Daily water stage estimated from satellite altimetric data for large river basin monitoring

被引:18
|
作者
Roux, Emmanuel [1 ]
Cauhope, Mathilde [1 ]
Bonnet, Marie-Paule [1 ]
Calmant, Stephane [2 ]
Vauchel, Philippe [1 ]
Seyler, Frederique [1 ]
机构
[1] Univ Toulouse 3, LMTG, CNRS, UMR5563,UR154,IRD, F-31400 Toulouse, France
[2] Univ Toulouse 3, LEGOS, CNRS, UMR5566,IRD,CNES, F-31400 Toulouse, France
关键词
satellite radar altimetry; Amazon basin; temporal resolution; interpolation; multi-objective optimization; simulation-based validation;
D O I
10.1623/hysj.53.1.81
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Satellite radar altimetry appears to be a highly promising method that could be used to complement in situ limnimetric station surveys in remote river basins. However, a major drawback of satellite altimetry is its poor temporal resolution. The sampling period ranges from 10 to 35 days, depending on the satellite. This paper proposes a methodology for obtaining time series with a one-day sampling period. The method is based on a linear model exploiting data at a limited number of in situ limnimetric stations. Three parameter estimation methods are proposed: the least square (LS) and weighted least square (WLS) methods, and an optimization method based on a multi-objective criterion (OPT). The model and parameter estimation methods are evaluated by means of simulated altimetric time series whose characteristics are as realistic as possible. The absolute precision of the interpolation and its sensitivity to model structures, missing values and random noise are investigated. The RMS of the interpolation residuals ranges from 0.6 to 40.9 cm. Results show that taking into account more than one in situ reference station significantly decreases the RMS errors. Taking into account time shifts between stations improves the results too, reducing the RMS error by 16.4 cm (32.7%) in one case. In the ideal case, i.e. with no missing values and random noise, the OPT technique provides slightly better absolute results than the LS method, and significantly better results than the WLS approach. The OPT method is the least sensitive to missing values and random noise, two artefacts that systematically affect radar altimetric data.
引用
收藏
页码:81 / 99
页数:19
相关论文
共 50 条
  • [21] Research on Satellite Based Drought Monitoring in the Yellow River Basin
    Chunqing, Wang
    Shuhui, Qiu
    Fangzhu, Zhang
    De Weirdt, Maijolein
    PROCEEDINGS OF THE 3RD INTERNATIONAL YELLOW RIVER FORUM ON SUSTAINABLE WATER RESOURCES MANAGEMENT AND DELTA ECOSYSTEM MAINTENANCE, VOL VI, 2007, : 108 - +
  • [22] Primary production estimated for large lakes and reservoirs in the Mekong River Basin
    Hiroki, Mikiya
    Tomioka, Noriko
    Murata, Tomoyoshi
    Imai, Akio
    Jutagate, Tuantong
    Preecha, Chatchai
    Avakul, Piyathap
    Phomikong, Pisit
    Fukushima, Michio
    SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 747
  • [23] Assessment of TRMM Satellite Precipitation Data and Its Impacts on the Water Balance of the Heihe River Basin
    Qu, Wei
    Lu, Jingxuan
    Pang, Zhiguo
    PIAGENG 2013: INTELLIGENT INFORMATION, CONTROL, AND COMMUNICATION TECHNOLOGY FOR AGRICULTURAL ENGINEERING, 2013, 8762
  • [24] Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite
    Shi, Xuanshuo
    Qiu, Zhongfeng
    Hu, Yunjian
    Zhao, Dongzhi
    Zhao, Aibo
    Lin, Hui
    Zhan, Yating
    Wang, Yu
    Zhang, Yuanzhi
    WATER, 2024, 16 (06)
  • [25] Monitoring Changes in Croplands Due to Water Stress in the Krishna River Basin Using Temporal Satellite Imagery
    Reddi, Venkata Ramana Murthy
    Gumma, Murali Krishna
    Pyla, Kesava Rao
    Eadara, Amminedu
    Gummapu, Jai Sankar
    LAND, 2017, 6 (04)
  • [26] Quality analysis of water level series obtained by altimetric radar satellite along the Sao Francisco River
    Martins, Luana Kessia Lucas Alves
    Maillard, Philippe
    Pinto, Eber Jose de Andrade
    Moreira, Daniel Medeiros
    RBRH-REVISTA BRASILEIRA DE RECURSOS HIDRICOS, 2021, 26
  • [27] Monitoring, assessment and modelling using water quality data in the Saale River Basin, Germany
    Bongartz, Klaus
    Steele, Timothy D.
    Baborowski, Martina
    Lindenschmidt, Karl-Erich
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2007, 135 (1-3) : 227 - 240
  • [28] Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis
    Hora Alves, Jose do Patrocinio
    Fonseca, Lucas Cruz
    Alves Chielle, Raisa de Siqueira
    Barreto Macedo, Lucia Calumby
    RBRH-REVISTA BRASILEIRA DE RECURSOS HIDRICOS, 2018, 23
  • [29] Monitoring, assessment and modelling using water quality data in the Saale River Basin, Germany
    Klaus Bongartz
    Timothy D. Steele
    Martina Baborowski
    Karl-Erich Lindenschmidt
    Environmental Monitoring and Assessment, 2007, 135 : 227 - 240
  • [30] SONGHUA RIVER BASIN FLOOD MONITORING USING MULTI-SOURCE SATELLITE REMOTE SENSING DATA
    Zheng, Wei
    Shao, Jiali
    Gao, Hao
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 9760 - 9763