Anker Højberg

Uncertainties in model structures have been recognised often to be the main source of uncertainty in predictive model simulations. Despite this knowledge, uncertainty studies are traditionally limited to a single deterministic model and the uncertainty addressed by a parameter uncertainty study. The extent to which a parameter uncertainty study may encompass model structure errors in a groundwater model is studied in a case study. Three groundwater models were constructed on the basis of three different hydrogeological interpretations. Each of the models was calibrated inversely against groundwater heads and streamflows. A parameter uncertainty analysis was carried out for each of the three conceptual models by Monte Carlo simulations. A comparison of the predictive uncertainties for the three conceptual models showed large differences between the uncertainty intervals. Most discrepancies were observed for data types not used in the model calibration. Thus uncertainties in the conceptual models become of increasing importance when predictive simulations consider data types that are extrapolates from the data types used for calibration.

The fate of selected pesticides under natural groundwater conditions was studied by natural gradient short and long term injection experiments in a shallow uncon- fined aerobic aquifer. Bentazone, DNOC, MCPP, dichlorprop, isoproturon, and BAM (dichlobenil metabolite) were injected in aqueous solution with bromide as a nonre- active tracer. The Bromide and pesticide plumes were sampled during the initial 25 m of migration in a dense monitoring net of multilevel samplers. The aquifer was physical and geochemical heterogeneous, which affected transport of several of the pesticides. A 3D reactive transport code was developed including one- and two-site linear/nonlinear equilibrium/nonequilibrium sorption and first-order as well as single Monod degradation kinetic coupled to microbial growth. Model simulations demon- strated that microbial growth was likely supported by the phenoxy acids MCPP and dichlorprop, while degradation of DNOC was adequately described by first-order degradation w...

Water management is divided among several actors in Denmark with different responsibilities at different physical scales and authoritative levels. A major challenge is thus to ensure that the administration across the different levels are founded on a consistent basis. Starting in 1996 the first version of a national hydrological model was developed, for assessment of the groundwater resource at national scale. During the period 2005 – 2009 an extensive model update was carried out in collaboration with the regional water authorities. During the update focus was that the model should not only perform well in hydrological sense, but should also be able to provide a common framework for water management at the different scales and levels. As a consequence, procedure and supporting software have been developed by which knowledge obtained by local scale detailed studies can be incorporated in databases by local users and transferred to the national model.

This paper presents a framework for characterising uncertainty in hydrological modelling and discusses the most difficult task with respect to model prediction, namely model predictions for cases where no data are available for constraining the model through calibration/validation either due to lack of data (ungauged catchments) or because predictions are made for situations different from the present conditions, e.g. for assessing impacts due to climate change or due to other human interventions such as land use change. The discussion is illustrated by a few examples.

ABSTRACT The question of which climate model bias correction methods and spatial scales for correction are optimal for both projecting future hydrological changes as well as removing initial model bias has so far received little attention. For 11 climate models (CMs), or CM – Global/Regional Climate Model pairing, this paper analyses the relationship between complexity and robustness of three distribution based scaling (DBS) bias correction methods applied to daily precipitation at various spatial scales. Hydrological simulations are forced by CM inputs to assess the spatial uncertainty of groundwater head and stream discharge given the various DBS methods. A unique metric is devised which allows for comparison of spatial variability in climate model bias and projected change in precipitation. It is found that the spatial variability in climate model bias is larger than in the climate change signals. The magnitude of spatial bias seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in hydrological outputs. Variables which integrate basin responses over time and space are more sensitive to mean spatial biases and less so on extremes. Hydrological simulations forced by the least parameterised DBS approach show the highest error in mean and maximum groundwater heads, however, the most highly parameterised DBS approach shows less robustness in future periods compared to the reference period it was trained in. For hydrological impacts studies, choice of bias correction method should depend on the spatial scale at which hydrological impacts variables are required and whether CM initial bias is spatially uniform or spatially varying. This article is protected by copyright. All rights reserved.

ABSTRACT In distributed and coupled surface water – groundwater modeling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of air-borne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analyzed using a Representative Elementary Scale (RES) concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. This article is protected by copyright. All rights reserved.

There is much to gain in joining monitoring and modelling efforts, especially in the present process of implementing the European Water Framework Directive. Nevertheless, it is rare to see forces combined in these two disciplines. To bring the monitoring and the modelling communities together, a number of workshops have been arranged with discussions on benefits and constraints in joint use of monitoring and modelling. The workshops have been attended by scientists, water managers, policy makers as well as stakeholders and consultants. Emphasis has been put on data availability and accessibility, remote sensing and data assimilation techniques, monitoring programmes and modelling support to the design or optimisation of these as well as potential benefits of using supporting modelling tools in the process of designing Programmes of Measures by impact assessment etc. The way models can support in extrapolation in time and space, in data analysis, in process understanding (conceptual models), in accessing correct interaction between pressures and impacts etc. have also been elaborated. Although practitioners have been open-minded to the presented ideas, they are somewhat reluctant towards how to implement this in their daily work. This paper presents some experiences from the workshops.

ABSTRACT In this study six hydrological models that only differ with respect to their conceptual geological models are established for a 465 km2 area. The performances of the six models are evaluated in differential split-sample tests against a unique data set with well documented groundwater head and discharge data for different periods with different groundwater abstractions. The calibration results of the six models are comparable, with no model being superior to the others. Though, the six models make very different predictions of changes in groundwater head and discharges as a response to changes in groundwater abstraction. This confirms the utmost importance of the conceptual geological model for making predictions of variables and conditions beyond the calibration situation. In most cases the observed changes in hydraulic head and discharge are within the range of the changes predicted by the six models implying that a multiple modeling approach can be useful in obtaining more robust assessments of likely prediction errors. We conclude that the use of multiple models appear to be a good alternative to traditional differential split-sample schemes. A model averaging analysis shows that model weights estimated from model performance in the calibration or validation situation in many cases are not optimal for making other predictions. Hence, the critical assumption that is always made in model averaging, namely that the model weights derived from the calibration situation are also optimal for model predictions, cannot be assumed to be generally valid.

A physically based, coupled and distributed hydrologic model has been set up for the Ringkøbing Fjord catchment, Denmark. This transient model, built with the MIKE SHE/MIKE 11 code, comprises all major components of the terrestrial water cycle, including a three-...

There is much to gain in joining monitoring and modelling efforts, especially in the present process of implementing the European Water Framework Directive and in the coming implementation of the Groundwater Directive. Nevertheless, present practises in the water management world suggest that most often models are not considered an option when monitoring obligations in the WFD are solved. The present paper analyses the constraints, such as perceived insufficiency of data for modelling, lack of explicit requirement for modelling in the WFD and its associated technical guidance documents, lack of awareness about what models can do and lack of confidence in models by water managers and policy makers. The findings have mainly emerged from a series of Harmoni-CA workshops aiming at bringing the monitoring and modelling communities together for a discussion of benefits and constraints in the joint use of monitoring and modelling. The workshops were attended by scientists, water managers, policy makers, stakeholders and consultants. The overall conclusion is that modelling can significantly improve the benefits of monitoring data; by quality assurance of data, interpolation and extrapolation in space and time, development of process understanding (conceptual models), and the assessment of impacts of pressures and effects of programmes of measures.

Groundwater modeling is undergoing a change from traditional stand-alone studies toward being an integrated part of holistic water resources management procedures. This is illustrated by the development in Denmark, where comprehensive national databases for geologic borehole data, groundwater-related geophysical data, geologic models, as well as a national groundwater-surface water model have been established and integrated to support water management. This has enhanced the benefits of using groundwater models. Based on insight gained from this Danish experience, a scientifically realistic scenario for the use of groundwater modeling in 2020 has been developed, in which groundwater models will be a part of sophisticated databases and modeling systems. The databases and numerical models will be seamlessly integrated, and the tasks of monitoring and modeling will be merged. Numerical models for atmospheric, surface water, and groundwater processes will be coupled in one integrated modeling system that can operate at a wide range of spatial scales. Furthermore, the management systems will be constructed with a focus on building credibility of model and data use among all stakeholders and on facilitating a learning process whereby data and models, as well as stakeholders' understanding of the system, are updated to currently available information. The key scientific challenges for achieving this are (1) developing new methodologies for integration of statistical and qualitative uncertainty; (2) mapping geological heterogeneity and developing scaling methodologies; (3) developing coupled model codes; and (4) developing integrated information systems, including quality assurance and uncertainty information that facilitate active stakeholder involvement and learning.

ABSTRACT It is generally acknowledged that water management must be based on an integrated approach, considering the entire freshwater cycle. This has in particularly been endorsed in Europe by the European Water Framework Directive (WFD) imposing integrated management considering all waters. Although not prescribed by the WFD, integrated hydrological modelling may be necessary to support the management according to the directive as also suggested by several research projects initiated by the EU commission. To ensure a coherent and consistent management across various institutions and authorities, having different responsibilities and operating at various scales, a common tool integrating all relevant knowledge and data is imperative. By the end of 2003, a numerical national water resources model was constructed for Denmark, which has been applied in several national assessments. At the regional level there has, however, been some reluctance to use the model, primarily because the model did not contain the most recent data and understanding obtained from detailed local studies. The model has therefore been subject to a comprehensive update focussing on utilising the system understanding from the local studies. This process was largely stakeholder driven by involvement of predominantly the technical staff at the regional water authorities. Local knowledge is continuously improved urging the model update to be an on-going process. Based on experience from the update of the Danish national water resources model, three levels of model updating have been identified: 1) Basic data update e keeping the model up-to-date with respect to input data, 2) improving the model description by including new or more detailed data, and 3) reconstructing the model concept. The three levels vary with respect to technical tasks, challenges and stakeholder involvement. Two utility programs developed to optimise the updating process and support the uptake of data and knowledge from local users are furthermore presented. Finally, some of the challenges in operating a national model with multiple users belonging to different institutions with varying demands are discussed.

In order to support the implementation of the Water Framework Directive, the European Commission has established a cluster on Integrated Catchment Water Modelling (CatchMod). The objective of this cluster is the development of common harmonised modelling tools and methodologies for the integrated management of water at river basin or sub-basin scales, including the interface to the coastal zone. Within the CatchMod cluster, the project Harmoni-CA has the objective to create a forum for unambiguous ...