Joint assimilation of piezometric heads and groundwater temperatures for improved modeling of river-aquifer interactions

Abstract

The ensemble Kalman filter (EnKF) is increasingly used to improve the real-time prediction of groundwater states and the estimation of uncertain hydraulic subsurface parameters through assimilation of measurement data like groundwater levels and concentration data. At the interface between surface water and groundwater, measured groundwater temperature data can provide an additional source of information for subsurface characterizations with EnKF. Additionally, an improved prediction of the temperature field itself is often desirable for groundwater management. In this work, we investigate the worth of a joint assimilation of hydraulic and thermal observation data on the state and parameter estimation with EnKF for two different model setups: (i) a simple synthetic model of a river-aquifer system where the parameters and simulation conditions were perfectly known and (ii) a model of the Limmat aquifer in Zurich (Switzerland) where an exhaustive set of real-world observations of groundwater levels (87) and temperatures (22) was available for assimilation (year 2007) and verification (year 2011). Results for the synthetic case suggest that a joint assimilation of piezometric heads and groundwater temperatures together with updating of uncertain hydraulic parameters gives the best estimation of states and hydraulic properties of the model. For the real-world case, the prediction of groundwater temperatures could also be improved through data assimilation with EnKF. For the validation period, it was found that parameter fields updated with piezometric heads reduced RMSE's of states significantly (heads 249%, temperature 215%), but an additional conditioning of parameters on groundwater temperatures only influenced the characterization of the temperature field.

Multivariate hydrological data assimilation of soil moisture and groundwater head

“…Rasmussen et al (2015) assimilated the same variables using the ensemble transform Kalman filter (ETKF) with the MIKE SHE model. Kurtz et al (2014) jointly assimilated groundwater heads and groundwater temperatures with EnKF using both synthetic and real-world models. Shi et al (2014) employed EnKF to assimilate multivariate hydrological states in a small catchment modelled by the Flux-PIHM land surface model, with a focus on parameter estimation.…”

State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

“…Most of these publications use synthetic (or twin) experiments with assimilation of artificially generated data. Examples include studies with simulated measurements of the groundwater table depth or hydraulic head (Franssen and Kinzelbach, 2008;Bailey and Baù, 2012;Kurtz et al, 2014;Shi et al, 2014;Song et al, 2014;Tang et al, 2015), discharge/streamflow (Bailey and Baù, 2012;Moradkhani et al, 2012;Vrugt et al, 2013;Rasmussen et al, 2015), groundwater temperature (Kurtz et al, 2014), soil moisture (Wu and Margulis, 2011;Plaza et al, 2012;Erdal et al, 2014;Shi et al, 2014;Song et al, 2014;Pasetto et al, 2015), brightness temperature from passive remote sensing (Montzka et al, 2013;Han et al, 2014), and contaminant concentration (Gharamti et al, 2013). These studies use a variety of different methods for joint parameter and state estimation, among which the EnKF (Franssen and Kinzelbach, 2008;Wu et al, 2011;Gharamti et al, 2013;Erdal et al, 2014;Kurtz et al, 2014;Shi et al, 2014;Pasetto et al, 2015), the iterative EnKF (Song et al, 2014), the extended KF (Pauwels et al, 2009), the local ensemble transform KF (Han et al, 2014), the ensemble transform KF (Rasmussen et al, 2015), and the normal score EnKF (Tang et al, 2015).…”

“…This finding is corroborated by results for real-world assimilation studies documented in a rapidly growing list of publications and involving model structural inadequacies, measurement errors of the atmospheric forcing variables and calibration (assimilation) data, inadequate characterization of the lower boundary condition (aquifer), and uncertainty of other, auxiliary, model inputs. This includes assimilation of measurements of the electrical conductivity (Wu and Margulis, 2013), hydraulic head in wells (Kurtz et al, 2014;L. Shi et al, 2015), groundwater temperature (Kurtz et al, 2014), streamflow and discharge Y.…”

“…This includes assimilation of measurements of the electrical conductivity (Wu and Margulis, 2013), hydraulic head in wells (Kurtz et al, 2014;L. Shi et al, 2015), groundwater temperature (Kurtz et al, 2014), streamflow and discharge Y. Shi et al, 2015), active remote sensing (Pauwels et al, 2009), passive brightness temperature (Qin et al, 2009), soil moisture from lysimeters (Lue et al, 2011;Wu and Margulis, 2013;Erdal et al, 2014;L.…”

“…Shi et al, 2015), active remote sensing (Pauwels et al, 2009), passive brightness temperature (Qin et al, 2009), soil moisture from lysimeters (Lue et al, 2011;Wu and Margulis, 2013;Erdal et al, 2014;L. Shi et al, 2015), land surface temperature (Bateni and Entekhabi, 2012), and sensible and latent heat fluxes (Y. using methods such as the PF (Qin et al, 2009), PMCMC , EnKF (Bateni and Entekhabi, 2012;Wu and Margulis, 2013;Erdal et al, 2014;Kurtz et al, 2014;Y. Shi et al, 2015), and the extended KF (Pauwels et al, 2009;Lue et al, 2011).…”