Hydrological seasonal forecast over France: feasibility and prospects

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1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 11: (2010) Published online 1 February 2010 in Wiley InterScience ( DOI: /asl.256 Hydrological seasonal forecast over France: feasibility and prospects J.-P. Céron, 1 * G. Tanguy, 2 L. Franchistéguy, 1 E. Martin, 2 F. Regimbeau 1 and J.-P. Vidal 1,2 1 Climatology Department, Météo-France, 42 Avenue G. Coriolis, F31057, Toulouse Cédex 01, France 2 CNRM-GAME (Météo-France, CNRS), 42 Avenue G. Coriolis, F31057, Toulouse Cédex 01, France *Correspondence to: J.-P. Céron, Climatology Department, Météo-France, 42 Avenue G. Coriolis, F31057, Toulouse Cedex 01, France. Jean-Pierre.Ceron@meteo.fr Received: 31 August 2009 Revised: 16 November 2009 Accepted: 6 December 2009 Abstract This article presents a first evaluation of a hydrological forecasting suite at seasonal time scales over France. The hydrometeorological model SAFRAN-ISBA-MODCOU is forced by seasonal forecasts from the DEMETER project for the March April May period. Despite a simple downscaling method, the atmospheric forcings are reasonably well represented at the finest scale. The computed soil moisture shows some predictability with large regions of correlation above 0.3. Probabilistic scores for soil moisture and river flows for four different catchments are higher than that for atmospheric variables. These results suggest to go further for building an operational hydrological seasonal forecast system. Copyright 2010 Royal Meteorological Society Keywords: seasonal forecast; hydrology; ensemble forecast; river flow; soil wetness index 1. Introduction Water resource and its management is becoming a major issue in our societies. Various organizations or bodies managing water resources local or national drought committees, government bodies, hydropower companies, basin managers need decision support tools in terms of river flow forecasting, especially in the range of a few months which corresponds to the typical time-range of meteorological seasonal forecasting. Recent studies demonstrated the feasibility and relevance of seasonal forecasts for near surface variables, such as temperature and precipitation (see e.g. Ogallo et al., 2008). This predictability is related to the low-frequency parameters of the climate system, particularly the sea surface temperature. Seasonal forecasts are now operational in major meteorological centres worldwide (see for example int/pages/prog/wcp/wcasp/clips/producers forecasts. html). Studies on river flow seasonal forecasts have been furthermore carried out in the USA (Wood and Maurer, 2002; Luo and Wood, 2008) with encouraging results for water resource management purposes. In France, Cemagref has also investigated this topic, using long-term mean values of meteorological data as input to the hydrological model (Sauquet et al., 2008). Over the last decade, Météo-France has built the SAFRAN-ISBA-MODCOU (SIM) hydrometeorological suite to compute surface water and energy budgets and corresponding hydrological variables soil water content, river flows and water table levels for major aquifers at the scale of France (Habets et al., 2008). Outputs from SIM among them an estimate of the soil moisture are reported monthly to the French National Water Resources Department through the Hydrological Monitoring Bulletin ( The objective of this study is first to demonstrate the feasibility of hydrological seasonal forecasts in France using SIM and then to have an insight into the predictability of the French hydrological system. This predictability is expected to be higher than for the atmosphere, mainly because of the slow evolution of surface conditions. As a first attempt, the spring period (March April May, MAM hereafter) is targeted here because it covers a large part of the snow melting period. Indeed, the snowpack corresponds to one of the main low-frequency parameters of the hydro-climate system. Moreover, decisions to anticipate and prepare for the low-flow summer period are to be taken during this period. This article will first introduce the hydrometeorological seasonal forecasting suite based on DEMETER hindcasts (Palmer et al., 2004) and the SIM hydrometeorological suite. It will then present summary results in terms of rainfall and soil moisture over France as well as river flows for four selected catchments. Hydrometerological forecasts will be assessed against a SIM run carried out in reanalysis mode. 2. The hydrological seasonal forecasting suite SIM is composed of three independent models. SAFRAN (Durand et al., 1993; Quintana-Seguí et al., 2008) is a meteorological mesoscale analysis system of near surface variables, based on the hypothesis of climatically homogeneous zones and running at a 6-h Copyright 2010 Royal Meteorological Society

2 Hydrological seasonal forecast over France 79 Figure 1. Schematic representation of the hydrological forecasting suite. time step. Its results are interpolated to the hourly time step and over a 8-km grid in order to force the soilvegetation-atmosphere transfer (SVAT) scheme ISBA (Noilhan and Planton, 1989). ISBA simulates the surface water and energy budgets and computes the soil wetness index (SWI), defined as follows: SWI = w w wilt w fc w wilt where w is the soil water content and w fc and w wilt are the water content at field capcity and wilting point, respectively. The soil depth varies over France according to the ECOCLIMAP database (Masson et al., 2003) and the SWI is integrated over the soil column. ISBA also computes the surface runoff and bottom drainage, which are used to drive the hydrogeological model MODCOU (Ledoux et al., 1989). MODCOU routes the surface runoff to the hydrographic network and computes the evolution of the main aquifers. The application and validation of SIM over France are described in detail by Habets et al. (2008). SAFRAN has been applied and validated over the period to constitute a high-resolution atmospheric reanalysis over France (Vidal et al., 2009). This reanalysis has then been used to force ISBA and MODCOU over this period and thus to provide SWI and streamflow data that will be used hereafter as a reference (1) for evaluating the relevance of the forecasted information and (2) for providing the initial state of ISBA and MODCOU models at the start of the MAM period. Hydrological forecasts are here obtained by replacing SAFRAN reanalysed data by seasonal atmospheric forecasts downscaled over France. The seasonal forecasting information is provided by hindcasts of the Météo-France Arpège model used in the DEMETER project (Palmer et al., 2004). We used the set of forecasts from 1st February, which correspond to a 1-month lead-time forecast for the MAM period. The DEMETER forecasts are here downscaled from a resolution of 2.5 to 8 km following a revised version of the two-step method proposed by Rousset- Regimbeau et al. (2007) for ensemble medium range river flow forecasts with SIM. Large-scale precipitation and temperature fields from DEMETER forecasts are first converted into anomalies by removing their mean values, and then standardized by dividing them with their interannual standard deviation (SD). These standardized anomaly fields are then interpolated with an inverse-square weighting onto the 615 climatically homogeneous zones considered in the SAFRAN atmospheric analysis (see Quintana-Seguí et al., 2008). They are finally combined with SAFRAN long-term means and SD to get actual precipitation and temperature fields that include local-scale spatial variability. The discrimination between snowfall and rainfall is based on a temperature threshold of 0.5 C. Following Rousset-Regimbeau et al. (2007), the other variables required to drive the land surface model ISBA come from the SAFRAN climatology over the period, which overlaps SIM reanalysis and DEME- TER dataset. ISBA and MODCOU have been run each year over a period of 120 days from 1st February to issue the forecast for the MAM period meaning that it corresponds to a 1-month lead-time forecast. Figure 1 summarizes the main features of the hydrological forecasting suite. 3. Results 3.1. Downscaled atmospheric forecasts Seasonal forecasts usually show limited performance for atmospheric parameters over France, and more generally for extra-tropical regions like Europe. However, the DEMETER project demonstrated the interest of such information for downstream applications through the use of multimodel approaches and downscaling techniques (e.g. Cantelaube and Terres, 2005). Three simulations using different downscaled fields were successively tested by using direct interpolated large-scale fields, forecast anomalies and standardized forecast anomalies as described earlier. In this article,

3 80 J.-P. Céron et al. Figure 2. Spatial representation of time correlation ( ) between the 3-month average (March April May) of the forecasted ensemble mean and the reference SWI. Colored zones in orange and red indicate regions with a meaningful predictability. only the results from the last simulation (and logically the one that provided the best results) are presented. Downscaled forecasted fields do not exhibit any bias in temperature or in total precipitation over France, but they show an overestimation of rainfall and an underestimation of snowfall. The dispersion of ensemble members appears satisfactory with a reasonably high interannual variability. Brier scores are of the same magnitude as the ones for DEMETER forecasts without interpolation (between 0.2 and 0.3). So, despite the simple method used, the downscaling is neutral with regard to the scores of the atmospheric forcing terms. Thus, it is clear that results are better on the 3-month period rather than on each individual month, and that are better for temperature than for precipitation Soil wetness index forecasts Figure 2 shows the correlation between forecasted and reference spring-averaged SWI over France ( ). Values are mostly positive over France and large regions show values above 0.3. Probabilistic scores for tercile categories show some potential of predictability. Brier skill scores averaged over France reach and 0.02 for the upper and lower terciles, respectively. This can be compared with lower corresponding Brier s skill scores (BSS) values ( 0.23 and 0.27) for downscaled precipitation forecasts. The reliability charts are all closer to the diagonal than that for downscaled precipitation, showing a higher reliability of probabilistic forecasts (not shown). In addition, the Relative Operating Characteristic (ROC) curves are reasonably well shaped with ROC scores close to 0.7 (0.5 for the climatology), i.e. significantly greater than those obtained for atmospheric forcings River flow forecasts The predictability of river flows was assessed on four catchments with diverse hydrological regimes (Figure 3). The Durance at Embrun and the Ariège at Foix are quite small catchments located in mountainous areas and consequently sensitive to snow melting during the MAM period. The Seine at Paris and the Garonne at Tonneins are large catchments, the aquifers being explicitly simulated by Modcou in the Seine river catchment. Ariège river flow forecasts are of the same magnitude as in the SIM reanalysis (see Figure 4), and the spread of the ensemble forecast is satisfactory. The mean bias is rather low and a large part of the interannual variability is well captured. Similar observations can be made for the other catchments, with the exception of an overestimation of low-flow values for the Seine river. The mean bias is within the range of 2% (Ariège) to 20% (Durance). Bias-sensitive Nash Sutcliffe scores (Table I) are positive or close to 0 for all catchments, meaning that the forecasts outperform the climatological strategy even when the bias becomes noticeable (e.g. for the Durance river). In addition, correlation coefficients between SIM reanalysis and forecasted river flows show some potential for flow forecasting, with correlation coefficients as high as 0.7 for the Ariège river (see Table I). The robustness of these results has been assessed through a cross-validation method using a 5-year moving window. ROC curves (not shown) and BSS for extreme terciles (see Table I) are all indicating that the probabilistic forecast is better than the climatology reference without any calibration. In order to have an insight into more extreme categories than the terciles, we considered categories close

4 Hydrological seasonal forecast over France 81 Figure 3. Location of the four studied river catchments. Figure 4. Time series of the 3-month average (March April May) river flow (m 3 /s) of the Ariège at Foix. In red the ensemble mean, in green the individual members and in blue the SIM reference. Table I. Nash Sutcliffe score, correlation coefficient (calibration/cross-validation) and Brier skill score for the upper and lower terciles for the four river catchments. The calibration period is and the window width is 5 years for the cross-validation. Nash Sutcliffe score Correlation coefficient BSS Upper tercile BSS lower tercile The Durance at / Embrun The Ariège at Foix / The Seine at Paris / The Garonne at Tonneins / to 20% of the observations and calibrated the forecasts using a linear discriminant analysis (LDA; Wilks, 2006). We found a reasonably high predictability with a good stability of LDA models between forecasted and reference river flows (not shown) but with sometimes high-false alarm rates. Thus, a first insight into monthly scores seems to highlight some intraseasonal predictability to be further investigated. We also tested a simpler benchmark method by building regressions between forecasted catchment rainfall and corresponding SIM reanalysis river flow. The quality of such an approach is very poor (correlation coefficient from 0.01 for the Durance river up to 0.24 for the Garonne river) and relationships are not stable in cross-validation, in accordance with the low predictability of precipitation over France. This highlights the interest of using a physically based forecasting suite (including a SVAT approach and a hydrological model) compared with a direct and simple regression method.

5 82 J.-P. Céron et al. 4. Conclusions and perspectives This study has first demonstrated the feasibility of hydrological seasonal forecasts in France by forcing the hydrometeorological model SIM with seasonal atmospheric forecasts from the DEMETER project. Despite the very simple downscaling method adopted, this study has also identified promising abilities of this system in forecasting hydrological variables, such as soil moisture and river flows. Correlations and probabilistic scores are indeed better for hydrological variables than for atmospheric variables, showing a higher predictability of the hydrological system as a result of the slow evolution of both soil moisture and snowpack. An important work will now focus on assessing the uncertainties of the forecasting system. Following results from the DEMETER project, the implementation of a multimodel approach should improve the robustness of forecasts. A first test led to similar scores using atmospheric forecasts from the ECMWF model included in the DEMETER database. It will also be beneficial to sample sources of uncertainties such as initial hydrological conditions provided to ISBA and MODCOU, namely snowpack, soil water content and water table levels. Such an analysis would be facilitated by the use of the 50-year SIM reanalysis and should document the impact of each component of the hydrological cycle in the overall predictability. It is for example expected that the predictability of flows in snow-fed rivers mainly comes from the snowpack volume at the start of the season. The uncertainty related to the hydrological model formulation can also be investigated. In addition, one can expect some improvement of atmospheric scores from using advanced downscaling methods (e.g. circulation regimes) and from adjusting the snowfall/rainfall discrimination threshold. Lastly, river flow forecasts could also be compared with observed flows and to outputs from other forecasting techniques already in use in order to demonstrate the actual additional information brought by this forecasting suite. The experiments mentioned earlier should constitute a roadmap for a better understanding of the seasonal predictability of the French hydrological system. From an operational point of view, results from the forecasting suite would hopefully provide relevant long-lead information for water resources managers (e.g. maps of probability of being above or below agreed thresholds), in line with what is already supplied for water resources monitoring. References Durand Y, Brun E, Merindol L, Guyomarc h G, Lesaffre B, Martin E A meteorological estimation of relevant parameters for snow schemes used in atmospheric models. 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