Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>Hyporheic exchange flow (HEF) at the streambed–water interface (SWI) has been shown to impact the pattern and rate of discharging groundwater flow (GWF) and the consequential transport of heat, solutes and contaminants from the subsurface into streams. However, the control of geographic and hydromorphological catchment characteristics on GWF–HEF interactions is still not fully understood. Here, the spatial variability in flow characteristics in discharge zones was investigated and averaged over three spatial scales in five geographically different catchments in Sweden. Specifically, the deep GWF discharge velocity at the SWI was estimated using steady-state numerical models, accounting for the real multiscale topography and heterogeneous geology, while an analytical model, based on power spectral analysis of the streambed topography and statistical assessments of the stream hydraulics, was used to estimate the HEF. The modeling resulted in large variability in deep GWF and HEF velocities, both within and between catchments, and a regression analysis was performed to explain this observed variability by using a set of independent variables representing catchment topography and geology as well as local stream hydromorphology. Moreover, the HEF velocity was approximately two orders of magnitude larger than the deep GWF velocity in most of the investigated stream reaches, indicating significant potential to accelerate the deep GWF velocity and reduce the discharge areas. The greatest impact occurred in catchments with low average slope and in reaches close to the catchment outlet, where the deep GWF discharge velocity was generally low.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Characterization of karst systems and forecast of their state variables are essential for groundwater management and engineering in karst regions. These objectives can be met by the use of process-based discrete-continuum models (DCMs). However, results of DCMs may suffer from inversion nonuniqueness. It has been demonstrated that the joint inversion of observations regulated by different natural processes can tackle the nonuniqueness issue in groundwater modeling. However, this has not been tested for DCMs thus far. This research proposes a methodology for the joint inversion of hydro-thermo-chemo-graphs, applying to two small-scale sink-to-spring experiments at Freiheit Spring, Minnesota, USA. In order to address conceptual uncertainty, a multimodel approach was implemented, featuring seven mutually exclusive variants. Spring hydro-thermo-chemo-graphs, for all the variants simulated by MODFLOW-CFPv2, were jointly inverted using a weighted least squares algorithm. Subsequently, models were compared in terms of inversion and forecast performances, as well as parameter uncertainties. Results reveal the suitability of the DCM approach for simultaneous inversion and forecast of hydro-physico-chemical behavior of karst systems, even at a scale of meters and seconds. The estimated volume of the tracer conduit passage ranges from approximately 46–51 m<jats:sup>3</jats:sup>, which is comparable to the estimate from the flood-pulse method. Moreover, it was demonstrated that the thermograph and hydrograph contain more information about aquifer characteristics than the chemograph. However, this finding can be site-specific and should depend on the analysis scale, the considered conceptual models, and the hydrological state, which are potentially affected by minor unaccountable processes and features.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Transmissivity is a significant hydrogeological parameter that affects the reliability of groundwater flow and transport models. This study demonstrates the improvement in the estimated transmissivity field of an unconfined detritic aquifer that can be obtained by using geostatistical methods to combine three types of data: hard transmissivity data obtained from pumping tests, soft transmissivity data obtained from lithological information from boreholes, and water head data. The piezometric data can be related to transmissivity by solving the hydrogeology inverse problem, i.e., including the observed water head to determine the unknown model parameters (log transmissivities). The geostatistical combination of all the available information is achieved by using three different geostatistical methodologies: ordinary kriging, ordinary co-kriging and inverse problem universal co-kriging. In addition, there are eight methodological cases to be compared according to which log-transmissivity data are considered as the primary variable in co-kriging and whether two or three variables are used in inverse-problem universal co-kriging. The results are validated by using the performance statistics of the direct modelling of the unconfined groundwater flow and comparing observed water heads with the modelled ones. Although the results show that the two sets of log-transmissivity data are incompatible, the set of log-transmissivity data from the lithofacies provides a good log-transmissivity image that can be improved by inverse modelling. The map provided by inverse-problem universal co-kriging provides the best results. Using three variables, rather than two in the inverse problem, gives worse results because of the incompatibility of the log-transmissivity data sets.</jats:p>