Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations

International audienceThis paper investigates the influence of mean areal rainfall estimation errors on a specific case study: the use of lumped conceptual rainfall-runoff models to simulate the flood hydrographs of three small to medium-sized catchments of the upper Loire river. This area (3200 km2) is densely covered by an operational network of stream and rain gauges. It is frequently exposed to flash floods and the improvement of flood forecasting models is then a crucial concern. Particular attention has been drawn to the development of an error model for rainfall estimation consistent with data in order to produce realistic streamflow simulation uncertainty ranges. The proposed error model combines geostatistical tools based on kriging and an autoregressive model to account for temporal dependence of errors. It has been calibrated and partly validated for hourly mean areal precipitation rates. Simulated error scenarios were propagated into two calibrated rainfall-runoff models using Monte Carlo simulations. Three catchments with areas ranging from 60 to 3200 km2 were tested to reveal any possible links between the sensitivity of the model outputs to rainfall estimation errors and the size of the catchment. The results show that a large part of the rainfall-runoff (RR) modelling errors can be explained by the uncertainties on rainfall estimates, especially in the case of smaller catchments. These errors are a major factor limiting accuracy and sharpness of rainfallrunoff simulations, and thus their operational use for flood forecasting

Introducing a moving time window in the analogue method for precipitation prediction to find better analogue situations at a sub-daily time step

Analogue methods (AMs) predict local weather variables (predictands), such as precipitation, by means of a statistical relationship with predictors at a synoptic scale. Predictors are extracted from reanalysis datasets that often have a six hourly time step. For precipitation forecasts, the predictand often consists of daily precipitation (06h to 30h UTC), given the length of their available archives, and the unavailability of equivalent archives at a finer time step. The optimal predictors to explain these daily precipitations have been obtained in a calibration procedure with fixed times of observation (e.g. geopotential heigths Z1000 at 12h UTC and Z500 at 24h UTC). In operational forecast, a new target situation is defined by its geopotential predictors at these fixed hours, i.e. Z1000 at 12h UTC and Z500 at 24h UTC. Usually, the search for candidate situations for this given target day is usually undertaken by comparing the state of the atmosphere at the same fixed hours of the day for both the target day and the candidate analogues. However, it can be expected that the best analogy among the past synoptic situations does not occur systematically at the same time of the day and that better candidates can be found by shifting to a different hour. With this assumption, a moving time window (MTW) was introduced to allow the search for candidates at different hours of the day (e.g. Z1000 at 00, 06, 12, 18 h UTC and Z500 at 12, 18, 24, 30 h UTC respectively). This MTW technique can only result in a better analogy in terms of the atmospheric circulation (compared to the method with fixed hours), with improved values of the analogy criterion on the entire distribution of analogue dates. A seasonal effect has also been identified, with larger improvements in winter than in summer. However, its interest in precipitation forecast can only be evaluated with an archive of the corresponding 24h-totals, i.e. not only 6-30h UTC totals, but also 0-24h, 12-12h and 18-18h totals). This was possible to assess on a set of stations from the Swiss hourly measurement network with rather long time-series. The prediction skill was found to have improved by the MTW, and even to a greater extent after recalibrating the AM parameters. Moreover, the improvement was greater for days with heavy precipitation, which are generally related to more dynamic atmospheric situations where timing is more specific. The use of the MTW in the AM can be considered for several applications in different contexts, may it be for operational forecasting or climate-related studies

Automatic and global optimization of the Analogue Method for statistical downscaling of precipitation - Which parameters can be determined by Genetic Algorithms?

The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predictand), often the daily precipitation total, on the basis of a statistical relationship with synoptic predictor variables. A certain number of similar situations are sampled in order to establish the empirical conditional distribution which is considered as the prediction for a given date. The method is used in operational medium-range forecasting in several hydropower companies or flood forecasting services, as well as in climate impact studies. The statistical relationship is usually established by means of a semi-automatic sequential procedure that has strong limitations: it is made of successive steps and thus cannot handle parameters dependencies, and it cannot automatically optimize certain parameters, such as the selection of the pressure levels and the temporal windows on which the predictors are compared. A global optimization technique based on Genetic Algorithms was introduced in order to surpass these limitations and to provide a fully automatic and objective determination of the AM parameters. The parameters that were previously assessed manually, such as the selection of the pressure levels and the temporal windows, on which the predictors are compared, are now automatically determined. The next question is: Are Genetic Algorithms able to select the meteorological variable, in a reanalysis dataset, that is the best predictor for the considered predictand, along with the analogy criteria itself? Even though we may not find better predictors for precipitation prediction that the ones often used in Europe, due to numerous other studies which consisted in systematic assessments, the ability of an automatic selection offers new perspectives in order to adapt the AM for new predictands or new regions under different meteorological influences

Using genetic algorithms to achieve an automatic and global optimization of analogue methods for statistical downscaling of precipitation

Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circulation, are likely to result in similar local or regional weather conditions. These methods consist of sampling a certain number of past situations, based on different synoptic-scale meteorological variables (predictors), in order to construct a probabilistic prediction for a local weather variable of interest (predictand). They are often used for daily precipitation prediction, either in the context of real-time forecasting, reconstruction of past weather conditions, or future climate impact studies. The relationship between predictors and predictands is defined by several parameters (predictor variable, spatial and temporal windows used for the comparison, analogy criteria, and number of analogues), which are often calibrated by means of a semi-automatic sequential procedure that has strong limitations. AMs may include several subsampling levels (e.g. first sorting a set of analogs in terms of circulation, then restricting to those with similar moisture status). The parameter space of the AMs can be very complex, with substantial co-dependencies between the parameters. Thus, global optimization techniques are likely to be necessary for calibrating most AM variants, as they can optimize all parameters of all analogy levels simultaneously. Genetic algorithms (GAs) were found to be successful in finding optimal values of AM parameters. They allow taking into account parameters inter-dependencies, and selecting objectively some parameters that were manually selected beforehand (such as the pressure levels and the temporal windows of the predictor variables), and thus obviate the need of assessing a high number of combinations. The performance scores of the optimized methods increased compared to reference methods, and this even to a greater extent for days with high precipitation totals. The resulting parameters were found to be relevant and spatially coherent. Moreover, they were obtained automatically and objectively, which reduces efforts invested in exploration attempts when adapting the method to a new region or for a new predictand. In addition, the approach allowed for new degrees of freedom, such as a weighting between the pressure levels, and non overlapping spatial windows. Genetic algorithms were then used further in order to automatically select predictor variables and analogy criteria. This resulted in interesting outputs, providing new predictor-criterion combinations. However, some limitations of the approach were encountered, and the need of the expert input is likely to remain necessary. Nevertheless, letting GAs exploring a dataset for the best predictor for a predictand of interest is certainly a useful tool, particularly when applied for a new predictand or a new region under different climatic characteristics

AtmoSwing, an analog technique model for statistical downscaling and forecasting

Analog methods (AMs) allow predicting local meteorological variables of interest (predictand), such as the daily precipitation, based on synoptic variables (predictors). They rely on the hypothesis that similar atmospheric conditions are likely to result in similar local effects. The statistical relationship is first defined (e.g. which predictors, and how many subsampling steps) and calibrated (e.g. which spatial domain, and how many analogues) before being applied to the target period, may it be for operational forecasting or for climate impact studies. A benefit of AMs is that they are lightweight and can provide valuable results for a negligible cost. AtmoSwing is an open source software that implements different AM variants in a very flexible way, so that they can be easily configured by means of XML files. It is written in C++, is object-oriented and multi-platform. AtmoSwing provides four tools: the Optimizer to establish the relationship between the predictand and predictors, the Downscaler to apply the method for climate impact studies, the Forecaster to perform operational forecasts, and the Viewer to display the results. The Optimizer provides a semi-automatic sequential approach, as well as Monte-Carlo analyses, and a global optimization technique by means of Genetic Algorithms. It calibrates the statistical relationship that can be later applied in a forecasting or climatic context. The Downscaler takes as input the outputs of climate models, either GCMs or RCMs in order to provide a downscaled time series of the predictand of interest at a local scale. The Forecaster automatically downloads and reads operational NWP outputs to provide operational forecasting of the predictand of interest. The processing of a forecast is extremely lightweight in terms of computing resources; it can indeed run on almost any computer. The Viewer displays the forecasts in an interactive GIS environment. It contains several layers of syntheses and details in order to provide a quick overview of the potential critical situations in the coming days, as well as the possibility for the user to go into the details of the forecasted predictand distribution

The catastrophic flash-flood event of 8–9 September 2002 in the Gard region, France: a first case study for the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory

The Cévennes–Vivarais Mediterranean Hydrometeorological Observatory (OHM-CV) is a research initiative aimed at improving the understanding and modeling of the Mediterranean intense rain events that frequently result in devastating flash floods in southern France. A primary objective is to bring together the skills of meteorologists and hydrologists, modelers and instrumentalists, researchers and practitioners, to cope with these rather unpredictable events. In line with previously published flash-flood monographs, the present paper aims at documenting the 8–9 September 2002 catastrophic event, which resulted in 24 casualties and an economic damage evaluated at 1.2 billion euros (i.e., about 1 billion U.S. dollars) in the Gard region, France. A description of the synoptic meteorological situation is first given and shows that no particular precursor indicated the imminence of such an extreme event. Then, radar and rain gauge analyses are used to assess the magnitude of the rain event, which was particularly remarkable for its spatial extent with rain amounts greater than 200 mm in 24 h over 5500 km2. The maximum values of 600–700 mm observed locally are among the highest daily records in the region. The preliminary results of the postevent hydrological investigation show that the hydrologic response of the upstream watersheds of the Gard and Vidourle Rivers is consistent with the marked space–time structure of the rain event. It is noteworthy that peak specific discharges were very high over most of the affected areas (5–10 m3 s−1 km−2) and reached locally extraordinary values of more than 20 m3 s−1 km−2. A preliminary analysis indicates contrasting hydrological behaviors that seem to be related to geomorphological factors, notably the influence of karst in part of the region. An overview of the ongoing meteorological and hydrological research projects devoted to this case study within the OHM-CV is finally presented

The VALUE perfect predictor experiment: evaluation of temporal variability

Temporal variability is an important feature of climate, comprising systematic vari-ations such as the annual cycle, as well as residual temporal variations such asshort-term variations, spells and variability from interannual to long-term trends.The EU-COST Action VALUE developed a comprehensive framework to evaluatedownscaling methods. Here we present the evaluation of the perfect predictorexperiment for temporal variability. Overall, the behaviour of the differentapproaches turned out to be as expected from their structure and implementation.The chosen regional climate model adds value to reanalysis data for most consid-ered aspects, for all seasons and for both temperature and precipitation. Bias cor-rection methods do not directly modify temporal variability apart from the annualcycle. However, wet day corrections substantially improve transition probabilitiesand spell length distributions, whereas interannual variability is in some cases dete-riorated by quantile mapping. The performance of perfect prognosis (PP) statisticaldownscaling methods varies strongly from aspect to aspect and method to method,and depends strongly on the predictor choice. Unconditional weather generatorstend to perform well for the aspects they have been calibrated for, but underrepre-sent long spells and interannual variability. Long-term temperature trends of thedriving model are essentially unchanged by bias correction methods. If precipita-tion trends are not well simulated by the driving model, bias correction furtherdeteriorates these trends. The performance of PP methods to simulate trendsdepends strongly on the chosen predictors.VALUE has been funded as EU COST Action ES1102

Contribution à l'analyse des données en hydrométéorologie, la prévision des phénomènes accidentels et l'analyse des champs spatiaux : application à la prévision des avalanches à Davos et à l'analyse des épisodes pluvieux cévenols

Dans ce travail nous tenterons participer à l'effort de rationalisation de l'instrumentation et de collecte d'informations, à propos de deux ensembles de données très représentatifs de ceux que l'on rencontre en Hydrométéorologie : Condenser l'information, en extraire le maximum, optimiser éventuellement le système de mesure, et mesurer son potentiel de prévision. Le premier exemple concerne la prévision des phénomènes "accidentels" connus le plus souvent de manière qualitative (occurence d'orages à grêle. nature des précipitations: pluie ou neige, risque de pollution, etc ... ) parmi lesquels le problème de la prévision des avalanches n'avait pas encore fait l'objet d'un effort intensif. Un autre problème, très courant en Hydrologie, concerne les variables "régionalisées", c'est-à-dire mesurées sur un réseau où la proximité géographique entre stations a un sens et doit donc être prise en compte. L'exemple du réseau pluviométrique des Cévennes est aussi intéressant par sa densité que spectaculaire par l'importance des précipitations qu'il mesure. Au passage, nous évoquerons, non pas l'ensemble des méthodes existantes en Analyse des Données, mais celles que nous avons personnellement utilisées, analysées, voire améliorées sur ces exemples. Grâce en particulier aux travaux de Pro BENZECRI et de ses collaborateurs, l'Analyse des données n'a plus rien aujourd'hui d'un domaine grossièrement défriché, mais c'est encore un jardin à l' anglaise où de nombreuses zones d'ombre subsistent ... Nous avons cherchéPas de résum

Contribution à l'analyse des données en hydrométéorologie, la prévision des phénomènes accidentels et l'analyse des champs spatiaux : application à la prévision des avalanches à Davos et à l'analyse des épisodes pluvieux cévenols

Dans ce travail nous tenterons participer à l'effort de rationalisation de l'instrumentation et de collecte d'informations, à propos de deux ensembles de données très représentatifs de ceux que l'on rencontre en Hydrométéorologie : Condenser l'information, en extraire le maximum, optimiser éventuellement le système de mesure, et mesurer son potentiel de prévision. Le premier exemple concerne la prévision des phénomènes "accidentels" connus le plus souvent de manière qualitative (occurence d'orages à grêle. nature des précipitations: pluie ou neige, risque de pollution, etc ... ) parmi lesquels le problème de la prévision des avalanches n'avait pas encore fait l'objet d'un effort intensif. Un autre problème, très courant en Hydrologie, concerne les variables "régionalisées", c'est-à-dire mesurées sur un réseau où la proximité géographique entre stations a un sens et doit donc être prise en compte. L'exemple du réseau pluviométrique des Cévennes est aussi intéressant par sa densité que spectaculaire par l'importance des précipitations qu'il mesure. Au passage, nous évoquerons, non pas l'ensemble des méthodes existantes en Analyse des Données, mais celles que nous avons personnellement utilisées, analysées, voire améliorées sur ces exemples. Grâce en particulier aux travaux de Pro BENZECRI et de ses collaborateurs, l'Analyse des données n'a plus rien aujourd'hui d'un domaine grossièrement défriché, mais c'est encore un jardin à l' anglaise où de nombreuses zones d'ombre subsistent ... Nous avons cherchéPas de résum