Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures

Freer, J. E.; McMillan, Hilary; McDonnell, Jeffrey J.; Beven, Keith

doi:10.1016/j.jhydrol.2003.12.037

Cited by 171 publications

(181 citation statements)

References 58 publications

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“…The second challenge is to devise meaningful ways to use research data for model evaluation, accounting for problems of data uncertainty and the incommensurability of models and measurements [Beven, 1993;Kuczera and Mroczkowski, 1998;Beven, 2001;Freer et al, 2004;Beven, 2007;Kuczera et al, 2010], and making use of qualitative insights from experimentalists to constrain model behavior [Seibert and McDonnell, 2002]. A key modeling problem is representing how interlinked physical processes affect the aggregate systemscale responses at larger spatial scales [Beven and Cloke, 2012;Wood et al, 2012], and thus a key focal area for model evaluation is understanding the merits of different methods to represent spatial variability and hydrologic connectivity.…”

Section: Process-based Model Evaluationmentioning

confidence: 99%

Improving the representation of hydrologic processes in Earth System Models

Clark

Fan

Lawrence

et al. 2015

Water Resources Research

456

404

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Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large-scale land models (as a component of Earth System Models, or ESMs) do not yet reflect the best hydrologic process understanding or utilize the large amount of hydrologic observations for model testing. This paper discusses the opportunities and key challenges to improve hydrologic process representations and benchmarking in ESM land models, suggesting that (1) land model development can benefit from recent advances in hydrology, both through incorporating key processes (e.g., groundwater-surface water interactions) and new approaches to describe multiscale spatial variability and hydrologic connectivity; (2) accelerating model advances requires comprehensive hydrologic benchmarking in order to systematically evaluate competing alternatives, understand model weaknesses, and prioritize model development needs, and (3) stronger collaboration is needed between the hydrology and ESM modeling communities, both through greater engagement of hydrologists in ESM land model development, and through rigorous evaluation of ESM hydrology performance in research watersheds or Critical Zone Observatories. Such coordinated efforts in advancing hydrology in ESMs have the potential to substantially impact energy, carbon, and nutrient cycle prediction capabilities through the fundamental role hydrologic processes play in regulating these cycles.

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Section: Process-based Model Evaluationmentioning

confidence: 99%

Improving the representation of hydrologic processes in Earth System Models

Clark

Fan

Lawrence

et al. 2015

Water Resources Research

456

404

View full text Add to dashboard Cite

show abstract

“…† Output data uncertainty: Uncertainty in observations of the system response has generally received less attention since it is often assumed to be much smaller than the uncertainty in the model input (e.g. Franks et al 1998;Freer et al 2004 Several studies found that inclusion of uncertainty in environmental modelling leads to better decision-making, which suggests that establishing uncertainty estimation as a standard for any environmental predictions (despite some remaining problems) represents an important step to make our work better suited for decision makers (Reichert & Borsuk 2005).…”

Section: Drivers Of Advancement First Driver: Uncertaintymentioning

confidence: 99%

Advances in the identification and evaluation of complex environmental systems models

Wagener

Reed

Werkhoven

et al. 2009

Journal of Hydroinformatics

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Advances in our ability to model complex environmental systems are currently driven by at least four needs: (1) the need for the inclusion of uncertainty in monitoring, modelling and decisionmaking; (2) the need to provide environmental predictions everywhere; (3) the need to predict the impacts of environmental change; and (4) the need to adaptively evolve observation networks to better resolve environmental systems and embrace sensing innovations. Satisfying these needs will require improved theory, improved models and improved frameworks for making and evaluating predictions. All of these improvements should result in the long-term evolution and improvement of observation systems. In the context of this paper we discuss current bottlenecks and opportunities for advancing environmental modelling with and without local observations of system response. More realistic representations of real-world thresholds, nonlinearities and feedbacks motivates the use of more complex models as well as the consequent need for more rigorous evaluations of model performance. In the case of gauged systems, we find that global sensitivity analysis provides a widely underused tool for evaluating models' assumptions and estimating the information content of data. In the case of ungauged systems, including the modelling of environmental change impacts, we propose that the definition of constraints on the expected system response provides a promising way forward. Examples of our own work are included to support the conclusions of this discussion paper. Overall, we conclude that an important bottleneck currently limiting environmental predictions lies in how our model evaluation and identification approaches are extracting, using and evolving the information available for environmental systems at the watershed scale.

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“…Thus, there may be scope for using other forms of likelihood or belief measures in hydrological modelling. Such informal likelihood measures have been defined based on limits of acceptability defined from evaluation-data uncertainty (Blazkova and Beven, 2009;Krueger et al, 2010;Liu et al, 2009) but also based on traditional performance measures (Freer et al, 2003). One of the most widely used performance measures in hydrology is the Nash-Sutcliffe model efficiency (R eff ).…”

Section: Introductionmentioning

confidence: 99%

Calibration of hydrological models using flow-duration curves

Westerberg

Guerrero

Younger

et al. 2011

Hydrol. Earth Syst. Sci.

242

243

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Abstract. The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested -based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WAS-MOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time Correspondence to: I. K. Westerberg (ida.westerberg@hyd.uu.se) series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

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Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures

Cited by 171 publications

References 58 publications

Improving the representation of hydrologic processes in Earth System Models

Improving the representation of hydrologic processes in Earth System Models

Advances in the identification and evaluation of complex environmental systems models

Calibration of hydrological models using flow-duration curves

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