Including prior information in the estimation of effective soil parameters in unsaturated zone modelling
Including prior information in the estimation of effective soil parameters in unsaturated zone modelling
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DOI：
10.1016/j.jhydrol.2004.02.011
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发表时间：
2004-07
影响因子：
6.4
通讯作者：
J. Mertens;H. Madsen;L. Feyen;D. Jacques;J. Feyen
中科院分区：
文献类型：
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作者：
J. Mertens;H. Madsen;L. Feyen;D. Jacques;J. Feyen
在本文中，我们提出了一种方法，包括先验信息在估计有效的土壤参数在非饱和区模拟土壤水分含量。利用室内实测数据对原状土芯进行了保湿曲线和导水曲线参数的估算。2001年，在一个80×20米山坡上沿三个样带和三个不同深度（表面，30和60厘米）的25个地点测量了土壤水分含量。在沿山坡的三个剖面坑的84个地点收集了土芯。利用实测参数值的联合概率分布作为先验信息来估计有效的土壤水力参数。建立了基于Richards方程的双水平单柱1D MIKE SHE模型，对沿坡中部样带的9个土壤湿度测点进行了测量。该模型的目标是模拟每个地点的土壤湿度剖面。shuffle complex evolution （SCE）算法已被应用于使用宽参数范围（称为“无先验”情况）或测量参数值的联合概率分布作为先验信息（“先验”情况）来估计有效的模型参数。当先验信息被纳入SCE优化时，模型预测的拟合优度仅比没有先验信息纳入时略差。然而，当纳入先验信息时，有效参数估计更为真实。对于无先验和先验情况，随后使用广义似然不确定性估计程序（GLUE）来估计模型预测的不确定性界限（UB）。在纳入先验信息后，采用更多的参数集来估计预测不确定性，参数值更加真实。此外，UB更好地附上了观察结果。因此，在GLUE中加入先验信息减少了获得足够行为参数集所需的模型评估量。结果表明先验信息在SCE和GLUE参数估计策略中的重要性。
In this paper we propose a methodology to include prior information in the estimation of effective soil parameters for modelling the soil moisture content in the unsaturated zone. Laboratory measurements on undisturbed soil cores were used to estimate the moisture retention curve and hydraulic conductivity curve parameters. The soil moisture content was measured at 25 locations along three transects and at three different depths (surface, 30 and 60 cm) on an 80×20 m hillslope for the year 2001. Soil cores were collected in 84 locations situated in three profile pits along the hillslope. For the estimation of the effective soil hydraulic parameters the joint probability distribution of measured parameter values was used as prior information. A two-horizon single column 1D MIKE SHE model based on Richards' equation was set-up for nine soil moisture measurement locations along the middle transect of the hillslope. The goal of the model is to simulate the soil moisture profile at each location. The shuffled complex evolution (SCE) algorithm has been applied to estimate effective model parameters using either wide parameter ranges, referred to as the ‘no-prior’ case, or the joint probability distribution of measured parameter values as prior information (‘prior’ case). When the prior information is incorporated in the SCE optimisation the goodness-of-fit of the model predictions is only slightly worse compared to when no-prior information is incorporated. However, the effective parameter estimates are more realistic when the prior information is incorporated. For both the no-prior and prior case the generalised likelihood uncertainty estimation procedure (GLUE) was subsequently used to estimate the uncertainty bounds (UB) on the model predictions. When incorporating the prior information more parameter sets were accepted for the estimation of the predictive uncertainty and the parameter values were more realistic. Moreover, UB better enclosed the observations. Thus, incorporating prior information in GLUE reduces the amount of model evaluations needed to obtain sufficient behavioural parameter sets. The results indicate the importance of prior information in the SCE and GLUE parameter estimation strategies.