In the latest performance report, we assess the effectiveness of BWI’s discharge forecast model for the Bourbre River, specifically at the Vernay outlet, which covers an area of 313 km². The drainage basin of the Bourbre river, located in southeastern France, is characterized by hills, plateaus overlooking valleys, and extensive marshes. The climate in the region is temperate, with both mountainous and continental influences due to its proximity to the Alps and the Rhône Valley. The model demonstrates strong performance on the testing dataset (NNSE = 0.73), accurately simulating baseflow. The underestimation of river discharge during summer months reflects the limited resolution of the available meteorological data.
Most countries worldwide have been experiencing the degradation of one or more types of freshwater ecosystems. As a result, effective water management, which has been off-track for many nations, is becoming increasingly essential. At the current pace of implementing integrated water management practices, the world is projected to achieve sustainable water management by 2049 (UNEP, 2024). Consequently, sustainable water management serves as a foundational element of our hydrological forecasting services.
This performance report explores the hydrological regime of the Bourbre River, which is moderately stable but has been experiencing summer floods. The following sections examine the performance of BWI’s semi-distributed hydrological model, which employs machine learning techniques to provide real-time estimates and forecasts of river discharge. Specifically, we focus on the Bourbre River at Vernay, situated in southeastern France.
The Bourbre River is located at the intersection of Grenoble, Chambéry, and the plains of eastern Lyon. It drains an area of 750 km² in the northern part of the Isère department, with elevations ranging between 200 m and 770 m. It originates in the commune of Burcin and is a tributary of the Rhône, joining it at Chavanoz. It has three main tributaries on its left bank: l’Hien, l’Agny, and le Bion. On the right bank, the Bourbre also receives water from the Canal du Catelan, a man-made outfall draining a vast marshy plain. The river basin is bounded to the north by the Crémieu plateau and to the south by the Fure basin, Étang du Grand Lemps, and the Bièvre Valloire (EpagebourbeBourbre, 2024).
Successive geological events have shaped this territory, characterized by hills, plateaus overlooking valleys, and extensive marshes. This unique geographical context has fostered rapidly expanding demographic and socio-economic dynamics, currently managed by the Epage Bourbre River Union.
In hydrological modeling, the components of performance metrics like the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) provide crucial insights into various aspects of discharge prediction. BWI considers both NSE and KGE for model performance evaluation and deploys the Normalized NSE (NNSE) to communicate the model performance evaluation with stakeholders.
NSE primarily assesses the overall agreement between observed and simulated discharge. It does not have specific components like KGE, but it indirectly provides information on the peak flow accuracy, overall variability, and bias (indirectly) of the discharges produced by the hydrological model at the catchment outlet. It ranges from −∞ to 1 and evaluates how well the simulated data matches the observed data. NNSE adjusts the NSE value to make it more comparable across different catchments or periods by standardizing to provide indices expressed between 0 and 1.
NSE is particularly sensitive to peak flows. It emphasizes how well the model captures high discharge events, such as floods. A high NSE value indicates that the model is accurately predicting peak discharges, reflecting the catchment’s response to extreme rainfall.
By comparing the squared differences between observed and simulated discharges to the variability in observed discharges, NSE indicates how well the model captures the overall variability of flows in the catchment.
Although NSE does not explicitly separate bias, a low NSE score could indicate that the model is consistently underestimating or overestimating the flow. It suggests that the simulated and observed discharges differ significantly, often due to bias in the model.
In a hydrological model, NNSE values below 0.5 indicate poor performance. Values above 0.67 suggest that baseflow correlates well with observations and that the timing of flood events is generally well-identified. Models with an NNSE of 0.67 or higher are deployed for 10-day discharge forecasts. Recognizing that NNSE is not an absolute performance metric, if other performance metrics (e.g., KGE) show good agreement, we accept NNSE starting from 0.65. When NNSE exceeds 0.85, the model demonstrates good agreement in the timing and magnitude of peak flows during extreme flood events. An NNSE value of 1 represents a perfect model.
The performance of “La Bourbre – Vernay” model is displayed in the figure below. The catchment map of the outlet is showcased on the left and the graphical comparison between observed discharges (in gold) and simulated discharges (in green) is shown on the right.
The model demonstrates high overall performance at the selected outlet, with NNSE 0.73 during the testing period. The well-simulated baseflow and flood detection, which could be enhanced with higher resolution of meteorological data, can prove valuable for water resource management, early warning systems, and planning of critical flood prevention infrastructures. As the model incorporates more detailed meteorological data, its ability to forecast and identify flood events will improve, addressing gaps such as those observed between May and September 2024, as illustrated in the performance report above.
Epage de la Bourbre (2024) ”Le bassin versant”. Available at: https://epagebourbre.fr/fr/rb/483199/le-bassin-versant (Accessed 30 October 2024).
UNEP (2024) “Half the world’s countries have degraded freshwater systems, UN finds”. Available at: https://www.unep.org/news-and-stories/press-release/half-worlds-countries-have-degraded-freshwater-systems-un-finds (Accessed 30 October 2024).
