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Application of data assimilation with the Root Zone Water Quality Model for soil moisture profile estimation in the upper Cedar Creek, Indiana (pages 1707–1719)

机译：根区水质模型数据同化在印第安纳州锡达河上游的土壤水分剖面估算中的应用（第1707–1719页）

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Data assimilation techniques have been proven as an effective tool to improve model forecasts by combining information aboutnobserved variables in many areas. This article examines the potential of assimilating surface soil moisture observations into anfield-scale hydrological model, the Root Zone Water Quality Model, to improve soil moisture estimation. The Ensemble KalmannFilter (EnKF), a popular data assimilation technique for nonlinear systems, was applied and compared with a simple directninsertion method. In situ soil moisture data at four different depths (5, 20, 40, and 60 cm) from two agricultural fields (AS1 andnAS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved soilnmoisture estimation compared with the direct insertion method and model results without assimilation, having more distinctnimprovement at the 5 and 20 cm depths than for deeper layers (40 and 60 cm). Local vertical soil property heterogeneity in AS1ndeteriorated soil moisture estimates with the EnKF. Removal of systematic bias in the forecast model was found to be critical fornmore successful soil moisture data assimilation studies. This study also demonstrates that a more frequent update generallyncontributes in enhancing the open loop simulation; however, large forecasting error can prevent more frequent update fromnproviding better results. In addition, results indicate that various ensemble sizes make little difference in the assimilation results.nAn ensemble of 100 members produced results that were comparable with results obtained from larger ensembles. Copyright ©n2011 John Wiley & Sons, Ltd.
机译：数据同化技术已被证明是一种有效的工具，可以通过结合许多领域中未观测到的变量的信息来改进模型预测。本文研究了将表层土壤水分观测值同化到田间规模的水文模型（根区水质模型）中以改善土壤水分估算的潜力。 Ensemble KalmannFilter（EnKF）是一种用于非线性系统的流行数据同化技术，并与简单的直接插入方法进行了比较。来自印第安纳州东北部两个农田（AS1和nAS2）的四个不同深度（5、20、40和60 cm）的原位土壤水分数据用于同化和验证。通过每日更新，与直接插入方法相比，EnKF改进了土壤水分估计，并且模型结果没有同化，在5和20 cm深度处比在更深层（40和60 cm）处具有更大的改进。用EnKF估计AS1中局部垂直土壤性质的异质性，并恶化土壤水分。发现消除预测模型中的系统偏差是成功进行土壤水分数据同化研究的关键。这项研究还表明，更频繁的更新通常有助于增强开环仿真。但是，较大的预测误差会阻止更频繁的更新提供更好的结果。此外，结果表明，各种合奏大小对同化结果影响不大。n由100个成员组成的合奏所产生的结果与从较大合奏获得的结果可比。版权所有©n2011 John Wiley＆Sons，Ltd.

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