Time stability of catchment model parameters: Implications for climate impact analyses

Merz, Ralf; Parajka, J.; Blöschl, Günter

doi:10.1029/2010wr009505

Cited by 363 publications

(380 citation statements)

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“…introduces uncertainty, both through the parameters (Vaze et al, 2010;Merz et al, 2011) and through the model structure, the conceptualization. The effect of model conceptualization on projected trends was underscored by a study on global trends in drought (Sheffield et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Mapping (dis)agreement in hydrologic projections

Melsen

Addor

Mizukami

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Hydrologic projections are of vital socioeconomic importance. However, they are also prone to uncertainty. In order to establish a meaningful range of storylines to support water managers in decision making, we need to reveal the relevant sources of uncertainty. Here, we systematically and extensively investigate uncertainty in hydrologic projections for 605 basins throughout the contiguous US. We show that in the majority of the basins, the sign of change in average annual runoff and discharge timing for the period 2070-2100 compared to 1985-2008 differs among combinations of climate models, hydrologic models, and parameters. Mapping the results revealed that different sources of uncertainty dominate in different regions. Hydrologic model induced uncertainty in the sign of change in mean runoff was related to snow processes and aridity, whereas uncertainty in both mean runoff and discharge timing induced by the climate models was related to disagreement among the models regarding the change in precipitation. Overall, disagreement on the sign of change was more widespread for the mean runoff than for the discharge timing. The results demonstrate the need to define a wide range of quantitative hydrologic storylines, including parameter, hydrologic model, and climate model forcing uncertainty, to support water resource planning.

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Section: Introductionmentioning

confidence: 99%

Mapping (dis)agreement in hydrologic projections

Melsen

Addor

Mizukami

et al. 2018

Hydrol. Earth Syst. Sci.

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“…Toutefois, quand on analyse le comportement des paramètres pendant cette période de non-stationnarité, pour les bassins où une non-stabilité des paramètres s'observe, le paramètre de la fonction de production a tendance à diminuer alors que le paramètre de la fonction de transfert a tendance à augmenter légèrement. Il serait étonnant que ce résultat global ne soit dû qu'à un artéfact du calage (Merz et al 2011) Quoi qu'il en soit, même si cette hypothèse s'avère fausse, on peut considérer qu'un jeu de paramètres calés correspond à la conjonction d'une situation climatique et d'une situation environnementale rencontrées à un moment ou pendant une période donnée sur un bassin versant, ces situations climatiques et environnementales pouvant conduire à la mise en jeu de processus dominants de génération des écoulements très différents selon les périodes (Coron et al 2012).…”

Section: Scenarisation Hydrologiqueunclassified

“…Il est d'ailleurs intéressant de voir s'il existe des relations entre les évolutions des paramètres du modèle GR2M et un indicateur climatique comme la pluie annuelle (Merz et al 2011). A première vue, on serait tenté de dire oui puisque l'évolution des deux paramètres du modèle GR2M change à la fin des années 1980 et au début des années 1990, tout comme les pluies qui ont eu tendance à augmenter après avoir diminué.…”

Section: Calages Du Modèle Hydrologiqueunclassified

Exercice de scénarisation hydrologique en Afrique de l’Ouest—Bassin du Bani

Paturel¹

2014

Hydrological Sciences Journal

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“…However, when the models are developed or calibrated using a long data set that encapsulates the different hydroclimate characteristics of different data periods, the models can generally reasonably simulate the hydrology through the different times (although not as well as if the model was calibrated only against data from the period it is simulating) (Vaze et al, 2010;Merz et al, 2011;Coron et al, 2012). Therefore, following on from the above Millennium Drought example, hydrological models developed and tested against long historical records are generally reliable until there is a significantly 'changed' condition (like the Millennium Drought).…”

Section: Hydrologic Nonstationarity and Implicationsoverviewmentioning

confidence: 99%

“…Examples include attempts at parameterising semidistributed hydrological models or adapting existing models to simulate processes important under extreme conditions like long dry spells (farm dam interception (Nathan et al, 2005) and surface-groundwater connectivity (Puspalatha et al, 2011)) and learning from catchments experiencing different or changing conditions (Wagener, 2007;Fenicia et al, 2008;Buytaert and Beven, 2009). Many studies use existing models, but with smart approaches to parameterise and cal-ibrate the model, for example (i) with time varying parameters dependent on storage levels (Smith et al, 2008;Merz et al, 2011); (ii) multi-criteria optimisation that also considers low flow simulations (Madsen, 2000;Oudin et al, 2006;Efstratiadis and Koutsoyiannis, 2010); and (iii) predicting the future with parameters from model calibration against a similar climate period as the future climate projections.…”

Section: Extrapolating Hydrological Models To Predict the Futurementioning

confidence: 99%

Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding

Chiew

Vaze

2015

Proc. IAHS

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Abstract. This paper provides an overview of this IAHS symposium and PIAHS proceeding on "hydrologic nonstationarity and extrapolating models to predict the future". The paper provides a brief review of research on this topic, presents approaches used to account for nonstationarity when extrapolating models to predict the future, and summarises the papers in this session and proceeding. Hydrologic nonstationarity and implicationsOverviewThe commentary by Milly et al. (2008) has initiated significant discussions and continuing progression of research on hydrologic nonstationarity. The term "hydrologic nonstationarity" has been used to describe many things, ranging from different climate-runoff relationships evident in different periods within a long hydroclimate time series to changes in hydroclimate characteristics and dominant hydrological processes in an increasingly warmer and higher CO 2 world. Hydrologists have always represented stationarity and nonstationarity (which is difficult to distinguish statistically in natural systems) as best they could and their implications on water resources and related systems, but modelling this adequately will become increasingly challenging in a world driven by anthropogenic changes. The constancy of laws and patterns has always been and will always be "stationary". It is our understanding or lack of these and the constancy of variables or characteristics at different times that may appear "nonstationary". For example, a hydroclimate time series can be considered "stationary" over thousands or millions of years, in that we can represent statistically or stochastically the characteristics and variability over time and space scales or even develop a precise understanding of the processes from the very long record. But of course, the characteristics of the different periods will always be different (exhibiting variability over different time and space scales), that is, nonstationary over time. The practical issue then is not whether hydroclimate systems are stationary or nonstationary, but whether the nonstationarity is substantial enough to require a change in existing system characterisation, conceptualisation or modelling for a particular hydrologic design, operation and planning.Hydrologists have excelled in developing models for numerous applications, through analysing and interpreting climate and hydrologic data to understand hydrologic processes, conceptualising the processes in hydrological models, and calibrating and testing models against observations. These models are particularly good in predicting the streamflow response to changes in the climate inputs and catchment characteristics. These models, when developed adequately using relatively long historical records that encapsulate the range of hydroclimate conditions, should be able to predict hydrologic responses to changes in the climate inputs over the near and medium term.However, extrapolating hydrological models to predict further into the future that is influenced by anthropogenic change is challenging as ...

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Time stability of catchment model parameters: Implications for climate impact analyses

Cited by 363 publications

References 53 publications

Mapping (dis)agreement in hydrologic projections

Mapping (dis)agreement in hydrologic projections

Exercice de scénarisation hydrologique en Afrique de l’Ouest—Bassin du Bani

Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding

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