Rémy Garçon

Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach

Abstract Medium-term quantitative precipitation forecasts (QPFs) up to several days ahead are required to issue early flood warnings and to allow optimum operation of hydraulic structures or reservoirs. This paper describes an approach which can be seen as an adaptation of deterministic meteorological model outputs. It involves searching for a sample of past situations similar to the current one from a long meteorological archive. The analogy is considered in terms of general circulation patterns over a window covering western Europe. For this restricted sample of days similar to the day at hand, the corresponding sample of observed daily precipitation is extracted for each catchment. The rainfall to be observed during the current day is assumed to follow the same distribution, known from this empirical sample. This provides a probabilistic forecast expressed, for example, by a central quantile and a confidence range. This paper describes the many choices underlying the optimisation of this approach: choice of predictor variables to characterise a meteorological situation, choice of similarity criterion between two situations, criterion for performance evaluation between two versions of the algorithm, etc. This method was calibrated over about 50 catchments located in France, Italy and Spain, using a meteorological and hydrological archive running from 1953 to 1996. Comparisons carried out over a validation sample (1995–1996) with three poor-man methods prove the interest of this approach, in a perfect prognosis context. In real-time operation, the use of forecast instead of observed predictor variables, essentially geopotential fields, produces only a minor decrease in performance. The use of the single-valued central quantile supplemented by the confidence interval provided a QPF that has proved effective and informative on the potential for extreme values.

Tall tales from the hydrological crypt: are models monsters?

Abstract “Bizarre,” “monstrous”: in society as well as in science, this is the way we are used to describing objects that deviate from an expected standard. Hydrology is no exception. The bizarre or the monstrous describes every object that has a low probability of occurring, or that our models fail to represent. Actually, the bizarre or the monstrous is often a demonstration of the limits of our models rather than an intrinsic characteristic of the objects we study. This article provides a reflection on the definition of bizarre and monstrous in the context of hydrology. We base our reflection on 60 years of experience in hydrometeorological operational management and applied research at the French national electricity company (EDF-DTG). First, we describe several classical a priori models or conceptions trusted by hydrologists, sometimes erroneously. These include classical rainfall or streamflow measurement issues, certain limits of the watershed concept or problems in the spatialization of local measurements. Then we attempt to show how the misuse of statistical models can generate bizarre or monstrous results. We give examples related to outliers and to the homogeneity and stationarity hypotheses. We show how difficult it may be for operational forecasters to anticipate and believe that extreme (monstrous) events will occur in the near future. Finally, we wish to show that the bizarre or the monstrous should not be rejected in hydrology, but instead is something to study in greater depth. We believe that this type of analysis offers new opportunities to improve the explanatory and predictive capacity of our models. Citation Mathevet, T. & Garçon, R. (2010) Tall tales from the hydrological crypt: are models monsters? Hydrol. Sci. J. 55(6), 857–871.

Hydrologically Aided Interpolation of Daily Precipitation and Temperature Fields in a Mesoscale Alpine Catchment

AbstractHydrological modeling in mountainous regions, where catchment hydrology is heavily influenced by snow (and possibly ice) processes, is a challenging task. The intrinsic complexity of local processes is added to the difficulty of estimating spatially distributed inputs such as precipitation and temperature, which often exhibit a high spatial heterogeneity that cannot be fully captured by measurement networks. Hence, an interpolation step is often required prior to the hydrological modeling step. Usually, the reconstruction of meteorological forcings and the calibration of the hydrological model are done sequentially. The outputs of the hydrological model (discharge estimates) may give some insight into the quality of the forcings used to feed it, but in this two-step independent analysis, it is not possible to easily feed the interpolation scheme back with the discrepancies between observed and simulated discharges. Yet, despite having undergone the rainfall–runoff (or snow–runoff) transformation, ...

Impact of model structure on flow simulation and hydrologicalrealism: from lumped to semi-distributed approach

Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration–validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.