Articles | Volume 24, issue 4
https://doi.org/10.5194/hess-24-2017-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-2017-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Apr 2020
Research article |
| 23 Apr 2020
| 23 Apr 2020
A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context
Lionel Berthet
CORRESPONDING AUTHOR
DREAL Centre-Val de Loire, Loire Cher & Indre Flood Forecasting Service, Orléans, France
François Bourgin
GERS-LEE, Univ Gustave Eiffel, IFSTTAR, 44344
Bouguenais, France
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Charles Perrin
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Julie Viatgé
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Renaud Marty
DREAL Centre-Val de Loire, Loire Cher & Indre Flood Forecasting Service, Orléans, France
Olivier Piotte
Ministry for the Ecological and Inclusive Transition, SCHAPI, Toulouse, France
Related authors
No articles found.
Taha-Abderrahman El Ouahabi, François Bourgin, Charles Perrin, and Vazken Andréassian
EGUsphere, https://doi.org/10.5194/egusphere-2025-3586, https://doi.org/10.5194/egusphere-2025-3586, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
To improve hydrological uncertainty estimation, recent studies have explored machine learning (ML)-based post-processing approaches. Among these, quantile random forests (QRF) are increasingly used for their balance between interpretability and performance. We develop a hydrologically informed QRF trained in a multi-site setting. Our results show that the regional QRF approach is beneficial, particularly in catchments where local information is insufficient.
Eric Sauquet, Guillaume Evin, Sonia Siauve, Ryma Aissat, Patrick Arnaud, Maud Bérel, Jérémie Bonneau, Flora Branger, Yvan Caballero, François Colléoni, Agnès Ducharne, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Benoît Hingray, Peng Huang, Tristan Jaouen, Alexis Jeantet, Sandra Lanini, Matthieu Le Lay, Claire Magand, Louise Mimeau, Céline Monteil, Simon Munier, Charles Perrin, Olivier Robelin, Fabienne Rousset, Jean-Michel Soubeyroux, Laurent Strohmenger, Guillaume Thirel, Flore Tocquer, Yves Tramblay, Jean-Pierre Vergnes, and Jean-Philippe Vidal
EGUsphere, https://doi.org/10.5194/egusphere-2025-1788, https://doi.org/10.5194/egusphere-2025-1788, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
The Explore2 project has provided an unprecedented set of hydrological projections in terms of the number of hydrological models used and the spatial and temporal resolution. The results have been made available through various media. Under the high-emission scenario, the hydrological models mostly agree on the decrease in seasonal flows in the south of France, confirming its hotspot status, and on the decrease in summer flows throughout France, with the exception of the northern part of France.
Olivier Delaigue, Guilherme Mendoza Guimarães, Pierre Brigode, Benoît Génot, Charles Perrin, Jean-Michel Soubeyroux, Bruno Janet, Nans Addor, and Vazken Andréassian
Earth Syst. Sci. Data, 17, 1461–1479, https://doi.org/10.5194/essd-17-1461-2025, https://doi.org/10.5194/essd-17-1461-2025, 2025
Short summary
Short summary
This dataset covers 654 rivers all flowing in France. The provided time series and catchment attributes will be of interest to those modelers wishing to analyze hydrological behavior and perform model assessments.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 29, 683–700, https://doi.org/10.5194/hess-29-683-2025, https://doi.org/10.5194/hess-29-683-2025, 2025
Short summary
Short summary
This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
Short summary
Short summary
We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
Short summary
Short summary
The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Ralph Bathelemy, Pierre Brigode, Vazken Andréassian, Charles Perrin, Vincent Moron, Cédric Gaucherel, Emmanuel Tric, and Dominique Boisson
Earth Syst. Sci. Data, 16, 2073–2098, https://doi.org/10.5194/essd-16-2073-2024, https://doi.org/10.5194/essd-16-2073-2024, 2024
Short summary
Short summary
The aim of this work is to provide the first hydroclimatic database for Haiti, a Caribbean country particularly vulnerable to meteorological and hydrological hazards. The resulting database, named Simbi, provides hydroclimatic time series for around 150 stations and 24 catchment areas.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
Short summary
Short summary
Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Laurent Strohmenger, Eric Sauquet, Claire Bernard, Jérémie Bonneau, Flora Branger, Amélie Bresson, Pierre Brigode, Rémy Buzier, Olivier Delaigue, Alexandre Devers, Guillaume Evin, Maïté Fournier, Shu-Chen Hsu, Sandra Lanini, Alban de Lavenne, Thibault Lemaitre-Basset, Claire Magand, Guilherme Mendoza Guimarães, Max Mentha, Simon Munier, Charles Perrin, Tristan Podechard, Léo Rouchy, Malak Sadki, Myriam Soutif-Bellenger, François Tilmant, Yves Tramblay, Anne-Lise Véron, Jean-Philippe Vidal, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023, https://doi.org/10.5194/hess-27-3375-2023, 2023
Short summary
Short summary
We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
Pierre Nicolle, Vazken Andréassian, Paul Royer-Gaspard, Charles Perrin, Guillaume Thirel, Laurent Coron, and Léonard Santos
Hydrol. Earth Syst. Sci., 25, 5013–5027, https://doi.org/10.5194/hess-25-5013-2021, https://doi.org/10.5194/hess-25-5013-2021, 2021
Short summary
Short summary
In this note, a new method (RAT) is proposed to assess the robustness of hydrological models. The RAT method is particularly interesting because it does not require multiple calibrations (it is therefore applicable to uncalibrated models), and it can be used to determine whether a hydrological model may be safely used for climate change impact studies. Success at the robustness assessment test is a necessary (but not sufficient) condition of model robustness.
Nabil Hocini, Olivier Payrastre, François Bourgin, Eric Gaume, Philippe Davy, Dimitri Lague, Lea Poinsignon, and Frederic Pons
Hydrol. Earth Syst. Sci., 25, 2979–2995, https://doi.org/10.5194/hess-25-2979-2021, https://doi.org/10.5194/hess-25-2979-2021, 2021
Short summary
Short summary
Efficient flood mapping methods are needed for large-scale, comprehensive identification of flash flood inundation hazards caused by small upstream rivers. An evaluation of three automated mapping approaches of increasing complexity, i.e., a digital terrain model (DTM) filling and two 1D–2D hydrodynamic approaches, is presented based on three major flash floods in southeastern France. The results illustrate some limits of the DTM filling method and the value of using a 2D hydrodynamic approach.
Pierre Nicolle, François Besson, Olivier Delaigue, Pierre Etchevers, Didier François, Matthieu Le Lay, Charles Perrin, Fabienne Rousset, Dominique Thiéry, François Tilmant, Claire Magand, Timothée Leurent, and Élise Jacob
Proc. IAHS, 383, 381–389, https://doi.org/10.5194/piahs-383-381-2020, https://doi.org/10.5194/piahs-383-381-2020, 2020
Léonard Santos, Guillaume Thirel, and Charles Perrin
Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, https://doi.org/10.5194/hess-22-4583-2018, 2018
Short summary
Short summary
The Kling and Gupta efficiency (KGE) is a score used in hydrology to evaluate flow simulation compared to observations. In order to force the evaluation on the low flows, some authors used the log-transformed flow to calculate the KGE. In this technical note, we show that this transformation should be avoided because it produced numerical flaws that lead to difficulties in the score value interpretation.
Léonard Santos, Guillaume Thirel, and Charles Perrin
Geosci. Model Dev., 11, 1591–1605, https://doi.org/10.5194/gmd-11-1591-2018, https://doi.org/10.5194/gmd-11-1591-2018, 2018
Short summary
Short summary
Many rainfall–runoff models are based on stores. However, the differential equations that describe the stores' evolution are rarely presented in literature.
This represents an issue when the temporal resolution changes. In this work, we propose and evaluate a state-space version of a simple rainfall–runoff model within a robust resolution scheme. The results show that the proposed model performs equally well or slightly better than the original one and is independent of the temporal resolution.
Louise Crochemore, Maria-Helena Ramos, Florian Pappenberger, and Charles Perrin
Hydrol. Earth Syst. Sci., 21, 1573–1591, https://doi.org/10.5194/hess-21-1573-2017, https://doi.org/10.5194/hess-21-1573-2017, 2017
Short summary
Short summary
The use of general circulation model outputs for streamflow forecasting has developed in the last decade. In parallel, traditional streamflow forecasting is commonly based on historical data. This study investigates the impact of conditioning historical data based on circulation model precipitation forecasts on seasonal streamflow forecast quality. Results highlighted a trade-off between the sharpness and reliability of forecasts.
Alban de Lavenne, Guillaume Thirel, Vazken Andréassian, Charles Perrin, and Maria-Helena Ramos
Proc. IAHS, 373, 87–94, https://doi.org/10.5194/piahs-373-87-2016, https://doi.org/10.5194/piahs-373-87-2016, 2016
Short summary
Short summary
Developing modelling tools that help to understand the spatial distribution of water resources is a key issue for better management. Ideally, hydrological models which discretise catchment space into sub-catchments should offer better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However we demonstrate that those model raise other issues related to the calibration strategy and to the identifiability of the parameters.
F. Bourgin, V. Andréassian, C. Perrin, and L. Oudin
Hydrol. Earth Syst. Sci., 19, 2535–2546, https://doi.org/10.5194/hess-19-2535-2015, https://doi.org/10.5194/hess-19-2535-2015, 2015
P. Nicolle, R. Pushpalatha, C. Perrin, D. François, D. Thiéry, T. Mathevet, M. Le Lay, F. Besson, J.-M. Soubeyroux, C. Viel, F. Regimbeau, V. Andréassian, P. Maugis, B. Augeard, and E. Morice
Hydrol. Earth Syst. Sci., 18, 2829–2857, https://doi.org/10.5194/hess-18-2829-2014, https://doi.org/10.5194/hess-18-2829-2014, 2014
L. Coron, V. Andréassian, C. Perrin, M. Bourqui, and F. Hendrickx
Hydrol. Earth Syst. Sci., 18, 727–746, https://doi.org/10.5194/hess-18-727-2014, https://doi.org/10.5194/hess-18-727-2014, 2014
F. Lobligeois, V. Andréassian, C. Perrin, P. Tabary, and C. Loumagne
Hydrol. Earth Syst. Sci., 18, 575–594, https://doi.org/10.5194/hess-18-575-2014, https://doi.org/10.5194/hess-18-575-2014, 2014
H. V. Gupta, C. Perrin, G. Blöschl, A. Montanari, R. Kumar, M. Clark, and V. Andréassian
Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, https://doi.org/10.5194/hess-18-463-2014, 2014
W. R. van Esse, C. Perrin, M. J. Booij, D. C. M. Augustijn, F. Fenicia, D. Kavetski, and F. Lobligeois
Hydrol. Earth Syst. Sci., 17, 4227–4239, https://doi.org/10.5194/hess-17-4227-2013, https://doi.org/10.5194/hess-17-4227-2013, 2013
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Uncertainty analysis
Quantifying spatiotemporal and elevational precipitation gauge network uncertainty in the Canadian Rockies
Toward merging MOPEX and CAMELS hydrometeorological datasets: compatibility and statistical comparison
Comparison of BARRA and ERA5 in Replicating Mean and Extreme Precipitation over Australia
Comparison of high-resolution climate reanalysis datasets for hydro-climatic impact studies
Diurnal Variability of Global Precipitation: Insights from Hourly Satellite and Reanalysis Datasets
On the visual detection of non-natural records in streamflow time series: challenges and impacts
Historical rainfall data in northern Italy predict larger meteorological drought hazard than climate projections
Daytime-only mean data enhance understanding of land–atmosphere coupling
Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning
Unraveling the contribution of potential evaporation formulation to uncertainty under climate change
Exploring hydrologic post-processing of ensemble streamflow forecasts based on affine kernel dressing and non-dominated sorting genetic algorithm II
Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems
Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products
Uncertainties and their interaction in flood hazard assessment with climate change
Bias-correcting input variables enhances forecasting of reference crop evapotranspiration
Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies
At which timescale does the complementary principle perform best in evaporation estimation?
Uncertainty in nonstationary frequency analysis of South Korea's daily rainfall peak over threshold excesses associated with covariates
Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability
The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden
A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products
Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality
Response of global evaporation to major climate modes in historical and future Coupled Model Intercomparison Project Phase 5 simulations
Cross-validating precipitation datasets in the Indus River basin
Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics
Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network
Influence of three phases of El Niño–Southern Oscillation on daily precipitation regimes in China
Dual-polarized quantitative precipitation estimation as a function of range
Reconstruction of droughts in India using multiple land-surface models (1951–2015)
Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system
Exploratory studies into seasonal flow forecasting potential for large lakes
Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China
Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate
Providing a non-deterministic representation of spatial variability of precipitation in the Everest region
Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada
Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones
Characteristics of rainfall events in regional climate model simulations for the Czech Republic
The rainfall erosivity factor in the Czech Republic and its uncertainty
Hierarchy of climate and hydrological uncertainties in transient low-flow projections
Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game
Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India
Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands
Uncertainties in calculating precipitation climatology in East Asia
Measurement and interpolation uncertainties in rainfall maps from cellular communication networks
Characterization of precipitation product errors across the United States using multiplicative triple collocation
Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework
Evaluation of land surface model simulations of evapotranspiration over a 12-year crop succession: impact of soil hydraulic and vegetation properties
Multi-objective parameter optimization of common land model using adaptive surrogate modeling
Testing gridded land precipitation data and precipitation and runoff reanalyses (1982–2010) between 45° S and 45° N with normalised difference vegetation index data
Evaluation of high-resolution precipitation analyses using a dense station network
André Bertoncini and John W. Pomeroy
Hydrol. Earth Syst. Sci., 29, 983–1000, https://doi.org/10.5194/hess-29-983-2025, https://doi.org/10.5194/hess-29-983-2025, 2025
Short summary
Short summary
Rainfall and snowfall spatial estimation for hydrological purposes is often compromised in cold mountain regions due to inaccessibility, creating sparse gauge networks with few high-elevation gauges. This study developed a framework for quantifying gauge network uncertainty, considering elevation to aid in future gauge placement in mountain regions. Results show that gauge placement above 2000 m is the most cost-effective measure to decrease gauge network uncertainty in the Canadian Rockies.
Katharine Owen Sink and Tom Brikowski
EGUsphere, https://doi.org/10.5194/egusphere-2024-4182, https://doi.org/10.5194/egusphere-2024-4182, 2025
Short summary
Short summary
This study compares two prominent hydrometeorological datasets across 47 shared watersheds in the United States to assess their compatibility, using R programming language. While daily temperature and precipitation data showed notable discrepancies, agreement improved at monthly, seasonal, and annual scales. The findings validate both datasets for previous hydrological studies and justification for merging them into a unified dataset, enhancing water resource management nationwide.
Kevin K. W. Cheung, Fei Ji, Nidhi Nishant, Jin Teng, James Bennett, and De Li Liu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-286, https://doi.org/10.5194/hess-2024-286, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This study evaluates two reanalysis datasets, which are critical in climate, weather research and water resources analysis, for the Australian region in simulating mean precipitation and six selected precipitation extremes. While spatial patterns of mean precipitation are well reproduced, substantial biases exist in precipitation variability, trends and extremes. Caution in applying these datasets is thus advised in terms of the latter aspects.
Raul R. Wood, Joren Janzing, Amber van Hamel, Jonas Götte, Dominik L. Schumacher, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2905, https://doi.org/10.5194/egusphere-2024-2905, 2024
Short summary
Short summary
Continuous and high-quality meteorological datasets are crucial to study extreme hydro-climatic events. We here conduct a comprehensive spatio-temporal evaluation of precipitation and temperature from four climate reanalysis datasets, focusing on mean and extreme metrics, variability, trends, and the representation of droughts and floods over Switzerland. Our analysis shows that all datasets have some merit when limitations are considered, and that one dataset performs better than the others.
Rajani Kumar Pradhan, Yannis Markonis, Francesco Marra, Efthymios I. Nikolopoulos, Simon Michael Papalexiou, and Vincenzo Levizzani
EGUsphere, https://doi.org/10.5194/egusphere-2024-1626, https://doi.org/10.5194/egusphere-2024-1626, 2024
Short summary
Short summary
This study compared global satellite and one reanalysis precipitation dataset to assess diurnal variability. We found that all datasets capture key diurnal precipitation patterns, with maximum precipitation in the afternoon over land and early morning over the ocean. However, there are differences in the exact timing and amount of precipitation. This suggests that it is better to use a combination of datasets for potential applications rather than relying on a single dataset.
Laurent Strohmenger, Eric Sauquet, Claire Bernard, Jérémie Bonneau, Flora Branger, Amélie Bresson, Pierre Brigode, Rémy Buzier, Olivier Delaigue, Alexandre Devers, Guillaume Evin, Maïté Fournier, Shu-Chen Hsu, Sandra Lanini, Alban de Lavenne, Thibault Lemaitre-Basset, Claire Magand, Guilherme Mendoza Guimarães, Max Mentha, Simon Munier, Charles Perrin, Tristan Podechard, Léo Rouchy, Malak Sadki, Myriam Soutif-Bellenger, François Tilmant, Yves Tramblay, Anne-Lise Véron, Jean-Philippe Vidal, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023, https://doi.org/10.5194/hess-27-3375-2023, 2023
Short summary
Short summary
We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
Rui Guo and Alberto Montanari
Hydrol. Earth Syst. Sci., 27, 2847–2863, https://doi.org/10.5194/hess-27-2847-2023, https://doi.org/10.5194/hess-27-2847-2023, 2023
Short summary
Short summary
The present study refers to the region of Bologna, where the availability of a 209-year-long daily rainfall series allows us to make a unique assessment of global climate models' reliability and their predicted changes in rainfall and multiyear droughts. Our results suggest carefully considering the impact of uncertainty when designing climate change adaptation policies for droughts. Rigorous use and comprehensive interpretation of the available information are needed to avoid mismanagement.
Zun Yin, Kirsten L. Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan
Hydrol. Earth Syst. Sci., 27, 861–872, https://doi.org/10.5194/hess-27-861-2023, https://doi.org/10.5194/hess-27-861-2023, 2023
Short summary
Short summary
Land–atmosphere (L–A) interactions typically focus on daytime processes connecting the land state with the overlying atmospheric boundary layer. However, much prior L–A work used monthly or daily means due to the lack of daytime-only data products. Here we show that monthly smoothing can significantly obscure the L–A coupling signal, and including nighttime information can mute or mask the daytime processes of interest. We propose diagnosing L–A coupling within models or archiving subdaily data.
Lei Xu, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen
Hydrol. Earth Syst. Sci., 26, 2923–2938, https://doi.org/10.5194/hess-26-2923-2022, https://doi.org/10.5194/hess-26-2923-2022, 2022
Short summary
Short summary
Precipitation forecasting has potential uncertainty due to data and model uncertainties. Here, an integrated predictive uncertainty modeling framework is proposed by jointly considering data and model uncertainties through an uncertainty propagation theorem. The results indicate an effective predictive uncertainty estimation for precipitation forecasting, indicating the great potential for uncertainty quantification of numerous predictive applications.
Thibault Lemaitre-Basset, Ludovic Oudin, Guillaume Thirel, and Lila Collet
Hydrol. Earth Syst. Sci., 26, 2147–2159, https://doi.org/10.5194/hess-26-2147-2022, https://doi.org/10.5194/hess-26-2147-2022, 2022
Short summary
Short summary
Increasing temperature will impact evaporation and water resource management. Hydrological models are fed with an estimation of the evaporative demand of the atmosphere, called potential evapotranspiration (PE). The objectives of this study were (1) to compute the future PE anomaly over France and (2) to determine the impact of the choice of the method to estimate PE. Our results show that all methods present similar future trends. No method really stands out from the others.
Jing Xu, François Anctil, and Marie-Amélie Boucher
Hydrol. Earth Syst. Sci., 26, 1001–1017, https://doi.org/10.5194/hess-26-1001-2022, https://doi.org/10.5194/hess-26-1001-2022, 2022
Short summary
Short summary
The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a conventional post-processing method of affine kernel dressing. NSGA-II showed its superiority in improving the forecast skill and communicating trade-offs with end-users. It allows the enhancement of the forecast quality since it allows for setting multiple specific objectives from scratch. This flexibility should be considered as a reason to implement hydrologic ensemble prediction systems (H-EPSs).
Emixi Sthefany Valdez, François Anctil, and Maria-Helena Ramos
Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, https://doi.org/10.5194/hess-26-197-2022, 2022
Short summary
Short summary
We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.
Haowen Yue, Mekonnen Gebremichael, and Vahid Nourani
Hydrol. Earth Syst. Sci., 26, 167–181, https://doi.org/10.5194/hess-26-167-2022, https://doi.org/10.5194/hess-26-167-2022, 2022
Short summary
Short summary
The development of high-resolution global precipitation forecasts and the lack of reliable precipitation forecasts over Africa motivates this work to evaluate the precipitation forecasts from the Global Forecast System (GFS) over the Niger river basin in Africa. The GFS forecasts, at a 15 d accumulation timescale, have an acceptable performance; however, the forecasts are highly biased. It is recommended to apply bias correction to GFS forecasts before their application.
Hadush Meresa, Conor Murphy, Rowan Fealy, and Saeed Golian
Hydrol. Earth Syst. Sci., 25, 5237–5257, https://doi.org/10.5194/hess-25-5237-2021, https://doi.org/10.5194/hess-25-5237-2021, 2021
Short summary
Short summary
The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis.
Qichun Yang, Quan J. Wang, Kirsti Hakala, and Yating Tang
Hydrol. Earth Syst. Sci., 25, 4773–4788, https://doi.org/10.5194/hess-25-4773-2021, https://doi.org/10.5194/hess-25-4773-2021, 2021
Short summary
Short summary
Forecasts of water losses from land surface to the air are highly valuable for water resource management and planning. In this study, we aim to fill a critical knowledge gap in the forecasting of evaporative water loss. Model experiments across Australia clearly suggest the necessity of correcting errors in input variables for more reliable water loss forecasting. We anticipate that the strategy developed in our work will benefit future water loss forecasting and lead to more skillful forecasts.
Mostafa Tarek, François Brissette, and Richard Arsenault
Hydrol. Earth Syst. Sci., 25, 3331–3350, https://doi.org/10.5194/hess-25-3331-2021, https://doi.org/10.5194/hess-25-3331-2021, 2021
Short summary
Short summary
It is not known how much uncertainty the choice of a reference data set may bring to impact studies. This study compares precipitation and temperature data sets to evaluate the uncertainty contribution to the results of climate change studies. Results show that all data sets provide good streamflow simulations over the reference period. The reference data sets also provided uncertainty that was equal to or larger than that related to general circulation models over most of the catchments.
Liming Wang, Songjun Han, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 25, 375–386, https://doi.org/10.5194/hess-25-375-2021, https://doi.org/10.5194/hess-25-375-2021, 2021
Short summary
Short summary
It remains unclear at which timescale the complementary principle performs best in estimating evaporation. In this study, evaporation estimation was assessed over 88 eddy covariance monitoring sites at multiple timescales. The results indicate that the generalized complementary functions perform best in estimating evaporation at the monthly scale. This study provides a reference for choosing a suitable time step for evaporation estimations in relevant studies.
Okjeong Lee, Jeonghyeon Choi, Jeongeun Won, and Sangdan Kim
Hydrol. Earth Syst. Sci., 24, 5077–5093, https://doi.org/10.5194/hess-24-5077-2020, https://doi.org/10.5194/hess-24-5077-2020, 2020
Short summary
Short summary
The uncertainty of the model interpreting rainfall extremes with temperature is analyzed. The performance of the model focuses on the reliability of the output. It has been found that the selection of temperatures suitable for extreme levels plays an important role in improving model reliability. Based on this, a methodology is proposed to quantify the degree of uncertainty inherent in the change in rainfall extremes due to global warming.
Chao Gao, Martijn J. Booij, and Yue-Ping Xu
Hydrol. Earth Syst. Sci., 24, 3251–3269, https://doi.org/10.5194/hess-24-3251-2020, https://doi.org/10.5194/hess-24-3251-2020, 2020
Short summary
Short summary
This paper studies the impact of climate change on high and low flows and quantifies the contribution of uncertainty sources from representative concentration pathways (RCPs), global climate models (GCMs) and internal climate variability in extreme flows. Internal climate variability was reflected in a stochastic rainfall model. The results show the importance of internal climate variability and GCM uncertainty in high flows and GCM and RCP uncertainty in low flows especially for the far future.
Marc Schleiss, Jonas Olsson, Peter Berg, Tero Niemi, Teemu Kokkonen, Søren Thorndahl, Rasmus Nielsen, Jesper Ellerbæk Nielsen, Denica Bozhinova, and Seppo Pulkkinen
Hydrol. Earth Syst. Sci., 24, 3157–3188, https://doi.org/10.5194/hess-24-3157-2020, https://doi.org/10.5194/hess-24-3157-2020, 2020
Short summary
Short summary
A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.
Xudong Zhou, Jan Polcher, Tao Yang, and Ching-Sheng Huang
Hydrol. Earth Syst. Sci., 24, 2061–2081, https://doi.org/10.5194/hess-24-2061-2020, https://doi.org/10.5194/hess-24-2061-2020, 2020
Short summary
Short summary
This article proposes a new estimation approach for assessing the uncertainty with multiple datasets by fully considering all variations in temporal and spatial dimensions. Comparisons demonstrate that classical metrics may underestimate the uncertainties among datasets due to an averaging process in their algorithms. This new approach is particularly suitable for overall assessment of multiple climatic products, but can be easily applied to other spatiotemporal products in related fields.
Shaoning Lv, Bernd Schalge, Pablo Saavedra Garfias, and Clemens Simmer
Hydrol. Earth Syst. Sci., 24, 1957–1973, https://doi.org/10.5194/hess-24-1957-2020, https://doi.org/10.5194/hess-24-1957-2020, 2020
Short summary
Short summary
Passive remote sensing of soil moisture has good potential to improve weather forecasting via data assimilation in theory. We use the virtual reality data set (VR01) to infer the impact of sampling density on soil moisture ground cal/val activity. It shows how the sampling error is growing with an increasing sampling distance for a SMOS–SMAP scale footprint in about 40 km, 9 km, and 3 km. The conclusion will help in understanding the passive remote sensing soil moisture products.
Thanh Le and Deg-Hyo Bae
Hydrol. Earth Syst. Sci., 24, 1131–1143, https://doi.org/10.5194/hess-24-1131-2020, https://doi.org/10.5194/hess-24-1131-2020, 2020
Short summary
Short summary
Here we investigate the response of global evaporation to main climate modes, including the Indian Ocean Dipole (IOD), the North Atlantic Oscillation (NAO) and the El Niño–Southern Oscillation (ENSO). Our results indicate that ENSO is an important driver of evaporation for many regions, while the impacts of NAO and IOD are substantial. This study allows us to obtain insight about the predictability of evaporation and, hence, may help to improve the early-warning systems of climate extremes.
Jean-Philippe Baudouin, Michael Herzog, and Cameron A. Petrie
Hydrol. Earth Syst. Sci., 24, 427–450, https://doi.org/10.5194/hess-24-427-2020, https://doi.org/10.5194/hess-24-427-2020, 2020
Short summary
Short summary
The amount of precipitation falling in the Indus River basin remains uncertain while its variability impacts 100 million inhabitants. A comparison of datasets from diverse sources (ground remote observations, model outputs) reduces this uncertainty significantly. Grounded observations offer the most reliable long-term variability but with important underestimation in winter over the mountains. By contrast, recent model outputs offer better estimations of total amount and short-term variability.
Kamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, and Eun-Sung Chung
Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, https://doi.org/10.5194/hess-23-4803-2019, 2019
Short summary
Short summary
This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and maximum and minimum temperature over Pakistan using spatial metrics (SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency) for the period 1961–2005. NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 were identified as the most suitable GCMs for simulating all three climate variables over Pakistan.
Sungmin O and Ulrich Foelsche
Hydrol. Earth Syst. Sci., 23, 2863–2875, https://doi.org/10.5194/hess-23-2863-2019, https://doi.org/10.5194/hess-23-2863-2019, 2019
Short summary
Short summary
We analyze heavy local rainfall to address questions regarding the spatial uncertainty due to the approximation of areal rainfall using point measurements. Ten years of rainfall data from a dense network of 150 rain gauges in southeastern Austria are employed, which permits robust examination of small-scale rainfall at various horizontal resolutions. Quantitative uncertainty information from the study can guide both data users and producers to estimate uncertainty in their own rainfall dataset.
Aifeng Lv, Bo Qu, Shaofeng Jia, and Wenbin Zhu
Hydrol. Earth Syst. Sci., 23, 883–896, https://doi.org/10.5194/hess-23-883-2019, https://doi.org/10.5194/hess-23-883-2019, 2019
Short summary
Short summary
ENSO-related changes in daily precipitation regimes are currently ignored by the scientific community. We analyzed the anomalies of daily precipitation and hydrological extremes caused by different phases of ENSO events, as well as the possible driving mechanisms, to reveal the influence of ENSO on China's daily precipitation regimes. Our results provide a valuable tool for daily precipitation prediction and enable the prioritization of adaptation efforts ahead of extreme events in China.
Micheal J. Simpson and Neil I. Fox
Hydrol. Earth Syst. Sci., 22, 3375–3389, https://doi.org/10.5194/hess-22-3375-2018, https://doi.org/10.5194/hess-22-3375-2018, 2018
Short summary
Short summary
Many researchers have expressed that one of the main difficulties in modeling watershed hydrology is that of obtaining continuous, widespread weather input data, especially precipitation. The overarching objective of this study was to provide a comprehensive study of three weather radars as a function of range. We found that radar-estimated precipitation was best at ranges between 100 and 150 km from the radar, with different radar parameters being superior at varying distances from the radar.
Vimal Mishra, Reepal Shah, Syed Azhar, Harsh Shah, Parth Modi, and Rohini Kumar
Hydrol. Earth Syst. Sci., 22, 2269–2284, https://doi.org/10.5194/hess-22-2269-2018, https://doi.org/10.5194/hess-22-2269-2018, 2018
Sanjib Sharma, Ridwan Siddique, Seann Reed, Peter Ahnert, Pablo Mendoza, and Alfonso Mejia
Hydrol. Earth Syst. Sci., 22, 1831–1849, https://doi.org/10.5194/hess-22-1831-2018, https://doi.org/10.5194/hess-22-1831-2018, 2018
Short summary
Short summary
We investigate the relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7). For this purpose, we develop and implement a regional hydrologic ensemble prediction system (RHEPS). Overall analysis shows that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.
Kevin Sene, Wlodek Tych, and Keith Beven
Hydrol. Earth Syst. Sci., 22, 127–141, https://doi.org/10.5194/hess-22-127-2018, https://doi.org/10.5194/hess-22-127-2018, 2018
Short summary
Short summary
The theme of the paper is exploration of the potential for seasonal flow forecasting for large lakes using a range of stochastic transfer function techniques with additional insights gained from simple analytical approximations. The methods were evaluated using records for two of the largest lakes in the world. The paper concludes with a discussion of the relevance of the results to operational flow forecasting systems for other large lakes.
Fan Yang, Hui Lu, Kun Yang, Jie He, Wei Wang, Jonathon S. Wright, Chengwei Li, Menglei Han, and Yishan Li
Hydrol. Earth Syst. Sci., 21, 5805–5821, https://doi.org/10.5194/hess-21-5805-2017, https://doi.org/10.5194/hess-21-5805-2017, 2017
Short summary
Short summary
In this paper, we show that CLDAS has the highest spatial and temporal resolution, and it performs best in terms of precipitation, while it overestimates the shortwave radiation. CMFD also has high resolution and its shortwave radiation data match well with the station data; its annual-mean precipitation is reliable but its monthly precipitation needs improvements. Both GLDAS and CN05.1 over mainland China need to be improved. The results can benefit researchers for forcing data selection.
Rachel Bazile, Marie-Amélie Boucher, Luc Perreault, and Robert Leconte
Hydrol. Earth Syst. Sci., 21, 5747–5762, https://doi.org/10.5194/hess-21-5747-2017, https://doi.org/10.5194/hess-21-5747-2017, 2017
Short summary
Short summary
Meteorological forecasting agencies constantly work on pushing the limit of predictability farther in time. However, some end users need proof that climate model outputs are ready to be implemented operationally. We show that bias correction is crucial for the use of ECMWF System4 forecasts for the studied area and there is a potential for the use of 1-month-ahead forecasts. Beyond this, forecast performance is equivalent to using past climatology series as inputs to the hydrological model.
Judith Eeckman, Pierre Chevallier, Aaron Boone, Luc Neppel, Anneke De Rouw, Francois Delclaux, and Devesh Koirala
Hydrol. Earth Syst. Sci., 21, 4879–4893, https://doi.org/10.5194/hess-21-4879-2017, https://doi.org/10.5194/hess-21-4879-2017, 2017
Short summary
Short summary
The central part of the Himalayan Range presents tremendous heterogeneity in terms of topography and climatology, but the representation of hydro-climatic processes for Himalayan catchments is limited due to a lack of knowledge in such poorly instrumented environments. The proposed approach is to characterize the effect of altitude on precipitation by considering ensembles of acceptable altitudinal factors. Ensembles of acceptable values for the components of the water cycle are then provided.
Jefferson S. Wong, Saman Razavi, Barrie R. Bonsal, Howard S. Wheater, and Zilefac E. Asong
Hydrol. Earth Syst. Sci., 21, 2163–2185, https://doi.org/10.5194/hess-21-2163-2017, https://doi.org/10.5194/hess-21-2163-2017, 2017
Short summary
Short summary
This study was conducted to quantify the spatial and temporal variability of the errors associated with various gridded precipitation products in Canada. Overall, WFDEI [GPCC] and CaPA performed best with respect to different performance measures, followed by ANUSPLIN and WEDEI [CRU]. Princeton and NARR demonstrated the lowest quality. Comparing the climate model-simulated products, PCIC ensembles generally performed better than NA-CORDEX ensembles in terms of reliability in four seasons.
Danlu Guo, Seth Westra, and Holger R. Maier
Hydrol. Earth Syst. Sci., 21, 2107–2126, https://doi.org/10.5194/hess-21-2107-2017, https://doi.org/10.5194/hess-21-2107-2017, 2017
Short summary
Short summary
This study assessed the impact of baseline climate conditions on the sensitivity of potential evapotranspiration (PET) to a large range of plausible changes in temperature, relative humidity, solar radiation and wind speed at 30 Australian locations. Around 2-fold greater PET changes were observed at cool and humid locations compared to others, indicating potential for elevated water loss in the future. These impacts can be useful to inform the selection of PET models under a changing climate.
Vojtěch Svoboda, Martin Hanel, Petr Máca, and Jan Kyselý
Hydrol. Earth Syst. Sci., 21, 963–980, https://doi.org/10.5194/hess-21-963-2017, https://doi.org/10.5194/hess-21-963-2017, 2017
Short summary
Short summary
The study presents validation of precipitation events as simulated by an ensemble of regional climate models for the Czech Republic. While the number of events per season, seasonal total precipitation due to heavy events and the distribution of rainfall depths are simulated relatively well, event maximum precipitation and event intensity are strongly underestimated. This underestimation cannot be explained by scale mismatch between point observations and area average (climate model simulations).
Martin Hanel, Petr Máca, Petr Bašta, Radek Vlnas, and Pavel Pech
Hydrol. Earth Syst. Sci., 20, 4307–4322, https://doi.org/10.5194/hess-20-4307-2016, https://doi.org/10.5194/hess-20-4307-2016, 2016
Short summary
Short summary
The paper is focused on assessment of the contribution of various sources of uncertainty to the estimated rainfall erosivity factor. It is shown that the rainfall erosivity factor can be estimated with reasonable precision even from records shorter than recommended, provided good spatial coverage and reasonable explanatory variables are available. The research was done as an update of the R factor estimates for the Czech Republic, which were later used for climate change assessment.
Jean-Philippe Vidal, Benoît Hingray, Claire Magand, Eric Sauquet, and Agnès Ducharne
Hydrol. Earth Syst. Sci., 20, 3651–3672, https://doi.org/10.5194/hess-20-3651-2016, https://doi.org/10.5194/hess-20-3651-2016, 2016
Short summary
Short summary
Possible transient futures of winter and summer low flows for two snow-influenced catchments in the southern French Alps show a strong decrease signal. It is however largely masked by the year-to-year variability, which should be the main target for defining adaptation strategies. Responses of different hydrological models strongly diverge in the future, suggesting to carefully check the robustness of evapotranspiration and snowpack components under a changing climate.
Louise Arnal, Maria-Helena Ramos, Erin Coughlan de Perez, Hannah Louise Cloke, Elisabeth Stephens, Fredrik Wetterhall, Schalk Jan van Andel, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3109–3128, https://doi.org/10.5194/hess-20-3109-2016, https://doi.org/10.5194/hess-20-3109-2016, 2016
Short summary
Short summary
Forecasts are produced as probabilities of occurrence of specific events, which is both an added value and a challenge for users. This paper presents a game on flood protection, "How much are you prepared to pay for a forecast?", which investigated how users perceive the value of forecasts and are willing to pay for them when making decisions. It shows that users are mainly influenced by the perceived quality of the forecasts, their need for the information and their degree of risk tolerance.
K. Sunilkumar, T. Narayana Rao, and S. Satheeshkumar
Hydrol. Earth Syst. Sci., 20, 1719–1735, https://doi.org/10.5194/hess-20-1719-2016, https://doi.org/10.5194/hess-20-1719-2016, 2016
Vincent Roth and Tatenda Lemann
Hydrol. Earth Syst. Sci., 20, 921–934, https://doi.org/10.5194/hess-20-921-2016, https://doi.org/10.5194/hess-20-921-2016, 2016
Short summary
Short summary
The Soil and Water Assessment Tool (SWAT) suggests using the CFSR global rainfall data for modelling discharge and soil erosion in data-scarce parts of the world. These data are freely available and ready to use for SWAT modelling. However, simulations with the CFSR data in the Ethiopian Highlands were unable to represent the specific regional climates and showed high discrepancies. This article compares SWAT simulations with conventional rainfall data and with CFSR rainfall data.
J. Kim and S. K. Park
Hydrol. Earth Syst. Sci., 20, 651–658, https://doi.org/10.5194/hess-20-651-2016, https://doi.org/10.5194/hess-20-651-2016, 2016
Short summary
Short summary
This study examined the uncertainty in climatological precipitation in East Asia, calculated from five gridded analysis data sets based on in situ rain gauge observations from 1980 to 2007. It is found that the regions of large uncertainties are typically lightly populated and are characterized by severe terrain and/or very high elevations. Thus, care must be taken in using long-term trends calculated from gridded precipitation analysis data for climate studies over such regions in East Asia.
M. F. Rios Gaona, A. Overeem, H. Leijnse, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 19, 3571–3584, https://doi.org/10.5194/hess-19-3571-2015, https://doi.org/10.5194/hess-19-3571-2015, 2015
Short summary
Short summary
Commercial cellular networks are built for telecommunication purposes. These kinds of networks have lately been used to obtain rainfall maps at country-wide scales. From previous studies, we now quantify the uncertainties associated with such maps. To do so, we divided the sources or error into two categories: from microwave link measurements and from mapping. It was found that the former is the source that contributes the most to the overall error in rainfall maps from microwave link network.
S. H. Alemohammad, K. A. McColl, A. G. Konings, D. Entekhabi, and A. Stoffelen
Hydrol. Earth Syst. Sci., 19, 3489–3503, https://doi.org/10.5194/hess-19-3489-2015, https://doi.org/10.5194/hess-19-3489-2015, 2015
Short summary
Short summary
This paper introduces a new variant of the triple collocation technique with multiplicative error model. The method is applied, for the first time, to precipitation products across the central part of continental USA. Results show distinctive patterns of error variance in each product that are estimated without a priori assumption of any of the error distributions. The correlation coefficients between each product and the truth are also estimated, which provides another performance perspective.
M. S. Raleigh, J. D. Lundquist, and M. P. Clark
Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, https://doi.org/10.5194/hess-19-3153-2015, 2015
Short summary
Short summary
A sensitivity analysis is used to examine how error characteristics (type, distributions, and magnitudes) in meteorological forcing data impact outputs from a physics-based snow model in four climates. Bias and error magnitudes were key factors in model sensitivity and precipitation bias often dominated. However, the relative importance of forcings depended somewhat on the selected model output. Forcing uncertainty was comparable to model structural uncertainty as found in other studies.
S. Garrigues, A. Olioso, J. C. Calvet, E. Martin, S. Lafont, S. Moulin, A. Chanzy, O. Marloie, S. Buis, V. Desfonds, N. Bertrand, and D. Renard
Hydrol. Earth Syst. Sci., 19, 3109–3131, https://doi.org/10.5194/hess-19-3109-2015, https://doi.org/10.5194/hess-19-3109-2015, 2015
Short summary
Short summary
Land surface model simulations of evapotranspiration are assessed over a 12-year Mediterranean crop succession. Evapotranspiration mainly results from soil evaporation when it is simulated over a Mediterranean crop succession. This leads to a high sensitivity to the soil parameters. Errors on soil hydraulic properties can lead to a large bias in cumulative evapotranspiration over a long period of time. Accounting for uncertainties in soil properties is essential for land surface modelling.
W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, and C. Miao
Hydrol. Earth Syst. Sci., 19, 2409–2425, https://doi.org/10.5194/hess-19-2409-2015, https://doi.org/10.5194/hess-19-2409-2015, 2015
S. O. Los
Hydrol. Earth Syst. Sci., 19, 1713–1725, https://doi.org/10.5194/hess-19-1713-2015, https://doi.org/10.5194/hess-19-1713-2015, 2015
Short summary
Short summary
The study evaluates annual precipitation (largely rainfall) amounts for the tropics and subtropics; precipitation was obtained from ground observations, satellite observations and numerical weather forecasting models.
- Annual precipitation amounts from ground and satellite observations were the most realistic.
- Newer weather forecasting models better predicted annual precipitation than older models.
- Weather forecasting models predicted inaccurate precipitation amounts for Africa.
A. Kann, I. Meirold-Mautner, F. Schmid, G. Kirchengast, J. Fuchsberger, V. Meyer, L. Tüchler, and B. Bica
Hydrol. Earth Syst. Sci., 19, 1547–1559, https://doi.org/10.5194/hess-19-1547-2015, https://doi.org/10.5194/hess-19-1547-2015, 2015
Short summary
Short summary
The paper introduces a high resolution precipitation analysis system which operates on 1 km x 1 km resolution with high frequency updates of 5 minutes. The ability of such a system to adequately assess the convective precipitation distribution is evaluated by means of an independant, high resolution station network. This dense station network allows for a thorough evaluation of the analyses under different convective situations and of the representativeness error of raingaue measurements.
Cited articles
Abaza, M., Anctil, F., Fortin, V., and Perreault, L.: On the incidence of
meteorological and hydrological processors: Effect of resolution, sharpness
and reliability of hydrological ensemble forecasts, J. Hydrol., 555, 371–384, https://doi.org/10.1016/j.jhydrol.2017.10.038, 2017. a
Abbaszadeh, P., Moradkhani, H., and Yan, H.: Enhancing hydrologic data
assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo, Adv, Water Resour,, 111, 192–204, https://doi.org/10.1016/j.advwatres.2017.11.011, 2018. a
Allamano, P., Laio, F., and Claps, P.: Effects of disregarding seasonality on
the distribution of hydrological extremes, Hydrol. Earth Syst. Sci., 15, 3207–3215, https://doi.org/10.5194/hess-15-3207-2011, 2011. a
Anctil, F., Perrin, C., and Andréassian, V.: Impact of the length of observed records on the performance of ANN and of conceptual parsimonious
rainfall-runoff forecasting models, Environ. Model. Softw., 19, 357–368, 2004. a
Andréassian, V., Hall, A., Chahinian, N., and Schaake, J.: Introduction and Synthesis: Why should hydrologists work on a large number of basin data
sets?, IAHS-AISH Publication no. 307, International Association of Hydrological Sciences, Wallingford, UK, 1–5, 2006. a
Andréassian, V., Perrin, C., Berthet, L., Le Moine, N., Lerat, J., Loumagne, C., Oudin, L., Mathevet, T., Ramos, M.-H., and Valéry, A.: HESS Opinions “Crash tests for a standardized evaluation of hydrological models”, Hydrol. Earth Syst. Sci., 13, 1757–1764, https://doi.org/10.5194/hess-13-1757-2009, 2009. a
Andréassian, V., Le Moine, N., Perrin, C., Ramos, M.-H., Oudin, L., Mathevet, T., Lerat, J., and Berthet, L.: All that glitters is not gold: the case of calibrating hydrological models, Hydrol. Process., 26, 2206–2210,
https://doi.org/10.1002/hyp.9264, 2012. a
Barbetta, S., Coccia, G., Moramarco, T., Brocca, L., and Todini, E.: The multi temporal/multi-model approach to predictive uncertainty assessment in
real-time flood forecasting, J. Hydrol., 551, 555–576,
https://doi.org/10.1016/j.jhydrol.2017.06.030, 2017. a, b
Bennett, J. C., Robertson, D. E., Shrestha, D. L., Wang, Q., Enever, D.,
Hapuarachchi, P., and Tuteja, N. K.: A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days, J. Hydrol., 519, 2832–2846, https://doi.org/10.1016/j.jhydrol.2014.08.010, 2014. a
Berthet, L.: Flood forecasting at the hourly time-step: for a better
assimilation of flow information in hydrological modelling, PhD thesis, Doctoral School GRN, AgroParisTech, Paris, Irstea, Antony, 2010. a
Berthet, L. and Piotte, O.: International survey for good practices in
forecasting uncertainty assessment and communication, in: vol. 16, EGU General Assembly, April and May 2014, Vienna, Austria, EGU2014-8579, 2014. a
Berthet, L., Andréassian, V., Perrin, C., and Javelle, P.: How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments, Hydrol. Earth Syst. Sci., 13, 819–831, https://doi.org/10.5194/hess-13-819-2009, 2009. a
Berthet, L., Andréassian, V., Perrin, C., and Loumagne, C.: How significant are quadratic criteria? Part 2. On the relative contribution of large flood events to the value of a quadratic criterion, Hydrolog. Sci. J.,
55, 1063–1073, https://doi.org/10.1080/02626667.2010.505891, 2010. a
Bock, A. R., Farmer, W. H., and Hay, L. E.: Quantifying uncertainty in
simulated streamflow and runoff from a continental-scale monthly water balance model, Adv. Water Resour., 122, 166–175, https://doi.org/10.1016/j.advwatres.2018.10.005, 2018. a
Bogner, K., Pappenberger, F., and Cloke, H. L.: Technical Note: The normal
quantile transformation and its application in a flood forecasting system,
Hydrol. Earth Syst. Sci., 16, 1085–1094, https://doi.org/10.5194/hess-16-1085-2012, 2012. a
Bourgin, F.: How to quantify predictive uncertainty in hydrological modelling? Exploratory work on a large sample of catchments, PhD thesis,
Doctoral School GRNE, AgroParisTech, Paris, Irstea, Antony, 2014. a
Bourgin, F., Andréassian, V., Perrin, C., and Oudin, L.: Transferring global uncertainty estimates from gauged to ungauged catchments, Hydrol. Earth Syst. Sci., 19, 2535–2546, https://doi.org/10.5194/hess-19-2535-2015, 2015. a
Box, G. E. P. and Cox, D. R.: An Analysis of Transformations, J. Roy. Stat. Soc. B, 26, 211–252, 1964. a
Breiman, L.: Statistical modeling: The two cultures, Stat. Sci., 16, 199–215, https://doi.org/10.1214/ss/1009213726, 2001. a
Bremnes, J. B.: Constrained Quantile Regression Splines for Ensemble
Postprocessing, Mon. Weather Rev., 147, 1769–1780, https://doi.org/10.1175/MWR-D-18-0420.1, 2019. a
Brigode, P., Oudin, L., and Perrin, C.: Hydrological model parameter instability: A source of additional uncertainty in estimating the
hydrological impacts of climate change?, J. Hydrol., 476, 410–425,
https://doi.org/10.1016/j.jhydrol.2012.11.012, 2013. a
Brigode, P., Génot, B., Lobligeois, F., Delaigue, O.: Summary sheets of watershed-scale hydroclimatic observed data for France, Portail Data INRAE,
https://doi.org/10.15454/UV01P1, 2020. a
Buzzati, D.: Il deserto dei Tartari (The Tartar Steppe), Rizzoli, Milano, 1940. a
Cigizoglu, H.: Estimation, forecasting and extrapolation of river flows by
artificial neural networks, Hydrolog. Sci. J., 48, 349–362, 2003. a
Coccia, G. and Todini, E.: Recent developments in predictive uncertainty
assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci., 15, 3253–3274, https://doi.org/10.5194/hess-15-3253-2011, 2011. a, b
Coron, L., Andreassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M., and
Hendrickx, F.: Crash testing hydrological models in contrasted climate
conditions: An experiment on 216 Australian catchments, Water Resour. Res., 48, W05552, https://doi.org/10.1029/2011wr011721, 2012. a
Del Giudice, D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., and
Rieckermann, J.: Improving uncertainty estimation in urban hydrological
modeling by statistically describing bias, Hydrol. Earth Syst. Sci., 17, 4209–4225, https://doi.org/10.5194/hess-17-4209-2013, 2013. a, b, c
Demargne, J., Wu, L., Regonda, S. K., Brown, J. D., Lee, H., He, M., Seo,
D.-J., Hartman, R., Herr, H. D., Fresch, M., Schaake, J., and Zhu, Y.: The
Science of NOAA's Operational Hydrologic Ensemble Forecast Service, B. Am. Meteorol. Soc., 95, 79–98, https://doi.org/10.1175/BAMS-D-12-00081.1, 2014. a, b
Demeritt, D., Cloke, H., Pappenberger, F., Thielen, J., Bartholmes, J., and
Ramos, M.-H.: Ensemble predictions and perceptions of risk, uncertainty, and
error in flood forecasting, Environ. Hazards, 7, 115–127, 2007. a
Dogulu, N., López López, P., Solomatine, D. P., Weerts, A. H., and Shrestha, D. L.: Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting
catchments, Hydrol. Earth Syst. Sci., 19, 3181–3201,
https://doi.org/10.5194/hess-19-3181-2015, 2015. a
Ficchì, A., Perrin, C., and Andréassian, V.: Impact of temporal resolution of inputs on hydrological model performance: An analysis based on 2400 flood events, J. Hydrol., 538, 454–470, https://doi.org/10.1016/j.jhydrol.2016.04.016, 2016. a, b
Furusho, C., Perrin, C., Viatgé, J., R., L., and Andréassian, V.:
Collaborative work between operational forecasters and scientists for better
flood forecasts, La Houille Blanche, 2016-4, 5–10, https://doi.org/10.1051/lhb/2016033, 2016. a
Gaume, E., Bain, V., Bernardara, P., Newinger, O., Barbuc, M., Bateman, A.,
Blaskovicová, L., Blöschl, G., Borga, M., Dumitrescu, A., Daliakopoulos, I., Garcia, J., Irimescu, A., Kohnova, S., Koutroulis, A., Marchi, L., Matreata, S., Medina, V., Preciso, E., Sempere-Torres, D., Stancalie, G., Szolgay, J., Tsanis, I., Velasco, D., and Viglione, A.: A compilation of data on European flash floods, J. Hydrol., 367, 70–78,
https://doi.org/10.1016/j.jhydrol.2008.12.028, 2009. a
Giustolisi, O. and Laucelli, D.: Improving generalization of artificial neural networks in rainfall-runoff modelling, Hydrolog. Sci. J., 50, 439–457, 2005. a
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts,
calibration and sharpness, J. Roy. Stat. Soc. B, 69, 243–268,
https://doi.org/10.1111/j.1467-9868.2007.00587.x, 2007. a, b
Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., and Andréassian, V.: Large-sample hydrology: a need to balance depth with breadth, Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, 2014. a
Hemri, S., Lisniak, D., and Klein, B.: Multivariate postprocessing techniques
for probabilistic hydrological forecasting, Water Resour. Res., 51, 7436–7451, https://doi.org/10.1002/2014WR016473, 2015. a, b
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2, 2000. a, b
Imrie, C., Durucan, S., and Korre, A.: River flow prediction using artificial
neural networks: Generalisation beyond the calibration range, J. Hydrol., 233, 138–153, 2000. a
Kelly, K. S. and Krzysztofowicz, R.: A bivariate meta-Gaussian density for use in hydrology, Stoch. Hydrol. Hydraul., 11, 17–31, 1997. a
Klemes̆, V.: Operational testing of hydrological simulation models, Hydrolog. Sci. J. – Journal Des Sciences Hydrologiques, 31, 13–24, https://doi.org/10.1080/02626668609491024, 1986. a, b
Krzysztofowicz, R. and Maranzano, C. J.: Hydrologic uncertainty processor for
probabilistic stage transition forecasting, J. Hydro., 293, 57–73, https://doi.org/10.1016/j.jhydrol.2004.01.003, 2004. a
Laio, F. and Tamea, S.: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrol. Earth Syst. Sci., 11, 1267–1277, https://doi.org/10.5194/hess-11-1267-2007, 2007. a
Lang, M., Pobanz, K., Renard, B., Renouf, E., and Sauquet, E.: Extrapolation of rating curves by hydraulic modelling, with application to flood frequency
analysis, Hydrolog. Sci. J., 55, 883–898, https://doi.org/10.1080/02626667.2010.504186, 2010. a
Legates, D. and McCabe Jr., G.: Evaluating the use of `goodness-of-fit'
measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233–241, 1999. a
Leleu, I., Tonnelier, I., Puechberty, R., Gouin, P., Viquendi, I., Cobos, L., Foray, A., Baillon, M., and Ndima, P.-O.: Re-founding the national information system designed to manage and give access to hydrometric data,
La Houille Blanche, 2014-1, 25–32, https://doi.org/10.1051/lhb/2014004, 2014. a
Li, M., Wang, Q. J., and Bennett, J.: Accounting for seasonal dependence in
hydrological model errors and prediction uncertainty, Water Resour. Res., 49, 5913–5929, https://doi.org/10.1002/wrcr.20445, 2013. a
Li, W., Duan, Q., Miao, C., Ye, A., Gong, W., and Di, Z.: A review on
statistical postprocessing methods for hydrometeorological ensemble forecasting, Wiley Interdisciplin. Rev.: Water, 4, e1246, https://doi.org/10.1002/wat2.1246, 2017. a, b
Liano, K.: Robust error measure for supervised neural network learning with
outliers, IEEE T. Neural Netw., 7, 246–250, 1996. a
Lobligeois, F., Andréassian, V., Perrin, C., Tabary, P., and Loumagne, C.: When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events, Hydrol. Earth
Syst. Sci., 18, 575–594, https://doi.org/10.5194/hess-18-575-2014, 2014. a
McInerney, D., Thyer, M., Kavetski, D., Lerat, J., and Kuczera, G.: Improving
probabilistic prediction of daily streamflow by identifying Pareto optimal
approaches for modeling heteroscedastic residual errors, Water Resour. Res., 53, 2199–2239, https://doi.org/10.1002/2016WR019168, 2017. a, b, c, d, e, f
Merz, R., Parajka, J., and Blöschl, G.: Time stability of catchment model
parameters: Implications for climate impact analyses, Water Resour. Res., 47, W02531, https://doi.org/10.1029/2010WR009505, 2011. a
Michel, C.: Que peut-on faire en hydrologie avec un modèle conceptuel à un seul paramètre?, La Houille Blanche, 1983-1, 39–44, https://doi.org/10.1051/lhb/1983004, 1983. a
Montanari, A.: Uncertainty of Hydrological Predictions, in: Treatise on Water Science, edited by: Wilderer, P., Elsevier, Oxford, 459–478, 2011. a
Montanari, A. and Grossi, G.: Estimating the uncertainty of hydrological
forecasts: A statistical approach, Water Resour. Res., 44, W00B08,
https://doi.org/10.1029/2008wr006897, 2008. a
Moradkhani, H., Hsu, K. L., Gupta, H., and Sorooshian, S.: Uncertainty
assessment of hydrologic model states and parameters: Sequential data
assimilation using the particle filter, Water Resour. Res., 41, W05012, https://doi.org/10.1029/2004wr003604, 2005a. a
Moradkhani, H., Sorooshian, S., Gupta, H. V., and Houser, P. R.: Dual
state–parameter estimation of hydrological models using ensemble Kalman
filter, Adv. Water Resour., 28, 135–147, https://doi.org/10.1016/j.advwatres.2004.09.002, 2005b. a
Morawietz, M., Xu, C.-Y., Gottschalk, L., and Tallaksen, L. M.: Systematic
evaluation of autoregressive error models as post-processors for a probabilistic streamflow forecast system, J. Hydrol., 407, 58–72,
https://doi.org/10.1016/j.jhydrol.2011.07.007, 2011. a, b
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andreassian, V., Anctil, F.,
and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall-runoff model? Part 2 – Towards a simple and efficient potential
evapotranspiration model for rainfall-runoff modelling, J. Hydrol., 303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005. a
Pagano, T. C., Shrestha, D. L., Wang, Q. J., Robertson, D., and Hapuarachchi,
P.: Ensemble dressing for hydrological applications, Hydrol. Process., 27, 106–116, https://doi.org/10.1002/hyp.9313, 2013. a
Pagano, T. C., Wood, A. W., Ramos, M.-H., Cloke, H. L., Pappenberger, F.,
Clark, M. P., Cranston, M., Kavetski, D., Mathevet, T., Sorooshian, S., and
Verkade, J. S.: Challenges of Operational River Forecasting, J. Hydrometeorol., 15, 1692–1707, https://doi.org/10.1175/JHM-D-13-0188.1, 2014. a
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302,
https://doi.org/10.1029/2005wr004820, 2006. a
Pappenberger, F., Thielen, J., and Del Medico, M.: The impact of weather
forecast improvements on large scale hydrology: analysing a decade of forecasts of the European Flood Alert System, Hydrol. Process., 25, 1091–1113, https://doi.org/10.1002/hyp.7772, 2011. a
Pappenberger, F., Pagano, T. C., Brown, J. D., Alfieri, L., Lavers, D. A.,
Berthet, L., Bressand, F., Cloke, H. L., Cranston, M., Danhelka, J., Demargne, J., Demuth, N., de Saint-Aubin, C., Feikema, P. M., Fresch, M. A.,
Garçon, R., Gelfan, A., He, Y., Hu, Y. Z., Janet, B., Jurdy, N., Javelle, P., Kuchment, L., Laborda, Y., Langsholt, E., Le Lay, M., Li, Z. J., Mannessiez, F., Marchandise, A., Marty, R., Meißner, D., Manful, D.,
Organde, D., Pourret, V., Rademacher, S., Ramos, M. H., Reinbold, D., Tibaldi, S., Silvano, P., Salamon, P., Shin, D., Sorbet, C., Sprokkereef, E.,
Thiemig, V., Tuteja, N. K., van Andel, S. J., Verkade, J. S., Vehviläinen, B., Vogelbacher, A., Wetterhall, F., Zappa, M., Van der Zwan, R. E., and Thielen-del Pozo, J.: Hydrological Ensemble Prediction Systems Around the Globe, Springer, Berlin, Heidelberg, 1–35, https://doi.org/10.1007/978-3-642-40457-3_47-1, 2016. a
Perrin, C., Michel, C., and Andreassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289,
https://doi.org/10.1016/s0022-1694(03)00225-7, 2003. a
Perrin, C., Oudin, L., Andréassian, V., Rojas-Serna, C., Michel, C., and
Mathevet, T.: Impact of limited streamflow data on the efficiency and the
parameters of rainfall-runoff models, Hydrolog. Sci. J., 52, 131–151, 2007. a
Ramos, M.-H., Bartholmes, J., and Pozo, J. T.-D.: Development of decision
support products based on ensemble forecasts in the European flood alert
system, Atmos. Sci. Lette., 8, 113–119, https://doi.org/10.1002/asl.161, 2007. a
Reichert, P. and Mieleitner, J.: Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters, Water
Resour. Res., 45, W10402, https://doi.org/10.1029/2009wr007814, 2009. a
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.:
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009wr008328, 2010. a, b, c, d
Salamon, P. and Feyen, L.: Assessing parameter, precipitation, and predictive
uncertainty in a distributed hydrological model using sequential data
assimilation with the particle filter, J. Hydrol., 376, 428–442,
https://doi.org/10.1016/j.jhydrol.2009.07.051, 2009. a, b
Schoups, G. and Vrugt, J. A.: A formal likelihood function for parameter and
predictive inference of hydrologic models with correlated, heteroscedastic,
and non-Gaussian errors, Water Resour. Res., 46, W10531,
https://doi.org/10.1029/2009wr008933, 2010. a, b
Seiller, G., Anctil, F., and Roy, R.: Design and experimentation of an
empirical multistructure framework for accurate, sharp and reliable
hydrological ensembles, J. Hydrol., 552, 313–340,
https://doi.org/10.1016/j.jhydrol.2017.07.002, 2017. a
Sharma, S., Siddique, R., Reed, S., Ahnert, P., Mendoza, P., and Mejia, A.:
Relative effects of statistical preprocessing and postprocessing on a
regional hydrological ensemble prediction system, Hydrol. Earth Syst. Sci., 22, 1831–1849, https://doi.org/10.5194/hess-22-1831-2018, 2018. a
Singh, S. K., McMillan, H., and Bardossy, A.: Use of the data depth function to differentiate between case of interpolation and extrapolation in hydrological model prediction, J. Hydrol., 477, 213–228,
https://doi.org/10.1016/j.jhydrol.2012.11.034, 2013. a, b
Solomatine, D. P. and Shrestha, D. L.: A novel method to estimate model
uncertainty using machine learning techniques, Water Resour. Res., 45, W00B11, https://doi.org/10.1029/2008wr006839, 2009. a
Tabary, P., Dupuy, P., L'Henaff, G., Gueguen, C., Moulin, L., Laurantin, O.,
Merlier, C., and Soubeyroux, J.-M.: A 10-year (1997–2006) reanalysis of
Quantitative Precipitation Estimation over France: methodology and first results, in: vol. 351, IAHS, Wallingford, UK, 255–260, 2012. a
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125–140, https://doi.org/10.5194/hess-13-125-2009, 2009. a
Todini, E.: Role and treatment of uncertainty in real-time flood forecasting,
Hydrol. Process., 18, 2743–2746, https://doi.org/10.1002/hyp.5687, 2004. a
Todini, E.: Hydrological catchment modelling: past, present and future, Hydrol. Earth Syst. Sci., 11, 468–482, https://doi.org/10.5194/hess-11-468-2007, 2007. a, b
Todini, E.: A model conditional processor to assess predictive uncertainty in
flood forecasting, Int. J. River Basin Manage., 6, 123–137, https://doi.org/10.1080/15715124.2008.9635342, 2008. a
Todini, E.: Predictive uncertainty assessment in real time flood forecasting,
in: Uncertainties in Environmental Modelling and Consequences for Policy
Making, edited by: Baveye, P. C., Laba, M., and Mysiak, J., Springer Netherlands, Dordrecht, 205–228, 2009. a
Valéry, A., Andréassian, V., and Perrin, C.: `As simple as possible but not simpler': What is useful in a temperature-based snow-accounting routine? Part 2 – Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments, J. Hydrol., 517, 1176–1187,
https://doi.org/10.1016/j.jhydrol.2014.04.058, 2014. a
Van Steenbergen, N., Ronsyn, J., and Willems, P.: A non-parametric data-based
approach for probabilistic flood forecasting in support of uncertainty
communication, Environ. Model. Softw., 33, 92–105,
https://doi.org/10.1016/j.envsoft.2012.01.013, 2012. a
Vaze, J., Post, D., Chiew, F., Perraud, J.-M., Viney, N., and Teng, J.: Climate non-stationarity – Validity of calibrated rainfall–runoff models for use in climate change studies, J. Hydrol., 394, 447–457,
https://doi.org/10.1016/j.jhydrol.2010.09.018, 2010. a
Velazquez, J. A., Anctil, F., and Perrin, C.: Performance and reliability of
multimodel hydrological ensemble simulations based on seventeen lumped models
and a thousand catchments, Hydrol. Earth Syst. Sci., 14, 2303–2317, https://doi.org/10.5194/hess-14-2303-2010, 2010. a
Verkade, J. S. and Werner, M. G. F.: Estimating the benefits of single value
and probability forecasting for flood warning, Hydrol. Earth Syst. Sci., 15, 3751–3765, https://doi.org/10.5194/hess-15-3751-2011, 2011. a
Verkade, J. S., Brown, J., Davids, F., Reggiani, P., and Weerts, A.: Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine, J. Hydrol., 555, 257–277, https://doi.org/10.1016/j.jhydrol.2017.10.024, 2017. a
Viatgé, J., Pinna, T., Perrin, C., Dorchies, D., and Garandeau, L.: Towards an enhanced temporal flexibility of the GRP flood forecasting operational model, in: Proceedings of the SHF conference “De la prévision des crues à la gestion de crise” (From flood forecasting to crisis management), 14–16 November 2018, Société Hydrotechnique de France, Avignon, France, p. 12, 2018. a
Viatgé, J., Berthet, L., Marty, R., Bourgin, F., Piotte, O., Ramos, M. H., and Perrin, C.: Towards the real-time production of predictive intervals
around streamflow forecasts in Vigicrues in France, La Houille Blanche, 2019-1, 63–71, https://doi.org/10.1051/lhb/2019016, 2019. a
Wang, Q. J., Robertson, D. E., and Chiew, F. H. S.: A Bayesian joint
probability modeling approach for seasonal forecasting of streamflows at
multiple sites, Water Resour. Res., 45, W05407, https://doi.org/10.1029/2008wr007355, 2009. a, b
Wang, Q. J., Shrestha, D. L., Robertson, D. E., and Pokhrel, P.: A log-sinh
transformation for data normalization and variance stabilization, Water Resour. Res., 48, W05514, https://doi.org/10.1029/2011wr010973, 2012. a
Wani, O., Beckers, J. V. L., Weerts, A. H., and Solomatine, D. P.: Residual
uncertainty estimation using instance-based learning with applications to
hydrologic forecasting, Hydrol. Earth Syst. Sci., 21, 4021–4036,
https://doi.org/10.5194/hess-21-4021-2017, 2017. a
Weerts, A. H., Winsemius, H. C., and Verkade, J. S.: Estimation of predictive
hydrological uncertainty using quantile regression: examples from the
National Flood Forecasting System (England and Wales), Hydrol. Earth Syst. Sci., 15, 255–265, https://doi.org/10.5194/hess-15-255-2011, 2011. a, b
Wilby, R. L.: Uncertainty in water resource model parameters used for climate
change impact assessment, Hydrol. Process., 19, 3201–3219,
https://doi.org/10.1002/hyp.5819, 2005. a
Woldemeskel, F., McInerney, D., Lerat, J., Thyer, M., Kavetski, D., Shin, D., Tuteja, N., and Kuczera, G.: Evaluating post-processing approaches for monthly and seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 22, 6257–6278, https://doi.org/10.5194/hess-22-6257-2018, 2018. a
Wright, D. P., Thyer, M., and Westra, S.: Influential point detection
diagnostics in the context of hydrological model calibration, J. Hydrol., 527, 1161–1172, https://doi.org/10.1016/j.jhydrol.2015.05.047, 2015. a
Yang, J., Reichert, P., and Abbaspour, K. C.: Bayesian uncertainty analysis in distributed hydrologic modeling: a case study in the Thur River basin
(Switzerland), Water Resour. Res., 43, W10401, https://doi.org/10.1029/2006wr005497, 2007a.
a
Yang, J., Reichert, P., Abbaspour, K. C., and Yang, H.: Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and
Bayesian inference, J. Hydrol., 340, 167–182, https://doi.org/10.1016/j.jhydrol.2007.03.006, 2007b.
a, b, c
Zalachori, I., Ramos, M. H., Garçon, R., Mathevet, T., and Gailhard, J.:
Statistical processing of forecasts for hydrological ensemble prediction: a
comparative study of different bias correction strategies, Adv. Sci. Res., 8, 135–141, https://doi.org/10.5194/asr-8-135-2012, 2012. a
Short summary
An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. We present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows and considerable variability among catchments and across lead times.
An increasing number of flood forecasting services assess and communicate the uncertainty...
