Jord Jurriaan Warmink

Identification and classification of uncertainties in the application of environmental models

In the support of environmental management, models are frequently used. The outcomes of these models however, rarely show a perfect resemblance to the real-world system behavior. This is due to uncertainties, introduced during the process of abstracting information about the system to include it in the model. To provide decision makers with realistic information about these model outcomes, uncertainty analysis is indispensable. Because of the multiplicity of frameworks available for uncertainty analysis, the outcomes of such analyses are rarely comparable. In this paper a method for structured identification and classification of uncertainties in the application of environmental models is presented. We adapted an existing uncertainty framework to enhance the objectivity in the uncertainty identification process. Two case studies demonstrate how it can help to obtain an overview of unique uncertainties encountered in a model. The presented method improves the comparability of uncertainty analyses in different model studies and leads to a coherent overview of uncertainties affecting model outcomes.

Two novel methods for field measurements of hydrodynamic density of floodplain vegetation using terrestrial laser scanning and digital parallel photography

Hydrodynamic vegetation density, the sum of the projected plant area per unit volume, is an important parameter for floodplain flow models. This paper compares two novel techniques to quantify this parameter in the field: terrestrial laser scanning (TLS) and digital parallel photography (PP). Separate field reference data were collected for the two methods, which consisted of (1) a stem map of 650 trees, aggregated into 23 plots in a single forest patch, (2) 17 manually measured forest plots in two floodplains. PP consists of a series of digital photographic images of vegetation against a contrasting background. The centre columns of all images were merged into a single composite parallel image. This mosaic was thresholded to determine the fractional coverage of the vegetation, which is converted to vegetation density using the optical point quadrat method. A sensitivity analysis proved that PP is insensitive to small errors on the selected number of centre columns, photograph spacing should not exceed 20 cm, photograph resolution is important and the plot depth should be measured accurately. TLS was carried out using a Leica HDS3000 time‐of‐flight laser scanner. Data processing of TLS data consisted of slicing the points around breast height. In a polar grid the vegetation density was predicted using the optical point quadrat method, corrected for missing points. Both methods were compared to the field reference data. PP (EF = 0.99; bias = 8.4×10−5 m−1) showed a higher modelling efficiency than the TLS method (EF = 0.63; bias = 0.015 m−1). An advantage of the TLS method is the ability to provide a detailed 2‐dimensional or even 3‐dimensional distribution of vegetation density. PP is cheaper, faster, and data processing is limited. We conclude that TLS and PP are two complementary techniques that show high accuracies for field measurements of vegetation density.

Coping with Uncertainty in River Management: Challenges and Ways Forward

Coping with uncertainties is inherent to river management planning and policymaking. Yet, policymakers often perceive uncertainty as a complicating factor. We examine the challenges faced by policymakers when coping with uncertainties and provide an action perspective on how to best cope with these challenges to inform the policy debate. Integrating social and natural scientist’s perspectives on uncertainties and learning theories, we present a holistic, management perspective for coping with uncertainty. Based on a literature review about uncertainty concepts, strategies and learning, we identify three challenges in current river management: balancing social and technical uncertainties, being conservative and avoiding to end up a lock-in situation. We then provide a step-wise strategy and concrete actions for policymakers, which are illustrated with several examples. We conclude that coping with uncertainty may require paradigm shifts that can only be achieved through organisational learning. This, we claim, requires reflection, learning and flexibility of policymakers and their organisation.

Modelling effects of an asphalt road at a dike crest on dike cover erosion onset during wave overtopping

Structures integrated in a grass-covered dike may increase erosion development. Currently, safety assessment methods for flood defences are only applicable for a conventional grass-covered dike and the effects of structures on dike cover erosion are poorly understood. Since many dikes have a road on top, it is important to study the effect of such a road structure on erosion onset during wave overtopping. To investigate this effect, a coupled hydrodynamic–erosion model was developed. The erosion onset caused by overtopping waves was predicted by combining the time-varying bed shear stresses from the hydrodynamic model with a depth-dependent erosion model. The results show that roads on top of a dike increase the erosion of the neighbouring grass cover. This increase in erosion may have a negative impact on dike stability. Therefore, we recommend considering effects of constructions on top of dike profiles during safety assessments. Explicitly, consideration of the roughness transitions in the safety assessments of dikes is recommended.

Piping erosion safety assessment of flood defences founded over sewer pipes

Piping erosion is one of the major causes of failure of flood defences. The occurrence of this failure mechanism is difficult to predict and it can be triggered during a flood event. The inclusion of hard structures under flood defences will change the probability of occurrence of this erosion process. The present study aimed at understanding the effect of an embedded sewer pipe under the flood defence foundation in its safety assessment for piping erosion failure. This was done by setting a probabilistic analysis framework based on emulation of finite element models of porous media flow and the fictitious permeability approach. The effects of size and location of the sewer pipe were evaluated via a deterministic safety factor approach. Afterwards, emulators of the most safe and unsafe models were trained and used for probabilistic assessments. The results showed that the embedment of a sewer pipe in the flood defence foundation has a significant effect on its safety. The magnitude of the effect is highly dependent on the size and location of the sewer pipe. Furthermore, the foundation permeability uncertainty shows a conditional effect with respect to the sewer pipe size and location.

Using expert elicitation to quantify catchment water balances and their uncertainties

Expert elicitation with the participation of 35 experts was used to estimate a water balance for the nested Ahlergaarde and Holtum catchments in Western Denmark. Average annual values of precipitation, evapotranspiration, and surface runoff as well as subsurface outflow and recharge and their uncertainty were estimated in a multistep elicitation, where experts first gave their opinion on the probability distribution of their water balance component of interest, then the average annual values and uncertainty of water balance components and catchment-scale water balances were obtained by reaching consensus during group discussions. The obtained water balance errors for the 1055 km2 Ahlergaarde catchment and 120 km2 Holtum catchment were −5 and −62 mm/yr, respectively, with an uncertainty of 66 and 86 mm/yr, respectively. As an advantage of the expert elicitation, drawing on the intuitive experience and capabilities of experts to assess complex, site-specific problems, the contribution of independent sources of uncertainties to the total uncertainty was also evaluated similarly to the subsurface outflow component, which traditionally is estimated as the residual of the water balance.

Uncertainty Analysis in River Modelling

Uncertainty analysis is an essential step in river modelling. Knowledge of the uncertainty is crucial for a meaningful interpretation of the model results. In this chapter we describe the whole process of an uncertainty analysis in four steps: identification, prioritization, quantification and propagation. In each step the rationale behind choosing a method is described and illustrated with an example of the design water level computation of the Dutch river Waal with a 2D hydrodynamic model. The sources of uncertainty related to the case study are identified and their (relative) importance is determined using expert opinions combined with a novel uncertainty identification method. Subsequently, the sources with the largest effect on the design water levels are individually quantified and propagated using Monte Carlo analysis to yield the quantified uncertainty in the design water levels. The uncertainty analysis provided information about the reliability of the model results and about further actions to possibly reduce the uncertainty and their benefits in terms of increased accuracy.

Unraveling uncertainties : the effect of hydraulic roughness on design water levels in river models

Flooding is a serious threat in many regions in the world and is a problem of international interest. Hydrodynamic models are used for the prediction of flood water levels to support flood safety and are often applied in a deterministic way. However, the modelling of river processes involves numerous uncertainties. Previous research has shown that the hydraulic roughness is one of the main sources of in hydrodynamic computations. Knowledge of the type and magnitude of uncertainties is crucial for a meaningful interpretation of the model outcomes and the usefulness of model outcomes in decision making. The objective of this thesis is to quantify the uncertainties in the hydraulic roughness that contribute most to the uncertainty in the water levels and quantify their contribution to the uncertainty for the 2D hydrodynamic WAQUA model for the river Waal under design conditions. This research showed that the uncertainty of a complex model factor, such as the hydraulic roughness, can be quantified explicitly. The hydraulic roughness has been unravelled in separate components, which have been quantified separately and then combined and propagated through the model. In chapter 2, a method is presented to identify the sources of uncertainty in an environmental model. In chapter 3, expert opinion is used to determine the sources of uncertainty that contributed most to the uncertainty in the design water levels. Chapter 4 describes the quantification of the uncertainty in the bedform roughness and in chapter 5 the uncertainty in bedform roughness is combined with the uncertainty in the vegetation roughness. The results show a best estimate of the uncertainty range under design conditions, due to roughness, given that we did not account for the effect of calibration. The final uncertainty range is significant in view of Dutch river management practise. The research demonstrates that the uncertainties in a modelling study can be made explicit. The process of uncertainty analysis helps in raising the awareness of the uncertainties and enhances communication about the uncertainties among both scientists and decision makers.

Correction to: Modelling effects of an asphalt road at a dike crest on dike cover erosion onset during wave overtopping

The article was published Open Access under the Dutch Compact Agreement; however, due to an internal system error, previous HTML rendering of the article did not reflect this.

Efficient uncertainty quantification for impact analysis of human interventions in rivers

Human interventions to optimise river functions are often contentious, disruptive, and expensive. To analyse the expected impact of an intervention before implementation, decision makers rely on computations with complex physics-based hydraulic models. The outcome of these models is known to be sensitive to uncertain input parameters, but long model runtimes render full probabilistic assessment infeasible with standard computer resources. In this paper we propose an alternative, efficient method for uncertainty quantification for impact analysis that significantly reduces the required number of model runs by using a subsample of a full Monte Carlo ensemble to establish a probabilistic relationship between pre- and post-intervention model outcome. The efficiency of the method depends on the number of interventions, the initial Monte Carlo ensemble size and the desired level of accuracy. For the cases presented here, the computational cost was decreased by 65%.