Predictability of soil moisture and river flows over France for the spring season

Singla, S.; Céron, J.-P.; Martin, E.; Regimbeau, Fabienne; Déqué, Michel; Habets, Florence; Vidal, Jean‐Philippe

doi:10.5194/hessd-8-7947-2011

Cited by 4 publications

(6 citation statements)

References 41 publications

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“…This could be due to a persistence of the skill from the previous winter through the land surface memory (i.e. groundwaterdriven streamflow or snowmelt-driven streamflow), as highlighted by Bierkens and van Beek (2009) Singla et al (2012) for parts of France, Lorenzo-Lacruz et al (2011) for the Iberian Peninsula and Meißner et al (2017) for the Rhine. Moreover, it could be that most of the gained predictability occurs in March, a transition month between the more predictable winter (as mentioned above) and spring, as discussed by Steirou et al (2017).…”

Section: Discussionmentioning

confidence: 99%

“…While climate-model-based seasonal streamflow forecasting experiments are more common outside of Europe, for example for the United States (Wood et al, 2002(Wood et al, , 2005Mo and Lettenmaier, 2014), Australia (Bennett et al, 2016), or Africa , they remain limited in Europe, with a few examples in France (Céron et al, 2010;Singla et al, 2012;Crochemore et al, 2016), in central Europe (Demirel et al, 2015;Meißner et al, 2017), in the United Kingdom Prudhomme et al, 2017) and at the global scale (Yuan et al, 2015a;Candogan Yossef et al, 2017). This is because, although the quality of seasonal climate forecasts has increased over the past decades, there remains limited skill in seasonal climate forecasts for the extra-tropics, particularly for the variables of interest for hydrology, notably precipitation and temperature (Arribas et al, 2010;DoblasReyes et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Skilful seasonal forecasts of streamflow over Europe?

Arnal

Cloke

Stephens

et al. 2018

Hydrol. Earth Syst. Sci.

113

104

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Abstract. This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Skilful seasonal forecasts of streamflow over Europe?

Arnal

Cloke

Stephens

et al. 2018

Hydrol. Earth Syst. Sci.

113

104

View full text Add to dashboard Cite

show abstract

“…One common problem encountered in hydrologic forecasting studies is the requirement of obtaining adequate initial state variables that represent the hydrologic state of the catchment at the time of forecast. To eliminate the initial mismatch between observed and simulated hydrologic conditions (flow and state variables), many studies implement a pseudo‐observed discharge that is typically a hydrologic model simulation produced by forcing the model with observed meteorological data (Bierkens & van Beek, 2009; Greuell et al, 2018; Shukla & Lettenmaier, 2011; Shukla et al, 2014; Singla et al, 2012). By doing so, the pseudo‐observations are always perfectly matched by the hydrologic model as they stem from the same source.…”

Section: Models and Datamentioning

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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“…Similar to ESP, the CM‐SHF also heavily relies on the ICs, i.e., the anomalies from initial soil moisture and snow etc. Unlike quantifying the contribution of ICs to seasonal climate predictability using land–atmosphere‐coupled modeling approaches, the contribution of ICs to seasonal hydrologic predictability is often investigated using hydrologic models in a standalone mode . One of the theoretical frameworks to separate the contributions of ICs and atmospheric boundary forcings is called reverse ESP (revESP) .…”

Section: Sources Of Seasonal Hydrologic Predictabilitymentioning

confidence: 99%

“…As an important land surface storage term, snow has long been recognized as a major source of seasonal hydrologic predictability, particularly over high altitude and high latitude regions, where its impact can last for 3–6 months especially during cold seasons . Because of the strong control from snow, the climate forecasts of temperature could be as important as precipitation for streamflow forecast in high‐latitude snow‐fed river basins through the temperature–snow melt relationship .…”

Section: Sources Of Seasonal Hydrologic Predictabilitymentioning

confidence: 99%

A review on climate‐model‐based seasonal hydrologic forecasting: physical understanding and system development

2015

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Climate-model-based seasonal hydrologic forecasting (CM-SHF) is an emerging area in recent decade because of the development of coupled atmosphere-ocean-land general circulation models (CGCMs) and land surface hydrologic models, and increasing needs for transferring the advances in climate research into hydrologic applications within the framework of climate services. In order to forecast terrestrial hydrology from monthly to seasonal time scales, a CM-SHF system should take advantage of important information from initial land surface conditions (ICs) as well as skillful seasonal predictions of atmospheric boundary conditions that mostly rely on the predictability of large-scale climate precursors such as the El Niño Southern Oscillation (ENSO). The progresses in the understanding of seasonal hydrologic predictability in terms of ICs and climate precursors are reviewed, and future emphases are discussed. Both the achievements and challenges of the CM-SHF system development, including multimodel ensemble prediction, seamless hydrologic forecasting, dynamical downscaling, hydrologic post-processing, and seasonal forecasting of hydrologic extremes with the hyper-resolution modeling framework that is able to address both the climate change and water resources management impacts on terrestrial hydrology, are presented. Regardless of great strides in CM-SHF, a grand challenge is the effective dissemination of the information provided by the seasonal hydrologic forecasting system to the decision-makers, which cannot be resolved without cross-disciplinary dialog and collaboration. © 2015 The Authors. WIREs Water published by Wiley Periodicals, Inc. How to cite this article:WIREs Water 2015Water , 2:523-536. doi: 10.1002Water /wat2.1088 INTRODUCTION G lobal change is influencing the frequency and severity of hydrologic extremes, including floods and droughts, 1 resulting in a number of issues with * Correspondence to: yuanxing@tea.ac.cn respect to food and water security. While decadal plans for infrastructure adaptation and capacity building are important for managing water resources under a changing climate, timely early (seasonal) warning, or so-called seasonal hydrologic forecasting (SHF), is essential for hydrologic hazard mitigation by increasing preparedness. Basically, the aim of SHF is to predict the land surface hydrologic variables (e.g., streamflow, soil moisture) at monthly to seasonal time scales. It is also named as long-term hydrologic forecasting in the hydrologic community because it is targeted for the forecasting of persistent land surface hydrologic This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. anomalies (e.g., drought), 2 which is different from short-term flood forecasting. 3 A successful SHF not only requires accurate initial land surface conditions (ICs) from upstream river flow, 4 snow c...

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Predictability of soil moisture and river flows over France for the spring season

Cited by 4 publications

References 41 publications

Skilful seasonal forecasts of streamflow over Europe?

Skilful seasonal forecasts of streamflow over Europe?

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

A review on climate‐model‐based seasonal hydrologic forecasting: physical understanding and system development

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