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Recent water availability and scarcity problems have highlighted the importance of surface–groundwater interactions. Thus, groundwater models are coupled with surface water models. However, this solution is complex, needing code modifications and long computation times. Recently, a new groundwater module (gwflow) was developed directly inside the SWAT code to tackle these issues. This research assesses gwflow's capabilities in representing surface–groundwater system interactions in the Dijle catchment (892.54 km2), a groundwater-driven watershed in Belgium. Additional developments were made in SWAT+gwflow to represent the interaction between the groundwater and soil (gwsoil). The model was calibrated for monthly mean streamflow at the catchment outlet (1983 to 1996) and validated for two periods (validation 1: 1975 to 1982 and validation 2: 1997 to 2002). It was found that the SWAT+gwflow model is better at representing the total flow (NSE of 0.6) than the standalone SWAT+ (NSE of 0.4). This was confirmed during two validation periods where the standalone model scored unsatisfactory monthly NSE (0.6 and 0.1), while the new model's NSE was 0.7 and 0.5. Additionally, the SWAT+gwflow model simulations better depict the groundwater via baseflow and attain proper water balance values. Thus, in a highly groundwater-driven catchment, the simplified representation of groundwater systems by the standalone SWAT+ model has pitfalls. In addition, the modification made to the gwflow module (gwsoil) improved the model's performance, which, without such adjustment, overestimates the streamflow via saturation excess flow. When including the gwsoil mechanism, thereby providing a more accurate representation of water storage and movement, groundwater is transferred to the soil profile, increasing the overall soil water content and thereby increasing lateral flow. This novel modification can also have implications for other distributed hydrological models to consider such exchanges in their modeling scheme. [ABSTRACT FROM AUTHOR]

Recent water availability and scarcity problems have highlighted the importance of surface water-groundwater interactions. Thus, groundwater models such as Modular ground-water Flow (MODFLOW) were coupled to the Soil and Water Assessment Tool (SWAT (+)). However, this solution is complex, needing code modifications, complex coupling, and high computation time. Lately, a new groundwater module (gwflow) was developed directly inside the SWAT+ code to tackle those issues. This research assesses gwflow’s capabilities in representing surface – groundwater systems interactions in the Dijle catchment, Belgium. A hydrological model was set up using the standalone SWAT+ and SWAT+gwflow. In addition, the interaction between the soil and the groundwater is not represented in the new module, hence, it was modified to account for such exchanges. Finally, pumping is also included in the module to enable the modeling of transient state conditions. Model comparison is made using Nash-Sutcliffe efficiency (NSE) for the calibration period (1986 to 1996) and two validation periods (1975 to 1983 and 1997 to 2002). It is found that the SWAT+gwflow model is better representative (NSE of 0.6) than the standalone SWAT+ (NSE of 0.4). This is signified during two validation periods where the standalone scored negative NSE while the new model’s NSE was 0.7 and 0.5. This shows that, in a highly groundwater-driven catchment of this type, the simplistic representation of groundwater systems by the standalone SWAT+ model has pitfalls. In addition, the modification we made on the gwflow module has improved the model performance as groundwater-soil interaction is inevitable whenever the water table reaches the soil profile. In conclusion, groundwater-surface water processes need to be appropriately designated in hydrological models; hence, the modification we made to the gwflow module (groundwater–soil interaction) is found to be critical. This novel modification can also have an implication for other distributed hydrological models to consider such exchanges in their modeling scheme. This paves a road towards refining coupled ground-surface water hydrogeological models. Finally, the modified SWAT+gwflow model is more appropriate for assessing the Dijle catchment hydrology than the standalone model. [ABSTRACT FROM AUTHOR]

Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation. [ABSTRACT FROM AUTHOR]

Streamflow is often the only variable used to constrain hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate a total of twelve hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we hypothesize that these twelve process-based models with similar streamflow performance have similar representations of internal states and fluxes. We test our hypothesis by comparing internal states and fluxes between models and we assess their plausibility using remotely-sensed products of evaporation, snow cover, soil moisture and total storage anomalies. Our results indicate that models with similar streamflow performance represent internal states and fluxes differently. Substantial dissimilarities between models are found for annual and seasonal evaporation and interception rates, the number of days per year with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. Relatively small root-zone storage capacities for several models lead to drying-out of the root-zone storage and significant reduction of evaporative fluxes each summer, which is not suggested by remotely-sensed estimates of evaporation and root-zone soil moisture. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Using remotely-sensed products, we could evaluate the plausibility of model representations only to some extent, as many of these internal variables remain unknown, highlighting the need for experimental research. We also encourage modelers to rely on multi-model and multi-parameter studies to reveal to decision-makers the uncertainties inherent to the heterogeneity of catchments and the lack of evaluation data. [ABSTRACT FROM AUTHOR]

We present a general and flexible Bayesian approach using uncertainty multipliers to simultaneously analyze the input and parameter uncertainty of a groundwater flow model with consideration of the heteroscedasticity of the groundwater level error. Groundwater recharge and groundwater abstraction multipliers are introduced to quantify the uncertainty of the spatially distributed input data of the groundwater model in addition to parameter uncertainty. The heteroscedasticity of the groundwater level error is also considered in our Bayesian approach by incorporating a new heteroscedastic error model. The proposed methodology is applied in an overexploited aquifer in Bangladesh where groundwater abstraction and recharge data are highly uncertain. The results of the study confirm that consideration of recharge and abstraction uncertainty through the use of recharge and abstraction multipliers is feasible even in a fully distributed physically based groundwater flow model. Heteroscedasticity is present in the groundwater level error and has an effect on the model predictions and parameter distributions. The input uncertainty affects the model predictions and parameter distributions and it is the dominant source of uncertainty in the groundwater flow prediction. Additionally, the approach described also provides a new way to optimize the spatially distributed recharge and abstraction data along with the parameter values under uncertain input conditions. We conclude that considering model input uncertainty along with parameter uncertainty and heteroscedasticity of the groundwater level error is important for obtaining realistic model predictions and a correct estimation of the uncertainty bounds. Key Points: Full Bayesian approach to simultaneously quantify input and parameter uncertainty of a groundwater flow modelNew heteroscedastic error model for groundwater level is includedUse of groundwater recharge and abstraction multipliers leads to physically more realistic results [ABSTRACT FROM AUTHOR]

Among the advances made in analytical and numerical analysis methods to quantify groundwater/surface-water interaction, one methodology that stands out is the use of heat as an environmental tracer. A large data set of river and riverbed temperature profiles from the Aa River in Belgium has been used to examine the spatial-temporal variations of groundwater/surface-water interaction. Exchange fluxes were calculated with the numerical heat-transport code STRIVE. The code was applied in transient mode to overcome previous limitations of steady-state analysis, and allowed for the calculation of model quality. In autumn and winter the mean exchange fluxes reached −90 mm d−1, while in spring and early summer fluxes were −42 mm d−1. Predominantly gaining conditions occurred along the river reach; however, in a few areas the direction of flow changed in time. The river banks showed elevated fluxes up to a factor of 3 compared to the center of the river. Higher fluxes were detected in the upstream section of the reach. Due to the influence of exchange fluxes along the river banks, larger temporal variations were found in the downstream section. The exchange fluxes at the river banks seemed more driven by variable local exchange flows, while the center of the river was dominated by deep and steady regional groundwater flows. These spatial and temporal differences in groundwater/surface-water exchange show the importance of long-term investigations on the driving forces of hyporheic processes across different scales. [ABSTRACT FROM AUTHOR]

International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although comparative studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes. We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of best performing parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. Nash-Sutcliffe Efficiency), clear differences could be observed for specific events. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the Ourthe catchment. This intercomparison enhanced the understanding of the hydrological functioning of the catchment and, above all, helped to evaluate each model against a set of alternative models. [ABSTRACT FROM AUTHOR]

Environmental models are often over-parameterized. A sensitivity analysis can identify influential model parameters for, e.g. the parameter estimation process, model development, research prioritization and so on. This paper presents the results of an extensive study of the Latin-Hypercube One-factor-At-a-Time (LH-OAT) procedure applied to the Soil and Water Assessment Tool (SWAT). The LH-OAT is a sensitivity analysis method that can be categorized as a screening method. The results of the sensitivity analyses for all output variables indicate that the SWAT model of the river Kleine Nete is mainly sensitive to flow related parameters. Rarely, water quality parameters get a high priority ranking. It is observed that the number of intervals used for the Latin-Hypercube sampling should be sufficiently high to achieve converged parameter rankings. Additionally, it is noted that the LH-OAT method can enhance the understanding of the model, e.g. on the use of water quality input data. [ABSTRACT FROM AUTHOR]