Weighted objective function selector algorithm for parameter estimation of SVAT models with remote sensing data

J. A. P. Pollacco, B. P. Mohanty, and A. Efstratiadis, Weighted objective function selector algorithm for parameter estimation of SVAT models with remote sensing data, Water Resources Research, 49 (10), 6959–6978, doi:10.1002/wrcr.20554, 2013.

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[English]

The objective function of the inverse problem in Soil Vegetation Atmosphere Transfer (SVAT) models can be expressed as the aggregation of two criteria, accounting for the uncertainties of surface soil moisture (θ) and evapotranspiration (ET), retrieved from remote sensing (RS). In this context, we formulate a Weighted Objective Function (WOF) with respect to model effective soil hydraulic parameters, comprising of two components for θ and ET, respectively, and a dimensionless coefficient w. Given that the sensitivity of θ is increased by omitting the periods when soil moisture decoupling occurs, we also introduce within the WOF a threshold, θd, which outlines the decoupling of the surface and root-zone moisture. The optimal values of w and θd are determined by using a novel framework, Weighted Objective Function Selector Algorithm (WOFSA). This performs numerical experiments, assuming known reference conditions. In particular, it solves the inverse problem for different sets of θ and ET, considering the uncertainties of retrieving them from RS, and then runs the hydrological model to obtain the simulated water fluxes and their residuals, ΔWF, against the reference responses. It estimates the two unknown variables, w and θd, by maximizing the linear correlation between the WOF and maximum ΔWF. The framework is tested using a modified Soil-Water-Atmosphere-Plant (SWAP) model, under 22 contrasting hydroclimatic scenarios. It is shown that for each texture class, w can be expressed as function of the average θ and ET-fraction, while that for all scenarios θd can be modeled as function of the average θ, average ET and standard deviation of ET. Based on the outcomes of this study, we also provide recommendations on the most suitable time period for soil moisture measurements for capturing its dynamics and thresholds. Finally, we propose the implementation of WOFSA within multiobjective calibration, as a generalized tool for recognizing robust solutions from the Pareto front.

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See also: http://dx.doi.org/10.1002/wrcr.20554

Our works referenced by this work:

1. A. Efstratiadis, and D. Koutsoyiannis, One decade of multiobjective calibration approaches in hydrological modelling: a review, Hydrological Sciences Journal, 55 (1), 58–78, doi:10.1080/02626660903526292, 2010.

Our works that reference this work:

1. E. Savvidou, A. Efstratiadis, A. D. Koussis, A. Koukouvinos, and D. Skarlatos, The curve number concept as a driver for delineating hydrological response units, Water, 10 (2), 194, doi:10.3390/w10020194, 2018.

Other works that reference this work (this list might be obsolete):

1. Mohanty, B. P., Soil hydraulic property estimation using remote sensing: a review, Vadose Zone Journal, 12(4), 2013.
2. Wöhling, T., S. Gayler, E. Priesack, J. Ingwersen, H.-D. Wizemann, P. Högy, M. Cuntz, S. Attinger, V. Wulfmeyer, and T. Streck, Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and CLM3.5, Water Resources Research, 49(12), 8200–8221, 2013.
3. #Gupta, M., N. K. Garg, P. K Srivastava, and T. Islam, Integration of TRMM rainfall in numerical model for pesticide prediction in subtropical climate, Proceedings of 11th International Conference on Hydroinformatics (HIC 2014), New York City, 2014.
4. Gong, W., Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, and C. Miao, Multi-objective parameter optimization of common land model using adaptive surrogate modelling, Hydrology and Earth System Sciences, 19, 2409–2425, doi:10.5194/hess-19-2409-2015, 2015.
5. Garg, N. K., and M. Gupta, Assessment of improved soil hydraulic parameters for soil water content simulation and irrigation scheduling, Irrigation Science, 33(4), 247-264, doi:10.1007/s00271-015-0463-7, 2015.
6. Larsen, M. A. D., J. C. Refsgaard, K. H. Jensen, M. B. Butts, S. Stisen, and M. Mollerup, Calibration of a distributed hydrology and land surface model using energy flux measurements, Agricultural and Forest Meteorology, 217, 74–88, doi:10.1016/j.agrformet.2015.11.012, 2016.
7. #Gupta, M., P. K Srivastava, and T. Islam, Integrative use of near-surface satellite soil moisture and precipitation for estimation of improved irrigation scheduling parameters, Satellite Soil Moisture Retrieval: Techniques and Applications , P. K. Srivastava, G. Petropoulos, and Y. H. Kerr (editors), 271-288, doi:10.1016/B978-0-12-803388-3.00014-0, 2016.
8. Maurya, S., P. K. Srivastava, M. Gupta, T. Islam, and D. Han, Integrating soil hydraulic parameter and microwave precipitation with morphometric analysis for watershed prioritization, Water Resources Management, 30(14), 5385–5405, doi:10.1007/s11269-016-1494-4, 2016.
9. Fernández-Gálvez, J., J. A. P. Pollacco, L. Lassabatere, R. Angulo-Jaramillo, and S. Carrick, A general Beerkan estimation of soil transfer parameters method predicting hydraulic parameters of any unimodal water retention and hydraulic conductivity curves: Application to the Kosugi soil hydraulic model without using particle size distribution data, Advances in Water Resources, doi:10.1016/j.advwatres.2019.05.005, 2019.

Tagged under: Hydrological models, Optimization, Papers initially rejected