Your search found 7 records
1 Wu, Y.; Falconer, R. A. 2000. A mass conservative 3-D numerical model for predicting solute fluxes in estuarine waters. Advances in Water Resources, 23(5):531-543.
(Location: IWMI-HQ Call no: PER Record No: H025836)
2 Xu, Y.; Wu, Y.; Beekman, H. E. 2003. The role of interflow in estimating recharge in mountainous catchments. In Xu, Y.; Beekman, H. E. (Eds.), Groundwater recharge estimation in Southern Africa. Paris, France: UNESCO. pp.135-145.
Catchment areas ; Mountains ; Recharge ; Estimation ; Hydrology ; Groundwater ; Flow ; Water balance / South Africa / Vermaaks River Valley
(Location: IWMI-HQ Call no: 553.79 G154 XU Record No: H037001)
3 Fan, X.; Miao, C.; Duan, Q.; Shen, C.; Wu, Y.. 2021. Future climate change hotspots under different 21st century warming scenarios. Earth’s Future, 9(6):e2021EF002027. [doi: https://doi.org/10.1029/2021EF002027]
Climate change ; Forecasting ; Global warming ; Extreme weather events ; Precipitation ; Temperature ; Emission ; Models ; Uncertainty ; Indicators / Central Africa / West Africa / Southern Africa / Central America / Arctic Region / Indonesia / Tibetan Plateau / Amazon
(Location: IWMI HQ Call no: e-copy only Record No: H050397)
https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2021EF002027
https://vlibrary.iwmi.org/pdf/H050397.pdf
https://vlibrary.iwmi.org/pdf/H050397.pdf
(4.65 MB) (4.65 MB)
Identifying climate change hotspot regions is critical for planning effective mitigation and adaptation activities. We use standard Euclidean distance (SED) to calculate integrated changes in precipitation and temperature means, interannual variability, and extremes between different future warming levels and a baseline period (1995–2014) using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model ensemble. We find consistent hotspots in the Amazon, central and western Africa, Indonesia and the Tibetan Plateau at warming levels of 1.5 °C, 2 °C and 3 °C for all scenarios explored; the Arctic, Central America and southern Africa emerge as hotspots at 4 °C warming and at the end of the 21st century under two Shared Socioeconomic Pathways scenarios, SSP3-7.0 and SSP5-8.5. CMIP6 models show higher SED values than CMIP5, suggesting stronger aggregated effects of climate change under the new scenarios. Hotspot time of emergence (TOE) is further investigated; TOE is defined as the year when the climate change signal first exceeds the noise of natural variability in 21st century projections. The results indicate that TOEs for warming would occur over all primary hotspots, with the earliest occurring in the Arctic and Indonesia. For precipitation, TOEs occur before 2100 in the Arctic, the Tibetan Plateau and Central America. Results using a geographical detector model show that patterns of SED are shaped by extreme hot and dry occurrences at low-to-medium warming, while precipitation and temperature means and extreme precipitation occurrences are the dominant influences under the high emission scenario and at high warming levels.
4 Yao, Y.; Zheng, C.; Andrews, C. B.; Scanlon, B. R.; Kuang, X.; Zeng, Z.; Jeong, S.-J.; Lancia, M.; Wu, Y.; Li, G. 2021. Role of groundwater in sustaining northern Himalayan rivers. Geophysical Research Letters, 48(10):e2020GL092354. [doi: https://doi.org/10.1029/2020GL092354]
Groundwater flow ; Rivers ; Sustainability ; Stream flow ; Groundwater recharge ; Discharges ; Hydrology ; Precipitation ; Highlands ; Models / China / Himalayan Rivers / Yarlung Zangbo Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050400)
https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2020GL092354
https://vlibrary.iwmi.org/pdf/H050400.pdf
https://vlibrary.iwmi.org/pdf/H050400.pdf
(4.10 MB) (4.10 MB)
The Himalayas are critical for supplying water for ~2 billion people who live downstream, and available water is highly sensitive to climate change. The role of the groundwater system in sustaining the northern Himalayan rivers remains unknown, and this compromises Asia's future water sustainability. Here, we quantify the spatiotemporal contribution of groundwater to river flows in the Yarlung Zangbo Basin (upper reaches of Brahmaputra). Our results show that the groundwater recharge represents ~23% of mean annual precipitation, translating into ~30 km3/yr of baseflow, which contributes ~55% of the total river discharge in the upstream reaches to ~27% in the downstream reaches. The percentage of groundwater contribution is inversely related to topographic steepness and total precipitation, with the steepest topography and highest precipitation in the eastern Himalayas. This study fills a knowledge gap on groundwater in the Himalayas and is a foundation for projecting water changes under climatic warming.
5 Wu, Y.; Xu, Y.; Yin, G.; Zhang, X.; Li, C.; Wu, L.; Wang, X.; Hu, Q.; Hao, F. 2021. A collaborated framework to improve hydrologic ecosystem services management with sparse data in a semi-arid basin. Hydrology Research, 52(5):1159-1172. [doi: https://doi.org/10.2166/nh.2021.146]
Hydrology ; Ecosystem services ; Semiarid zones ; Frameworks ; Models ; Water resources ; Water supply ; Water yield ; Sediment ; Runoff ; Precipitation ; Vegetation ; Land cover ; Hydropower / China / Yixunhe River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050811)
https://iwaponline.com/hr/article-pdf/52/5/1159/950726/nh0521159.pdf
https://vlibrary.iwmi.org/pdf/H050811.pdf
https://vlibrary.iwmi.org/pdf/H050811.pdf
(0.52 MB) (536 KB)
Applying various models to assess hydrologic ecosystem services (HESs) management has the potential to encourage efficient water resources allocation. However, can a single model designed on these principles be practical to carry out hydrologic ecosystem services management for all purposes? We address this question by fully discussing the advantages of the variable infiltration capacity (VIC) model, the soil and water assessment tool (SWAT), and the integrated valuation of ecosystem services and tradeoffs (InVEST) model. The analysis is carried both qualitatively and quantitatively at the Yixunhe River basin, China, with a semi-arid climate. After integrating the advantages of each model, a collaborated framework and model selection method have been proposed and validated for optimizing the HESs management at the data sparse scenario. Our study also reveals that the VIC and SWAT model presents the better runoff reproducing ability of the hydrological cycle. Though the InVEST model has less accuracy in runoff simulation, the interannual change rate is similar to the other two models. Furthermore, the InVEST model (1.08 billion m3) has larger simulation result than the SWAT model (0.86 billion m3) for the water yield, while both models have close results for assessment of sediment losses.
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.
7 Zhang, Q.; Sun, J.; Zhang, G.; Liu, X.; Wu, Y.; Sun, J.; Hu, B. 2023. Spatiotemporal dynamics of water supply-demand patterns under large-scale paddy expansion: implications for regional sustainable water resource management. Agricultural Water Management, 285:108388. (Online first) [doi: https://doi.org/10.1016/j.agwat.2023.108388]
Water supply ; Water resources ; Water requirements ; Rice ; Growth period ; Climate change ; Precipitation ; Crop water use ; Irrigation water ; Water demand ; Water shortage ; Evapotranspiration / China / Sanjiang Plain / Songhua River / Wusuli River
(Location: IWMI HQ Call no: e-copy only Record No: H051983)
https://www.sciencedirect.com/science/article/pii/S0378377423002536/pdfft?md5=c08be234799e27a6e78d439d8bd87d74&pid=1-s2.0-S0378377423002536-main.pdf
https://vlibrary.iwmi.org/pdf/H051983.pdf
https://vlibrary.iwmi.org/pdf/H051983.pdf
(14.80 MB) (14.8 MB)
Climate change and large-scale paddy field expansion have altered the balance of water supply–demand in the Sanjiang Plain, a substantial commercial grain base in the high-latitude region of China. However, the matching pattern of water supply–demand throughout the growing period during the rapid expansion processes of paddy fields remains unknown. Hence, this study aimed to analyze the spatial–temporal variation characteristics of effective precipitation (Pem), crop water demand (ETc), supply–demand matching degree (MD), and irrigation water demand (IR) for different growing periods of paddy fields in the Sanjiang Plain using high-resolution meteorological and multi-period rice distribution data sets. The results showed that the area of paddy fields increased by 446% (20,064 km2) from 1990 to 2020 and almost completely covered the lowland of the Sanjiang Plain in 2020. ETc showed a slightly increasing trend initially and decreased afterward, while Pem and MD marginally increased at first and considerably increased subsequently during 1990–1995 and 2000–2020, respectively. MD has largely increased since 2000 in the Jiansanjiang area and the lower reaches of the Songhua River, where the largest paddy field expansion was experienced. However, the regional IR increased rapidly after 2000, which was associated with the expansion of paddy fields and further exceeded the carrying capacity of regional water resources. The efficiency of water resource utilization should be urgently improved, and integrated water resource planning and management should be implemented considering precipitation, surface water (regional water resources and transit water resources), and groundwater to promote the sustainable development of regional agriculture.
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