Keith Beven

A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant

A hydrological forecasting model is presented that attempts to combine the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple lumped parameter basin models. Quick response flow is predicted from a storage/contributing area relationship derived analytically from the topographic structure of a unit within a basin. Average soil water response is represented by a constant leakage infiltration store and an exponential subsurface water store. A simple non-linear routing procedure related to the link frequency distribution of the channel network completes the model and allows distinct basin sub-units, such as headwater and sideslope areas to be modelled separately. The model parameters are physically based in the sense that they may be determined directly by measurement and the model may be used at ungauged sites. Procedures for applying the model and tests with data from the Crimple Beck basin are described. Using only measured and estimated parameter values, without optimization, the model makes satisfactory predictions of basin response. The modular form of the model structure should allow application over a range of small and medium sized basins while retaining the possibility of including more complex model components when suitable data are available.

Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology

It may be endemic to mechanistic modelling of complex environmental systems that there are many different model structures and many different parameter sets within a chosen model structure that may be behavioural or acceptable in reproducing the observed behaviour of that system. This has been called the equifinality concept. The generalised likelihood uncertainty estimation (GLUE) methodology for model identification allowing for equifinality is described. Prediction within this methodology is a process of ensemble forecasting using a sample of parameter sets from the behavioural model space, with each sample weighted according to its likelihood measure to estimate prediction quantiles. This allows that different models may contribute to the ensemble prediction interval at different time steps and that the distributional form of the predictions may change over time. Any effects of model nonlinearity, covariation of parameter values and errors in model structure, input data or observed variables, with which the simulations are compared, are handled implicitly within this procedure. GLUE involves a number of choices that must be made explicit and can be therefore subjected to scrutiny and discussion. These include ways of combining information from different types of model evaluation or from different periods in a data assimilation context. An example application to rainfall-runoff modelling is used to illustrate the methodology, including the updating of likelihood measures.

Changing ideas in hydrology — The case of physically-based models

This paper argues that there are fundamental problems in the application of physically-based models for practical prediction in hydrology. These problems result from limitations of the model equations relative to a heterogeneous reality; the lack of a theory of subgrid scale integration; practical constraints on solution methodologies; and problems of dimensionality in parameter calibration. It is suggested that most current applications of physically-based models use them as lumped conceptual models at the grid scale. Recent papers on physically-based models have misunderstood and misrepresented these limitations. There are practical hydrological problems requiring physically-based predictions, and there will continue to be a need for physically-based models but ideas about their capabilities must change so that future applications attempt to obtain realistic estimates of the uncertainty associated with their predictions, particularly in the case of evaluating future scenarios of the effects of management strategies.

Prophecy, reality and uncertainty in distributed hydrological modelling

Difficulties in defining truly mechanistic model structures and difficulties of model calibration and validation suggest that the application of distributed hydrological models is more an exercise in prophecy than prediction. One response to these problems is outlined in terms of a realistic assessment of uncertainty in hydrological prophecy, together with a framework (GLUE) within which such ideas can be implemented. It is suggested that a post-modernistic hydrology will recognise the uncertainties inherent in hydrological modelling and will focus attention on the value of data in conditioning hydrological prophecies.

BAYESIAN ESTIMATION OF UNCERTAINTY IN RUNOFF PREDICTION AND THE VALUE OF DATA : AN APPLICATION OF THE GLUE APPROACH

This paper addresses the problem of evaluating the predictive uncertainty of TOPMODEL using the Bayesian Generalised Likelihood Uncertainty Estimation (GLUE) methodology in an application to the small Ringelbach research catchment in the Vosges, France. The wide range of parameter sets giving acceptable simulations is demonstrated, and uncertainty bands are presented based on different likelihood measures. It is shown how the distributions of predicted discharges are non-Gaussian and vary in shape through time and with discharge. Updating of the likelihood weights using Bayes equation is demonstrated after each year of record and it is shown how the additional data can be evaluated in terms of the way they constrain the uncertainty bands.

Effects of spatial variability and scale with implications to hydrologic modeling

This paper reports the results of a preliminary investigation into the existence of a Representative Elementary Area (REA) in the context of hydrologic modeling at the catchment scale. The investigation was carried out for an actual catchment topography as represented by Coweeta River experimental basin with synthetic realizations for rainfall and soils. The hydrologic response of this catchment was modeled by a modified version of topmodel{black star} {black star} Beven and Kirkby (1979) which is capable of modeling both infiltration excess and saturation excess runoff and incorporating the spatial variability of soils, topography, and rainfall. The effect of scale was analyzed by first dividing the catchment into smaller subcatchments and determining the average water fluxes for each subcatchment. The preliminary results lead to the following conclusions: (1) a Representative Elementary Area (REA) exists in the context of catchment hydrologic responses; (2) the REA is strongly influenced by the topography; and (3) based on our initial results, the length scale of rainfall seems to have only a secondary role in determining the size of the REA; however, increases in the variability of rainfall and soils between subcatchments increase the variability of runoff generation between subcatchments.

TOPMODEL: A critique

TOPMODEL (a TOPography based hydrological MODEL) is now 20 years old and has been the subject of numerous applications to a wide variety of catchments. This paper represents a critical review of some of the issues involved in application of the TOPMODEL concepts, including the basic assumptions involved; the derivation of topographic index distributions from digital terrain data; additional model components; meaning and calibration of the model parameters; and issues involved in model validation and predictive uncertainty. The aim is to provoke a thoughtful approach to hydrological modelling and the interaction of modelling and field work. Some recommendations are made for future modelling practice.

Ignorance is bliss: Or seven reasons not to use uncertainty analysis

Uncertainty analysis of models has received increasing attention over the last two decades in water resources research. However, a significant part of the community is still reluctant to embrace the estimation of uncertainty in hydrological and hydraulic modeling. In this paper, we summarize and explore seven common arguments: uncertainty analysis is not necessary given physically realistic models; uncertainty analysis cannot be used in hydrological and hydraulic hypothesis testing; uncertainty (probability) distributions cannot be understood by policy makers and the public; uncertainty analysis cannot be incorporated into the decision-making process; uncertainty analysis is too subjective; uncertainty analysis is too difficult to perform; uncertainty does not really matter in making the final decision. We will argue that none of the arguments against uncertainty analysis rehearsed are, in the end, tenable. Moreover, we suggest that one reason why the application of uncertainty analysis is not normal and expected part of modeling practice is that mature guidance on methods and applications does not exist. The paper concludes with suggesting that a Code of Practice is needed as a way of formalizing such guidance.

Panta Rhei-Everything Flows: Change in hydrology and society-The IAHS Scientific Decade 2013-2022

Abstract The new Scientific Decade 2013–2022 of IAHS, entitled “Panta Rhei—Everything Flows”, is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013–2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes. Editor Z.W. Kundzewicz Citation Montanari, A., Young, G., Savenije, H.H.G., Hughes, D., Wagener, T., Ren, L.L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S.J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V., 2013. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrological Sciences Journal. 58 (6) 1256–1275.

Testing a physically-based flood forecasting model (TOPMODEL) for three U.K. catchments

A previously developed model has been tested on three catchments: Crimple Beck (8 km2) near Harrogate, Hodge Beck (36 km2) on the North York Moors and the Wye headwater (10.5 km2) in central Wales. The model was originally validated on subcatchments within Crimple Beck. For this study forecasts were made over a period of one year, based only on rainfall and evaporation data. The model parameters were derived from a defined program of field measurements over a period of 2–4 weeks, and no formal optimization procedures were carried out before comparing forecasts with the measured stream discharge record. As a result of the comparisons, the model is seen as a useful approach for ungauged catchments of up to 500 km2 in humid-temperature climates.