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| 2. |
- Beven, Keith, et al.
(författare)
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Epistemic uncertainties and natural hazard risk assessment - Part 1 : A review of different natural hazard areas
- 2018
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Ingår i: Natural hazards and earth system sciences. - : COPERNICUS GESELLSCHAFT MBH. - 1561-8633 .- 1684-9981. ; 18:10, s. 2741-2768
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Forskningsöversikt (refereegranskat)abstract
- This paper discusses how epistemic uncertainties are currently considered in the most widely occurring natural hazard areas, including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. Our aim is to provide an overview of the types of epistemic uncertainty in the analysis of these natural hazards and to discuss how they have been treated so far to bring out some commonalities and differences. The breadth of our study makes it difficult to go into great detail on each aspect covered here; hence the focus lies on providing an overview and on citing key literature. We find that in current probabilistic approaches to the problem, uncertainties are all too often treated as if, at some fundamental level, they are aleatory in nature. This can be a tempting choice when knowledge of more complex structures is difficult to determine but not acknowledging the epistemic nature of many sources of uncertainty will compromise any risk analysis. We do not imply that probabilistic uncertainty estimation necessarily ignores the epistemic nature of uncertainties in natural hazards; expert elicitation for example can be set within a probabilistic framework to do just that. However, we suggest that the use of simple aleatory distributional models, common in current practice, will underestimate the potential variability in assessing hazards, consequences, and risks. A commonality across all approaches is that every analysis is necessarily conditional on the assumptions made about the nature of the sources of epistemic uncertainty. It is therefore important to record the assumptions made and to evaluate their impact on the uncertainty estimate. Additional guidelines for good practice based on this review are suggested in the companion paper (Part 2).
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| 3. |
- Beven, Keith, et al.
(författare)
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So just why would a modeller choose to be incoherent?
- 2008
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 354:1-4, s. 15-32
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Tidskriftsartikel (refereegranskat)abstract
- This article provides an extended response to the criticisms of the GLUE methodology by Mantovan and Todini [Mantovan, P., Todini, E., 2006. Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology. J. Hydrol. 330, 368-381]. It is shown that the formal Bayesian identification of models is a special case of GLUE that can be used where the modeller is prepared to make very strong assumptions about the nature of the modelling errors. Under such assumptions, GLUE can be coherent in the sense of Manotvan and Todini. In real applications, however, with multiple sources of uncertainty including model structural error, their strong definition of coherence is shown to be inapplicable to the extent that the choice of a format likelihood function based on a simple error structure may be an incoherent choice. It is demonstrated by some relatively minor modifications of their hypothetical example that misspecification of the error model and the non-stationarities associated with the presence of input error and model structural error in the Bayes approach will then produce well-defined but incorrect parameter distributions. This empirical result is quite independent of GLUE, but the flexibility of the GLUE approach may then prove to be an advantage in providing more coherent and robust choices of model evaluation in these cases and, by analogy, in other non-ideal cases for real applications. At the current time it is difficult to make a reasoned choice between methods of uncertainty estimation for real applications because of a lack of understanding of the real information content of data in conditioning models.
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| 4. |
- Dean, Sarah, et al.
(författare)
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Uncertainty assessment of a process-based integrated catchment model of phosphorus
- 2009
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Ingår i: Stochastic environmental research and risk assessment (Print). - : Springer Science and Business Media LLC. - 1436-3240 .- 1436-3259. ; 23, s. 991-1010
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Tidskriftsartikel (refereegranskat)abstract
- Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km(2)), a River Wye tributary, on the England-Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R (2)) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters.
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| 5. |
- Di Baldassarre, Giuliano, et al.
(författare)
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Flood-plain mapping : a critical discussion of deterministic and probabilistic approaches
- 2010
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Ingår i: Hydrological Sciences Journal. - : Informa UK Limited. - 0262-6667 .- 2150-3435. ; 55:3, s. 364-376
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Tidskriftsartikel (refereegranskat)abstract
- Different methodologies for flood-plain mapping are analysed and discussed by comparing deterministic and probabilistic approaches using hydrodynamic numerical solutions. In order to facilitate the critical discussion, state-of-art techniques in the field of flood inundation modelling are applied to a specific test site (River Dee, UK). Specifically, different flood-plain maps are derived for this test site. A first map is built by applying an advanced deterministic approach: use of a fully two-dimensional finite element model (TELEMAC-2D), calibrated against a historical flood extent, to derive a 1-in-100 year flood inundation map. A second map is derived by using a probabilistic approach: use of a simple raster-based inundation model (LISFLOOD-FP) to derive an uncertain flood extent map predicting the 1-in-100 year event conditioned on the extent of the 2006 flood. The flood-plain maps are then compared and the advantages and disadvantages of the two different approaches are critically discussed.
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| 6. |
- Kreibich, Heidi, et al.
(författare)
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Panta Rhei benchmark dataset : Socio-hydrological data of paired events of floods and droughts
- 2023
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Ingår i: Earth System Science Data. - : Copernicus Publications. - 1866-3508 .- 1866-3516. ; 15:5, s. 2009-2023
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Tidskriftsartikel (refereegranskat)abstract
- As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human-water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, 10.5880/GFZ.4.4.2023.001).
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| 7. |
- Kreibich, Heidi, et al.
(författare)
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The challenge of unprecedented floods and droughts in risk management
- 2022
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Ingår i: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687. ; 608:7921, s. 80-86
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Tidskriftsartikel (refereegranskat)abstract
- Risk management has reduced vulnerability to floods and droughts globally, yet their impacts are still increasing. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change.
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| 8. |
- Liu, Yanli, et al.
(författare)
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Towards a limits of acceptability approach to the calibration of hydrological models : Extending observation error
- 2009
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 367:1-2, s. 93-103
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Tidskriftsartikel (refereegranskat)abstract
- Within the GLUE methodology, there are a number of advantages of taking a limits of acceptability approach to model evaluation for non-ideal applications where the strong assumptions of statistical identification might be difficult to justify. However, there is a question of how the limits of acceptability might be specified in a way that reflects the different Sources of uncertainty in the modeling process. Here, a novel method for identifying behavioural models in an extended GLUE methodology is developed and applied to an application of Dynamic TOPMODEL to the Attert catchment in Luxemburg with semi-distributed inputs to nested sub-catchments. The results raise some important issues about testing model structures as hypotheses of catchment responses. (C) 2009 Elsevier B.V. All rights reserved.
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| 9. |
- Metcalfe, Peter, et al.
(författare)
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Dynamic TOPMODEL : A new implementation in R and its sensitivity to time and space steps
- 2015
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Ingår i: Environmental Modelling & Software. - : Elsevier BV. - 1364-8152 .- 1873-6726. ; 72, s. 155-172
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Tidskriftsartikel (refereegranskat)abstract
- In 2001, Beven and Freer introduced a "dynamic" variant of TOPMODEL that addressed some of the limitations of the original model whilst retaining its computational and parametric efficiency. The original assumption of a quasi-steady water table was replaced by time-dependent kinematic routing within hydrological similar areas. The new formulation allows a more flexible discretisation, variable upslope drainage areas and spatially variable physical properties. There has, however, never been a freely distributable version of dynamic TOPMODEL. Here, we describe a new, open source, version developed in the R environment. It incorporates handling of geo-referenced spatial data that allows it to integrate with modern GIS. It makes use of data storage and vectorisation features of the language that will allow efficient scaling of the problem domain. The implementation is evaluated with data from a small catchment. The formulation of the model in terms of a flow distribution matrix is described and its use illustrated for treatment of surface and subsurface flow routing. The model uses an improved implicit solution for updating the subsurface storages and fluxes. The paper focuses on the robustness of the predicted output variables to changes in the time and space discretisations.
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| 10. |
- Mitchell, Stephen, et al.
(författare)
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Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange
- 2009
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Ingår i: Ecological Modelling. - : Elsevier BV. - 0304-3800 .- 1872-7026. ; 220:23, s. 3259-3270
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Tidskriftsartikel (refereegranskat)abstract
- Net ecosystem CO2 exchange (NEE) is typically measured directly by eddy covariance towers or is estimated by ecosystem process models, yet comparisons between the data obtained by these two methods can show poor correspondence. There are three potential explanations for this discrepancy. First, estimates of NEE as measured by the eddy-covariance technique are laden with uncertainty and can potentially provide a poor baseline for models to be tested against. Second, there could be fundamental problems in model structure that prevent an accurate simulation of NEE. Third, ecosystem process models are dependent on ecophysiological parameter sets derived from field measurements in which a single parameter for a given species can vary considerably. The latter problem suggests that with such broad variation among multiple inputs, any ecosystem modeling scheme must account for the possibility that many combinations of apparently feasible parameter values might not allow the model to emulate the observed NEE dynamics of a terrestrial ecosystem, as well as the possibility that there may be many parameter sets within a particular model structure that can successfully reproduce the observed data. We examined the extent to which these three issues influence estimates of NEE in a widely used ecosystem process model, Biome-BGC, by adapting the generalized likelihood uncertainty estimation (GLUE) methodology. This procedure involved 400,000 model runs, each with randomly generated parameter values from a uniform distribution based on published parameter ranges, resulting in estimates of NEE that were compared to daily NEE data from young and mature Ponderosa pine stands at Metolius, Oregon. Of the 400,000 simulations run with different parameter sets for each age class (800,000 total), over 99% of the simulations underestimated the magnitude of net ecosystem CO2 exchange, with only 4.07% and 0.045% of all simulations providing satisfactory simulations of the field data for the young and mature stands, even when uncertainties in eddy-covariance measurements are accounted for. Results indicate fundamental shortcomings in the ability of this model to produce realistic carbon flux data over the course of forest development, and we suspect that much of the mismatch derives from an inability to realistically model ecosystem respiration. However, difficulties in estimating historic climate data are also a cause for model-data mismatch, particularly in a highly ecotonal region such as central Oregon. This latter difficulty may be less prevalent in other ecosystems, but it nonetheless highlights a challenge in trying to develop a dynamic representation of the terrestrial biosphere.
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