Monitoring top soil moisture through remote sensing to aid hydrologic modelling
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1 Monitoring top soil moisture through remote sensing to aid hydrologic modelling N. Verhoest 1, H. Lievens 1, L. Hoffmann 2, P. Matgen 2, M. Montanari 2, M. Vanclooster 3, S. Lambot 3, J. Minet 3, B. De Baets 4, H. Vernieuwe 4, E. De Keyser 4, V. Pauwels 5, D. Plaza 5 1 Laboratory of Hydrology and Water Management, Research Unit Water Resources Monitoring, Ghent University 2 Centre de Recherche Public-Gabriel Lippmann 3 Department of Environmental Sciences and Land Use Planning, Université catholique de Louvain 4 Department of Applied Mathematics, Biometrics and Process Control, Ghent University 5 Laboratory of Hydrology and Water Management, Research Unit Hydrologic Modeling, Ghent University
2 How can soil moisture information improve hydrologic modelling? Objective of hydrologic modelling within the HYDRASENS project To improve flood predictions after coupling with a hydraulic model Hydrologic model should accurately predict upstream inflow hydrograms
3 How can soil moisture information improve hydrologic modelling? Soil moisture is important state variable in hydrologic model as it determines most hydrologic processes (infiltration, runoff, evapotranspiration, ) Use soil moisture observations to update state variables in the hydrologic model
4 How can soil moisture information improve hydrologic modelling? Soil moisture is important state variable in hydrologic model as it determines most hydrologic processes (infiltration, runoff, evapotranspiration, ) Use soil moisture observations to update state variables in the hydrologic model Data assimilation
5 How can soil moisture information improve hydrologic modelling? Soil moisture is important state variable in hydrologic model as it determines most hydrologic processes (infiltration, runoff, evapotranspiration, ) Use soil moisture observations to update state variables in the hydrologic model Data assimilation Remote sensing
6 Test sites Dijle catchment
7 Test sites Alzette catchment
8 Types of remote sensing under investigation Ground Penetrating Radar (GPR) High resolution soil moisture patterns Spatial scaling of soil moisture Synthetic Aperture Radar (SAR) Basin-wide soil moisture patterns Spatial scaling of soil moisture
9 Ground Penetrating Radar (GPR) Principle Pulse is emitted by antenna Pulse scatters at surface Part of scattered pulse is received by antenna. Amount of backscattered energy depends on: - dielectric constant of soil (related to soil moisture) - soil roughness - vegetation - electromagnetic properties of the microwave
10 Ground Penetrating Radar (GPR) Mounted on a Multi-sensor hydrogeophysical platform GPR system: VNA + Antenna Schwarzbeck UWB Antenna 0,2 2 GHz EM38 GPS Leica 1200 Field laptop Vinyl Ester reinforced Fiberglass & PVC Weight: ~100 kg Dimensions: 4 x 1,8 x 1,1 m Batteries
11 Ground Penetrating Radar (GPR) Mounted on a Multi-sensor hydrogeophysical platform Developing a graphical User Interface integrating GPS, GPR system and EM38 GPS Laptop VNA Antenna EM38 Developing EM-models accounting for soil (moisture) layering
12 Ground Penetrating Radar (GPR) Results from field experiments
13 Ground Penetrating Radar (GPR) Results from field experiments
14 Synthetic Aperture Radar (SAR) Backscattered energy depends on: - soil moisture content - soil roughness - local incidence angle - vegetation - electromagnetic properties of the microwave Transmitting pulse Receiving pulse
15 Synthetic Aperture Radar (SAR) Main difficulty: characterization of soil roughness Prone to errors! Largely influences accuracy of soil moisture retrieval
16 Synthetic Aperture Radar (SAR) Development of retrieval technique that allows for a fuzzy description of soil roughness Tillage classes Possibility 1 0 min very likely max Roughness parameter Advantage: technique allows for estimating uncertainty in retrieved soil moisture
17 Synthetic Aperture Radar (SAR) Retrieval procedure: 1 0 RMS Backscattering value Incidence angle Frequency Polarization Autocorrelation function (Exponential) IEM Correlation length Possibility 1 0 Moisture content
18 Synthetic Aperture Radar (SAR) High uncertainty Why? Relationship between roughness parameters: RMS heights correlation length Possibility 1 0 Moisture content
19 Synthetic Aperture Radar (SAR) Current research: accounting for interactivity between roughness parameters. Possibilistic clustering (Gustafson-Kessel fuzzy clustering)
20 Synthetic Aperture Radar (SAR) Example No interactivity Interactivity High uncertainty (wide possibility distributions) Less uncertainty (narrower possibility distributions) Interactivity reduces uncertainty due to inaccurate knowledge of roughness in retrieved soil moisture
21 Next steps GPR Validate EM-modelling based retrieval techniques Study soil moisture scaling Assimilate soil moisture maps in small scale hydrologic model SAR Apply possibilistic retrieval to SAR time series Validate methodology for identifying roughness classes Assimilate soil moisture maps in catchment-scale hydrologic model Hydrologic modelling Develop data assimilation schemes for coupled hydrologic/hydraulic model Develop data assimilation schemes that allow for fuzzy observations Develop data assimilation scheme that accounts for spatial scaling of soil moisture Validate merit of (uncertain) remotely sensed soil moisture for flood forecasting
22 Monitoring top soil moisture through remote sensing to aid hydrologic modelling N. Verhoest 1, H. Lievens 1, L. Hoffmann 2, P. Matgen 2, M. Montanari 2, M. Vanclooster 3, S. Lambot 3, J. Minet 3, B. De Baets 4, H. Vernieuwe 4, E. De Keyser 4, V. Pauwels 5, D. Plaza 5 1 Laboratory of Hydrology and Water Management, Research Unit Water Resources Monitoring, Ghent University 2 Centre de Recherche Public-Gabriel Lippmann 3 Department of Environmental Sciences and Land Use Planning, Université catholique de Louvain 4 Department of Applied Mathematics, Biometrics and Process Control, Ghent University 5 Laboratory of Hydrology and Water Management, Research Unit Hydrologic Modeling, Ghent University
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