Traditionally, groundwater and surface water flow models have been calibrated against two observation types: hydraulic heads and surface water discharge. It has repeatedly been demonstrated, however, that these classical observations do not contain sufficient information to calibrate flow models. To reduce the predictive uncertainty of flow models, the consideration of other observation types constitutes a promising way forward. Despite the ever-increasing availability of other observation types, however, they are still unconventional when it comes to flow model calibration. By reviewing studies that included nonclassical observations in flow model calibration, benefits and challenges associated with their integration in flow model calibration were identified, and their information content was analyzed. While explicit simulation of mass transport processes in flow models poses challenges, even simplified approaches to integrate tracer concentrations yield significantly better calibration results than using only classical observations. For a majority of calibrated flow models, observations of tracer concentrations and of exchange fluxes were beneficial. Temperature observations improved the simulation of heat transport but often worsened all other model outcomes. Only when temperature observations were made within 2 m of the surface water-groundwater interface did they have the potential to also improve flow and mass transport simulations. Surprisingly, many models were calibrated manually rather than with the widely available, mathematically robust and automated tools. There is a clear need for more systematic implementation of unconventional observations and automated flow model calibration as well as for more systematic quantification of the information content of unconventional observations. Plain Language Summary Traditionally, groundwater and surface water flow models, which are critical for water resources assessment, have been calibrated against only two classical observation types: groundwater levels and surface water discharge. In the past, it has repeatedly been demonstrated that these classical observations do not contain sufficient information to calibrate the parameters required for the simulation of groundwater and surface water flow systems. Owing to the rapid development of measurement techniques throughout the last three decades, however, many other observations of hydrological systems have become widely available. Despite this, observation types other than the classical ones are still unconventional when it comes to flow model calibration. The overall goal of this review is to identify optimal observation types and procedures for flow model calibration and hydrological predictions. We found that observations of tracer concentrations and exchange fluxes are beneficial for most flow models. Temperatures improve the simulation of heat transport but often worsen other flow model outcomes, unless temperatures are measured within 2 m of the surface water-groundwater interface. We identified a need for...

To provide a sound understanding of the sources, pathways, and residence times of groundwater water in alluvial river‐aquifer systems, a combined multitracer and modeling experiment was carried out in an important alluvial drinking water wellfield in Switzerland. 222Rn, 3H/3He, atmospheric noble gases, and the novel 37Ar‐method were used to quantify residence times and mixing ratios of water from different sources. With a half‐life of 35.1 days, 37Ar allowed to successfully close a critical observational time gap between 222Rn and 3H/3He for residence times of weeks to months. Covering the entire range of residence times of groundwater in alluvial systems revealed that, to quantify the fractions of water from different sources in such systems, atmospheric noble gases and helium isotopes are tracers suited for end‐member mixing analysis. A comparison between the tracer‐based mixing ratios and mixing ratios simulated with a fully‐integrated, physically‐based flow model showed that models, which are only calibrated against hydraulic heads, cannot reliably reproduce mixing ratios or residence times of alluvial river‐aquifer systems. However, the tracer‐based mixing ratios allowed the identification of an appropriate flow model parametrization. Consequently, for alluvial systems, we recommend the combination of multitracer studies that cover all relevant residence times with fully‐coupled, physically‐based flow modeling to better characterize the complex interactions of river‐aquifer systems.

Online data acquisition, data assimilation and integrated hydrological modelling have become more and more important in hydrological science. In this study, we explore cloud computing for integrating field data acquisition and stochastic, physically-based hydrological modelling in a data assimilation and optimisation framework as a service to water resources management. For this purpose, we developed an ensemble Kalman filter-based data assimilation system for the fully-coupled, physically-based hydrological model HydroGeoSphere, which is able to run in a cloud computing environment. A synthetic data assimilation experiment based on the widely used tilted V-catchment problem showed that the computational overhead for the application of the data assimilation platform in a cloud computing environment is minimal, which makes it well-suited for practical water management problems. Advantages of the cloud-based implementation comprise the independence from computational infrastructure and the straightforward integration of cloud-based observation databases with the modelling and data assimilation platform.

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For the simulation of winter hydrological processes a gap in the availability of flow models existed: one either had the choice between (1) physically‐based and fully‐integrated, but computationally very intensive, or (2) simplified and compartamentalized, but computationally less expensive, simulators. To bridge this gap, we here present the integration of a computationally efficient representation of winter hydrological processes (snowfall, snow accumulation, snowmelt, pore water freeze–thaw) in a fully‐integrated surface water‐groundwater flow model. This allows the efficient simulation of catchment‐scale hydrological processes in locations significantly influenced by winter processes. Snow accumulation and snowmelt are based on the degree‐day method and pore water freeze–thaw is calculated with a vertical heat conduction approach. This representation of winter hydrological processes is integrated into the fully‐coupled surface water‐groundwater flow model HydroGeoSphere. A benchmark for pore water freeze–thaw as well as two illustrative examples are provided.

The presence of unsaturated zones at the river‐aquifer interface has large implications on numerous hydraulic and chemical processes. However, the hydrological and geological controls that influence the development of unsaturated zones have so far only been analyzed with simplified conceptualizations of flow processes, or homogeneous conceptualizations of the hydraulic conductivity in either the aquifer or the riverbed. We systematically investigated the influence of heterogeneous structures in both the riverbed and the aquifer on the development of unsaturated zones. A stochastic 1‐D criterion that takes both riverbed and aquifer heterogeneity into account was developed using a Monte Carlo sampling technique. The approach allows the reliable estimation of the upper bound of the spatial extent of unsaturated areas underneath a riverbed. Through systematic numerical modeling experiments, we furthermore show that horizontal capillary forces can reduce the spatial extent of unsaturated zones under clogged areas. This analysis shows how the spatial structure of clogging layers and aquifers influence the propensity for unsaturated zones to develop: In riverbeds where clogged areas are made up of many small, spatially disconnected patches with a diameter in the order of 1 m, unsaturated areas are less likely to develop compared to riverbeds where large clogged areas exist adjacent to unclogged areas. A combination of the stochastic 1‐D criterion with an analysis of the spatial structure of the clogging layers and the potential for resaturation can help develop an appropriate conceptual model and inform the choice of a suitable numerical simulator for river‐aquifer systems.

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Flood events can change the riverbed topography as well as the riverbed texture and structure, which in turn can influence the riverbed hydraulic conductivity (Krb) and river‐aquifer exchange fluxes. A major flood event occurred in the Emme River in Switzerland in 2014, with major implications for the riverbed structure. The event was simulated with the fully integrated hydrological model HydroGeoSphere. The aim was to investigate the effect of the spatial and temporal variability of riverbed topography and Krb on predictions of hydraulic states and fluxes and to test whether data assimilation (DA) based on the ensemble Kalman filter (EnKF) can better reproduce flood‐induced changes to hydraulic states and parameters with the help of riverbed topography changes recorded with an unmanned aerial vehicle (UAV) and through‐water photogrammetry. The performance of DA was assessed by evaluating the reproduction of the hydraulic states for the year 2015. While the prediction of surface water discharge was not affected much by the changes in riverbed topography and in Krb, using the UAV‐derived postflood instead of the preflood riverbed topography reduced the root‐mean‐square error of predicted heads (RMSE [h]) by 24%. If, in addition to using the postflood riverbed topography, also Krb and aquifer hydraulic conductivity (Kaq) were updated through DA after the flood, the RMSE (h) was reduced by 55%. We demonstrate how updating of Krb and Kaq based on EnKF and UAV‐based observations of riverbed topography transience after a major flood event strongly improve predictions of postflood hydraulic states.