THE<i>CEQUEAU</i>MODEL: DESCRIPTION AND EXAMPLES OF ITS USE IN PROBLEMS RELATED TO WATER RESOURCE MANAGEMENT / Le modèle CEQUEAU: description et exemples d'utilisation dans le cadre de problèmes reliés à l'aménagement

Charbonneau, R.; Fortin, Jean‐Pierre; Morin, Guy

doi:10.1080/02626667709491704

Cited by 34 publications

(10 citation statements)

References 1 publication

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“…For example, Abaza et al (2015) assimilated streamflow at the outlet using the EnKF to update two state variables (soil moisture in the intermediate and deep layers of the hydrological model used in their study) using a time step of 3 h. The resulting gain in CRPS was high for the first time step and decreased quickly as a function of the forecast horizon such that mainly the first 24 h benefited from the data assimilation. Chen et al (2013) found similar results with multiple performance criteria when assimilating streamflow using a variant of the EnKF (the ensemble square root filter) to update various state variables represented by conceptual reservoirs. The observed improvement duration was even shorter, lasting less than 12 h during flash flood events.…”

Section: Streamflow Data Assimilationsupporting

confidence: 54%

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Bergeron¹,

Trudel²,

Leconte³

2016

Preprint

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Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the Ensemble Kalman Filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British-Columbia, Canada. Synthetic data includes daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than its individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

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Section: Streamflow Data Assimilationsupporting

confidence: 54%

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Bergeron¹,

Trudel²,

Leconte³

2016

Preprint

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“…CEQUEAU is a grid-based distributed hydrologic model with a modular structure allowing to select snowmelt and evapotranspiration formulations to compute runoff availability (Charbonneau et al, 1977). The flow routing is based on a cell-to-cell approach that is calibrated using time delays from one cell to the next.…”

Section: Hydrologic Model Cequeaumentioning

confidence: 99%

Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting

Mai

Arsenault

Tolson

et al. 2020

Water Resources Research

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Streamflow forecasting is essential in many applications, including the management of hydropower reservoirs and of flood risks. To increase the performance of these hydrologic forecasts, a hydrological model is typically updated with optimal initial model states. These states are usually selected a priori and adjusted to obtain the optimal initial setup. The model states to adjust are generally selected by experts with deep knowledge of the model's behavior. These relationships are rarely documented and formalized, leading to difficulties in reproducibility and transferability to other models. The method here provides a fully automatized system to find the model states based on the parameter-free sensitivity method of efficient elementary effects (EEE), which identifies the most informative variables of the hydrologic model CEQUEAU. The analysis is applied to 1,826 model setups for four Canadian basins. The identified states are formalized using clustering techniques to obtain adjustment "recipes" conditioned on given meteorological conditions. Several recipes are tested and compared to expert-based recipes. The recipes are tested according to their ability to (a) obtain optimal initial states and (b) their forecast performance. The results show that (1) the EEE-based recipes perform similarly or better than the expert-based recipes in both objectives; (2) the recipes are similar across the four basins, highlighting their potential of spatial transferability; and (3) the adjustment of hydrologic model inputs significantly decreases forecast performance. The proposed approach to use EEE-based recipes to obtain optimal initial states for forecasting is reproducible, objective, and suitable to be applied to other models and basins.

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“…1) The hydrological model CEQUEAU (Charbonneau et al, 1977) is calibrated on each of the five sub-basins using the Dynamically Dimensioned Search algorithm (DDS) (Tolson and Shoemaker, 2007) according to the procedure in Arsenault et al (2014). CEQUEAU is a grid-based distributed model which is set up on a 10km resolution grid on the SLSJ basin.…”

Section: Preparation Of Esp Forecastsmentioning

confidence: 99%

Analysis of the effects of biases in ESP forecasts on electricity production in hydropower reservoir management

Arsenault

Côté

2018

Preprint

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Abstract. This paper presents an analysis of the effects of biased Extended Streamflow Prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon testbed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data was fed to the hydrological model and the synthetic streamflow thus generated was considered as a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method’s response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test-bed in which the entire system is precisely and accurately modeled. The ensemble climate data forecasts, the hydrological modeling and ESP forecast generation, optimization model and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focused on one hydropower system both with and without minimum base load constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore increases the odds of forced spillage by attempting to maximize short-term profit by keeping a higher net head. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider optimal trajectories.

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THECEQUEAUMODEL: DESCRIPTION AND EXAMPLES OF ITS USE IN PROBLEMS RELATED TO WATER RESOURCE MANAGEMENT / Le modèle CEQUEAU: description et exemples d'utilisation dans le cadre de problèmes reliés à l'aménagement

Cited by 34 publications

References 1 publication

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting

Analysis of the effects of biases in ESP forecasts on electricity production in hydropower reservoir management

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