Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
Abstract
Aim of study: To investigate the monitoring strategies that let us to build effective models able to best estimate water contents, θ and pressure heads, h with the least amount of data.
Area of study: Field data was acquired in an experimental plot at Bahía Blanca (Argentina).
Material and methods: Field data of θ(t), h(t) for six soil depth were used to optimize the SHP (θr, θs, α, n and Ks) by inverse modeling with HYDRUS 1D. Several scenarios of available data from θ(t) and h(t) were considered: (1) six monitoring depths (6-MD); (2) five monitoring depths (5-MD); (3) four monitoring depths (4-MD). Model accuracy was assessed by comparing the measured and predicted θ and h for each monitoring strategy. Additionally, field measured SHP with independent methods were compared to inversely optimized SHP.
Main results: The best fit between predicted and observed θ and h was achieved with the 6-MD strategy. Nevertheless, deterioration of statistics EF and rRMSE in the 5-MD or 4-MD schemes were lower than 10%, depending on the location of the missing data. The observation points that had less importance in parameter prediction corresponded to the intermediate vadose zone and to the deeper layers. The proposed strategies presented a better performance than field measured SHP to reproduce soil water retention curves for each layer of the soil profile.
Research highlights: By reducing the number of vertical observations in the profile without harming the final SHP estimation, the resources needed in data monitoring strategies can be greatly enhanced.
Downloads
References
Abbaspour KC, Sonnleitner MA, Schulin R, 1999. Uncertainty in estimation of soil hydraulic parameters by inverse modeling: example lysimeter experiments. Soil Sci Soc Am J 63: 501-509. https://doi.org/10.2136/sssaj1999.03615995006300030012x
Allen RG, Pereira LS, Raes D, Smith M, 1998. Crop evapotranspiration. FAO Irrig & Drain Paper N° 56:
Altafi Dadgar M, Nakhaei M, Porhemmat J, Eliasi B, Biswas A, 2020. Potential groundwater recharge from deep drainage of irrigation water. Sci Total Environ 716: 1-12. https://doi.org/10.1016/j.scitotenv.2020.137105
Blake GR, Hartge KH, 1986. Bulk density. In: Methods of soil analysis, Part 1-Physical and mineralogical methods; Klute A (Ed.). SSSA Book Series, Madison, pp. 363-382. https://doi.org/10.2136/sssabookser5.1.2ed.c13
Bordoni M, Bittelli M, Valentino R, Chersich S, Meisina C, 2017. Improving the estimation of complete field soil water characteristic curves through field monitoring data. J Hydrol 552: 283-305. https://doi.org/10.1016/j.jhydrol.2017.07.004
Brooks RH, Corey AT, 1964. Hydraulic properties of porous media: hydrology papers. Colorado State University, Fort Collins.
Cornelis WM, Khlosi M, Hartmann R, van Meirvenne M, De Vos B, 2005. Comparison of unimodal analytical expressions for the soil‐water retention curve. Soil Sci Soc Am J 69(6): 1902-1911. https://doi.org/10.2136/sssaj2004.0238
Da Silva AJP, Pinheiro EAR, de Jong van Lier Q, 2020. Determination of soil hydraulic properties and its implications for mechanistic simulations and irrigation management. Irrig Sci 38: 223-234. https://doi.org/10.1007/s00271-020-00664-5
Dane JH, Topp CG, 2002. Methods of soil analysis, Part 4: Physical methods. John Wiley & Sons. https://doi.org/10.2136/sssabookser5.4
Durner W, 1994. Hydraulic conductivity estimation for soils with heterogeneous pore structure. Water Resour Res 30: 211-223. https://doi.org/10.1029/93WR02676
Feddes RA, Bresler E, Neuman SP, 1974. Field test of a modified numerical model for water uptake by root systems. Water Resour Res 10: 1199-1206. https://doi.org/10.1029/WR010i006p01199
Filipović V, Mallmann FJK, Coquet Y, Šimůnek J, 2014. Numerical simulation of water flow in tile and mole drainage systems. Agric Water Manage 146: 105-114. https://doi.org/10.1016/j.agwat.2014.07.020
Fredlund DG, Xing A, 1994. Equations for the soil-water characteristic curve. Can Geotech J 31(4): 521-532. https://doi.org/10.1139/t94-061
Gabriel JL, Quemada M, Martín-Lammerding D, Vanclooster M, 2019. Assessing the cover crop effect on soil hydraulic properties by inverse modelling in a 10-year field trial. Agric Water Manage 222: 62-71. https://doi.org/10.1016/j.agwat.2019.05.034
Gee GH, Bauder JW, 1986. Particle size analysis, 2nd ed. Methods of Soil Analysis, Part 1-Physical and Mineralogical Methods. SSSA Book Series, Madison.
Graham SL, Srinivasan MS, Faulkner N, Carrick S, 2018. Soil hydraulic modeling outcomes with four parameterization methods: comparing soil description and inverse estimation approaches. Vadose Zone J 17: 170002. https://doi.org/10.2136/vzj2017.01.0002
Groenevelt PH, Grant CD, 2004. A new model for the soil-water retention curve that solves the problem of residual water contents. Eur J Soil Sci 55: 479-485. https://doi.org/10.1111/j.1365-2389.2004.00617.x
Groh J, Stumpp C, Lücke A, Pütz T, Vanderborght J, Vereecken H, 2018. Inverse estimation of soil hydraulic and transport parameters of layered soils from water stable isotope and lysimeter data. Vadose Zone J 17(1): 1-19. https://doi.org/10.2136/vzj2017.09.0168
Guo X, Sun X, Ma J, Lei T, Zheng L, Wang P, 2018. Simulation of the water dynamics and root water uptake of winter wheat in irrigation at different soil depths. Water 10: 1033. https://doi.org/10.3390/w10081033
Hulsemann J, 1966. On the routine analysis of carbonates in unconsolidated sediments. J Sediment Res 36 (2): 622-625.
Ket P, Oeurng C, Degré A, 2018. Estimating soil water retention curve by inverse modelling from combination of in situ dynamic soil water content and soil potential data. Soil Syst 2: 1-23. https://doi.org/10.3390/soilsystems2040055
Khlosi M, Cornelis WM, Douaik A, van Genuchten MT, Gabriels D, 2008. Performance evaluation of models that describe the soil water retention curve between saturation and oven dryness. Vadose Zone J 7(1): 87-96. https://doi.org/10.2136/vzj2007.0099
Kosugi K, 1999. General model for unsaturated hydraulic conductivity for soils with log-normal pore-size distribution. Soil Sci Soc Am J 63: 270-277. https://doi.org/10.2136/sssaj1999.03615995006300020003x
Legates DR, McCabe GJ, 1999. Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resour Res 35: 233-241. https://doi.org/10.1029/1998WR900018
Marquardt DW, 1963. An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11: 431-441. https://doi.org/10.1137/0111030
Mualem Y, 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour Res 12: 513-522. https://doi.org/10.1029/WR012i003p00513
Musters PAD, Bouten W, 2000. A method for identifying optimum strategies of measuring soil water contents for calibrating a root water uptake model. J Hydrol 227: 273-286. https://doi.org/10.1016/S0022-1694(99)00187-0
Musters PAD, Bouten W, Verstraten JM, 2000. Potentials and limitations of modelling vertical distributors of root water uptake of an Austrian pine forest on a sandy soil. Hydrol Process 14: 103-115. 3.0.CO;2-5" target="_blank">https://doi.org/10.1002/(SICI)1099-1085(200001)14:1<103::AID-HYP913>3.0.CO;2-5
Nash JE, Sutcliffe JV, 1970. River flow forecasting through conceptual models part I - A discussion of principles. J Hydrol 10: 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
Qu W, Bogena HR, Huisman JA, Martinez G, Pachepsky YA, Vereecken H, 2014. Effects of soil hydraulic properties on the spatial variability of soil water content: evidence from sensor network data and inverse modeling. Vadose Zone J 13(12): 1-12. https://doi.org/10.2136/vzj2014.07.0099
Ramos TB, Goncalves MC, Martins JC, van Genuchten MT, Pires FP, 2006. Estimation of soil hydraulic properties from numerical inversion of tension disk infiltrometer data. Vadose Zone J 5(2): 684-696. https://doi.org/10.2136/vzj2005.0076
Ritter A, Hupet F, Muñoz-Carpena R, Lambot S, Vanclooster M, 2003. Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods. Agric Water Manage 59: 77-96. https://doi.org/10.1016/S0378-3774(02)00160-9
Schaap MG, Leij FJ, van Genuchten MTh, 2001. ROSETTA: a computer program for estimating soil hydraulic properties with hierarchical pedotransfer functions. J Hydrol 251: 163-176. https://doi.org/10.1016/S0022-1694(01)00466-8
Scharnagl B, Vrugt JA, Vereecken H, Herbst M, 2011. Inverse modelling of in situ soil water dynamics: Investigating the effect of different prior distributions of the soil hydraulic parameters. Hydrol Earth Syst Sci 15: 3043-3059. https://doi.org/10.5194/hess-15-3043-2011
Schelle H, Durner W, Schlüter S, Vogel HJ, Vanderborght J, 2013. Virtual soils: moisture measurements and their interpretation by inverse modeling. Vadose Zone J 12: 1-12. https://doi.org/10.2136/vzj2012.0168
Scherger LE, Zanello V, Lexow C, 2019. Evaluation of recharge as vector flow for solute transport in the industrial area of Bahía Blanca (Argentina). 2nd Int Conf on the Environment and 4th Nat Conf on the Environment, Tandil.
Scherger LE, Zanello V, Lexow C, 2020. Comparison of the inverse modeling approach and traditional methods to estimate the unsaturated hydraulic properties in a sandy loam soil. Revista Aguas Subterrâneas 34: 310-324. https://doi.org/10.14295/ras.v34i3.29929
Šimůnek J, Hopmans JW, 2002. Parameter optimization and nonlinear fitting. In: Methods of soil analysis: Part 4 Physical methods; Dane JH & Topp GC (Eds.). SSSA Book Series, pp. 139-157. https://doi.org/10.2136/sssabookser5.4.c7
Šimůnek J, van Genuchten MTh, Wendroth O, 1998. Parameter estimation analysis of the evaporation method for determining soil hydraulic properties. Soil Sci Soc Am J 62: 894-905. https://doi.org/10.2136/sssaj1998.03615995006200040007x
Šimůnek J, Šejna MA, Saito H, Sakai M, van Genuchten MTh, 2013. The HYDRUS-1D software package for simulating the movement of water, heat, and multiple solutes in variably saturated media, version 4.17.
Tan X, Shao D, Liu H, 2014. Simulating soil water regime in lowland paddy fields under different water managements using HYDRUS-1D. Agric Water Manage 132: 69-78. https://doi.org/10.1016/j.agwat.2013.10.009
Thomas N, Schilling KE, Amado AA, Streeter M, Weber L, 2017. Inverse modeling of soil hydraulic properties in a two-layer system and comparisons with measured soil conditions. Vadose Zone J 16(2): 1-14. https://doi.org/10.2136/vzj2016.08.0072
Too V, Omuto C, Biamah E, Obiero J, 2014. Review of soil water retention characteristic (SWRC): Models between saturation and oven dryness. Open J Modern Hydrol 4: 173-182. https://doi.org/10.4236/ojmh.2014.44017
Valdes-Abellan J, Jiménez-Martínez J, Candela L, Tamoh K, 2015. Comparison among monitoring strategies to assess water flow dynamic and soil hydraulic properties in agricultural soils. Span J Agric Res 13: e1201. https://doi.org/10.5424/sjar/2015131-6323
Valdes-Abellan J, Pachepsky Y, Martinez G, Pla C, 2019. How critical is the assimilation frequency of water content measurements for obtaining soil hydraulic parameters with data assimilation? Vadose Zone J 18(1): 1-10. https://doi.org/10.2136/vzj2018.07.0142
van Genuchten MTh, 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44: 892-898. https://doi.org/10.2136/sssaj1980.03615995004400050002x
van Genuchten MTh, Leij FJ, Yates SR, 1991. The RETC code for quantifying the hydraulic functions of unsaturated soils. EPA 600: 2-91.
Ventrella D, Castellini M, di Prima S, Garofalo P, Lassabatère L, 2019. Assessment of the physically-based Hydrus-1D model for simulating the water fluxes of a Mediterranean cropping system. Water 11: 1-19. https://doi.org/10.3390/w11081657
Vereecken H, Weynants M, Javaux M, Pachepsky Y, Schaap MG, van Genuchten MT, 2010. Using pedotransfer functions to estimate the van Genuchten-Mualem soil hydraulic properties: A review. Vadose Zone J 9(4): 795-820. https://doi.org/10.2136/vzj2010.0045
Wang X, Li Y, Chen X, Wang H, Li L, Yao N, Liu DL, Biswas A, Sun S, 2021. Projection of the climate change effects on soil water dynamics of summer maize grown in water repellent soils using APSIM and HYDRUS-1D models. Comput Electron Agric 185: 106142. https://doi.org/10.1016/j.compag.2021.106142
Wesseling JG, 1991. Meerjarige simulatie van grondwaterstroming voor verschillende bodemprofielen, grondwatertrappen en gewassen met het model SWATRE. Rapport 152, Staring Centrum, Wageningen 1-63.
Yakirevich A, Gish TJ, Šimunek J, van Genuchten MTh, Pachepsky YA, Nicholson TJ, Cady RE, 2010. Potential impact of a seepage face on solute transport to a pumping well. Vadose Zone J 9: 686-696. https://doi.org/10.2136/vzj2009.0054
Yetbarek E, Kumar S, Ojha R, 2020. Effects of soil heterogeneity on subsurface water movement in agricultural fields: A numerical study. J Hydrol 590: 125420. https://doi.org/10.1016/j.jhydrol.2020.125420
Copyright (c) 2022 Spanish Journal of Agricultural Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
© CSIC. Manuscripts published in both the printed and online versions of this Journal are the property of Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a “Creative Commons Attribution 4.0 International” (CC BY 4.0) License. You may read here the basic information and the legal text of the license. The indication of the CC BY 4.0 License must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the published by the Editor, is not allowed.
