alexa Statistical Assessment of a Numerical Model Simulating
ISSN: 2168-9768

Irrigation & Drainage Systems Engineering
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Research Article

Statistical Assessment of a Numerical Model Simulating Agro Hydrochemical Processes in Soil under Drip Fertigated Mandarin Tree

Phogat V1*, Skewes MA1, Cox JW1,2 and Simunek J3

1South Australian Research and Development Institute, Australia

2The University of Adelaide, Australia

3Department of Environmental Sciences, University of California, USA

*Corresponding Author:
Phogat V
South Australian Research and Development Institute
Australia
Tel: +61 8 83039567
E-mail: [email protected]

Received January 19, 2016; Accepted January 22, 2016; Published January 25, 2016

Citation: Phogat V, Skewes MA, Cox JW, Simunek J (2016) Statistical Assessment of a Numerical Model Simulating Agro Hydro-chemical Processes in Soil under Drip Fertigated Mandarin Tree. Irrigat Drainage Sys Eng 5:155. doi:10.4172/2168-9768.1000155

Copyright: © 2016 Phogat V, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Qualitative assessment of model performance is essential because reliable statistical comparison of observed data with simulated behaviour of a model reflects the performance and consistency of the mathematical tool under defined conditions. In this study we compared the measured temporal and spatial distribution of water content, soil solution salinity (ECsw), and nitrate (NO3 --N) concentration in the soil beneath a drip-fertigated mandarin tree during a complete season with corresponding HYDRUS-2D simulated values using a range of standard statistical techniques, comprising mean error (ME), mean absolute error (MAE), root mean square error (RMSE), paired t-test (tcal), coefficient of determination (R2), Nash and Sutcliffe model efficiency (E), index of agreement (IA), relative model efficiency (Erel), relative index of agreement (IArel), modified E (E1) and IA (IA1). Temporal and spatial values of ME, MAE, and RMSE for water content (-0.04 to 0.05 cm3.cm-3) and salinity (-0.42- 0.93 dSm-1) were within an acceptable range. However, a relatively wider range in MAE (1.44-27.65 mg.L-1) and RMSE (2.00-39.57 mg.L-1) values were obtained for NO3 --N concentrations measured weekly or at the 25-cm depth (MAE = 21.2 and RMSE = 30.7 mg.L-1). Temporal and spatial RMSE were higher than MAE, which suggests a slight bias in RMSE due to squared differences between measured and simulated values. Similarly, the paired t-test (tcal) showed significant differences for NO3 --N during the mid-season (85-140 DOY) for temporal (weekly) comparison and at several depths for water content (10, 25, 80 and 110 cm), salinity (100 and 150 cm) and NO3 --N concentration (25, 100 and 150 cm). The R2 values varied in a narrow range (0.5 to 0.59). Similarly, values for E (0.12-0.43), IA (0.80-0.84), and E1 (0.26- 0.32) and IA1 (0.61-0.69) suggest that the model precisely predicted water content, salinity and nitrate concentration over the season, however, Erel (-319.25) and IArel (-71.3) values were highly negative for nitrate concentration, indicating a mismatch. It was concluded that none of the evaluated measures described and tested the performance of the model for water, salinity and nitrate ideally. Each criterion had its specific advantages and disadvantages, which should be taken into account. Hence, sound model performance evaluation requires the use of a combination of different statistical criteria, which consider both absolute and relative errors. Judicious use of statistical criteria should lead to improvements in the modelling assessment of water, salinity and nitrate dynamics in soil under cropped conditions.

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