A vision for hydrological prediction

IMproving PRedictions and management of hydrological EXtremes (IMPREX) was a European Union Horizon 2020 project that ran from September 2015 to September 2019. Its aim was to improve society’s ability to anticipate and respond to future extreme hydrological events in Europe across a variety of uses in the water-related sectors (flood forecasting, drought risk assessment, agriculture, navigation, hydropower, and water supply utilities). Through the engagement with stakeholders and continuous feedback between model outputs and water applications, progress was achieved in better understanding the way hydrological predictions can be useful to (and operationally incorporated into) problem solving in the water sector. The work and discussions carried out during the project nurtured further reflections towards a common vision for hydrological prediction. In this article, we summarize the main findings of the IMPREX project within a broader overview of hydrological prediction, providing a vision for improving such predictions. In so doing, we firstly present a synopsis of hydrological and weather forecasting, with a focus on medium-range to seasonal scales of prediction for increased preparedness. Second, the lessons learnt from IMPREX are discussed. The key findings are the gaps highlighted in the global observing system of the hydrological cycle, the degree of accuracy of hydrological models and the techniques of post-processing to correct biases, the origin of seasonal hydrological skill in Europe, and user requirements of hydrometeorological forecasts to ensure their appropriate use in decision-making models and practices. Lastly, a vision for how to improve these forecast systems/products in the future is expounded and these include advancing numerical weather and hydrological models, improved earth monitoring, and more frequent interaction between forecasters and users to tailor the forecasts to applications. We conclude that if these improvements can be implemented in the coming years, earth system and hydrological modelling will become more skilful, thus leading to socioeconomic benefits for the citizens of Europe and beyond

Prévision saisonnière des débits pour la gestion de réservoirs

(trad auto)The objective of this thesis is to advance knowledge on seasonal forecasting for the management of multi-use reservoirs. In the first part, the work assessed the quality of seasonal rainfall and flow forecasts in sixteen French basins. New flow forecasting methods have been proposed and tested in these basins, particularly for low-flow forecasting. A second part is devoted to seasonal flow forecasts for reservoir management. A tool for assessing the risks of water shortage in the Arzal reservoir in Brittany has been developed and the role of seasonal forecasts in managing reservoirs in a risk context has been analysed.L'objectif de cette thèse est de faire progresser les connaissances sur la prévision saisonnière pour la gestion de réservoirs multi-usage. Dans un premier volet, les travaux ont évalué la qualité des prévisions saisonnières de pluies et de débits dans seize bassins français. De nouvelles méthodes de prévision des débits ont été proposées et testées dans ces bassins, notamment pour la prévision des étiages. Un deuxième volet est consacré aux prévisions saisonnières de débits pour la gestion de réservoirs. Un outil d'évaluation des risques de pénurie d'eau dans le réservoir d'Arzal, en Bretagne, a été développé et le rôle des prévisions saisonnières dans la gestion de réservoirs en contexte de risque a été analysé

Catchment memory of climate anomalies

This study presents a new approach to quantify multi-year hydrological memory, using exclusively streamflow and climate data. The rainfall-runoff relationship is analysed through the concept of elasticity. The anomaly in runoff yield is predicted using the anomaly in humidity index of the same year and, if adding value, those of previous years. To describe the rate at which the impact of these climate anomalies fades with time, we define Catchment Forgetting Curves (CFCs), which we parameterise using a Gamma distribution. We identify the CFCs of two OZCAR observatories, and we compare them to those of a set of 500 French catchments. As expected, catchments with significant multi-year memory are groundwater-dominated in France. Interestingly, the aridity index appears to be one of the main drivers of catchment memory, with longer memory generally observed in dry hydroclimatic regions. Catchment area, often pointed out as a driver of catchment memory in the literature, does not seem to play a first-order role. Additionally, we show that the reliability of the assessment of elasticity indices is improved when accounting for catchment memory with a much more spatially coherent organisation of the elasticity indices. The elasticity indices were also well related to aridity, with humid catchments showing lower elasticity

Can Continental Models Convey Useful Seasonal Hydrologic Information at the Catchment Scale?

International audienceThe development and availability of climate forecasting systems have allowed theimplementation of seasonal hydroclimatic services at the continental scale. User guidance and quality ofthe forecast information are key components to ensure user engagement and service uptake, yet forecastquality depends on the hydrologic model setup. Here, we address how seasonal forecasts from continentalservices can be used to address user needs at the catchment scale. We compare a continentally calibratedprocessbased model (EHYPE) and a catchmentspecic parsimonious model (GR6J) to forecast streamowin a set of French catchments. Results show that despite expected high performance from the catchmentsetup against observed streamow, the continental setup can, in some catchments, match or evenoutperform the catchmentspecic setup for 3month aggregations and threshold exceedance. Forecastsystems can become comparable when looking at statistics relative to model climatology, such as anomalies,and adequate initial conditions are the main source of skill in both systems. We highlight the need forconsistency in data used in modeling chains and in tailoring service outputs for use at the catchment scale.Finally, we show that the spread in internal model states varies largely between the two systems, reectingthe differences in their setups and calibration strategies, and highlighting that caution is needed beforeextracting hydrologic variables other than streamow. We overall argue that continental hydroclimaticservices show potential on addressing needs at the catchment scale, yet guidance is needed to extract, tailorand use the information provide

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

International audienceMeteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability

Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam

The recent advances in the skill of hydroclimatic services are motivating the need for quantifying their value in informing decisions. State-of-the-art forecasts proved to be skillful over seasonal and longer time scales especially in regions where climate teleconnections, such as El Nino Southern Oscillation, or particular hydrological characteristics, such as snow- and/or baseflow-dominance, enable predictability over such long lead times. Recent studies have investigated the value of seasonal streamflow forecasts in informing the operations of water systems in order to improve reservoir management strategies. However, how to best inform the operations of hydropower systems is still an open question because hydropower reservoir operations benefit from hydroclimatic services over a broad range of time scales, from short-term to seasonal and decadal time horizons, for combining daily and sub-daily operational decisions with strategic planning on the medium- to long- term. In this work, we propose a machine-learning based framework to quantify the value of hydroclimatic services as their contribution to increasing the hydropower production of the Grand Ethiopian Renaissance Dam (GERD) in Ethiopia. The GERD, with an installed capacity of more than 6,000 MW is considered the largest hydroelectric power plant in Africa and the seventh largest in the world. Its construction is part of the strategic hydropower development plan in Ethiopia that aims to serve the growing domestic and foreign electricity demands. The quantification of the forecasts value relies on the Information Selection Assessment framework, which is applied to a service based on bias adjusted ECMWF SEAS5 seasonal forecasts used as input to the World-wide HYPE hydrological model. First, we evaluate the expected value of perfect information as the potential maximum improvement of a baseline operating policy relying on a basic information with respect to an ideal operating policy designed under the assumption of perfect knowledge of future conditions. Second, we select the most informative lead times of inflow forecast by employing input variable selection techniques, namely the Iterative Input Selection algorithm. Finally, we assess the expected value of sample Information as the performance improvement that could be achieved when the inflow forecast for the selected lead time is used to inform operational decisions. In addition, we analyze the potential value of forecast information under different future climate scenarios. Preliminary results show that the maximum space for increasing the hydropower production of the GERD baseline operations not informed by any forecast is relatively small. This potential gain becomes larger when we focus on the performance during the heavy rainy season from June to September (Kiremt season), making room for the uptake of forecast information. The added production obtained with the forecast-informed operations of the GERD may represent an additional option in the current negotiations about the dam impacts on the downstream countries

Prévision des débits à longue échéance: Quel apport des services large échelle ? Quelles pistes d’amélioration aux échelles locales ?

International audienc

What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?

Recent technological advances in representation of processes in numerical climate models have led to skillful predictions, which can consequently increase the confidence of hydrological predictions and usability of hydroclimatic services. Given that many water-related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability. Here we analyze the seasonal forecasts of streamflow volumes across about 35,400 basins in Europe, which lie along a strong gradient in terms of climatology, scale, and hydrological regime. We then link the seasonal volumetric errors to various physiographic-hydroclimatic descriptors and meteorological biases in order to identify the key drivers controlling predictability. Streamflow volumes over Europe are well predicted, yet with some geographic and seasonal variability; however, the predictability deteriorates with increasing lead time particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of descriptors, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin's hydrological regime, with limited predictability in relatively flashy basins. On the contrary, snow and/or baseflow dominated regions with long recessions show high streamflow predictability. Finally, climatology and precipitation forecast biases are also related to streamflow predictability, highlighting the importance of developing robust bias adjustment methods. Overall, this investigation shows that the seasonal streamflow predictability can be clustered, and hence regionalized, based on a priori knowledge of local hydroclimatic conditions