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Article: Detection of large-scale groundwater storage variability over the karstic regions in Southwest China
Title | Detection of large-scale groundwater storage variability over the karstic regions in Southwest China |
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Authors | |
Keywords | Drought Forward modelling GRACE Groundwater storage Karst |
Issue Date | 2019 |
Publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jhydrol |
Citation | Journal of Hydrology, 2019, v. 569, p. 409-422 How to Cite? |
Abstract | The 2003–2013 monthly groundwater storage (GWS) anomalies in the highly karstic region (HKR) and low karstic region (LKR) in the Southwest China are estimated from the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) data by using the ancillary data of surface water storage (SWS) and soil moisture storage (SMS) from the WaterGAP model simulations. The leakage errors in the estimated GWS anomalies are corrected through using the iterative forward modelling approach. The estimated GWS anomalies compare well with in situ groundwater-level observations with the correlation coefficient r = 0.71 and the root-mean-square-error (RMSE) = 42 mm. For both HKR and LKR, ∼60% of temporal variability of TWS is contributed by GWS variability, while SMS contributes to 18% (HKR) and 28% (LKR), and SWS contributes to 22% (HKR) and 14% (LKR) of the TWS variability. Due to the higher permeability of the epi-karst zones and their better connection with the subsurface aquifers, GWS anomalies in HKR show larger correlations with SWS (r = 0.73, RMSE = 51 mm) and SMS (r = 0.68, RMSE = 47 mm) and a shorter lag to precipitation than that in LKR (SWS: r = 0.56, RMSE = 50 mm, and SMS: r = 0.48, RMSE = 49 mm). During the extreme drought in 2009, GWS loss in HKR (LKR) was 74.3 mm/yr (42.7 mm/yr), accounting for 66% (62%) of total TWS loss. The severe GWS loss was mainly due to larger discharges through the well-developed subsurface drainage system rather than human depletion, since groundwater resources are still under-exploited in Southwest China (∼4 km3/yr, 12% of the potentially exploitable amounts). A quicker recovery of GWS from 2009 drought can be observed in HKR than LKR due to the larger and earlier (approximately one month) precipitation and infiltration, and quicker response of groundwater to precipitation in HKR. © 2018 Elsevier B.V. |
Persistent Identifier | http://hdl.handle.net/10722/274942 |
ISSN | 2023 Impact Factor: 5.9 2023 SCImago Journal Rankings: 1.764 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Huang, Z | - |
dc.contributor.author | Yeh, P | - |
dc.contributor.author | Pan, Y | - |
dc.contributor.author | Jiao, JJJ | - |
dc.contributor.author | Gong, H | - |
dc.contributor.author | Li, X | - |
dc.contributor.author | Güntner, A | - |
dc.contributor.author | Zhu, Y | - |
dc.contributor.author | Zhang, C | - |
dc.contributor.author | Zheng, L | - |
dc.date.accessioned | 2019-09-10T02:32:05Z | - |
dc.date.available | 2019-09-10T02:32:05Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Journal of Hydrology, 2019, v. 569, p. 409-422 | - |
dc.identifier.issn | 0022-1694 | - |
dc.identifier.uri | http://hdl.handle.net/10722/274942 | - |
dc.description.abstract | The 2003–2013 monthly groundwater storage (GWS) anomalies in the highly karstic region (HKR) and low karstic region (LKR) in the Southwest China are estimated from the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) data by using the ancillary data of surface water storage (SWS) and soil moisture storage (SMS) from the WaterGAP model simulations. The leakage errors in the estimated GWS anomalies are corrected through using the iterative forward modelling approach. The estimated GWS anomalies compare well with in situ groundwater-level observations with the correlation coefficient r = 0.71 and the root-mean-square-error (RMSE) = 42 mm. For both HKR and LKR, ∼60% of temporal variability of TWS is contributed by GWS variability, while SMS contributes to 18% (HKR) and 28% (LKR), and SWS contributes to 22% (HKR) and 14% (LKR) of the TWS variability. Due to the higher permeability of the epi-karst zones and their better connection with the subsurface aquifers, GWS anomalies in HKR show larger correlations with SWS (r = 0.73, RMSE = 51 mm) and SMS (r = 0.68, RMSE = 47 mm) and a shorter lag to precipitation than that in LKR (SWS: r = 0.56, RMSE = 50 mm, and SMS: r = 0.48, RMSE = 49 mm). During the extreme drought in 2009, GWS loss in HKR (LKR) was 74.3 mm/yr (42.7 mm/yr), accounting for 66% (62%) of total TWS loss. The severe GWS loss was mainly due to larger discharges through the well-developed subsurface drainage system rather than human depletion, since groundwater resources are still under-exploited in Southwest China (∼4 km3/yr, 12% of the potentially exploitable amounts). A quicker recovery of GWS from 2009 drought can be observed in HKR than LKR due to the larger and earlier (approximately one month) precipitation and infiltration, and quicker response of groundwater to precipitation in HKR. © 2018 Elsevier B.V. | - |
dc.language | eng | - |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jhydrol | - |
dc.relation.ispartof | Journal of Hydrology | - |
dc.subject | Drought | - |
dc.subject | Forward modelling | - |
dc.subject | GRACE | - |
dc.subject | Groundwater storage | - |
dc.subject | Karst | - |
dc.title | Detection of large-scale groundwater storage variability over the karstic regions in Southwest China | - |
dc.type | Article | - |
dc.identifier.email | Jiao, JJJ: jjiao@hku.hk | - |
dc.identifier.authority | Jiao, JJJ=rp00712 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.jhydrol.2018.11.071 | - |
dc.identifier.scopus | eid_2-s2.0-85059130951 | - |
dc.identifier.hkuros | 302735 | - |
dc.identifier.volume | 569 | - |
dc.identifier.spage | 409 | - |
dc.identifier.epage | 422 | - |
dc.identifier.isi | WOS:000457952900030 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 0022-1694 | - |