Articles | Volume 23, issue 2
https://doi.org/10.5194/hess-23-1163-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-1163-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2019
Research article |
| 28 Feb 2019
| 28 Feb 2019
Covariance resampling for particle filter – state and parameter estimation for soil hydrology
Daniel Berg
CORRESPONDING AUTHOR
Institute of Environmental Physics (IUP), Heidelberg University,
Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Hannes H. Bauser
Institute of Environmental Physics (IUP), Heidelberg University,
Heidelberg, Germany
HGS MathComp, Heidelberg University, Heidelberg, Germany
Kurt Roth
Institute of Environmental Physics (IUP), Heidelberg University,
Heidelberg, Germany
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
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Data assimilation methods are used throughout the geosciences to combine information from uncertain models and uncertain measurement data. In this study, we distinguish between the characteristics of geophysical systems, i.e., divergent systems (initially nearby states will drift apart) and convergent systems (initially nearby states will coalesce), and demonstrate the implications for sequential ensemble data assimilation methods, which require a sufficient divergent component.
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Data assimilation methods like the ensemble Kalman filter (EnKF) can combine models and measurements to estimate states and parameters, but require a proper representation of uncertainties. In soil hydrology, model errors typically vary rapidly in space and time, which is difficult to represent. Inflation methods can account for unrepresented model errors. To improve estimations in soil hydrology, we designed a method that can adjust the inflation of states and parameters to fast varying errors.
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The representation of soil water movement comes with uncertainties in all model components. We assess the key uncertainties for the case of a one-dimensional soil profile with measured water contents. We employ a data assimilation method to represent and reduce the key uncertainties. For intermittent phases where model assumptions are violated, we introduce a "closed-eye period" to bridge the gap. We also demonstrate the need to include heterogeneity.
Hannes Helmut Bauser, Daniel Berg, and Kurt Roth
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Data assimilation methods are used throughout the geosciences to combine information from uncertain models and uncertain measurement data. In this study, we distinguish between the characteristics of geophysical systems, i.e., divergent systems (initially nearby states will drift apart) and convergent systems (initially nearby states will coalesce), and demonstrate the implications for sequential ensemble data assimilation methods, which require a sufficient divergent component.
Edoardo Martini, Matteo Bauckholt, Simon Kögler, Manuel Kreck, Kurt Roth, Ulrike Werban, Ute Wollschläger, and Steffen Zacharias
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We present the in situ data available from the soil monitoring network
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This study suggests an efficient approach to obtain plot-scale soil hydraulic properties for the shallow structural soils via non-invasive ground-penetrating radar measurements. Facilitated by spatial information of lateral water flow, this approach is more efficient than the widely used inversion approaches relying on intensive soil moisture monitoring. The acquisition of such quantitative information is of great interest to fields such as hydrology and precision agriculture.
Hannes H. Bauser, Daniel Berg, Ole Klein, and Kurt Roth
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Data assimilation methods like the ensemble Kalman filter (EnKF) can combine models and measurements to estimate states and parameters, but require a proper representation of uncertainties. In soil hydrology, model errors typically vary rapidly in space and time, which is difficult to represent. Inflation methods can account for unrepresented model errors. To improve estimations in soil hydrology, we designed a method that can adjust the inflation of states and parameters to fast varying errors.
Stefan Jaumann and Kurt Roth
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Ground-penetrating radar (GPR) is a noninvasive and nondestructive measurement method to monitor the hydraulic processes precisely and efficiently. We analyze synthetic as well as measured data from the ASSESS test site and show that the analysis yields accurate estimates for the soil hydraulic material properties as well as for the subsurface architecture by comparing the results to references derived from time domain reflectometry (TDR) and subsurface architecture ground truth data.
Julia Boike, Inge Juszak, Stephan Lange, Sarah Chadburn, Eleanor Burke, Pier Paul Overduin, Kurt Roth, Olaf Ippisch, Niko Bornemann, Lielle Stern, Isabelle Gouttevin, Ernst Hauber, and Sebastian Westermann
Earth Syst. Sci. Data, 10, 355–390, https://doi.org/10.5194/essd-10-355-2018, https://doi.org/10.5194/essd-10-355-2018, 2018
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A 20-year data record from the Bayelva site at Ny-Ålesund, Svalbard, is presented on meteorology, energy balance components, surface and subsurface observations. This paper presents the data set, instrumentation, calibration, processing and data quality control. The data show that mean annual, summer and winter soil temperature data from shallow to deeper depths have been warming over the period of record, indicating the degradation and loss of permafrost at this site.
Stefan Jaumann and Kurt Roth
Hydrol. Earth Syst. Sci., 21, 4301–4322, https://doi.org/10.5194/hess-21-4301-2017, https://doi.org/10.5194/hess-21-4301-2017, 2017
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We investigate the quantitative effect of neglected sensor position, small-scale heterogeneity, and lateral flow on soil hydraulic material properties. Thus, we analyze a fluctuating water table experiment in a 2-D architecture (ASSESS) with increasingly complex studies based on time domain reflectometry and hydraulic potential data. We found that 1-D studies may yield biased parameters and that estimating sensor positions as well as small-scale heterogeneity improves the model significantly.
Sina Muster, Kurt Roth, Moritz Langer, Stephan Lange, Fabio Cresto Aleina, Annett Bartsch, Anne Morgenstern, Guido Grosse, Benjamin Jones, A. Britta K. Sannel, Ylva Sjöberg, Frank Günther, Christian Andresen, Alexandra Veremeeva, Prajna R. Lindgren, Frédéric Bouchard, Mark J. Lara, Daniel Fortier, Simon Charbonneau, Tarmo A. Virtanen, Gustaf Hugelius, Juri Palmtag, Matthias B. Siewert, William J. Riley, Charles D. Koven, and Julia Boike
Earth Syst. Sci. Data, 9, 317–348, https://doi.org/10.5194/essd-9-317-2017, https://doi.org/10.5194/essd-9-317-2017, 2017
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Waterbodies are abundant in Arctic permafrost lowlands. Most waterbodies are ponds with a surface area smaller than 100 x 100 m. The Permafrost Region Pond and Lake Database (PeRL) for the first time maps ponds as small as 10 x 10 m. PeRL maps can be used to document changes both by comparing them to historical and future imagery. The distribution of waterbodies in the Arctic is important to know in order to manage resources in the Arctic and to improve climate predictions in the Arctic.
Xicai Pan, Stefan Jaumann, Jiabao Zhang, and Kurt Roth
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-77, https://doi.org/10.5194/tc-2017-77, 2017
Revised manuscript not accepted
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This study proposes a new method for estimating hydraulic properties of active layers using ground-penetrating radar (GPR) and 2D inverse hydrological modeling. This method creatively turns over the adverse features of undulating frost table for 1D inverse estimation of hydraulic parameters to assets for 2D inverse estimation. Its advantages include non-destructive observations, a bigger scale of the soil hydraulic properties and efficiency for permafrost studies.
Hannes H. Bauser, Stefan Jaumann, Daniel Berg, and Kurt Roth
Hydrol. Earth Syst. Sci., 20, 4999–5014, https://doi.org/10.5194/hess-20-4999-2016, https://doi.org/10.5194/hess-20-4999-2016, 2016
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The representation of soil water movement comes with uncertainties in all model components. We assess the key uncertainties for the case of a one-dimensional soil profile with measured water contents. We employ a data assimilation method to represent and reduce the key uncertainties. For intermittent phases where model assumptions are violated, we introduce a "closed-eye period" to bridge the gap. We also demonstrate the need to include heterogeneity.
Xicai Pan, Yanping Li, Qihao Yu, Xiaogang Shi, Daqing Yang, and Kurt Roth
The Cryosphere, 10, 1591–1603, https://doi.org/10.5194/tc-10-1591-2016, https://doi.org/10.5194/tc-10-1591-2016, 2016
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Using a 9-year dataset in conjunction with a process-based model, we verify that the common assumption of a considerably smaller thermal conductivity in the thawed season than the frozen season is not valid at a site with a stratified active layer on the Qinghai–Tibet Plateau (QTP). The unique hydraulic and thermal mechanism in the active layer challenges the concept of thermal offset used in conceptual permafrost models and hints at the reason for rapid permafrost warming on the QTP.
P. Klenk, S. Jaumann, and K. Roth
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-12215-2015, https://doi.org/10.5194/hessd-12-12215-2015, 2015
Revised manuscript has not been submitted
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The main goal of our work is to quantify near-surface soil water dynamics by advancing non-invasive measurement methods such as surface-based Ground-Penetrating Radar (GPR). Here, we observe soil infiltration processes with a novel dual-frequency GPR system. The high precision of our approach allows (i) closely investigating the dynamic evolution of specific subsurface signals in different materials and (ii) monitoring the longterm effect of infiltration pulses over the course of several months.
P. Klenk, S. Jaumann, and K. Roth
Hydrol. Earth Syst. Sci., 19, 1125–1139, https://doi.org/10.5194/hess-19-1125-2015, https://doi.org/10.5194/hess-19-1125-2015, 2015
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In this study, we analyze a set of high-resolution, surface-based, 2-D ground-penetrating radar (GPR) observations of artificially induced subsurface water dynamics. In particular, we place close scrutiny on the evolution of the capillary fringe in a highly dynamic regime with surface-based time-lapse GPR. We thoroughly explain all observed phenomena based on theoretical soil physical considerations and numerical simulations of both subsurface water flow and the expected GPR response.
A. Dagenbach, J. S. Buchner, P. Klenk, and K. Roth
Hydrol. Earth Syst. Sci., 17, 611–618, https://doi.org/10.5194/hess-17-611-2013, https://doi.org/10.5194/hess-17-611-2013, 2013
Related subject area
Subject: Vadose Zone Hydrology | Techniques and Approaches: Stochastic approaches
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Short summary
Particle filters are becoming popular for state and parameter estimations in hydrology. The renewal of the ensemble (resampling) is crucial in preventing filter degeneration. We introduce a resampling method that uses the weighted covariance of the ensemble, which contains information between observed and unobserved dimensions, to generate new ensemble members. This allows us to estimate the state and parameters for a rough initial guess in a synthetic hydrological case with just 100 particles.
Particle filters are becoming popular for state and parameter estimations in hydrology. The...
