Abstract
Sensitivity analysis and multiobjective optimisation are established diagnostic instruments for the identification of uncertainty factors and structural deficits in environmental simulation models. Although the application of both techniques provides a comprehensive understanding of model behaviour, they are seldom practised in combination. In this study, the Sobol global sensitivity method and the multiobjective algorithm AMALGAM are combined to assess the agro-hydrological SWAP model for simulating the soil water balance of different sole and mixed crops based on hydrological and phenological field observations. Fifteen unknown model parameters are subjected to the sensitivity analysis (GSA) with the aim of finding their importance to model performance of matric potential (F 1) and soil water content (F 2). Subsequently, sensitive parameters are calibrated by optimising F 1 and F 2 simultaneously. The GSA showed that the description of the rooting density and potential evapotranspiration is of crucial important to F 1, and that soil properties are most relevant for F 2. Parameter interactions played a primary role in the response of matric potential, being irrelevant for F 2. Structural model deficiencies in reproducing both objectives simultaneously were found in the multiobjective analysis, meaning that deterioration in the fit to one of the objectives is in favour of the other. However, solutions exist that produce satisfying fits to both observational types, suggesting that the SWAP model has the capability of simulating the soil water balance of the crops considered. The results of the evaluation period revealed model deficiencies in simulating the process under an environmental regime significantly different from that of the calibration period, indicating the necessity of acquiring a broader spectrum of environmental regimes for parameter calibration. Overall, this study demonstrates how complex and variable the relationship between parameters and model outputs can be in environmental models and highlights the value of combining global sensitivity analysis and multiobjective optimisation in order to improve model performances.
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References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO irrigation and drainage paper, vol 56. Food and Agriculture Organization of the United States, Rome
Baroni G, Tarantola S (2014) A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: a hydrological case study. Environ Model Softw 51:26–34. doi:10.1016/j.envsoft.2013.09.022
Bastidas LA, Gupta HV, Sorooshian S, Shuttleworth WJ, Yang ZL (1999) Sensitivity analysis of a land surface scheme using multicriteria methods. J Geophys Res Atmos 104(D16):19481–19490
Bedoussac L, Journet EP, Hauggaard-Nielsen H, Naudin C, Corre-Hellou G, Jensen ES, Prieur L, Justes E (2015) Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review. Agron Sustain Dev 35(3):911–935. doi:10.1007/s13593-014-0277-7
Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320(1–2):18–36. doi:10.1016/j.jhydrol.2005.07.007
Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydrol 249(1–4):11–29. doi:10.1016/S0022-1694(01)00421-8
Bohne K, Horn R, Baumgartl T (1993) Bereitstellung von van-Genuchten-Parametern zur Charakterisierung der hydraulischen Bodeneigenschaften. Pflanzenernährung Bodenkunde 156:229–233
Brisson N (ed) (2008) Conceptual basis, formalisations and parameterization of the STICS crop model. Collection Update sciences & technologies. Éditions Quæ, Versailles
Cibin R, Sudheer KP, Chaubey I (2010) Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydrol Process 24(9):1133–1148. doi:10.1002/hyp.7568
de Jong van Lier Q, Wendroth O, van Dam JC (2015) Prediction of winter wheat yield with the SWAP model using pedotransfer functions: an evaluation of sensitivity, parameterization and prediction accuracy. Agric Water Manag 154:29–42. doi:10.1016/j.agwat.2015.02.011
Delta-T Devices Ltd (2016) User manual: Sun Scan Canopy Analysis System type SS1. Delta-T Devices Limited, Cambridge
Djaman K, Irmak S (2013) Actual crop evapotranspiration and alfalfa- and grass-reference crop coefficients of maize under full and limited irrigation and rainfed conditions. J Irrig Drain Eng 139(6):433–446. doi:10.1061/(ASCE)IR.1943-4774.0000559
Efron B, Tibshirani RJ (1998) An introduction to the bootstrap, [Nachdr.]. Monographs on statistics and applied probability, vol 57. Chapman & Hall, Boca Raton
Efstratiadis A, Koutsoyiannis D (2010) One decade of multi-objective calibration approaches in hydrological modelling: a review|Une décennie d’approches de calage multi-objectifs en modélisation hydrologique: Une revue. Hydrol Sci J 55(1):58–78. doi:10.1080/02626660903526292
Feddes RA, Kowalik PJ, Zaradny H (1978) Simulation of filed water use and crop yield. Simulation Monographs. Pudoc, Wageningen
Fustec J, Lesuffleur F, Mahieu S, Cliquet JB (2010) Nitrogen rhizodeposition of legumes. A review. Agron Sustain Dev 30(1):57–66. doi:10.1051/agro/2009003
Gan Y, Duan Q, Gong W, Tong C, Sun Y, Chu W, Ye A, Miao C, Di Z (2014) A comprehensive evaluation of various sensitivity analysis methods: a case study with a hydrological model. Environ Model Softw 51:269–285. doi:10.1016/j.envsoft.2013.09.031
Gao Y, Duan A, Sun J, Li F, Liu Z, Liu H, Liu Z (2009) Crop coefficient and water-use efficiency of winter wheat/spring maize strip intercropping. Field Crops Res 111(1–2):65–73. doi:10.1016/j.fcr.2008.10.007
Gerwitz A, Page ER (1974) An empirical mathematical model to describe plant root systems. J Appl Ecol 11(2):773. doi:10.2307/2402227
Ghasemizade M, Baroni G, Abbaspour K, Schirmer M (2017) Combined analysis of time-varying sensitivity and identifiability indices to diagnose the response of a complex environmental model. Environ Model Softw 88:22–34. doi:10.1016/j.envsoft.2016.10.011
Gupta HV, Sorooshian S, Yapo PO (1998) Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resour Res 34(4):751–763
Guttmann-Bond E (2014) Productive landscapes: a global perspective on sustainable agriculture. Landscapes 15(1):59–76. doi:10.1179/1466203514Z.00000000024
Hansen S, Abrahamsen P, Petersen CT, Styczen M (2012) Daisy: model use, calibration, and validation. Trans ASABE 55(4):1317–1335. doi:10.13031/2013.42244
Hauggaard-Nielsen H, Ambus P, Jensen ES (2003) The comparison of nitrogen use and leaching in sole cropped versus intercropped pea and barley. Nutr Cycl Agroecosyst 65(3):289–300. doi:10.1023/A:1022612528161
IMKO (1995) User manual: TRIME-EZ/EC. IMKO Micromodultechnik GmbH, Etlingen
JRC (2015) Routines for sensitivity analysis. https://ec.europa.eu/jrc/en/samo/simlab?search. Accessed 10 June 2015
Kroes J, van Dam JC, Groenendijk P, Hendriks RFA, Jacobs CMJ (2008) SWAP version 3.2: theory description and user manual, Update02. Alterra report, 1649(02). Alterra, Wageningen
Kroese DP, Taimre T, Botev ZI (2011) Handbook of Monte Carlo methods. Wiley Series in Probability and Statistics, vol 706. Wiley, Hoboken
Kutschera L, Lichtenegger E, Sobotik M (2009) Wurzelatlas der Kulturpflanzen gemäßigter Gebiete mit Arten des Feldgemüsebaues. Wurzelatlasreihe, vol 7. DLG-Verl., Frankfurt am Main
Li L, Sun J, Zhang F, Guo T, Bao X, Smith FA, Smith SE (2006) Root distribution and interactions between intercropped species. Oecologia 147(2):280–290. doi:10.1007/s00442-005-0256-4
Li L, Tilman D, Lambers H, Zhang F-S (2014) Plant diversity and overyielding: insights from belowground facilitation of intercropping in agriculture. New Phytol 203(1):63–69. doi:10.1111/nph.12778
Madsen H (2000) Automatic calibration of a conceptual rainfall–runoff model using multiple objectives. J Hydrol 235(3–4):276–288. doi:10.1016/S0022-1694(00)00279-1
Meier U (2001) Growth stages of mono- and dicotyledonous plants: BBCH Monograph, 2. Edition
Mualem Y (1976) A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour Res 12(3):513–522. doi:10.1029/WR012i003p00513
Naseem B, Ajami H, Cordery I, Sharma A (2015) A multi-objective assessment of alternate conceptual ecohydrological models. J Hydrol 529:1221–1234. doi:10.1016/j.jhydrol.2015.08.060
Pappa VA, Rees RM, Walker RL, Baddeley JA, Watson CA (2011) Nitrous oxide emissions and nitrate leaching in an arable rotation resulting from the presence of an intercrop. Agric Ecosyst Environ 141(1–2):153–161. doi:10.1016/j.agee.2011.02.025
Peters A, Durner W, Wessolek G (2011) Consistent parameter constraints for soil hydraulic functions. Adv Water Resour 34(10):1352–1365. doi:10.1016/j.advwatres.2011.07.006
Pfannerstill M, Guse B, Reusser D, Fohrer N (2015) Process verification of a hydrological model using a temporal parameter sensitivity analysis. Hydrol Earth Syst Sci 19(10):4365–4376. doi:10.5194/hess-19-4365-2015
Piccinni G, Ko J, Marek T, Howell T (2009) Determination of growth-stage-specific crop coefficients (KC) of maize and sorghum. Agric Water Manag 96(12):1698–1704. doi:10.1016/j.agwat.2009.06.024
Ren W, Hu L, Zhang J, Sun C, Tang J, Yuan Y, Chen X (2014) Can positive interactions between cultivated species help to sustain modern agriculture? Front Ecol Environ 12(9):507–514. doi:10.1890/130162
Richards LA (1931) Capillary conduction of liquids through porous mediums. J Appl Phys 1(5):318–333. doi:10.1063/1.1745010
Richter D (1995) Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Messfehlers des Hellmann-Niederschlagsmessers, vol 194. Berichte des Deutschen Wetterdienstes, Offenbach a. M
Saltelli A (2008) Global sensitivity analysis: the primer. Wiley, Chichester
Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270. doi:10.1016/j.cpc.2009.09.018
Schittenhelm S, Schroetter S (2014) Comparison of drought tolerance of maize, sweet sorghum and sorghum-sudangrass hybrids. J Agron Crop Sci 200(1):46–53. doi:10.1111/jac.12039
Sobol IM (1993) Sensitivity analysis for nonlinear mathematical models. Math Model Comput Exp 1:407–414
Sobol IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55(1–3):271–280. doi:10.1016/S0378-4754(00)00270-6
Sobol IM, Turchaninov VI, Levitan Yu. L., Shukhman BV (1992) Quasirandom sequence generators. Keldysh Institute of Applied Mathematics Russian Academy of Sciences IMP ZAK. (No. 30)
Taylor SA, Ashcroft GL (1972) Physical edaphology: the physics of irrigated and nonirrigated soils. Freeman, San Francisco
Tyagi NK, Sharma DK, Luthra SK (2000) Evapotranspiration and crop coefficients of wheat and sorghum. J Irrig Drain Eng 126(4):215–222. doi:10.1061/(ASCE)0733-9437(2000)126:4(215)
UMS (2012) User Manual: T4/T4e Druckaufnehmer-Tensiometer. UMS GmbH, Munich
USDA Soil Texture Calculator (2016) https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_054167. Accessed 13 Dec 2016
van Bavel CHM, Ahmed J (1976) Dynamic simulation of water depletion in the root zone. Ecol Model 2:189–212
van Genuchten MT (1980) Closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44(5):892–898
Varella H, Guérif M, Buis S, Beaudoin N (2010) Soil properties estimation by inversion of a crop model and observations on crops improves the prediction of agro-environmental variables. Eur J Agron 33(2):139–147. doi:10.1016/j.eja.2010.04.005
Vereecken H, Huisman JA, Bogena H, Vanderborght J, Vrugt JA, Hopmans JW (2008) On the value of soil moisture measurements in vadose zone hydrology: a review. Water Resour Res. doi:10.1029/2008WR006829
Vrugt JA (2016) Multi-criteria optimization using the AMALGAM software package: theory, concepts, and MATLAB implementation. http://faculty.sites.uci.edu/jasper/files/2016/04/manual_AMALGAM.pdf. Accessed 26 Apr 2016
Vrugt JA, Robinson BA (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci USA 104(3):708–711. doi:10.1073/pnas.0610471104
Vrugt JA, Gupta HV, Bastidas LA, Bouten W, Sorooshian S (2003) Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour Res 39(8):SWC51–SWC519
Werisch S, Grundmann J, Al-Dhuhli H, Algharibi E, Lennartz F (2014) Multiobjective parameter estimation of hydraulic properties for a sandy soil in Oman. Environ Earth Sci 72(12):4935–4956. doi:10.1007/s12665-014-3537-6
Wöhling T, Vrugt JA (2011) Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data. Water Resour Res. doi:10.1029/2010WR009265
Wöhling T, Vrugt JA, Barkle GF (2008) Comparison of three multiobjective optimization algorithms for inverse modeling of vadose zone hydraulic properties. Soil Sci Soc Am J 72(2):305–319. doi:10.2136/sssaj2007.0176
Wöhling T, Gayler S, Priesack E, Ingwersen J, Wizemann H-D, Högy P, Cuntz M, Attinger S, Wulfmeyer V, Streck T (2013a) Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and CLM3.5. Water Resour Res 49(12):8200–8221. doi:10.1002/2013WR014536
Wöhling T, Samaniego L, Kumar R (2013b) Evaluating multiple performance criteria to calibrate the distributed hydrological model of the upper Neckar catchment. Environ Earth Sci 69(2):453–468. doi:10.1007/s12665-013-2306-2
Xia H-Y, Zhao J-H, Sun J-H, Bao X-G, Christie P, Zhang F-S, Li L (2013) Dynamics of root length and distribution and shoot biomass of maize as affected by intercropping with different companion crops and phosphorus application rates. Field Crops Res 150:52–62. doi:10.1016/j.fcr.2013.05.02
Acknowledgements
The authors would like to thank the German Federal Ministry of Food and Agriculture (BMEL) and the Agency for Renewable Resources (FNR) for funding this research under the Project No. FNR-22030111. We gratefully acknowledge the assistance of Dr. Petra Kahle for characterising soil physical properties at the study site.
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Stahn, P., Busch, S., Salzmann, T. et al. Combining global sensitivity analysis and multiobjective optimisation to estimate soil hydraulic properties and representations of various sole and mixed crops for the agro-hydrological SWAP model. Environ Earth Sci 76, 367 (2017). https://doi.org/10.1007/s12665-017-6701-y
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DOI: https://doi.org/10.1007/s12665-017-6701-y