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International audience; Rainfall is one of the most important inputs of hydrologic models. Lumped models use the areal rainfall as input (i.e. the rainfall spatially averaged within the catchment boundaries), while distributed models use the whole rainfall field. In both cases, understanding and quantifying the spatial variability of rainfall is required to improve the calibration and prediction of hydrologic models. Unfortunately, spatial rainfall is not measured directly. Instead, it is generally estimated based on: (i) (sparse) measurements from the raingauge network; (ii) (imprecise) indirect radar estimates; or (iii) a combination of them. Such estimates are affected by significant uncertainties that need to be quantified. This poster focuses on the estimation of spatial rainfall based on raingauge measurements, and aims at assessing the reliability of a geostatistical approach to quantify the uncertainty in spatial rainfall. This approach is based on a turning-band based spatio-temporal rainfall generator used in conditional simulation (CS) mode: the generated rainfall fields match the observed values at the raingauge locations, but differ elsewhere. In other words, the rainfall generator simulates multiple realizations of the rainfall in between raingauges, therefore quantifying the uncertainty associated with the incomplete spatial coverage of the raingauge network. A case study is used to illustrate the application of the rainfall generator and to assess the reliability of its conditional simulations. It is based on hourly data from 64 raingauges over the period 2000-2008, located within or near the Ardèche catchment at Sauze (South-Eastern France, 2240 km²). 25 raingauges are used to calibrate the rainfall generator, which includes: (i) a clustering procedure to define a restricted number of homogeneous “rainfall types”; (ii) the estimation of the geostatistical properties of rainfall within each type (spatial and temporal variograms, local distribution). The conditional simulation of 100 replicates (using the 25 calibration raigauges as conditioning values) is then performed. Those 100 CS replicates are finally evaluated against 39 validation raingauges, which were use neither for calibration nor for conditioning. Overall, this validation exercise highlights an acceptable reliability of the CS replicates. However, the reliability differs significantly for different rainfall types: in general, CS replicates are far more reliable for heavy autumnal rainfall types than for types corresponding to small and scattered rainfall events. These results suggest several avenues to improve the geostatistical model behind the simulation.

This study explores the decomposition of predictive uncertainty in hydrological modeling into its contributing sources. This is pursued by developing data-based probability models describing uncertainties in rainfall and runoff data, and incorporating them into the Bayesian Total Error Analysis methodology (BATEA). A case study based on the Yzeron catchment (France) and the conceptual rainfall-runoff model GR4J is presented. It exploits a calibration period where dense raingauge data is available to characterize the uncertainty in the catchment-average rainfall using geostatistical conditional simulation. The inclusion of information about rainfall and runoff data uncertainties overcomes ill-posedness problems and enables simultaneous estimation of forcing and structural errors as part of the Bayesian inference. This yields more reliable predictions than approaches that ignore or lump different sources of uncertainty in a simplistic way (e.g., standard least squares). It is shown that independently-derived data quality estimates are needed to decompose the total uncertainty in the runoff predictions into the individual contributions of rainfall, runoff and structural errors. In this case study, the total predictive uncertainty appears dominated by structural errors. Although further research is needed to interpret and verify this decomposition, it can provide strategic guidance for investments in environmental data collection and/or modeling improvement. More generally, this study demonstrates the power of the Bayesian paradigm to improve the reliability of environmental modeling using independent estimates of sampling and instrumental data uncertainties.

Hydrological calibration and prediction using conceptual models is affected by forcing/response data uncertainty and structural model error. The Bayesian Total Error Analysis methodology (BATEA) uses a hierarchical framework to represent individual sources of uncertainty. However, it is shown that standard multi-block Metropolis-within-Gibbs samplers commonly used in traditional Bayesian hierarchical Markov Chain Monte Carlo (MCMC) are exceedingly computationally expensive when applied to hydrologic models based on recursive numerical solution of coupled nonlinear differential equations describing the evolution of catchment states such as soil and groundwater storages. This note develops a limited-memory algorithm for accelerating multi-block MCMC sampling from the posterior distributions of such models using low-dimensional jump distributions. The new algorithm exploits the decaying memory of hydrological systems to provide accurate tolerance-based approximations of traditional fullmemory MCMC methods and is orders of magnitude more efficient than the latter.

Meaningful quantification of data and structural uncertainties in conceptual rainfall-runoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios. Several Bayesian inference schemes are investigated, differing in the treatment of rainfall and structural uncertainties, and in the precision of the priors describing rainfall uncertainty. Compared with traditional lumped additive-error approaches, the quantification of the total predictive uncertainty in the runoff is improved when rainfall and/or structural errors are characterized explicitly. However, the decomposition of the total uncertainty into individual sources is more challenging. In particular, poor identifiability may arise when the inference scheme represents rainfall and structural errors using separate probabilistic models. The inference becomes ill-posed unless sufficiently precise prior knowledge of data uncertainty is supplied; this ill-posedness can often be detected from the behavior of the Monte Carlo sampling algorithm. Moreover, the priors on the data quality must also be sufficiently accurate if the inference is to be reliable and support meaningful uncertainty decomposition. Our findings highlight the inherent limitations of inferring inaccurate hydrologic models using rainfall-runoff data with large unknown errors. Bayesian total error analysis (BATEA) can overcome these problems using independent prior information. The need for deriving independent descriptions of the uncertainties in the input and output data is clearly demonstrated.

International audience; Uncertainty in the rainfall inputs, which constitute a primary forcing of hydrological systems, considerably affects the calibration and predictive use of hydrological models. In a recent paper, Ajami et al. [2007] proposed the Integrated Bayesian Uncertainty Estimator (IBUNE) to quantify input, parameter and model uncertainties. This comment analyzes two interpretations of the IBUNE method and compares them to the Bayesian Total Error Analysis (BATEA) method [Kavetski et al., 2002, 2006a]. It is shown that BATEA and IBUNE are based on the same hierarchical conceptualization of the input uncertainty. However, in interpretation A of IBUNE, the likelihood function, and hence the posterior distribution, are random functions of the inferred variables, which violates a standard requirement for probability density functions (pdf). A synthetic study shows that IBUNE-A inferences are inconsistent with the correct parameter values and model predictions. In the second interpretation, IBUNE-B, it is shown that a specific implementation of IBUNE is equivalent to a special Metropolis-Hastings sampler for the full Bayesian posterior, directly including the rainfall multipliers as latent variables (but not necessarily storing their samples). Consequently, IBUNE-B does not reduce the dimensionality of the sampling problem. Moreover, the jump distribution for the latent variables embedded in IBUNE-B is computationally inefficient and leads to prohibitively slow convergence. Modifications of these jump rules can cause convergence to incorrect posterior distributions. The primary conclusion of this comment is that, unless the hydrological model and the structure of data uncertainty allow specialized treatment, Bayesian hierarchical models invariably lead to high-dimensional computational problems, whether working with the full posterior (high-dimensional sampling problem) or with the marginal posterior (highdimensional integration problem each time the marginal posterior is evaluated).

The lack of a robust framework for quantifying the parametric and predictive uncertainty of conceptual rainfall-runoff (CRR) models remains a key challenge in hydrology. The Bayesian total error analysis (BATEA) methodology provides a comprehensive framework to hypothesize, infer, and evaluate probability models describing input, output, and model structural error. This paper assesses the ability of BATEA and standard calibration approaches (standard least squares (SLS) and weighted least squares (WLS)) to address two key requirements of uncertainty assessment: (1) reliable quantification of predictive uncertainty and (2) reliable estimation of parameter uncertainty. The case study presents a challenging calibration of the lumped GR4J model to a catchment with ephemeral responses and large rainfall gradients. Postcalibration diagnostics, including checks of predictive distributions using quantile-quantile analysis, suggest that while still far from perfect, BATEA satisfied its assumed probability models better than SLS and WLS. In addition, WLS/SLS parameter estimates were highly dependent on the selected rain gauge and calibration period. This will obscure potential relationships between CRR parameters and catchment attributes and prevent the development of meaningful regional relationships. Conversely, BATEA provided consistent, albeit more uncertain, parameter estimates and thus overcomes one of the obstacles to parameter regionalization. However, significant departures from the calibration assumptions remained even in BATEA, e.g., systematic overestimation of predictive uncertainty, especially in validation. This is likely due to the inferred rainfall errors compensating for simplified treatment of model structural error.

This study tests the 1D/2D coupled modelling approach on its ability to emulate the âroadâ portion of runoff which is conveyed in the kerb/gutter alignment. The event modelled is an approximately 1 in 1 year ARI event in magnitude. Given itâs small ARI it may be supposed that it is mainly comprised of runoff from impervious surfaces (10% roof and 90% road and others). In the event flow from the road moves as sheet flow to the kerb where it is routed to inflow pits. Via the pits it enters the pipe and at the downstream end of the pipe it is gauged. The modelling system is found to be capable of matching the gauged hydrograph for the relatively small event very well. What is also found however is that model parameters that might, in a 1D model, be relatively well informed by field inspection, are instead required to be altered outside recommended ranges in order to optimise the fit between gauged and modelled flow. That is, there is a degree of conceptualisation in the 1D/2D system that was unexpected.

A number of areas associated with current practices for floodplain modelling have been highlighted during the revision of ARR as requiring further investigation. One of these areas and hence the reason for one of the revision projects is the need for guidance on 2D modelling in urban areas. This need for guidance has increased as 2D models have become the tool of choice for prediction of flood behaviour within urban floodplains throughout Australia. These 2D models also are being used to simulate rainfall-runoff processes that traditionally have been modelled using conceptual lumped storage rainfall-runoff routing models. The revision project implemented to satisfy this need adopted a non-traditional approach. Instead of inviting a small group of authors to write the project report, a team of authors that represented model suppliers, experienced users, academics and clients from across Australia was assembled. The team used an online wiki tool to develop content collaboratively. Outlined in this paper are the process, experiences learned and details of the final report arising from this project.

Since ARR 1987 improved statistical techniques have been presented which are ideal for producing regression relationships relating different catchment characteristics to peak flow. A unique feature of the quantile regression technique is that as different catchment characteristics become more or less important to peak flow their varying significance can be incorporated. For example the importance of losses declines for larger longer events where previous areas become saturated and start to contribute runoff. As a first step towards carrying out the quantile regression work, a database of available gauged data has been compiled. The data base is still being finalised however at the end of the proofing process, whereby gauges are checked for length of record, completeness, quality, ratings, stability of upstream development, it is expected that only a handful of gauges will be found to be suitable. Given the paucity of data it is not possible to establish relationships, by regression, between various catchment characteristics and peak flow for urban catchment. Thus the paper calls for a focus on urban gauging in Australia. This paper suggests that a goal be established, for the establishment of urban gauging sites across Australia (and the maintenance of existing sites) with the suggestion being that such work can be funded as part of climate change research.

Since publication of ARR87, available computer resources have increased thus enabling the analysis of a pluviograph record. This analysis typically involves extracting the significant storm events and determining the temporal patterns of these storms. These extracted temporal patterns will then be scaled for the design rainfall depth and filtered if necessary. Presented herein are the results of a study into the observed and filtered temporal patterns derived from the Sydney Observatory Hill pluviograph record. It was found that filtering changed the ranking of periods within the storm, changed the depth of antecedent rainfall prior to the intense part of the rainfall event, and changed the percentage of the storm occurring in the core of the storm (most intense part of the storm). Furthermore, if aggressive filtering of the observed temporal pattern is required, the user is probably better to not filter at all as the pattern will no longer resemble the original pattern.

An important component of estimating the flood flow is the temporal pattern associated with the design rainfall burst. The purpose of this paper, as part of the current revision of ARR, is to develop a new methodology for developing rainfall temporal patterns which will be presented together with a discussion of the implication for design flood flow estimation. Historical storms at two locations, namely Sydney and Richmond (New South Wales, Australia), were divided arbitrarily by meteorological origin into frontal or convective storms. These categories of historical storms were subdivided further by the location of the centre of mass of the storm. The relevant statistical properties for these storms were extracted for each of these categories and used in the development of the design temporal patterns. The proposed approach is based on a non-dimensional conditional random walk to generate an indefinite number of synthetic storms that preserve the internal structure of real storms. This approach allowed better representation of the temporal variability than the AVM method however, some problems with internal burst frequency were observed. Finally, rather than developing a single rainfall temporal pattern, the proposed approach develops a suite of synthetic design temporal patterns which enables the designer to assess the sensitivity of the peak flow estimate to the temporal pattern of rainfall.