Multivariate hydrological data assimilation of soil moisture and groundwater head

Abstract

Abstract. Observed groundwater head and soil moisture profiles are assimilated into an integrated hydrological model. The study uses the ensemble transform Kalman filter (ETKF) data assimilation method with the MIKE SHE hydrological model code. The method was firstly tested on synthetic data in a catchment of less complexity (the Karup catchment in Denmark), and later implemented using data from real observations in a larger and more complex catchment (the Ahlergaarde catchment in Denmark). In the Karup model, several experiments were designed with respect to different observation types, ensemble sizes and localization schemes, to investigate the assimilation performance. The results showed the necessity of using localization, especially when assimilating both groundwater head and soil moisture. The proposed scheme with both distance localization and variable localization was shown to be more robust and provide better results. Using the same assimilation scheme in the Ahlergaarde model, groundwater head and soil moisture were successfully assimilated into the model. The hydrological model with assimilation showed an overall improved performance compared to the model without assimilation.

Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach

“…Since then, significant progress has been made in areas such as model code development (Clark et al, 2015;Maxwell and Kollet, 2008;Samaniego et al, 2010), remote sensing (Lettenmaier et al, 2015) and data assimilation (Zhang et al, 2016;Ridler et al, 2014). Nevertheless, explicit spatial pattern evaluation of distributed hydrological models remains a rarity.…”

Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model

“…These include for instance Bayesian total error analysis (Kavetski, Kuczera, & Franks, 2006a, 2006b and integrated Bayesian uncertainty estimator (Ajami, Duan, & Sorooshian, 2007). Other authors resort to data assimilation techniques, for example, Vrugt, Diks, Gupta, Bouten, and Verstraten (2005) who performed uncertainty analysis using simultaneous optimization and data assimilation; Salamon and Feyen (2009), who used a particle filter; and Zhang et al (2016) who analysed how different assumptions on uncertainty formulations affected the efficiency of a Kalman filter.…”

Application of soil moisture information for operational water management

“…The goal is to assess whether the EnKF scheme improves the accuracy of soil moisture model estimates when all error statistics are known. The twin experiment is performed for the in situ station locations and for a period of two months: May 1 In general, increasing the ensemble size will lead to a better representation of model uncertainty (Zhang et al, 2016). However, due to computational limitations, one has to nd a balance between computational costs and appropriate ensemble size (He et al, 2019).…”

“…Therefore, it would make sense to update both the soil moisture state as well as groundwater heads in a data assimilation procedure to update phreatic groundwater levels. For example, Zhang et al (2016) found that univariate assimilation of either soil moisture or groundwater head states improves the accuracy of the variable being assimilated, but does not improve the accuracy of other variables. However, they show that the simultaneous assimilation of soil moisture and groundwater head into a hydrological model improves the accuracy of both the soil moisture and groundwater head simulations.…”

“…Data assimilation aims to nd an optimal combination of merging hydrological model state estimates with observations. Several studies have shown the value of data assimilation schemes for integrated surface-subsurface modelling (Camporese et al, 2009a,b;Zhang et al, 2016;Botto et al, 2018;Zhao and Yang, 2018), some speci cally focusing on operational applications (Hendricks Franssen et al, 2011;De Rosnay et al, 2013;Kurtz et al, 2017;He et al, 2019).…”