Application of Sentinel-1 soil moisture information for improving groundwater simulations

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    Abstract

    To support robust water management, water managers should have access to up-to-date information about their water system. For example, Dutch regional water authorities are interested in temporally and spatially distributed groundwater level information. The Netherlands Hydrological Model LHM is often used for retrieving such information on several spatial scales in the Netherlands (De Lange et al., 2014). LHM is a physically-based distributed integrated hydrological model for simulating surface water, unsaturated zone and saturated zone dynamics. However, a validation of saturated zone simulations shows that, on a local to regional scale, deviations occur between observations and simulations of groundwater levels. The availability of high-resolution remotely sensed hydrological information has led to new possibilities for hydrological model improvements. Assimilating soil moisture information can improve both unsaturated and saturated zone simulations (Camporese et al., 2009; Zhang et al., 2016). Recently, a fine-resolution surface soil moisture product based on the freely available Sentinel-1 imagery has been developed. We use this new soil moisture information in combination with an Ensemble Kalman Filter to improve groundwater simulations of the LHM and to develop an accurate system for real-time groundwater simulations and forecasts. The open-source data assimilation framework OpenDA is used to implement the filter technique. The Twente region in the Netherlands serves as a case study. The availability of in-situ soil moisture and groundwater level measurement networks enables validation of the results. The results of this study show the potential of using high-resolution Sentinel-1 satellite imagery for water management. Water managers can use this knowledge to improve forecasts of groundwater levels and to estimate effects of control measures. Furthermore, water managers can use the results to explore the use of soil moisture information for water management. References Camporese, M., Paniconi, C., Putti, M., & Salandin, P. (2009). Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow. Water Resources Research, 45(10). doi:10.1029/2008wr007031. De Lange, W. J., Prinsen, G. F., Hoogewoud, J. C., Veldhuizen, A. A., Verkaik, J., Oude Essink, G. H. P., van Walsum,P.E.V.,Delsman,J.R.,Hunink,J.C.,Massop,H.T.L.,&Kroon,T.(2014).Anoperational,multi-scale, multi-model system for consensus-based, integrated water management and policy analysis: The Netherlands Hydrological Instrument. Environmental Modelling & Software, 59, 98-108. doi:10.1016/j.envsoft.2014.05.009. Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., & Refsgaard, J. C. (2016). Multivariate hydrological data assimilation of soil moisture and groundwater head. Hydrology and Earth System Sciences,
    Original languageEnglish
    Number of pages1
    Publication statusPublished - 2018
    EventEGU General Assembly 2018 - Vienna, Austria
    Duration: 8 Apr 201813 Apr 2018
    https://www.egu2018.eu/

    Conference

    ConferenceEGU General Assembly 2018
    Abbreviated titleEGU 2018
    CountryAustria
    CityVienna
    Period8/04/1813/04/18
    Internet address

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    soil moisture
    groundwater
    water management
    phreatic zone
    simulation
    data assimilation
    vadose zone
    water
    environmental modeling
    policy analysis
    Kalman filter
    satellite imagery
    hydrology
    imagery
    water resource
    catchment
    surface water
    software
    forecast

    Cite this

    @conference{f7e6cdcadabc4860a3ccde3f0b380ce7,
    title = "Application of Sentinel-1 soil moisture information for improving groundwater simulations",
    abstract = "To support robust water management, water managers should have access to up-to-date information about their water system. For example, Dutch regional water authorities are interested in temporally and spatially distributed groundwater level information. The Netherlands Hydrological Model LHM is often used for retrieving such information on several spatial scales in the Netherlands (De Lange et al., 2014). LHM is a physically-based distributed integrated hydrological model for simulating surface water, unsaturated zone and saturated zone dynamics. However, a validation of saturated zone simulations shows that, on a local to regional scale, deviations occur between observations and simulations of groundwater levels. The availability of high-resolution remotely sensed hydrological information has led to new possibilities for hydrological model improvements. Assimilating soil moisture information can improve both unsaturated and saturated zone simulations (Camporese et al., 2009; Zhang et al., 2016). Recently, a fine-resolution surface soil moisture product based on the freely available Sentinel-1 imagery has been developed. We use this new soil moisture information in combination with an Ensemble Kalman Filter to improve groundwater simulations of the LHM and to develop an accurate system for real-time groundwater simulations and forecasts. The open-source data assimilation framework OpenDA is used to implement the filter technique. The Twente region in the Netherlands serves as a case study. The availability of in-situ soil moisture and groundwater level measurement networks enables validation of the results. The results of this study show the potential of using high-resolution Sentinel-1 satellite imagery for water management. Water managers can use this knowledge to improve forecasts of groundwater levels and to estimate effects of control measures. Furthermore, water managers can use the results to explore the use of soil moisture information for water management. References Camporese, M., Paniconi, C., Putti, M., & Salandin, P. (2009). Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow. Water Resources Research, 45(10). doi:10.1029/2008wr007031. De Lange, W. J., Prinsen, G. F., Hoogewoud, J. C., Veldhuizen, A. A., Verkaik, J., Oude Essink, G. H. P., van Walsum,P.E.V.,Delsman,J.R.,Hunink,J.C.,Massop,H.T.L.,&Kroon,T.(2014).Anoperational,multi-scale, multi-model system for consensus-based, integrated water management and policy analysis: The Netherlands Hydrological Instrument. Environmental Modelling & Software, 59, 98-108. doi:10.1016/j.envsoft.2014.05.009. Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., & Refsgaard, J. C. (2016). Multivariate hydrological data assimilation of soil moisture and groundwater head. Hydrology and Earth System Sciences,",
    author = "Michiel Pezij and Denie Augustijn and Dimmie Hendriks and Suzanne Hulscher",
    year = "2018",
    language = "English",
    note = "EGU General Assembly 2018, EGU 2018 ; Conference date: 08-04-2018 Through 13-04-2018",
    url = "https://www.egu2018.eu/",

    }

    Pezij, M, Augustijn, D, Hendriks, D & Hulscher, S 2018, 'Application of Sentinel-1 soil moisture information for improving groundwater simulations' EGU General Assembly 2018, Vienna, Austria, 8/04/18 - 13/04/18, .

    Application of Sentinel-1 soil moisture information for improving groundwater simulations. / Pezij, Michiel ; Augustijn, Denie; Hendriks, Dimmie; Hulscher, Suzanne.

    2018. Abstract from EGU General Assembly 2018, Vienna, Austria.

    Research output: Contribution to conferenceAbstract

    TY - CONF

    T1 - Application of Sentinel-1 soil moisture information for improving groundwater simulations

    AU - Pezij, Michiel

    AU - Augustijn, Denie

    AU - Hendriks, Dimmie

    AU - Hulscher, Suzanne

    PY - 2018

    Y1 - 2018

    N2 - To support robust water management, water managers should have access to up-to-date information about their water system. For example, Dutch regional water authorities are interested in temporally and spatially distributed groundwater level information. The Netherlands Hydrological Model LHM is often used for retrieving such information on several spatial scales in the Netherlands (De Lange et al., 2014). LHM is a physically-based distributed integrated hydrological model for simulating surface water, unsaturated zone and saturated zone dynamics. However, a validation of saturated zone simulations shows that, on a local to regional scale, deviations occur between observations and simulations of groundwater levels. The availability of high-resolution remotely sensed hydrological information has led to new possibilities for hydrological model improvements. Assimilating soil moisture information can improve both unsaturated and saturated zone simulations (Camporese et al., 2009; Zhang et al., 2016). Recently, a fine-resolution surface soil moisture product based on the freely available Sentinel-1 imagery has been developed. We use this new soil moisture information in combination with an Ensemble Kalman Filter to improve groundwater simulations of the LHM and to develop an accurate system for real-time groundwater simulations and forecasts. The open-source data assimilation framework OpenDA is used to implement the filter technique. The Twente region in the Netherlands serves as a case study. The availability of in-situ soil moisture and groundwater level measurement networks enables validation of the results. The results of this study show the potential of using high-resolution Sentinel-1 satellite imagery for water management. Water managers can use this knowledge to improve forecasts of groundwater levels and to estimate effects of control measures. Furthermore, water managers can use the results to explore the use of soil moisture information for water management. References Camporese, M., Paniconi, C., Putti, M., & Salandin, P. (2009). Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow. Water Resources Research, 45(10). doi:10.1029/2008wr007031. De Lange, W. J., Prinsen, G. F., Hoogewoud, J. C., Veldhuizen, A. A., Verkaik, J., Oude Essink, G. H. P., van Walsum,P.E.V.,Delsman,J.R.,Hunink,J.C.,Massop,H.T.L.,&Kroon,T.(2014).Anoperational,multi-scale, multi-model system for consensus-based, integrated water management and policy analysis: The Netherlands Hydrological Instrument. Environmental Modelling & Software, 59, 98-108. doi:10.1016/j.envsoft.2014.05.009. Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., & Refsgaard, J. C. (2016). Multivariate hydrological data assimilation of soil moisture and groundwater head. Hydrology and Earth System Sciences,

    AB - To support robust water management, water managers should have access to up-to-date information about their water system. For example, Dutch regional water authorities are interested in temporally and spatially distributed groundwater level information. The Netherlands Hydrological Model LHM is often used for retrieving such information on several spatial scales in the Netherlands (De Lange et al., 2014). LHM is a physically-based distributed integrated hydrological model for simulating surface water, unsaturated zone and saturated zone dynamics. However, a validation of saturated zone simulations shows that, on a local to regional scale, deviations occur between observations and simulations of groundwater levels. The availability of high-resolution remotely sensed hydrological information has led to new possibilities for hydrological model improvements. Assimilating soil moisture information can improve both unsaturated and saturated zone simulations (Camporese et al., 2009; Zhang et al., 2016). Recently, a fine-resolution surface soil moisture product based on the freely available Sentinel-1 imagery has been developed. We use this new soil moisture information in combination with an Ensemble Kalman Filter to improve groundwater simulations of the LHM and to develop an accurate system for real-time groundwater simulations and forecasts. The open-source data assimilation framework OpenDA is used to implement the filter technique. The Twente region in the Netherlands serves as a case study. The availability of in-situ soil moisture and groundwater level measurement networks enables validation of the results. The results of this study show the potential of using high-resolution Sentinel-1 satellite imagery for water management. Water managers can use this knowledge to improve forecasts of groundwater levels and to estimate effects of control measures. Furthermore, water managers can use the results to explore the use of soil moisture information for water management. References Camporese, M., Paniconi, C., Putti, M., & Salandin, P. (2009). Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow. Water Resources Research, 45(10). doi:10.1029/2008wr007031. De Lange, W. J., Prinsen, G. F., Hoogewoud, J. C., Veldhuizen, A. A., Verkaik, J., Oude Essink, G. H. P., van Walsum,P.E.V.,Delsman,J.R.,Hunink,J.C.,Massop,H.T.L.,&Kroon,T.(2014).Anoperational,multi-scale, multi-model system for consensus-based, integrated water management and policy analysis: The Netherlands Hydrological Instrument. Environmental Modelling & Software, 59, 98-108. doi:10.1016/j.envsoft.2014.05.009. Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., & Refsgaard, J. C. (2016). Multivariate hydrological data assimilation of soil moisture and groundwater head. Hydrology and Earth System Sciences,

    M3 - Abstract

    ER -