287 research outputs found
A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers
The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes
A Past Discharges Assimilation System for Ensemble Streamflow Forecasts over France - Part 1: Description and Validation of the Assimilation System
Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using streamflow observations in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.JRC.H.7-Climate Risk Managemen
A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system
International audienceTwo Ensemble Streamflow Prediction Systems (ESPSs) have been set up at M´et´eo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using streamflow observations in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations
A Past Discharge Assimilation System for Ensemble Streamflow Forecasts over France - Part 2: Impact on the Ensemble Streamflow Forecasts
The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.JRC.H.7-Climate Risk Managemen
Technical note: Pitfalls in using log-transformed flows within the KGE criterion
Log-transformed discharge is often used to calculate performance criteria to
better focus on low flows. This prior transformation limits the
heteroscedasticity of model residuals and was largely applied in criteria
based on squared residuals, like the Nash–Sutcliffe efficiency (NSE). In the
recent years, NSE has been shown to have mathematical limitations and the
Kling–Gupta efficiency (KGE) was proposed as an alternative to provide more
balance between the expected qualities of a model (namely representing the
water balance, flow variability and correlation). As in the case of NSE,
several authors used the KGE criterion (or its improved version KGE′) with a prior
logarithmic transformation on flows. However, we show that the use of this
transformation is not adapted to the case of the KGE (or KGE′) criterion and
may lead to several numerical issues, potentially resulting in a biased
evaluation of model performance. We present the theoretical underpinning
aspects of these issues and concrete modelling examples, showing that KGE′
computed on log-transformed flows should be avoided. Alternatives are
discussed.</p
A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts
International audienceThe use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBAMODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at M´et´eo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of M´et´eo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones
A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment
The accuracy of hydrological predictions in
snow-dominated regions deeply depends on the quality of the snowpack
simulations, with dynamics that strongly affect the local
hydrological regime, especially during the melting period. With the aim of
reducing the modelling uncertainty, data assimilation techniques are
increasingly being implemented for operational purposes. This study aims to
investigate the performance of a multivariate sequential importance
resampling – particle filter scheme, designed to jointly assimilate several
ground-based snow observations. The system, which relies on a multilayer
energy-balance snow model, has been tested at three Alpine sites: Col de
Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The
implementation of a multivariate data assimilation scheme faces several
challenging issues, which are here addressed and extensively discussed:
(1)Â the effectiveness of the perturbation of the meteorological forcing data
in preventing the sample impoverishment; (2) the impact of the parameter
perturbation on the filter updating of the snowpack state; the system
sensitivity to (3) the frequency of the assimilated observations, and (4) the
ensemble size.The perturbation of the meteorological forcing data generally turns out to be
insufficient for preventing the sample impoverishment of the particle sample,
which is highly limited when jointly perturbating key model parameters. However, the
parameter perturbation sharpens the system sensitivity to
the frequency of the assimilated observations, which can be successfully
relaxed by introducing indirectly estimated information on snow-mass-related
variables. The ensemble size is found not to greatly impact the filter
performance in this point-scale application.</p
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