Abstract
Crop growth models are multi-outputs and can be valuable tools for the quantification of crop Growth and production. However, these models usually require several input data, which are costly, time-consuming, and sometimes impossible to measure. These model parameters are mostly estimated by calibration and inverse solving. In this study, five output variables of the AquaCrop model, including soil evaporation, crop transpiration, evapotranspiration, crop biomass at maturity, and grain yield, were investigated to study 47 genotypic model parameters on the output time series of the model for wheat in the Qazvin Synoptic Station. The main objective of this study was to find the most critical variables of the AquaCrop model as well as find the probabilistic behavior of inputs to estimate the missing values. The SAFE toolbox in the Matlab was used to study the global sensitivity analysis (GSA) and uncertainty of inputs and their impact on outputs. The uncertainty in the outputs of the AquaCrop model in simulating wheat yield, in the Qazvin Synoptic Station, over 36 years was analyzed using the Generalized Likelihood Uncertainty Estimation (GLUE) method. Using RMSE < 0.9 as the threshold in a 95% confidence level, the best parameter sets included all the observations. Results showed that evaporation and yield rates are the least reliable outputs of the AquaCrop model that have not been calibrated, while others consider them reliable. After that, the new domain of each output was determined based on the two indexes. Then we modified the domain to reduce its size. Finally, the probabilistic distribution of each inputs were introduced by the Easy Fit software. The main result of this study is that the probabilistic distribution of the model parameter that is calibrated for a particular output variable can differ from other output variables. Also, when we trust a specific run of the model (calibrated run) as observed data, the uncertainty bounds covering are very high. So we can find an efficient bound of uncertainty which was one of the main goals of the study. Finally, we utilized the GLUE to optimize multi-output models by introducing one unique, optimized Probability Density Function (PDF) for each model parameter for all outputs estimated by collecting all accepted output series of all target outputs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from the first author, upon reasonable request.
References
Adabi V, Azizian A, Ramezani Etedali H, Kaviani A Ababaei B (2020) Local sensitivity analysis of AquaCrop model for wheat and maize in Qazvin Plain and Moghan Pars-Abad in Iran. Iran J Irrig Drain, 13(6): 1565–1579. http://idj.iaid.ir/article_103136.html
Adeboye OB, Schultz B, Adekalu KO, Prasad KC (2019) Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans (Glycine max L. merr) in Ile-Ife Nigeria. Agric Water Manag. https://doi.org/10.1016/j.agwat.2018.11.006
Ahmadi M, Ascough JC, DeJonge KC, Arabi M (2014) Multisite-multivariable sensitivity analysis of distributed watershed models: enhancing the perceptions from computationally frugal methods. Ecol Model. https://doi.org/10.1016/j.ecolmodel.2014.02.013
Ahmadi M, Etedali HR, Elbeltagi A (2021) Evaluation of the effect of climate change on maize water footprint under RCPs scenarios in Qazvin plain, Iran. Agric Water Manag 254:106969. https://doi.org/10.1016/j.agwat.2021.106969
Andarzian B, Bannayan M, Steduto P, Mazraeh H, Barati ME, Barati MA, Rahnama A (2011) Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agric Water Manag. https://doi.org/10.1016/j.agwat.2011.08.023
Anderson B, Borgonovo E, Galeotti M, Roson R (2014) Uncertainty in climate change modeling: Can global sensitivity analysis be of help? Risk Anal. https://doi.org/10.1111/risa.12117
Arabi M, Govindaraju RS, Hantush MM (2007) A probabilistic approach for analysis of uncertainty in the evaluation of watershed management practices. J Hydrol. https://doi.org/10.1016/j.jhydrol.2006.09.012
Araya A, Habtu S, Hadgu KM, Kebede A, Dejene T (2010) Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agric Water Manag. https://doi.org/10.1016/j.agwat.2010.06.021
Baranyai L, Zude M (2009) Analysis of laser light propagation in kiwifruit using backscattering imaging and Monte Carlo simulation. Comput Electron Agric. https://doi.org/10.1016/j.compag.2009.06.011
Battilani A, Letterio T, Chiari G (2015) AquaCrop model calibration and validation for processing tomato crop in a sub-humid climate. Acta Hortic. https://doi.org/10.17660/ActaHortic.2015.1081.19
Berends KD, Warmink JJ, Hulscher SJMH (2018) Efficient uncertainty quantification for impact analysis of human interventions in rivers. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2018.05.021
Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process. https://doi.org/10.1002/hyp.3360060305
Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydrol. https://doi.org/10.1016/S0022-1694(01)00421-8
Beven K, Smith P, Freer J (2007) Comment on “Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology” by Pietro Mantovan and Ezio Todini. J Hydrol. https://doi.org/10.1016/j.jhydrol.2007.02.023
Blasone RS, Vrugt JA, Madsen H, Rosbjerg D, Robinson BA, Zyvoloski GA (2008) Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Adv Water Resour. https://doi.org/10.1016/j.advwatres.2007.12.003
Bouman BAM, Van Keulen H, Van Laar HH, Rabbinge R (1996) The “School of de Wit” crop growth simulation models: a pedigree and historical overview. Agric Syst. https://doi.org/10.1016/0308-521X(96)00011-X
Calera A, Martínez C, Meliá J (2001) A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. Int J Remote Sens. https://doi.org/10.1080/01431160010020100
Campolongo F, Cariboni J, Saltelli A (2007) An effective screening design for sensitivity analysis of large models. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2006.10.004
Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ. https://doi.org/10.1016/S0034-4257(97)00104-1
Christensen S (2004) A synthetic groundwater modelling study of the accuracy of GLUE uncertainty intervals. Nord Hydrol. https://doi.org/10.2166/nh.2004.0004
Ciric C, Ciffroy P, Charles S (2012) Use of sensitivity analysis to identify influential and non-influential parameters within an aquatic ecosystem model. Ecol Model. https://doi.org/10.1016/j.ecolmodel.2012.06.024
Cooper HM, Zhang C, Davis SE, Troxler TG (2019) Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2018.11.003
Cukier RI, Fortuin CM, Shuler KE, Petschek AG, Schaibly JH (1973) Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory. J Chem Phys. https://doi.org/10.1063/1.1680571
Darvishi A, Fakheran S, Soffianian A (2015) Monitoring landscape changes in Caucasian black grouse (Tetrao mlokosiewiczi) habitat in Iran during the last two decades. Environ Monit Assess. https://doi.org/10.1007/s10661-015-4659-3
DeJonge KC, Ahmadi M, Ascough JC, Kinzli KD (2015) Sensitivity analysis of reference evapotranspiration to sensor accuracy. Comput Electron Agric. https://doi.org/10.1016/j.compag.2014.11.013
Delgoda D, Malano H, Saleem SK, Halgamuge MN (2016) Irrigation control based on model predictive control (MPC): Formulation of theory and validation using weather forecast data and AQUACROP model. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2015.12.012
Doorenbos J, Kassam AH, Bentvelsen C, Uittenbogaard G (1980) Yield response to water. Irrig Agric Dev. https://doi.org/10.1016/b978-0-08-025675-7.50021-2
Droutsas I, Challinor AJ, Swiderski M, Semenov MA (2019) New modelling technique for improving crop model performance—application to the GLAM model. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2019.05.005
Elbeltagi A, Rizwan M, Mokhtar A, Deb P, Abdullahi G, Kushwaha NL, Peroni L, Malik A, Kumar N, Deng J (2020) Spatial and temporal variability analysis of green and blue evapotranspiration of wheat in the Egyptian Nile Delta from 1997 to 2017. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125662
Er-Raki S, Bouras E, Rodriguez JC, Watts CJ, Lizarraga-Celaya C, Chehbouni A (2021) Parameterization of the AquaCrop model for simulating table grapes growth and water productivity in an arid region of Mexico. Agric Water Manag. https://doi.org/10.1016/j.agwat.2020.106585
Fan YR, Huang GH, Baetz BW, Li YP, Huang K, Li Z, Chen X, Xiong LH (2016) Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: a hybrid sequential data assimilation and probabilistic collocation method. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2016.09.012
Foster T, Brozović N, Butler AP, Neale CMU, Raes D, Steduto P, Fereres E, Hsiao TC (2017) AquaCrop-OS: an open source version of FAO’s crop water productivity model. Agric Water Manag. https://doi.org/10.1016/j.agwat.2016.11.015
Freer J, Beven K, Ambroise B (1996) Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour Res. https://doi.org/10.1029/95WR03723
Freni G, Mannina G, Viviani G (2009) Urban runoff modelling uncertainty: comparison among Bayesian and pseudo-Bayesian methods. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2009.03.003
Ganot Y, Dahlke HE (2021) A model for estimating Ag-MAR flooding duration based on crop tolerance, root depth, and soil texture data. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.107031
Green DM, Whittemore CT (2005) Calibration and sensitivity analysis of a model of the growing pig for weight gain and composition. Agric Syst. https://doi.org/10.1016/j.agsy.2004.06.017
Gupta HV, Sorooshian S, Yapo PO (1998) Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resour Res. https://doi.org/10.1029/97WR03495
Hamby DM (1994) A review of techniques for parameter sensitivity analysis of environmental models. Environ Monit Assess. https://doi.org/10.1007/BF00547132
Hamm NAS, Hall JW, Anderson MG (2006) Variance-based sensitivity analysis of the probability of hydrologically induced slope instability. Comput Geosci. https://doi.org/10.1016/j.cageo.2005.10.007
Han E, Ines AVM, Koo J (2019) Development of a 10-km resolution global soil profile dataset for crop modeling applications. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2019.05.012
Hassan AE, Bekhit HM, Chapman JB (2009) Using Markov Chain Monte Carlo to quantify parameter uncertainty and its effect on predictions of a groundwater flow model. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2008.11.002
Hellal F, Mansour H, Abdel-Hady M, El-Sayed S, Abdelly C (2019) Assessment water productivity of barley varieties under water stress by AquaCrop model. AIMS Agric Food. https://doi.org/10.3934/agrfood.2019.3.501
Heng L, Kheng Heng L, Hsiao T, Evett S, Howell T, Steduto P (2009) Symposium Papers 488 A gronomy. Agron J 101(3):488–498. https://doi.org/10.2134/agronj2008.0029xs
Hossard L, Bregaglio S, Philibert A, Ruget F, Resmond R, Cappelli G, Delmotte S (2017) A web application to facilitate crop model comparison in ensemble studies. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2017.08.008
Huang X, Ni S, Wu C, Zorn C, Zhang W, Yu C (2020) GDNDC: An integrated system to model water-nitrogen-crop processes for agricultural management at regional scales. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2020.104807
Jalil A, Akhtar F, Awan UK (2020) Evaluation of the AquaCrop model for winter wheat under different irrigation optimization strategies at the downstream Kabul River Basin of Afghanistan. Agric Water Manag. https://doi.org/10.1016/j.agwat.2020.106321
Jeremiah E, Sisson SA, Sharma A, Marshall L (2012) Efficient hydrological model parameter optimization with Sequential Monte Carlo sampling. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2012.07.001
Jin X, Li Z, Nie C, Xu X, Feng H, Guo W, Wang J (2018) Parameter sensitivity analysis of the AquaCrop model based on extended fourier amplitude sensitivity under different agro-meteorological conditions and application. Field Crop Res. https://doi.org/10.1016/j.fcr.2018.07.002
Johnson LF, Trout TJ (2012) Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in california’s san Joaquin Valley. Remote Sens. https://doi.org/10.3390/rs4020439
Kelly TD, Foster T (2021) AquaCrop-OSPy: Bridging the gap between research and practice in crop-water modeling. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.106976
Kim D, Kaluarachchi J (2015) Validating FAO AquaCrop using Landsat images and regional crop information. Agric Water Manag. https://doi.org/10.1016/j.agwat.2014.10.013
Klepper O, Scholten H, Van Kamer JPGD (1991) Prediction uncertainty in an ecological model of the oosterschelde estuary. J Forecast. https://doi.org/10.1002/for.3980100111
Kroese DP, Brereton T, Taimre T, Botev ZI (2014) Why the Monte Carlo method is so important today. Wiley Interdiscip Rev Comput Stat. https://doi.org/10.1002/wics.1314
Kuczera G, Parent E (1998) Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. J Hydrol. https://doi.org/10.1016/S0022-1694(98)00198-X
Li J, Zhu T, Mao X, Adeloye AJ (2016) Modeling crop water consumption and water productivity in the middle reaches of Heihe River Basin. Comput Electron Agric. https://doi.org/10.1016/j.compag.2016.02.021
Linker R, Ioslovich I, Sylaios G, Plauborg F, Battilani A (2016) Optimal model-based deficit irrigation scheduling using AquaCrop: a simulation study with cotton, potato and tomato. Agric Water Manag. https://doi.org/10.1016/j.agwat.2015.09.011
Liu Y, Gupta HV (2007) Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water Resour Res. https://doi.org/10.1029/2006WR005756
Liu Z, Cheng L, Lin K, Cai H (2021) A hybrid bayesian vine model for water level prediction. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2021.105075
López-Urrea R, Domínguez A, Pardo JJ, Montoya F, García-Vila M, Martínez-Romero A (2020) Parameterization and comparison of the AquaCrop and MOPECO models for a high-yielding barley cultivar under different irrigation levels. Agric Water Manag. https://doi.org/10.1016/j.agwat.2019.105931
Madsen H (2000) Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. J Hydrol. https://doi.org/10.1016/S0022-1694(00)00279-1
Madsen H (2003) Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv Water Resour. https://doi.org/10.1016/S0309-1708(02)00092-1
Mantovan P, Todini E (2006) Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology. J Hydrol. https://doi.org/10.1016/j.jhydrol.2006.04.046
Metropolis N, Ulam S (1949) The Monte Carlo Method. J Am Stat Assoc 44(247):335–341. https://doi.org/10.1080/01621459.1949.10483310
Midingoyi CA, Pradal C, Enders A, Fumagalli D, Raynal H, Donatelli M, Athanasiadis IN, Porter C, Hoogenboom G, Holzworth D, Garcia F, Thorburn P, Martre P (2021) Crop2ML: an open-source multi-language modeling framework for the exchange and reuse of crop model components. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2021.105055
Migliaccio KW, Chaubey I (2008) Spatial distributions and stochastic parameter influences on SWAT flow and sediment predictions. J Hydrol Eng. https://doi.org/10.1061/(asce)1084-0699(2008)13:4(258)
Mohamed Sallah AH, Tychon B, Piccard I, Gobin A, Van Hoolst R, Djaby B, Wellens J (2019) Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived fractional vegetation cover data. Agric Water Manag. https://doi.org/10.1016/j.agwat.2019.03.016
Mojtabavi SA, Shokoohi A, Ramezani Etedali H, Singh V (2018) Using regional virtual water trade and water footprint accounting for optimizing crop patterns to mitigate water crises in dry regions. Irrig Drain. https://doi.org/10.1002/ird.2170
Montanari A (2005) Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfall-runoff simulations. Water Resour Res. https://doi.org/10.1029/2004WR003826
Montoya F, Camargo D, Ortega JF, Córcoles JI, Domínguez A (2016) Evaluation of Aquacrop model for a potato crop under different irrigation conditions. Agric Water Manag. https://doi.org/10.1016/j.agwat.2015.10.019
Moradkhani H, Hsu KL, Gupta H, Sorooshian S (2005) Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter. Water Resour Res. https://doi.org/10.1029/2004WR003604
Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics. https://doi.org/10.1080/00401706.1991.10484804
Nash D, Hannah M (2011) Using Monte-Carlo simulations and Bayesian networks to quantify and demonstrate the impact of fertiliser best management practices. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2011.03.009
Natale L, Savi F (2007) Monte Carlo analysis of probability of inundation of Rome. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2006.12.004
Nunes HGGC, Farias VDS, Sousa DP, Costa DLP, Pinto JVN, Moura VB, Teixeira EO, Lima MJA, Ortega-Farias S, Souza PJOP (2021) Parameterization of the AquaCrop model for cowpea and assessing the impact of sowing dates normally used on yield. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.106880
Pappenberger F, Beven KJ, Ratto M, Matgen P (2008) Multi-method global sensitivity analysis of flood inundation models. Adv Water Resour. https://doi.org/10.1016/j.advwatres.2007.04.009
Pastres R, Chan K, Solidoro C, Dejak C (1999) Global sensitivity analysis of a shallow-water 3D eutrophication model. Comput Phys Commun. https://doi.org/10.1016/S0010-4655(98)00164-7
Pianosi F, Wagener T (2015) A simple and efficient method for global sensitivity analysis based oncumulative distribution functions. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2015.01.004
Pianosi F, Sarrazin F, Wagener T (2015) A Matlab toolbox for global sensitivity analysis. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2015.04.009
Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T (2016) Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2016.02.008
Piri J, Pirzadeh B, Keshtegar B, Givehchi M (2021) A hybrid statistical regression technical for prediction wastewater inflow. Comput Electron Agric. https://doi.org/10.1016/j.compag.2021.106115
Poulose T, Kumar S, Ganjegunte GK (2021) Robust crop water simulation using system dynamic approach for participatory modeling. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2020.104899
Qin J, Lu R (2009) Monte Carlo simulation for quantification of light transport features in apples. Comput Electron Agric. https://doi.org/10.1016/j.compag.2009.04.002
Raes D, Steduto P, Hsiao TC, Fereres E (2009) Aquacrop-The FAO crop model to simulate yield response to water: II. main algorithms and software description. Agronomy J 1:1–5. https://doi.org/10.2134/agronj2008.0140s
Razzaghi F, Zhou Z, Andersen MN, Plauborg F (2017) Simulation of potato yield in temperate condition by the AquaCrop model. Agric Water Manag. https://doi.org/10.1016/j.agwat.2017.06.008
Ruane AC, Hudson NI, Asseng S, Camarrano D, Ewert F, Martre P, Boote KJ, Thorburn PJ, Aggarwal PK, Angulo C, Basso B, Bertuzzi P, Biernath C, Brisson N, Challinor AJ, Doltra J, Gayler S, Goldberg R, Grant RF, Wolf J (2016) Multi-wheat-model ensemble responses to interannual climate variability. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2016.03.008
Saltelli A, Tarantola S, Chan KPS (1999) A quantitative model-independent method for global sensitivity analysis of model output. Technometrics. https://doi.org/10.1080/00401706.1999.10485594
Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global Sensitivity Analysis The Primer. Global Sens Anal Primer. https://doi.org/10.1002/9780470725184
Sandhu R, Irmak S (2019) Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation. Agric Water Manag. https://doi.org/10.1016/j.agwat.2019.105687
Sarrazin F, Pianosi F, Wagener T (2016) Global sensitivity analysis of environmental models: convergence and validation. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2016.02.005
Shelia V, Hansen J, Sharda V, Porter C, Aggarwal P, Wilkerson CJ, Hoogenboom G (2019) A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2019.02.006
Shirazi SZ, Mei X, Liu B, Liu Y (2021) Assessment of the AquaCrop model under different irrigation scenarios in the North China Plain. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.107120
Sieber A, Uhlenbrook S (2005) Sensitivity analyses of a distributed catchment model to verify the model structure. J Hydrol. https://doi.org/10.1016/j.jhydrol.2005.01.004
Sobol IM (1993) Sensitivity analysis for non-linear mathematical models. Math Model Comput Exp
Spear RC, Hornberger GM (1980) Eutrophication in peel inlet-II. Identification of critical uncertainties via generalized sensitivity analysis. Water Res. https://doi.org/10.1016/0043-1354(80)90040-8
Steduto P, Hsiao TC, Raes D, Fereres E (2009) Aquacrop-the FAO crop model to simulate yield response to water: I. concepts and underlying principles. Agronomy J. https://doi.org/10.2134/agronj2008.0139s
Stella T, Frasso N, Negrini G, Bregaglio S, Cappelli G, Acutis M, Confalonieri R (2014) Model simplification and development via reuse, sensitivity analysis and composition: a case study in crop modelling. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2014.05.007
Thorp KR, DeJonge KC, Marek GW, Evett SR (2020) Comparison of evapotranspiration methods in the DSSAT Cropping System Model: I. Global sensitivity analysis. Comput Electr Agric. https://doi.org/10.1016/j.compag.2020.105658
Tsakmakis ID, Kokkos NP, Gikas GD, Pisinaras V, Hatzigiannakis E, Arampatzis G, Sylaios GK (2019) Evaluation of AquaCrop model simulations of cotton growth under deficit irrigation with an emphasis on root growth and water extraction patterns. Agric Water Manag. https://doi.org/10.1016/j.agwat.2018.10.029
van Griensven A, Meixner T, Grunwald S, Bishop T, Diluzio M, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol. https://doi.org/10.1016/j.jhydrol.2005.09.008
Van Straten GT, Keesman KJ (1991) Uncertainty propagation and speculation in projective forecasts of environmental change: a lake-eutrophication example. J Forecast. https://doi.org/10.1002/for.3980100110
Vanuytrecht E, Raes D, Steduto P, Hsiao TC, Fereres E, Heng LK, Garcia Vila M, Mejias Moreno P (2014a) AquaCrop: FAO’s crop water productivity and yield response model. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2014.08.005
Vanuytrecht E, Raes D, Willems P (2014b) Global sensitivity analysis of yield output from the water productivity model. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2013.10.017
Vazquez-Cruz M, RG-C- and E (2014) undefined. (n.d.). Global sensitivity analysis by means of EFAST and Sobol’methods and calibration of reduced state-variable TOMGRO model using genetic algorithms. Elsevier. Retrieved May 29, 2021, from https://www.sciencedirect.com/science/article/pii/S0168169913002482
Vogel RM, Stedinger JR, Batchelder R, Lee SU (2008) Appraisal of the generalized likelihood uncertainty estimation (GLUE) method. World Environmental and Water Resources Congress 2008: Ahupua’a—Proceedings of the World Environmental and Water Resources Congress 2008. https://doi.org/10.1061/40976(316)611
Vrugt JA (2016) Markov chain Monte Carlo simulation using the DREAM software package: theory, concepts, and MATLAB implementation. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2015.08.013
Vrugt JA, Robinson BA (2007) Treatment of uncertainty using ensemble methods: comparison of sequential data assimilation and Bayesian model averaging. Water Resour Res. https://doi.org/10.1029/2005WR004838
Vrugt JA, Bouten W, Weerts AH (2001) Information content of data for identifying soil hydraulic parameters from outflow experiments. Soil Sci Soc Am J. https://doi.org/10.2136/sssaj2001.65119x
Vrugt JA, Gupta HV, Bouten W, Sorooshian S (2003) A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour Res. https://doi.org/10.1029/2002WR001642
Vrugt JA, Diks CGH, Gupta HV, Bouten W, Verstraten JM (2005) Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation. Water Resour Res. https://doi.org/10.1029/2004WR003059
Wagener T, Kollat J (2007) Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2006.06.017
Wagener T, McIntyre N, Lees MJ, Wheater HS, Gupta HV (2003) Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis. Hydrol Process. https://doi.org/10.1002/hyp.1135
Wang J, Li X, Lu L, Fang F (2013) Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2013.06.007
Wellens J, Sallah AH, Tychon B, Piccard I, Gobin A, Curnel Y, Leclef A, Goffart D, Planchon V, Goffart JP, Delloye C, Defourny P (2017) Assessment of AquaCrop for winter wheat using satellite derived fCover data. In: 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017. https://doi.org/10.1109/Multi-Temp.2017.8035224
Xing HM, Xu XG, Li ZH, Chen YJ, Feng HK, Yang GJ, Chen ZX (2017) Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test. J Integr Agric. https://doi.org/10.1016/S2095-3119(16)61626-X
Xu X, Sun C, Huang G, Mohanty BP (2016) Global sensitivity analysis and calibration of parameters for a physically-based agro-hydrological model. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2016.05.013
Xu J, Bai W, Li Y, Wang H, Yang S, Wei Z (2019) Modeling rice development and field water balance using AquaCrop model under drying-wetting cycle condition in eastern China. Agric Water Manag. https://doi.org/10.1016/j.agwat.2018.10.028
Yang J (2011) Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2010.10.007
Yapo PO, Gupta HV, Sorooshian S (1998) Multi-objective global optimization for hydrologic models. J Hydrol. https://doi.org/10.1016/S0022-1694(97)00107-8
Yin J, Medellín-Azuara J, Escriva-Bou A, Liu Z (2021) Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.144715
Yousefi M, Darvishi A, Padró R, Barghjelveh S, Mobarghaee Dinan N, Marull J (2020) An energy-landscape integrated analysis to evaluate agroecological scarcity. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.139998
Yu Q, Kang S, Hu S, Zhang L, Zhang X (2021) Modeling soil water-salt dynamics and crop response under severely saline condition using WAVES: searching for a target irrigation volume for saline water irrigation. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.107100
Zhai B, Fu Q, Li T, Liu D, Ji Y, Li M, Cui S (2019) Rice irrigation schedule optimization based on the AquaCrop model: Study of the Longtouqiao irrigation district. Water (switzerland). https://doi.org/10.3390/w11091799
Zhang J, Li K, Gao Y, Feng D, Zheng C, Cao C, Sun J, Dang H, Hamani AKM (2022) Evaluation of saline water irrigation on cotton growth and yield using the AquaCrop crop simulation model. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.107355
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ramezani Etedali, H., Adabi, V., Gorgin, F. et al. The probabilistic behavior of AquaCrop parameters: a Monte-Carlo study. Stoch Environ Res Risk Assess 37, 717–734 (2023). https://doi.org/10.1007/s00477-022-02309-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-022-02309-9