Using Kalman Filtering to improve and quantify the uncertainty of numerical groundwater simulations 2. Application to monitoring network design
article
Integrating Kalman filtering and a deterministic groundwater flow model not only improves and quantifies the uncertainty of numerical groundwater simulation, but also provides the dynamic relation between the variance of the estimation error and the measurement strategy. This relation is applied to Zhengzhou city and Spannenburg for the analysis and design of networks for monitoring groundwater levels. For both cases, the network is designed in such a way that the network density is minimized under the constraint of given threshold values for the standard deviations of the estimation errors. Several network alternatives are analyzed and a best alternative is selected by trial and error. In the case of Zhengzhou city, much attention is also paid to the sensitivity analysis of the deterministic and stochastic parameters of the standard deviation of the estimation error. The results indicate that the density of the network depends on the characteristics of geohydrological systems. The Zhengzhou city and Spannenburg cases are compared and the difference between them is discussed using a hypothetical example. It is found that the difference in the behavior of the estimation variance in time is attributable to the characteristic response time of the system on which the network density and observation frequency depend.
TNO Identifier
469027
Source
Water resources research, 27(8), pp. 1995-2006.
Pages
1995-2006
Files
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