气候变化领域集成服务门户(CCPortal): Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product

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DOI	10.5194/hess-22-2135-2018
	Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product
	Emery C.M.; Paris A.; Biancamaria S.; Boone A.; Calmant S.; Garambois P.-A.; Da Silva J.S.
发表日期	2018
ISSN	1027-5606
起始页码	2135
结束页码	2162
卷号	22 期号:4
英文摘要	Land surface models (LSMs) are widely used to study the continental part of the water cycle. However, even though their accuracy is increasing, inherent model uncertainties can not be avoided. In the meantime, remotely sensed observations of the continental water cycle variables such as soil moisture, lakes and river elevations are more frequent and accurate. Therefore, those two different types of information can be combined, using data assimilation techniques to reduce a model's uncertainties in its state variables or/and in its input parameters. The objective of this study is to present a data assimilation platform that assimilates into the large-scale ISBA-CTRIP LSM a punctual river discharge product, derived from ENVISAT nadir altimeter water elevation measurements and rating curves, over the whole Amazon basin. To deal with the scale difference between the model and the observation, the study also presents an initial development for a localization treatment that allows one to limit the impact of observations to areas close to the observation and in the same hydrological network. This assimilation platform is based on the ensemble Kalman filter and can correct either the CTRIP river water storage or the discharge. Root mean square error (RMSE) compared to gauge discharges is globally reduced until 21% and at Óbidos, near the outlet, RMSE is reduced by up to 52% compared to ENVISAT-based discharge. Finally, it is shown that localization improves results along the main tributaries. © 2018 Author(s).
语种	英语
scopus关键词	Digital storage; Mean square error; Rivers; Soil moisture; Uncertainty analysis; Data assimilation techniques; Ensemble Kalman Filter; Initial development; Land surface models; Large scale hydrological model; Model uncertainties; Remotely-sensed observations; Root mean square errors; Geodetic satellites; correction; data assimilation; Envisat; hydrological cycle; hydrological modeling; Kalman filter; nadir; river discharge; satellite altimetry; soil moisture; tributary; Amazon Basin; Brazil; Obidos; Para [Brazil]
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160064
作者单位	Emery, C.M., LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France, JPL, Pasadena, CA, United States; Paris, A., LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France, GET, Université de Toulouse, UPS, CNRS, IRD, Toulouse, France, LMI OCE IRD/UNB, Campus Darcy Ribeiro, Brasilia, Brazil, CLS, Ramonville-Saint-Agne, France; Biancamaria, S., LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France; Boone, A., Meteo France CNRS, CNRM UMR 3589, Toulouse, France; Calmant, S., LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France; Garambois, P.-A., ICUBE - UMR 7357, Fluid Mechanics Team, INSA, Strasbourg, France; Da Silva, J.S., CESTU, Universidade Do Estado Do Amazonas, Manaus, Brazil
推荐引用方式 GB/T 7714	Emery C.M.,Paris A.,Biancamaria S.,et al. Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product[J],2018,22(4).
APA	Emery C.M..,Paris A..,Biancamaria S..,Boone A..,Calmant S..,...&Da Silva J.S..(2018).Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product.Hydrology and Earth System Sciences,22(4).
MLA	Emery C.M.,et al."Large-scale hydrological model river storage and discharge correction using a satellite altimetry-based discharge product".Hydrology and Earth System Sciences 22.4(2018).