Articles | Volume 21, issue 10
https://doi.org/10.5194/hess-21-5375-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-5375-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Oct 2017
Research article |
| 26 Oct 2017
Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo
Khan Zaib Jadoon
CORRESPONDING AUTHOR
Department of the Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
now at: Department of Civil Engineering, International Islamic University, Islamabad 44000, Pakistan
Muhammad Umer Altaf
Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Earth Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Matthew Francis McCabe
Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Ibrahim Hoteit
Earth Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Nisar Muhammad
Department of the Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
Davood Moghadas
Brandenburg University of Technology, Research Center Landscape Development and Mining Landscapes, 03046 Cottbus, Germany
Lutz Weihermüller
Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, GmbH, 52425 Jülich, Germany
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Oliver Miguel López Valencia, Kasper Johansen, Bruno José Luis Aragón Solorio, Ting Li, Rasmus Houborg, Yoann Malbeteau, Samer AlMashharawi, Muhammad Umer Altaf, Essam Mohammed Fallatah, Hari Prasad Dasari, Ibrahim Hoteit, and Matthew Francis McCabe
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Rui Sun, Aneesh C. Subramanian, Arthur J. Miller, Matthew R. Mazloff, Ibrahim Hoteit, and Bruce D. Cornuelle
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K. Johansen, M. J. L. Morton, Y. Malbeteau, B. Aragon, S. Al-Mashharawi, M. Ziliani, Y. Angel, G. Fiene, S. Negrao, M. A. A. Mousa, M. A. Tester, and M. F. McCabe
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Geosci. Model Dev., 11, 3071–3088, https://doi.org/10.5194/gmd-11-3071-2018, https://doi.org/10.5194/gmd-11-3071-2018, 2018
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Mehdi Rahmati, Lutz Weihermüller, Jan Vanderborght, Yakov A. Pachepsky, Lili Mao, Seyed Hamidreza Sadeghi, Niloofar Moosavi, Hossein Kheirfam, Carsten Montzka, Kris Van Looy, Brigitta Toth, Zeinab Hazbavi, Wafa Al Yamani, Ammar A. Albalasmeh, Ma'in Z. Alghzawi, Rafael Angulo-Jaramillo, Antônio Celso Dantas Antonino, George Arampatzis, Robson André Armindo, Hossein Asadi, Yazidhi Bamutaze, Jordi Batlle-Aguilar, Béatrice Béchet, Fabian Becker, Günter Blöschl, Klaus Bohne, Isabelle Braud, Clara Castellano, Artemi Cerdà, Maha Chalhoub, Rogerio Cichota, Milena Císlerová, Brent Clothier, Yves Coquet, Wim Cornelis, Corrado Corradini, Artur Paiva Coutinho, Muriel Bastista de Oliveira, José Ronaldo de Macedo, Matheus Fonseca Durães, Hojat Emami, Iraj Eskandari, Asghar Farajnia, Alessia Flammini, Nándor Fodor, Mamoun Gharaibeh, Mohamad Hossein Ghavimipanah, Teamrat A. Ghezzehei, Simone Giertz, Evangelos G. Hatzigiannakis, Rainer Horn, Juan José Jiménez, Diederik Jacques, Saskia Deborah Keesstra, Hamid Kelishadi, Mahboobeh Kiani-Harchegani, Mehdi Kouselou, Madan Kumar Jha, Laurent Lassabatere, Xiaoyan Li, Mark A. Liebig, Lubomír Lichner, María Victoria López, Deepesh Machiwal, Dirk Mallants, Micael Stolben Mallmann, Jean Dalmo de Oliveira Marques, Miles R. Marshall, Jan Mertens, Félicien Meunier, Mohammad Hossein Mohammadi, Binayak P. Mohanty, Mansonia Pulido-Moncada, Suzana Montenegro, Renato Morbidelli, David Moret-Fernández, Ali Akbar Moosavi, Mohammad Reza Mosaddeghi, Seyed Bahman Mousavi, Hasan Mozaffari, Kamal Nabiollahi, Mohammad Reza Neyshabouri, Marta Vasconcelos Ottoni, Theophilo Benedicto Ottoni Filho, Mohammad Reza Pahlavan-Rad, Andreas Panagopoulos, Stephan Peth, Pierre-Emmanuel Peyneau, Tommaso Picciafuoco, Jean Poesen, Manuel Pulido, Dalvan José Reinert, Sabine Reinsch, Meisam Rezaei, Francis Parry Roberts, David Robinson, Jesús Rodrigo-Comino, Otto Corrêa Rotunno Filho, Tadaomi Saito, Hideki Suganuma, Carla Saltalippi, Renáta Sándor, Brigitta Schütt, Manuel Seeger, Nasrollah Sepehrnia, Ehsan Sharifi Moghaddam, Manoj Shukla, Shiraki Shutaro, Ricardo Sorando, Ajayi Asishana Stanley, Peter Strauss, Zhongbo Su, Ruhollah Taghizadeh-Mehrjardi, Encarnación Taguas, Wenceslau Geraldes Teixeira, Ali Reza Vaezi, Mehdi Vafakhah, Tomas Vogel, Iris Vogeler, Jana Votrubova, Steffen Werner, Thierry Winarski, Deniz Yilmaz, Michael H. Young, Steffen Zacharias, Yijian Zeng, Ying Zhao, Hong Zhao, and Harry Vereecken
Earth Syst. Sci. Data, 10, 1237–1263, https://doi.org/10.5194/essd-10-1237-2018, https://doi.org/10.5194/essd-10-1237-2018, 2018
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D. Turner, A. Lucieer, M. McCabe, S. Parkes, and I. Clarke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 379–384, https://doi.org/10.5194/isprs-archives-XLII-2-W6-379-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W6-379-2017, 2017
Matthew F. McCabe, Matthew Rodell, Douglas E. Alsdorf, Diego G. Miralles, Remko Uijlenhoet, Wolfgang Wagner, Arko Lucieer, Rasmus Houborg, Niko E. C. Verhoest, Trenton E. Franz, Jiancheng Shi, Huilin Gao, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, https://doi.org/10.5194/hess-21-3879-2017, 2017
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Carsten Montzka, Michael Herbst, Lutz Weihermüller, Anne Verhoef, and Harry Vereecken
Earth Syst. Sci. Data, 9, 529–543, https://doi.org/10.5194/essd-9-529-2017, https://doi.org/10.5194/essd-9-529-2017, 2017
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Stephen D. Parkes, Matthew F. McCabe, Alan D. Griffiths, Lixin Wang, Scott Chambers, Ali Ershadi, Alastair G. Williams, Josiah Strauss, and Adrian Element
Hydrol. Earth Syst. Sci., 21, 533–548, https://doi.org/10.5194/hess-21-533-2017, https://doi.org/10.5194/hess-21-533-2017, 2017
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Determining atmospheric moisture sources is required for understanding the water cycle. The role of land surface fluxes is a particular source of uncertainty for moisture budgets. Water vapour isotopes have the potential to improve constraints on moisture sources. In this work relationships between water vapour isotopes and land–atmosphere exchange are studied. Results show that land surface evaporative fluxes play a minor role in the daytime water and isotope budgets in semi-arid environments.
Jason P. Evans, Xianhong Meng, and Matthew F. McCabe
Hydrol. Earth Syst. Sci., 21, 409–422, https://doi.org/10.5194/hess-21-409-2017, https://doi.org/10.5194/hess-21-409-2017, 2017
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This work demonstrates that changes in surface albedo and vegetation, caused by the millennium drought in south-east Australia, affected the atmosphere in a way that decreased precipitation further. This land–surface feedback increased the severity of the drought by 10 %. This suggests that climate models need to simulate changes in surface characteristics (other than soil moisture) in response to a developing drought if they are to capture this kind of multi-year drought.
Oliver López, Rasmus Houborg, and Matthew Francis McCabe
Hydrol. Earth Syst. Sci., 21, 323–343, https://doi.org/10.5194/hess-21-323-2017, https://doi.org/10.5194/hess-21-323-2017, 2017
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The study evaluated the spatial and temporal consistency of satellite-based hydrological products based on the water budget equation, including three global evaporation products. The products were spatially matched using spherical harmonics analysis. The results highlighted the difficulty in obtaining agreement between independent satellite products, even over regions with simple water budgets. However, imposing a time lag on water storage data improved results considerably.
Mohamad E. Gharamti, Johan Valstar, Gijs Janssen, Annemieke Marsman, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 4561–4583, https://doi.org/10.5194/hess-20-4561-2016, https://doi.org/10.5194/hess-20-4561-2016, 2016
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The paper addresses the issue of sampling errors when using the ensemble Kalman filter, in particular its hybrid and second-order formulations. The presented work is aimed at estimating concentration and biodegradation rates of subsurface contaminants at the port of Rotterdam in the Netherlands. Overall, we found that accounting for both forecast and observation sampling errors in the joint data assimilation system helps recover more accurate state and parameter estimates.
Raghavendra B. Jana, Ali Ershadi, and Matthew F. McCabe
Hydrol. Earth Syst. Sci., 20, 3987–4004, https://doi.org/10.5194/hess-20-3987-2016, https://doi.org/10.5194/hess-20-3987-2016, 2016
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Interactions between soil moisture and terrestrial evaporation affect responses between land surface and the atmosphere across scales. We present an analysis of the link between soil moisture and evaporation estimates from three distinct models. The relationships were examined over nearly 2 years of observation data. Results show that while direct correlations of raw data were mostly not useful, the root-zone soil moisture and the modelled evaporation estimates reflect similar distributions.
Boujemaa Ait-El-Fquih, Mohamad El Gharamti, and Ibrahim Hoteit
Hydrol. Earth Syst. Sci., 20, 3289–3307, https://doi.org/10.5194/hess-20-3289-2016, https://doi.org/10.5194/hess-20-3289-2016, 2016
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We derive a new dual ensemble Kalman filter (EnKF) for state-parameter estimation. The derivation is based on the one-step-ahead smoothing formulation, and unlike the standard dual EnKF, it is consistent with the Bayesian formulation of the state-parameter estimation problem and uses the observations in both state smoothing and forecast. This is shown to enhance the performance and robustness of the dual EnKF in experiments conducted with a two-dimensional synthetic groundwater aquifer model.
Simone Bircher, Mie Andreasen, Johanna Vuollet, Juho Vehviläinen, Kimmo Rautiainen, François Jonard, Lutz Weihermüller, Elena Zakharova, Jean-Pierre Wigneron, and Yann H. Kerr
Geosci. Instrum. Method. Data Syst., 5, 109–125, https://doi.org/10.5194/gi-5-109-2016, https://doi.org/10.5194/gi-5-109-2016, 2016
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At the Finnish Meteorological Institute in Sodankylä and the Danish Center for Hydrology, calibration functions for organic surface layers were derived for two in situ soil moisture sensors to be used in the validation of coarse-resolution soil moisture from satellites and land surface models. There was no clear difference in the data from a variety of humus types, strengthening confidence that these calibrations are applicable over a wide range of conditions as encountered in the large areas.
D. G. Miralles, C. Jiménez, M. Jung, D. Michel, A. Ershadi, M. F. McCabe, M. Hirschi, B. Martens, A. J. Dolman, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, https://doi.org/10.5194/hess-20-823-2016, 2016
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The WACMOS-ET project aims to advance the development of land evaporation estimates on global and regional scales. Evaluation of current evaporation data sets on the global scale showed that they manifest large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into several components. Different models perform better under different conditions, highlighting the potential for considering biome- or climate-specific model ensembles.
D. Michel, C. Jiménez, D. G. Miralles, M. Jung, M. Hirschi, A. Ershadi, B. Martens, M. F. McCabe, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 803–822, https://doi.org/10.5194/hess-20-803-2016, https://doi.org/10.5194/hess-20-803-2016, 2016
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In this study a common reference input data set from satellite and in situ data is used to run four established evapotranspiration (ET) algorithms using sub-daily and daily input on a tower scale as a testbed for a global ET product. The PT-JPL model and GLEAM provide the best performance for satellite and in situ forcing as well as for the different temporal resolutions. PM-MOD and SEBS perform less well: the PM-MOD model generally underestimates, while SEBS generally overestimates ET.
M. F. McCabe, A. Ershadi, C. Jimenez, D. G. Miralles, D. Michel, and E. F. Wood
Geosci. Model Dev., 9, 283–305, https://doi.org/10.5194/gmd-9-283-2016, https://doi.org/10.5194/gmd-9-283-2016, 2016
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In an effort to develop a global terrestrial evaporation product, four models were forced using both a tower and grid-based data set. Comparisons against flux-tower observations from different biome and land cover types show considerable inter-model variability and sensitivity to forcing type. Results suggest that no single model is able to capture expected flux patterns and response. It is suggested that a multi-model ensemble is likely to provide a more stable long-term flux estimate.
H. Ajami, J. P. Evans, M. F. McCabe, and S. Stisen
Hydrol. Earth Syst. Sci., 18, 5169–5179, https://doi.org/10.5194/hess-18-5169-2014, https://doi.org/10.5194/hess-18-5169-2014, 2014
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A new hybrid approach was developed to reduce the computational burden of the spin-up procedure by using a combination of model simulations and an empirical depth-to-water table function. Results illustrate that the hybrid approach reduced the spin-up period required for an integrated groundwater--surface water--land surface model (ParFlow.CLM) by up to 50%. The methodology is applicable to other coupled or integrated modeling frameworks when initialization from an equilibrium state is required.
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
A. D. Griffiths, S. D. Parkes, S. D. Chambers, M. F. McCabe, and A. G. Williams
Atmos. Meas. Tech., 6, 207–218, https://doi.org/10.5194/amt-6-207-2013, https://doi.org/10.5194/amt-6-207-2013, 2013
Related subject area
Subject: Vadose Zone Hydrology | Techniques and Approaches: Stochastic approaches
Detecting hydrological connectivity using causal inference from time series: synthetic and real karstic case studies
Covariance resampling for particle filter – state and parameter estimation for soil hydrology
State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction
Kalman filters for assimilating near-surface observations into the Richards equation – Part 1: Retrieving state profiles with linear and nonlinear numerical schemes
Kalman filters for assimilating near-surface observations into the Richards equation – Part 2: A dual filter approach for simultaneous retrieval of states and parameters
Kalman filters for assimilating near-surface observations into the Richards equation – Part 3: Retrieving states and parameters from laboratory evaporation experiments
State-space approach to evaluate spatial variability of field measured soil water status along a line transect in a volcanic-vesuvian soil
Damien Delforge, Olivier de Viron, Marnik Vanclooster, Michel Van Camp, and Arnaud Watlet
Hydrol. Earth Syst. Sci., 26, 2181–2199, https://doi.org/10.5194/hess-26-2181-2022, https://doi.org/10.5194/hess-26-2181-2022, 2022
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Causal inference methods (CIMs) aim at identifying causal links from temporal dependencies found in time-series data. Using both synthetic data and real-time series from a karst system, we study and discuss the potential of four CIMs to reveal hydrological connections between variables in hydrological systems. Despite the ever-present risk of spurious hydrological connections, our results highlight that the nonlinear and multivariate CIM has a substantially lower false-positive rate.
Daniel Berg, Hannes H. Bauser, and Kurt Roth
Hydrol. Earth Syst. Sci., 23, 1163–1178, https://doi.org/10.5194/hess-23-1163-2019, https://doi.org/10.5194/hess-23-1163-2019, 2019
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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.
Hongjuan Zhang, Harrie-Jan Hendricks Franssen, Xujun Han, Jasper A. Vrugt, and Harry Vereecken
Hydrol. Earth Syst. Sci., 21, 4927–4958, https://doi.org/10.5194/hess-21-4927-2017, https://doi.org/10.5194/hess-21-4927-2017, 2017
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Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We find that all DA methods can improve prediction of states, and that differences between DA methods were limited but that the differences between LSMs were much larger.
Roland Baatz, Harrie-Jan Hendricks Franssen, Xujun Han, Tim Hoar, Heye Reemt Bogena, and Harry Vereecken
Hydrol. Earth Syst. Sci., 21, 2509–2530, https://doi.org/10.5194/hess-21-2509-2017, https://doi.org/10.5194/hess-21-2509-2017, 2017
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Soil moisture is a major variable that affects regional climate, weather and hydrologic processes on the Earth's surface. In this study, real-world data of a network of cosmic-ray sensors were assimilated into a regional land surface model to improve model states and soil hydraulic parameters. The results show the potential of these networks for improving model states and parameters. It is suggested to widen the number of observed variables and to increase the number of estimated parameters.
G. B. Chirico, H. Medina, and N. Romano
Hydrol. Earth Syst. Sci., 18, 2503–2520, https://doi.org/10.5194/hess-18-2503-2014, https://doi.org/10.5194/hess-18-2503-2014, 2014
H. Medina, N. Romano, and G. B. Chirico
Hydrol. Earth Syst. Sci., 18, 2521–2541, https://doi.org/10.5194/hess-18-2521-2014, https://doi.org/10.5194/hess-18-2521-2014, 2014
H. Medina, N. Romano, and G. B. Chirico
Hydrol. Earth Syst. Sci., 18, 2543–2557, https://doi.org/10.5194/hess-18-2543-2014, https://doi.org/10.5194/hess-18-2543-2014, 2014
A. Comegna, A. Coppola, V. Comegna, G. Severino, A. Sommella, and C. D. Vitale
Hydrol. Earth Syst. Sci., 14, 2455–2463, https://doi.org/10.5194/hess-14-2455-2010, https://doi.org/10.5194/hess-14-2455-2010, 2010
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Short summary
In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity in an agriculture field irrigated with a drip irrigation system. Electromagnetic model parameters and uncertainty were estimated using adaptive Bayesian Markov chain Monte Carlo (MCMC). Application of the MCMC-based inversion to the synthetic and field measurements demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil.
In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity...
