Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother

Abstract

Abstract. Groundwater flow models are important tools in assessing baseline conditions and investigating management alternatives in groundwater systems. The usefulness of these models, however, is often hindered by insufficient knowledge regarding the magnitude and spatial distribution of the spatially-distributed parameters, such as hydraulic conductivity (K), that govern the response of these models. Proposed parameter estimation methods frequently are demonstrated using simplified aquifer representations, when in reality the groundwater regime in a given watershed is influenced by strongly-coupled surface-subsurface processes. Furthermore, parameter estimation methodologies that rely on a geostatistical structure of K often assume the parameter values of the geostatistical model as known or estimate these values from limited data.In this study, we investigate the use of a data assimilation algorithm, the Ensemble Smoother, to provide enhanced estimates of K within a catchment system using the fullycoupled, surface-subsurface flow model CATHY. Both water table elevation and streamflow data are assimilated to condition the spatial distribution of K. An iterative procedure using the ES update routine, in which geostatistical parameter values defining the true spatial structure of K are identified, is also presented. In this procedure, parameter values are inferred from the updated ensemble of K fields and used in the subsequent iteration to generate the K ensemble, with the process proceeding until parameter values are converged upon. The parameter estimation scheme is demonstrated via a synthetic three-dimensional tilted v-shaped catchment system incorporating stream flow and variably-saturated subsurface flow, with spatio-temporal variability in forcing terms. Results indicate that the method is successful in providing improved estimates of the K field, and that the iterative scheme can be used to identify the geostatistical parameter values of the aquifer system. In general, water table data have a much greater ability than streamflow data to condition K. Future research includes applying the methodology to an actual regional study site.

Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation

“…In Li et al (2012), the EnKF was used to map the hydraulic conductivity and porosity fields by assimilating dynamic piezometric data and multiple concentration data. In Bailey and Baú (2012), the ES was iteratively applied to estimate the parameters of a geostatistical model through assimilation of water table elevation data. Tong et al (2012) used the EnKF in a synthetic two-dimensional aquifer to estimate the hydraulic conductivity by assimilating solute concentration data measured in a large number of observation wells.…”

Estimating spatially-variable rate constants of denitrification in irrigated agricultural groundwater systems using an Ensemble Smoother

“…The latter is referred to as return mass (RM) throughout the remainder of this paper. The ES scheme is used due to its computational efficiency (Bailey and Baù, 2012), a particularly constraining requirement when dealing with physically-based distributed, data-intensive models such as reactive transport models. The methodology is applied to a 1-year transient simulation for a synthetic aquifer system characterized by a comprehensive suite of hydrological and chemical forcing terms, processes, and system parameters that approaches a real-world setting.…”

Data assimilation in integrated hydrological modelling in the presence of observation bias

“…Rasmussen et al (2015) used the ensemble transform Kalman filter (ETKF) to assimilate groundwater head and stream discharge in a catchment-scale integrated hydrological model for both state updating and parameter estimation. Other studies that focus on joint state updating and parameter estimation in integrated hydrological modelling include Bailey and Baù (2012), in which a smoother was used to calibrate hydraulic conductivity using streamflow and head observations, and Kurtz et al (2013), which used head observations to calibrate heterogenous riverbed conductivities.…”