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| DOI：10.13522/j.cnki.ggps.2017.0637 |
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| Estimating Aquifer Parameters in Irrigation District Using Inverse Method Coupled with the Ensemble Kalman Filter |
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ZHANG Yuxue, ZHU Yan, YANG Jinzhong
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State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
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| Abstract: |
| 【Objective】 The objective of this paper is to present a coupling method to inversely estimate parameters of shallow aquifers at irrigation district scale. 【Method】 We proposed a multi-parameter inversion groundwater flow model, SWF2D_DA, based on the ensemble Kalman filter method to estimate aquifer parameters, and a two-dimensional groundwater flow model, SWF2D, to simulate water flow. We then applied SWF2D_DA to inversely estimate the parameters of a shallow aquifer in the Sub-Irrigation District of Yongji in Hetao Irrigation District. The model involved eight parameters, including the specific yield, infiltration coefficient of precipitation, two infiltration coefficients for two-stage irrigation, two evaporation parameters for two-stage evaporation, and two critical evaporation depths. We studied two cases: one was to determine all the eight parameters and the other one was to determine six of the eight parameters. 【Result】 The SWF2D_DA model worked well in multi-parameter inversion with the mean relative error, MRE, of 0.124%, and the root mean square error, RMSE, of 0.002 663 when six parameters needed to be determined; and MRE of 0.376% and RMSE of 0.003 283 when all the eight parameters needed to be determined. We also investigated the impact of measurement errors on inversion accuracy by artificially setting the error variance of five measurements to be 0.01, 0.000 1, 0.002 5, 0.1, 0.64 m2, respectively. When the measurement error variance was greater than 0.1 m2, MRE, RMSE and Spread increased steadily with the measurement errors. When the measurement error variance was from 0.000 1 to 0.01 m2, MRE and RMSE remained almost unchanged, indicating that, as long as the measurement errors were controlled to a certain range, the model can give accurate and robust results. 【Conclusion】 The SWF2D_DA model is able to inversely estimate aquifer parameters at regional scale, and that the less parameters needed to be calibrated, the less assimilation steps the model would take to give reliable results. Its was also found that the tolerable measurement error variance was 0.01 m2, which, for the aquifer we investigated, corresponded to a relative error of 4.4% in groundwater table measurement. The model yielded accurate results when the measured errors were in this range. |
| Key words: multi-parameter joint inversion; data assimilation; observation error; ensemble Kalman filter; ground water; model |
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