Abstract
Combining metamodels with data assimilation schemes allows the incorporation of up-to-date information in metamodels, offering new opportunities for operational water resources management. We developed a data assimilation scheme for the unsaturated zone metamodel MetaSWAP using OpenDA, which is an open source data assimilation framework. A twin experiment showed the feasibility of applying an Ensemble Kalman filter as a data assimilation method for updating metamodels. Furthermore, we assessed the accuracy of root zone soil moisture model estimates when assimilating the regional SMAP L3 Enhanced surface soil moisture product. The model accuracy is assessed using in situ soil moisture measurements collected at 12 locations in the Twente region, the Netherlands. Although the accuracy of the model estimates does not improve in terms of correlation coefficient, the accuracy does improve in terms of Root Mean Square Error and bias. Therefore, the assimilation of surface soil moisture observations has value for updating root zone soil moisture model estimates. In addition, the accuracy of the model estimates improves on both regional and local spatial scales. The increasing availability of remotely sensed soil moisture data will lead to new possibilities for integrating metamodelling and data assimilation in operational water resources management. However, we expect that significant investments in computational capacities are necessary for effective implementation in decision-making.
Highlights
The application of integrated physically-based hydrological models is increasing in water resources management (Guswa et al, 2014; Kurtz et al, 2017)
We assessed the applicability of satellite-based regional-scale surface soil moisture observations to increase the accuracy of root zone soil moisture estimates of a metamodel
This study shows that combining metamodels with data assimilation schemes allows incorporating new information in metamodels
Summary
The application of integrated physically-based hydrological models is increasing in water resources management (Guswa et al, 2014; Kurtz et al, 2017). Such modelling tools are typically used for water resources management on various spatial and temporal scales. Water managers can use model output for decision-making while taking into account uncertainties of, among others, input data, boundary and initial conditions, and model structure (Beven and Alcock, 2012). To reduce the uncertainties inherent to integrated physically-based hydrological modelling, data assimilation schemes are often applied (Liu et al, 2012; Weerts et al, 2014). Several studies have shown the value of data assimilation schemes for integrated surface–subsurface modelling (Camporese et al., 2009a; Camporese et al, 2009b; Zhang et al, 2016; Botto et al, 2018; Zhao and Yang, 2018), some focusing on operational applications (Hendricks Franssen et al, 2011; De Rosnay et al, 2013; Kurtz et al, 2017; He et al, 2019)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
