Jens Christian Refsgaard

Uncertainty in the environmental modelling process - A framework and guidance

A terminology and typology of uncertainty is presented together with a framework for the modelling process, its interaction with the broader water management process and the role of uncertainty at different stages in the modelling processes. Brief reviews have been made of 14 different (partly complementary) methods commonly used in uncertainty assessment and characterisation: data uncertainty engine (DUE), error propagation equations, expert elicitation, extended peer review, inverse modelling (parameter estimation), inverse modelling (predictive uncertainty), Monte Carlo analysis, multiple model simulation, NUSAP, quality assurance, scenario analysis, sensitivity analysis, stakeholder involvement and uncertainty matrix. The applicability of these methods has been mapped according to purpose of application, stage of the modelling process and source and type of uncertainty addressed. It is concluded that uncertainty assessment is not just something to be added after the completion of the modelling work. Instead uncertainty should be seen as a red thread throughout the modelling study starting from the very beginning, where the identification and characterisation of all uncertainty sources should be performed jointly by the modeller, the water manager and the stakeholders.

Operational Validation and Intercomparison of Different Types of Hydrological Models

A theoretical framework for model validation, based on the methodology originally proposed by Klemes [1985, 1986], is presented. It includes a hierarchial validation testing scheme for model application to runoff prediction in gauged and ungauged catchments subject to stationary and nonstationary climate conditions. A case study on validation and intercomparison of three different models on three catchments in Zimbabwe is described. The three models represent a lumped conceptual modeling system (NAM), a distributed physically based system (MIKE SHE), and an intermediate approach (WATBAL). It is concluded that all models performed equally well when at least 1 years data were available for calibration, while the distributed models performed marginally better for cases where no calibration was allowed.

Assessing the effect of land use change on catchment runoff by combined use of statistical tests and hydrological modelling: Case studies from Zimbabwe

The purpose of this study was to identify and assess long-term impacts of land use change on catchment runoff in semi-arid Zimbabwe, based on analyses of long hydrological time series (25–50 years) from six medium-sized (200–1000 km2) non-experimental rural catchments. A methodology combining common statistical methods with hydrological modelling was adopted in order to distinguish between the effects of climate variability and the effects of land use change. The hydrological model (NAM) was in general able to simulate the observed hydrographs very well during the reference period, thus providing a means to account for the effects of climate variability and hence strengthening the power of the subsequent statistical tests. In the test period the validated model was used to provide the runoff record which would have occurred in the absence of land use change. The analyses indicated a decrease in the annual runoff for most of the six catchments, with the largest changes occurring for catchments located within communal land, where large increases in population and agricultural intensity have taken place. However, the decrease was only statistically significant at the 5% level for one of the catchments.

Methodology for construction, calibration and validation of a national hydrological model for Denmark

An integrated groundwater/surface water hydrological model with a 1 km2 grid has been constructed for Denmark covering 43,000 km2. The model is composed of a relatively simple root zone component for estimating the net precipitation, a comprehensive three-dimensional groundwater component for estimating recharge to and hydraulic heads in different geological layers, and a river component for streamflow routing and calculating stream–aquifer interaction. The model was constructed on the basis of the MIKE SHE code and by utilising comprehensive national databases on geology, soil, topography, river systems, climate and hydrology. The present paper describes the modelling process for the 7330 km2 island of Sjaelland with emphasis on the problems experienced in combining the classical paradigms of groundwater modelling, such as inverse modelling of steady-state conditions, and catchment modelling, focussing on dynamic conditions and discharge simulation. Three model versions with different assumptions on input data and parameter values were required until the performance of the final, according to pre-defined accuracy criteria, model was evaluated as being satisfactory. The paper highlights the methodological issues related to establishment of performance criteria, parameterisation and assessment of parameter values from field data, calibration and validation test schemes. Most of the parameter values were assessed directly from field data, while about 10 ‘free’ parameters were subject to calibration using a combination of inverse steady-state groundwater modelling and manual trial-and-error dynamic groundwater/surface water modelling. Emphasising the importance of tests against independent data, the validation schemes included combinations of split-sample tests (another period) and proxy-basin tests (another area).

Large scale modelling of groundwater contamination from nitrate leaching

Abstract Groundwater pollution from non-point sources, such as nitrate from agricultural activities, is a problem of increasing concern. Comprehensive modelling tools of the physically based type are well proven for small-scale applications with good data availability, such as plots or small experimental catchments. The two key problems related to large-scale simulation are data availability at the large scale and model upscaling/aggregation to represent conditions at larger scale. This paper presents a methodology and two case studies for large-scale simulation of aquifer contamination due to nitrate leaching. Readily available data from standard European level databases such as GISCO, EUROSTAT and the European Environment Agency (EEA) have been used as the basis of modelling. These data were supplemented by selected readily available data from national sources. The model parameters were all assessed from these data by use of various transfer functions, and no model calibration was carried out. The adopted upscaling procedure combines upscaling from point to field scale using effective parameters with a statistically based aggregation procedure from field to catchment scale, preserving the areal distribution of soil types, vegetation types and agricultural practices on a catchment basis. The methodology was tested on two Danish catchments with good simulation results on water balance and nitrate concentration distributions in groundwater. The upscaling/aggregation procedure appears to be applicable in many areas with regard to root zone processes such as runoff generation and nitrate leaching, while it has important limitations with regard to hydrograph shape due to its lack of accounting for scale effects in relation to stream aquifer interaction.

Quality assurance in model based water management - review of existing practice and outline of new approaches

Quality assurance (QA) is defined as protocols and guidelines to support the proper application of models. In the water management context we classify QA guidelines according to how much focus is put on the dialogue between the modeller and the water manager as: (Type 1) Internal technical guidelines developed and used internally by the modellers organisation; (Type 2) Public technical guidelines developed in a public consensus building process; and (Type 3) Public interactive guidelines developed as public guidelines to promote and regulate the interaction between the modeller and the water manager throughout the modelling process. State-of-the-art QA practices vary considerably between different modelling domains and countries. It is suggested that these differences can be explained by the scientific maturity of the underlying discipline and differences in modelling markets in terms of volume of jobs outsourced and level of competition. The structure and key aspects of new generic guidelines and a set of electronically based supporting tools that are under development within the HarmoniQuA project are presented. Model credibility can be enhanced by a proper modeller-manager dialogue, rigorous validation tests against independent data, uncertainty assessments, and peer reviews of a model at various stages throughout its development.

Assessment of uncertainty in simulation of nitrate leaching to aquifers at catchment scale

Deterministic models are used to predict the risk of groundwater contamination from non-point sources and to evaluate the effect of alleviation measures. Such model predictions are associated with considerable uncertainty due to uncertainty in the input data used, especially when applied at large scales. The present paper presents a case study related to prediction of nitrate concentrations in groundwater aquifers using a spatially distributed catchment model. Input data were primarily obtained from databases at an European level. The model parameters were all assessed from these data by use of transfer functions, and no model calibration was carried out. The Monte Carlo simulation technique was used to analyse how uncertainty in input data propagates to model output. It appeared that the magnitude of the uncertainty depends significantly on the considered temporal and spatial scale. Thus simulations of flux concentrations leaving the root zone at grid level were associated with large uncertainties, whereas uncertainties in simulated concentrations at aquifer level on catchment scale was much smaller.

A methodology to support multidisciplinary model-based water management

Quality assurance in model based water management is needed because of some frequently perceived shortcomings, e.g. a lack of mutual understanding between modelling team members, malpractice and a tendency of modellers to oversell model capabilities. Initiatives to support quality assurance focus on single domains and often follow a textbook approach with guidelines and checklists. A modelling process involves a complex set of activities executed by a team. To manage this complex, usually multidisciplinary process, to guide users through it and enhance the reproducibility of modelling work a software product has been developed, aiming at supporting the full modelling process by offering an ontological knowledge base (KB) and a Modelling Support Tool (MoST). The KB consists of a generic part for modelling, but also parts specific for various water management domains, for different types of users and for different levels of modelling complexity. MoSTs guiding component filters relevant knowledge from the KB depending on the user profile and needs. Furthermore, MoST supports different types of users by monitoring what they actually do and by producing customized reports for diverse audiences. In this way MoST facilitates co-operation in teams, modelling project audits and re-use of experiences of previous modelling projects.

A framework for testing the ability of models to project climate change and its impacts

Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections.

The role of uncertainty in climate change adaptation strategies—A Danish water management example

We propose a generic framework to characterize climate change adaptation uncertainty according to three dimensions: level, source and nature. Our framework is different, and in this respect more comprehensive, than the present UN Intergovernmental Panel on Climate Change (IPCC) approach and could be used to address concerns that the IPCC approach is oversimplified. We have studied the role of uncertainty in climate change adaptation planning using examples from four Danish water related sectors. The dominating sources of uncertainty differ greatly among issues; most uncertainties on impacts are epistemic (reducible) by nature but uncertainties on adaptation measures are complex, with ambiguity often being added to impact uncertainties. Strategies to deal with uncertainty in climate change adaptation should reflect the nature of the uncertainty sources and how they interact with risk level and decision making: (i) epistemic uncertainties can be reduced by gaining more knowledge; (ii) uncertainties related to ambiguity can be reduced by dialogue and knowledge sharing between the different stakeholders; and (iii) aleatory uncertainty is, by its nature, non-reducible. The uncertainty cascade includes many sources and their propagation through technical and socio-economic models may add substantially to prediction uncertainties, but they may also cancel each other. Thus, even large uncertainties may have small consequences for decision making, because multiple sources of information provide sufficient knowledge to justify action in climate change adaptation.