Your search found 3 records
1 Qinke, Y.; Rui, L.; Zhang, X.; Hu, L.. 2002. Regional evaluation of soil erosion by water: A case study on the Loess Plateau of China. In McVicar, T. R.; Rui, L.; Walker, J.; Fitzpatrick, R. W.; Changming, L. (Eds.), Regional water and soil assessment for managing sustainable agriculture in China and Australia. Canberra, Australia: ACIAR. pp.304-310.
(Location: IWMI-HQ Call no: 631.7.1 G592 MCV Record No: H033008)
2 Xie, J.; Zhang, K.; Hu, L.; Pavelic, Paul; Wang, Y.; Chen, M. 2015. Field-based simulation of a demonstration site for carbon dioxide sequestration in low-permeability saline aquifers in the Ordos Basin, China. Hydrogeology Journal, 23(7):1465-1480. [doi: https://doi.org/10.1007/s10040-015-1267-9]
Carbon dioxide ; Carbon sequestration ; Saline water ; Aquifers ; River basins ; Geological process ; Reservoir storage ; Wells ; Temperature ; Porosity ; Permeability / China / Ordos Basin
(Location: IWMI HQ Call no: e-copy only Record No: H047063)
(3.84 MB)
Saline formations are considered to be candidates for carbon sequestration due to their great depths, large storage volumes, and widespread occurrence. However, injecting carbon dioxide into low-permeability reservoirs is challenging. An active demonstration project for carbon dioxide sequestration in the Ordos Basin, China, began in 2010. The site is characterized by a deep, multi-layered saline reservoir with permeability mostly below 1.0×10-14 m2. Field observations so far suggest that only small-to-moderate pressure buildup has taken place due to injection. The Triassic Liujiagou sandstone at the top of the reservoir has surprisingly high injectivity and accepts approximately 80 % of the injected mass at the site. Based on these key observations, a three-dimensional numerical model was developed and applied, to predict the plume dynamics and pressure propagation, and in the assessment of storage safety. The model is assembled with the most recent data and the simulations are calibrated to the latest available observations. The model explains most of the observed phenomena at the site. With the current operation scheme, the CO2 plume at the uppermost reservoir would reach a lateral distance of 658 m by the end of the project in 2015, and approximately 1,000 m after 100 years since injection. The resulting pressure buildup in the reservoir was below 5 MPa, far below the threshold to cause fracturing of the sealing cap (around 33 MPa).
3 Sun, J.; Hu, L.; Li, D.; Sun, K.; Yang, Z. 2022. Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management. Journal of Hydrology, 608: 127630. [doi: https://doi.org/10.1016/j.jhydrol.2022.127630]
Groundwater management ; Groundwater table ; Forecasting ; Water supply ; Rivers ; Aquifers ; Precipitation ; Neural networks ; Models / China / Beijing
(Location: IWMI HQ Call no: e-copy only Record No: H051102)
(5.75 MB)
The overexploitation of groundwater resource and its delicacy management has gained increasing attentions in recent years worldwide because of causing a series of serious environmental and geological problems. Currently, accurately predicting the groundwater level (GWL) is an important issue in effective groundwater management across scales. In the present study, three popularly-used data-driven models, which are an autoregressive integrated moving average (ARIMA), a back-propagation artificial neural network (BP-ANN) and long short-term memory (LSTM), were established in five zones with different hydrogeological properties to explore the model’s accuracy in predicting the GWL at monthly and daily scales in a Northern Plain in China. The developed models were evaluated by both the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results indicate that the performance of the LSTM model is best at monthly time scales with the NSEs greater than 0.76 and RMSEs smaller than 1.15 m in each zone during the training period and demonstrate a good performance at daily time scales with the NSEs greater than 0.9 and the RMSEs smaller than 0.55 m at a local area. Meanwhile, the tempo-spatial distribution of the probability of drawdowns from the LSTM model was estimated by using the object-oriented spatial statistical (O2S2) method. The results show that cumulative drawdowns greater than 10 m are mainly concentrated in water source areas, with probabilities over 0.7 from 2003 to 2010 and declining to less than 0.3 from 2011 to 2014. The GWL rose generally in the study area from 2015 to 2018, but the probability of a drawdown with more than 5 m exceeded 0.8 in Zone V because of continuing groundwater exploitation. This study formulates a framework on developing effective data-driven models for predicting the GWL across scales which have the potential to aid groundwater management.
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