Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2661-2023
© Author(s) 2023. 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-27-2661-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2023
Research article |
| 19 Jul 2023
| 19 Jul 2023
Data worth analysis within a model-free data assimilation framework for soil moisture flow
Yakun Wang
Key Laboratory of Agricultural Soil and Water Engineering of in Arid and Semiarid Areas, Ministry of Education, Northwest A & F University, Yangling, Shaanxi 712100, China
Xiaolong Hu
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
Lijun Wang
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
Jinmin Li
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
Lin Lin
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
Kai Huang
Guangxi Key Laboratory of Water Engineering Materials and Structures, Guangxi Institute of Water Resources Research, Nanning 530023, China
Liangsheng Shi
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Sciences, Wuhan University, Wuhan, Hubei 430072, China
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Cited articles
Akhtar, K., Wang, W., Khan, A., Ren, G., Afridi, M. Z., Feng, Y., and Yang,
G.: Wheat straw mulching offset soil moisture deficient for improving
physiological and growth performance of summer sown soybean, Agric. Water
Manage., 211, 16–25, https://doi.org/10.1016/j.agwat.2018.09.031, 2019.
Amro, A., Al-Akhras, M., Hindi, K. E., Habib, M., and Shawar, B. A.:
Instance Reduction for Avoiding Overfitting in Decision Trees, J. Intell.
Syst., 30, 438–459, https://doi.org/10.1515/jisys-2020-0061, 2021.
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data
assimilation and machine learning to emulate a dynamical model from sparse
and noisy observations: A case study with the Lorenz 96 model, J. Comput.
Sci., 44, 101171, https://doi.org/10.1016/j.jocs.2020.101171, 2020.
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data
assimilation and machine learning to infer unresolved scale parametrization,
Philos. T. Roy. Soc. A, 379, 20200086, https://doi.org/10.1098/rsta.2020.0086, 2021.
Bresler, E., Heller, J., Diner, N., Ben-Asher, I., Brandt, A., and Goldberg,
D.: Infiltration from a Trickle Source: II. Experimental Data and
Theoretical Predictions, Soil Sci. Soc. Am. J., 35, 683–689,
https://doi.org/10.2136/sssaj1971.03615995003500050019x, 1971.
Chandrashekar, G. and Sahin, F.: A survey on feature selection methods,
Comput. Electr. Eng., 40, 16–28, https://doi.org/10.1016/j.compeleceng.2013.11.024, 2014.
Dai, C., Xue, L., Zhang, D., and Guadagnini, A.: Data-worth analysis through
probabilistic collocation-based Ensemble Kalman Filter, J. Hydrol., 540,
488–503, https://doi.org/10.1016/j.jhydrol.2016.06.037, 2016.
Dausman, A. M., Doherty, J., Langevin, C. D., and Sukop, M. C.: Quantifying
Data Worth Toward Reducing Predictive Uncertainty, Groundwater, 48, 729–740, https://doi.org/10.1111/j.1745-6584.2010.00679.x, 2010.
Dobriyal, P., Qureshi, A., Badola, R., and Hussain, S. A.: A review of the
methods available for estimating soil moisture and its implications for
water resource management, J. Hydrol., 458–459, 110–117,
https://doi.org/10.1016/j.jhydrol.2012.06.021, 2012.
Dunne, S. and Entekhabi, D.: An ensemble-based reanalysis approach to land
data assimilation, Water Resour. Res., 41, W02013, https://doi.org/10.1029/2004WR003449, 2005.
Fienen, M. N., Doherty, J. E., Hunt, R. J., and Reeves, H. W.: Using
prediction uncertainty analysis to design hydrologic monitoring networks:
example applications from the Great Lakes water availability pilot project,
US Geological Survey, https://pubs.usgs.gov/sir/2010/5159/ (last access: 15 July 2023), 2010.
Finsterle, S.: Practical notes on local data-worth analysis, Water Resour.
Res., 51, 9904–9924, https://doi.org/10.1002/2015WR017445, 2015.
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., and Herrera, F.: Big data preprocessing: methods and prospects, Big Data Anal., 1, 9, https://doi.org/10.1186/s41044-016-0014-0, 2016.
García-Gil, D., Luengo, J., García, S., and Herrera, F.: Enabling
Smart Data: Noise filtering in Big Data classification, Inform. Sci., 479,
135–152, https://doi.org/10.1016/j.ins.2018.12.002, 2019.
Gu, H., Lin, Z., Guo, W., and Deb, S.: Retrieving Surface Soil Water Content
Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System
Images, Remote Sens., 13, 145, https://doi.org/10.3390/rs13010145, 2021.
Hall, M. A.: Correlation-based feature selection for machine learning, The University of Waikato, https://researchcommons.waikato.ac.nz/handle/10289/15043 (last access: 15 July 2023), 1999.
Hamilton, F., Berry, T., and Sauer, T.: Kalman-Takens filtering in the
presence of dynamical noise, Eur. Phys. J. Spec. Top., 226, 3239–3250,
https://doi.org/10.1140/epjst/e2016-60363-2, 2017.
Hill, M. C. and Tiedeman, C. R.: Effective groundwater model calibration:
with analysis of data, sensitivities, predictions, and uncertainty, John
Wiley & Sons, https://wwwbrr.cr.usgs.gov/projects/GW_ModUncert/hill_tiedeman_book/exercise-files-UCODE_2005/ExerciseInstructions-mfi05-uc-v17.pdf (last access: 15 July 2023), 2006.
ISMN – International Soil Moisture Network: Welcome to the International Soil Moisture Network, https://ismn.geo.tuwien.ac.at/en/ (last access: 15 July 2023), 2023.
Hughes, G.: On the mean accuracy of statistical pattern recognizers, IEEE
T. Inform. Theory, 14, 55–63, https://doi.org/10.1109/TIT.1968.1054102, 1968.
Ju, L., Zhang, J., Meng, L., Wu, L., and Zeng, L.: An adaptive Gaussian
process-based iterative ensemble smoother for data assimilation, Adv. Water
Resour., 115, 125–135, https://doi.org/10.1016/j.advwatres.2018.03.010, 2018.
Kashif Gill, M., Kemblowski, M. W., and McKee, M.: Soil Moisture Data
Assimilation Using Support Vector Machines and Ensemble Kalman Filter1, J. Am. Water Resour. Assoc., 43, 1004–1015, https://doi.org/10.1111/j.1752-1688.2007.00082.x, 2007.
Kisekka, I., Migliaccio, K. W., Muñoz-Carpena, R., Schaffer, B., and Khare, Y.: Modelling soil water dynamics considering measurement uncertainty, Hydrol. Process., 29, 692–711, https://doi.org/10.1002/hyp.10173, 2015.
Lannoy, G. J. M. D., Verhoest, N. E. C., Houser, P. R., Gish, T. J., and
Meirvenne, M. V.: Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE3), J. Hydrol., 331, 719–730, https://doi.org/10.1016/j.jhydrol.2006.06.016, 2006.
Leube, P. C., Geiges, A., and Nowak, W.: Bayesian assessment of the expected
data impact on prediction confidence in optimal sampling design, Water Resour. Res., 48, W02501, https://doi.org/10.1029/2010WR010137, 2012.
Li, C. and Ren, L.: Estimation of Unsaturated Soil Hydraulic Parameters Using the Ensemble Kalman Filter, Vadose Zone J., 10, 1205–1227,
https://doi.org/10.2136/vzj2010.0159, 2011.
Li, P., Zha, Y., Shi, L., Tso, C.-H. M., Zhang, Y., and Zeng, W.: Comparison
of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics, J. Hydrol., 584, 124692, https://doi.org/10.1016/j.jhydrol.2020.124692, 2020.
Li, X., Shi, L., Zha, Y., Wang, Y., and Hu, S.: Data assimilation of soil
water flow by considering multiple uncertainty sources and spatial–temporal
features: a field-scale real case study, Stoch. Environ. Res. Risk A., 32, 2477–2493, https://doi.org/10.1007/s00477-018-1541-1, 2018.
Liu, H. L., Yang, J. Y., Tan, C. S., Drury, C. F., Reynolds, W. D., Zhang, T. Q., Bai, Y. L., Jin, J., He, P., and Hoogenboom, G.: Simulating water content, crop yield and nitrate-N loss under free and controlled tile drainage with subsurface irrigation using the DSSAT model, Agr. Water Manage., 98, 1105–1111, https://doi.org/10.1016/j.agwat.2011.01.017, 2011.
Liu, K., Huang, G., Jiang, Z., Xu, X., Xiong, Y., Huang, Q., and Šimůnek, J.: A gaussian process-based iterative Ensemble Kalman Filter for parameter estimation of unsaturated flow, J. Hydrol., 589, 125210, https://doi.org/10.1016/j.jhydrol.2020.125210, 2020.
Man, J., Zhang, J., Li, W., Zeng, L., and Wu, L.: Sequential ensemble-based
optimal design for parameter estimation, Water Resour. Res., 52, 7577–7592,
https://doi.org/10.1002/2016WR018736, 2016.
Minns, A. W. and Hall, M. J.: Artificial neural networks as rainfall-runoff
models, Hydrolog. Sci. J., 41, 399–417, https://doi.org/10.1080/02626669609491511, 1996.
Montzka, C., Moradkhani, H., Weihermüller, L., Franssen, H.-J. H., Canty, M., and Vereecken, H.: Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter, J. Hydrol., 399, 410–421, https://doi.org/10.1016/j.jhydrol.2011.01.020, 2011.
Neuman, S. P., Xue, L., Ye, M., and Lu, D.: Bayesian analysis of data-worth
considering model and parameter uncertainties, Adv. Water Resour., 36, 75–85, https://doi.org/10.1016/j.advwatres.2011.02.007, 2012.
Nowak, W., Rubin, Y., and de Barros, F. P. J.: A hypothesis-driven approach
to optimize field campaigns, Water Resour. Res., 48, W06509, https://doi.org/10.1029/2011WR011016, 2012.
Olvera-López, J. A., Carrasco-Ochoa, J. A., Martínez-Trinidad, J. F., and Kittler, J.: A review of instance selection methods, Artif. Intell. Rev., 34, 133–143, https://doi.org/10.1007/s10462-010-9165-y, 2010.
Pechenizkiy, M., Tsymbal, A., Puuronen, S., and Pechenizkiy, O.: Class Noise
and Supervised Learning in Medical Domains: The Effect of Feature Extraction, in: 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 22–23 June 2006, Salt Lake City, UT, USA, 708713, https://doi.org/10.1109/CBMS.2006.65, 2006.
Rasmussen, C. E.: Gaussian Processes in Machine Learning, in: Advanced
Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia,
February 2–14, 2003, Tübingen, Germany, August 4–16, 2003, Revised
Lectures, edited by: Bousquet, O., von Luxburg, U., and Rätsch, G.,
Springer, Berlin, Heidelberg, 63–71, https://doi.org/10.1007/978-3-540-28650-9_4, 2004.
Rasmussen, C. E. and Williams, C. K.: GPML Matlab Code version 4.2, http://www.gaussianprocess.org/gpml/code/matlab/doc/ (last access: 15 July 2023), 2006.
Reichle, R. H., Crow, W. T., and Keppenne, C. L.: An adaptive ensemble
Kalman filter for soil moisture data assimilation, Water Resour. Res., 44,
W03423, https://doi.org/10.1029/2007WR006357, 2008.
Richards, L. A.: Capillary Conduction Of Liquids Through Porous Mediums,
Physics, 1, 318–333, https://doi.org/10.1063/1.1745010, 1931.
Richardson, L. F.: Weather prediction by numerical process, Cambridge University Press, UK, https://doi.org/10.1017/CBO9780511618291, 1922.
Ross, P. J.: Modeling Soil Water and Solute Transport – Fast, Simplified Numerical Solutions, Agron. J., 95, 1352–1361, https://doi.org/10.2134/agronj2003.1352, 2003.
Shannon, C. E.: Communication in the Presence of Noise, Proc. IRE, 37, 10–21, https://doi.org/10.1109/JRPROC.1949.232969, 1949.
Shi, C., Xie, Z., Qian, H., Liang, M., and Yang, X.: China land soil moisture EnKF data assimilation based on satellite remote sensing data, Sci. China Earth Sci., 54, 1430–1440, https://doi.org/10.1007/s11430-010-4160-3, 2011.
Shuwen, Z., Haorui, L., Weidong, Z., Chongjian, Q., and Xin, L.: Estimating
the soil moisture profile by assimilating near-surface observations with the
ensemble Kaiman filter (EnKF), Adv. Atmos. Sci., 22, 936–945,
https://doi.org/10.1007/BF02918692, 2005.
Šimůnek, J., Van Genuchten, M. T., and Šejna, M.: The HYDRUS software package for simulating two-and three-dimensional movement of water, heat, and multiple solutes in variably saturated media, Tech. Man. Version 1, 241 pp., https://www.researchgate.net/profile/Jiri-Jirka-Simunek/publication/236901785_The_HYDRUS-2D_Software_Package_for_Simulating_Water (last access: 15 July 2023), 2006.
Singh, K., Sandu, A., Jardak, M., Bowman, K. W., and Lee, M.: A Practical
Method to Estimate Information Content in the Context of 4D-Var Data Assimilation, SIAMASA J. Uncertain. Quantif., 1, 106–138,
https://doi.org/10.1137/120884523, 2013.
Song, X., Shi, L., Ye, M., Yang, J., and Navon, I. M.: Numerical Comparison
of Iterative Ensemble Kalman Filters for Unsaturated Flow Inverse Modeling,
Vadose Zone J., 13, vzj2013.05.0083, https://doi.org/10.2136/vzj2013.05.0083, 2014.
van Dam, J. C. and Feddes, R. A.: Numerical simulation of infiltration,
evaporation and shallow groundwater levels with the Richards equation, J.
Hydrol., 233, 72–85, https://doi.org/10.1016/S0022-1694(00)00227-4, 2000.
Vauclin, M., Khanji, D., and Vachaud, G.: Experimental and numerical study of a transient, two-dimensional unsaturated-saturated water table recharge problem, Water Resour. Res., 15, 1089–1101, https://doi.org/10.1029/WR015i005p01089, 1979.
Wang, Y., Shi, L., Zha, Y., Li, X., Zhang, Q., and Ye, M.: Sequential data-worth analysis coupled with ensemble Kalman filter for soil water flow:
A real-world case study, J. Hydrol., 564, 76–88, https://doi.org/10.1016/j.jhydrol.2018.06.059, 2018.
Wang, Y., Shi, L., Lin, L., Holzman, M., Carmona, F., and Zhang, Q.: A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning, Vadose Zone J., 19, e20026, https://doi.org/10.1002/vzj2.20026, 2020.
Wang, Y., Shi, L., Xu, T., Zhang, Q., Ye, M., and Zha, Y.: A nonparametric
sequential data assimilation scheme for soil moisture flow, J. Hydrol., 593,
125865, https://doi.org/10.1016/j.jhydrol.2020.125865, 2021a.
Wang, Y., Shi, L., Zhang, Q., and Qiao, H.: A gradient-enhanced sequential
nonparametric data assimilation framework for soil moisture flow, J. Hydrol., 603, 126857, https://doi.org/10.1016/j.jhydrol.2021.126857, 2021b.
Wierenga, P. J., Gelhar, L. W., Simmons, C. S., Gee, G. W., and Nicholson, T. J.: Validation of stochastic flow and transport models for unsaturated soils: a comprehensive field study, United States, OSTI.GOV, https://www.osti.gov/biblio/5367083 (last access: 15 July 2023), 1986.
Xu, Q.: Measuring information content from observations for data assimilation: relative entropy versus shannon entropy difference, Tellus A, 59, 198–209, https://doi.org/10.1111/j.1600-0870.2006.00222.x, 2007.
Xu, T. and Valocchi, A. J.: Data-driven methods to improve baseflow prediction of a regional groundwater model, Comput. Geosci., 85, 124–136,
https://doi.org/10.1016/j.cageo.2015.05.016, 2015.
Yamanaka, A., Maeda, Y., and Sasaki, K.: Ensemble Kalman filter-based data
assimilation for three-dimensional multi-phase-field model: Estimation of
anisotropic grain boundary properties, Mater. Des., 165, 107577,
https://doi.org/10.1016/j.matdes.2018.107577, 2019.
Yang, J., Li, B., and Shiping, L.: A large weighing lysimeter for
evapotranspiration and soil-water–groundwater exchange studies, Hydrol.
Process., 14, 1887–1897, https://doi.org/10.1002/1099-1085(200007)14:10<1887::AID-HYP69>3.0.CO;2-B, 2000.
Yeh, T.-C. J., Gelhar, L. W., and Gutjahr, A. L.: Stochastic Analysis of
Unsaturated Flow in Heterogeneous Soils: 1. Statistically Isotropic Media,
Water Resour. Res., 21, 447–456, https://doi.org/10.1029/WR021i004p00447, 1985.
Zha, Y., Shi, L., Ye, M., and Yang, J.: A generalized Ross method for two-
and three-dimensional variably saturated flow, Adv. Water Resour., 54,
67–77, https://doi.org/10.1016/j.advwatres.2013.01.002, 2013.
Zhang, J., Zeng, L., Chen, C., Chen, D., and Wu, L.: Efficient Bayesian
experimental design for contaminant source identification, Water Resour. Res., 51, 576–598, https://doi.org/10.1002/2014WR015740, 2015.
Zhang, Q., Shi, L., Holzman, M., Ye, M., Wang, Y., Carmona, F., and Zha, Y.:
A dynamic data-driven method for dealing with model structural error in soil
moisture data assimilation, Adv. Water Resour., 132, 103407,
https://doi.org/10.1016/j.advwatres.2019.103407, 2019.
Zhu, X. and Wu, X.: Class Noise vs. Attribute Noise: A Quantitative Study,
Artif. Intell. Rev., 22, 177–210, https://doi.org/10.1007/s10462-004-0751-8, 2004.
Short summary
To avoid overloaded monitoring cost from redundant measurements, this study proposed a non-parametric data worth analysis framework to assess the worth of future soil moisture data regarding the model-free unsaturated flow models before data gathering. Results indicated that (1) the method can quantify the data worth of alternative monitoring schemes to obtain the optimal one, and (2) high-quality and representative small data could be a better choice than unfiltered big data.
To avoid overloaded monitoring cost from redundant measurements, this study proposed a...
