater were higher than those of deep groundwater in region C (55.3 vs. 28.9 mg/L, respectively). The NO3- concentrations in region A and region B did not exceed the WHO standard of 50 mg/L. However, 56.3% and 22.2% of the shallow and deep groundwater samples have NO3- concentrations exceeding the standard in region C, respectively. The average electrical conductivity (EC) values of springs in region A and shallow groundwater in regions B and C were 438, 525, and 753 µs/cm, respectively. Concentrations of Ca2+, Mg2+, Na+, Cl-, and HCO3- ions and nitrogen isotope values increased from region A to region C, indicating that intensive land use change has modified groundwater hydrochemical composition, and deteriorated groundwater quality. This study has highlighted the significant effect of intensive land use of orchards at the small catchment scale on the groundwater quality.
7 Dai, C.; Tang, J.; Li, Z.; Duan, Y.; Qu, Y.; Yang, Y.; Lyu, H.; Zhang, D.; Wang, Y. 2022. Index system of water resources development and utilization level based on water-saving society. Water, 14(5):802. [doi: https://doi.org/10.3390/w14050802]
Water resources ; Water conservation ; Water use efficiency ; Economic development ; Water supply ; Domestic water ; Industrial water use ; Urbanization ; Mineral waters ; Ecological factors ; Indicators ; Sensitivity analysis ; Case studies / China / Jingyu County
(Location: IWMI HQ Call no: e-copy only Record No: H051043)
(2.05 MB) (2.05 MB)
The notion of a ‘Water-saving society’ may help China achieve sustainable development and high-quality development. In this paper, the concept of water resources development and utilization level is discussed from the perspective of a water-saving society, and an evaluation index system including 33 indicators is constructed. This paper takes the evaluation of water resources development and utilization level of Jingyu County from 2009 to 2018 as an example to verify the rationality of the indicator system of this study. Additionally, by changing the sensitivity analysis method of indicator weights, the indicators with greater influence on the evaluation results are screened to reduce the uncertainty of too many indicators and low correlation. The results show that the evaluation value of water resources development and utilization level in Jingyu County from 2009 to 2018 was improved from V to II, and the improvement of industrial and domestic water use efficiency and effectiveness improved the water resource problems in the study area. Sensitivity analysis showed that the sensitivity parameters are the degree of water resources development and utilization (8.7%), water consumption per CNY 10,000 of industrial value added (11.2%), water consumption per CNY 10,000 of GDP (9.3%), leakage rate of the urban water supply network (8.4%), per capita water resources (10.1%), per capita COD emissions (9.3%) and urbanization rate (8.2%).
8 Yan, C.; Li, Z.; Zhang, Z.; Sun, Y.; Wang, Y.; Xin, Q. 2023. High-resolution mapping of paddy rice fields from unmanned airborne vehicle images using enhanced-transunet. Computers and Electronics in Agriculture, 210:107867. (Online first) [doi: https://doi.org/10.1016/j.compag.2023.107867]
Rice fields ; Remote sensing ; Mapping ; Neural networks ; Moderate resolution imaging spectroradiometer ; Vegetation ; Farmland ; Villages ; Tillering / China / Zengcheng / Guangzhou / Dapu / Lijing / Zhukeng / Shagang
(Location: IWMI HQ Call no: e-copy only Record No: H051977)
(6.93 MB)
Through remote sensing to obtain accurate information on the area of rice fields is of great significance for precision agriculture. Currently, rice extraction is primarily based on multi-temporal but low spatial resolution remote sensing images, which are unsuitable for a wide range of applications in efficient agricultural management and production. Exploring new methods for acquiring very-high processing resolution (VHR) images from Unmanned Aerial Vehicles (UAV) is a viable research avenue. Given that emerging deep learning networks have shown potential in image processing and object detection, this research proposed a deep learning network named Enhanced-TransUnet (ETUnet) for identifying paddy rice fields from VHR images. The developed network utilizes a dilated convolution approach and introduces the Convolutional Block Attention Module (CBAM) to the feature extraction layer in the convolutional neural networks to reduce unnecessary feature extractions by combining the self-attention mechanism in the Transformer. We applied the developed deep-learning network to extract rice fields from UAV images at three different growth stages, including transplanting, tilling, and maturing in Guangzhou city in China. The results demonstrate that ETUnet can accurately extract paddy fields during the phases of transplanting, tillering, and maturing, where the attained F1 scores are 94.87 %, 95.05 %, and 92.95 %, respectively. The attained IoUs are 90.24 %, 90.55 %, and 87.84 %, respectively, and the Kappa coefficients obtained are 93.13 %, 93.07 %, and 90.15 %, respectively. We identified that training samples had a substantial impact on the performance of the deep neural networks. The study revealed that both the timing of image acquisition and the model architecture affected paddy rice mapping using deep learning networks based on UAV data. It provides reference and help for studying the changes of crop phenology.
9 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.
10 Ding, B.; Zhang, J.; Zheng, P.; Li, Z.; Wang, Y.; Jia, G.; Yu, X. 2024. Water security assessment for effective water resource management based on multi-temporal blue and green water footprints. Journal of Hydrology, 632:130761. [doi: https://doi.org/10.1016/j.jhydrol.2024.130761]
Water security ; Assessment ; Water resources ; Water management ; Water footprint ; Climate change ; Land use ; Water storage ; Precipitation ; Vegetation ; Vulnerability ; Downstream ; Water scarcity ; Water flow ; Runoff ; Indicators ; Evapotranspiration ; Models ; Soil water ; Water availability / China / Weihe River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H052747)
(13.30 MB)
Climate change and land use change have significantly altered the water cycle, thus affecting watershed water security. Quantitative assessment of water security using the water footprint concept can improve water resource management at the watershed scale. Therefore, this study aims to investigate the impacts of climate change and land use change on water security based on the water footprint concept, with the goal of enhancing water resource management at the watershed level. In this study, we employed the Soil and Water Assessment Tool (SWAT) to quantify the spatial and temporal distribution of blue and green water resources in the Weihe River Basin (WRB), China. Multiple water security evaluation indicators, including scarcity and vulnerability, were integrated to quantify water security. Significantly, statistical analysis methods were employed to identify key factors influencing the changes in blue and green water. The results indicate that the interannual and monthly coefficient of variation for blue water is higher than that for green water, with the order being blue water > green water storage > green water flow. Hotspots of the blue water crisis are concentrated from February to July, with agricultural water use exhibiting the highest crisis (BWvulnerability = 0.814), while hotspots of the green water crisis are concentrated from April to October. Blue water is primarily influenced by climate change, particularly precipitation, while the changes in green water flow and green water storage are influenced by the interactive effects of climate change and land use change. Specifically, in the upstream, blue water is mainly influenced by precipitation (r = 0.703), while green water is influenced by precipitation, temperature, and pasture. In the midstream, blue water is mainly influenced by precipitation, temperature, and agriculture, while green water is additionally influenced by forest and pasture. In the downstream, the key influencing factors for blue and green water are similar to those in the midstream, with the difference that blue water is negatively correlated with the population (r = -0.421). Developing water-saving agriculture can effectively improve water security in the midstream and downstream. This study has identified key factors for optimizing the allocation of water resources upstream, midstream, and downstream, providing valuable insights for future research on water security at the basin scale.
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