Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-197-2022
© Author(s) 2022. 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-26-197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2022
Research article |
| 14 Jan 2022
| 14 Jan 2022
Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems
Emixi Sthefany Valdez
CORRESPONDING AUTHOR
Dept. of Civil and Water Engineering, Université Laval, 1065 Avenue de la Médecine, Quebec G1V 0A6, Canada
François Anctil
Dept. of Civil and Water Engineering, Université Laval, 1065 Avenue de la Médecine, Quebec G1V 0A6, Canada
Maria-Helena Ramos
Université Paris-Saclay, INRAE, UR HYCAR, 1 Rue Pierre-Gilles de Gennes, 92160 Antony, France
Related authors
No articles found.
Kh Rahat Usman, Rodolfo Alvarado Montero, Tadros Ghobrial, François Anctil, and Arnejan van Loenen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-116, https://doi.org/10.5194/gmd-2024-116, 2024
Preprint under review for GMD
Short summary
Short summary
Rivers in cold climate regions such as Canada undergo freeze up during winters which makes the estimation forecasting of under-ice discharge very challenging and uncertain since there is no reliable method other than direct measurements. The current study explored the potential of deploying a coupled modelling framework for the estimation and forecasting of this parameter. The framework showed promising potential in addressing the challenge of estimating and forecasting the under-ice discharge.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
Short summary
Short summary
Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
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.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-78, https://doi.org/10.5194/hess-2024-78, 2024
Revised manuscript under review for HESS
Short summary
Short summary
Observations from a study site network in eastern Canada showed a temperature interval the overlapping probabilities for rain, snow or a mix of both. Models using random forest algorithms were developed to classify the precipitation phase using meteorological data to evaluate operational applications. They showed significantly improved phase classification compared to benchmarks, but misclassification led to costlier errors. However, accurate prediction of mixed phase remains a challenge.
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and François Anctil
Hydrol. Earth Syst. Sci., 27, 2375–2395, https://doi.org/10.5194/hess-27-2375-2023, https://doi.org/10.5194/hess-27-2375-2023, 2023
Short summary
Short summary
A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. The proposed approach supports the participation of end-users in interpreting the impact of climate change on water resources.
Maryse Charpentier-Noyer, Daniela Peredo, Axelle Fleury, Hugo Marchal, François Bouttier, Eric Gaume, Pierre Nicolle, Olivier Payrastre, and Maria-Helena Ramos
Nat. Hazards Earth Syst. Sci., 23, 2001–2029, https://doi.org/10.5194/nhess-23-2001-2023, https://doi.org/10.5194/nhess-23-2001-2023, 2023
Short summary
Short summary
This paper proposes a methodological framework designed for event-based evaluation in the context of an intense flash-flood event. The evaluation adopts the point of view of end users, with a focus on the anticipation of exceedances of discharge thresholds. With a study of rainfall forecasts, a discharge evaluation and a detailed look at the forecast hydrographs, the evaluation framework should help in drawing robust conclusions about the usefulness of new rainfall ensemble forecasts.
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).
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
Short summary
Short summary
The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
Short summary
Short summary
Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Simon Ricard, Philippe Lucas-Picher, and François Anctil
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-451, https://doi.org/10.5194/hess-2021-451, 2021
Revised manuscript not accepted
Short summary
Short summary
We propose a simplified hydroclimatic modelling workflow for producing hydrologic scenarios without resorting to meteorological observations. This innovative approach preserves trends and physical consistency between simulated climate variables, allows the implementation of modelling cascades despite observation scarcity, and supports the participation of end-users in producing and interpreting climate change impacts on water resources.
Etienne Guilpart, Vahid Espanmanesh, Amaury Tilmant, and François Anctil
Hydrol. Earth Syst. Sci., 25, 4611–4629, https://doi.org/10.5194/hess-25-4611-2021, https://doi.org/10.5194/hess-25-4611-2021, 2021
Short summary
Short summary
The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.
Manon Cassagnole, Maria-Helena Ramos, Ioanna Zalachori, Guillaume Thirel, Rémy Garçon, Joël Gailhard, and Thomas Ouillon
Hydrol. Earth Syst. Sci., 25, 1033–1052, https://doi.org/10.5194/hess-25-1033-2021, https://doi.org/10.5194/hess-25-1033-2021, 2021
Antoine Thiboult, Gregory Seiller, Carine Poncelet, and François Anctil
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-6, https://doi.org/10.5194/hess-2020-6, 2020
Preprint withdrawn
Short summary
Short summary
HOOPLA, the HydrOlOgical Prediction LAboratory, is a toolbox that converts precipitation into river runoff. It relies on numerical models to compute snow accumulation and melting, water loss to the atmosphere, and the main on-land water cycle processes. HOOPLA includes several techniques to handle forecast uncertainty. In particular, it adopts a probabilistic approach to describe the model structure, the initial condition, and the meteorological uncertainties.
Audrey Maheu, Islem Hajji, François Anctil, Daniel F. Nadeau, and René Therrien
Hydrol. Earth Syst. Sci., 23, 3843–3863, https://doi.org/10.5194/hess-23-3843-2019, https://doi.org/10.5194/hess-23-3843-2019, 2019
Short summary
Short summary
We tested a new method to simulate terrestrial evaporation in a hydrological model. Given physical constraints imposed by this model, it should help avoid the overestimation of terrestrial evaporation in climate change assessments. We show the good performance of the model by comparing simulated terrestrial evaporation to observations at three sites with different climates and vegetation. Overall, this research proposes a method that will improve our ability to make streamflow projections.
Étienne Gaborit, Vincent Fortin, Xiaoyong Xu, Frank Seglenieks, Bryan Tolson, Lauren M. Fry, Tim Hunter, François Anctil, and Andrew D. Gronewold
Hydrol. Earth Syst. Sci., 21, 4825–4839, https://doi.org/10.5194/hess-21-4825-2017, https://doi.org/10.5194/hess-21-4825-2017, 2017
Short summary
Short summary
The work presents an original methodology for optimizing streamflow simulations with the distributed hydrological model GEM-Hydro.
While minimizing the computational time required for automatic calibration, the approach allows us to end up with a spatially coherent and transferable parameter set. The GEM-Hydro model is useful because it allows simulation of all physical components of the hydrological cycle in every part of a domain.
It proves to be competitive with other distributed models.
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.
Louise Crochemore, Maria-Helena Ramos, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, https://doi.org/10.5194/hess-20-3601-2016, 2016
Short summary
Short summary
This study investigates the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. Eight variants of bias correction approaches based on the linear scaling and the distribution mapping methods are applied to the precipitation forecasts prior to generating the streamflow forecasts. One of the main results of the study is that distribution mapping of daily values is successful in improving forecast reliability.
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.
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.
Antoine Thiboult, François Anctil, and Marie-Amélie Boucher
Hydrol. Earth Syst. Sci., 20, 1809–1825, https://doi.org/10.5194/hess-20-1809-2016, https://doi.org/10.5194/hess-20-1809-2016, 2016
Short summary
Short summary
Issuing a good hydrological forecast is challenging because of the numerous sources of uncertainty that lay in the description of the hydrometeorological processes. Several modeling techniques are investigated in this paper to assess how they contribute to the forecast quality. It is shown that the best modeling approach uses several dissimilar techniques that each tackle one source of uncertainty.
G. Seiller and F. Anctil
Hydrol. Earth Syst. Sci., 18, 2033–2047, https://doi.org/10.5194/hess-18-2033-2014, https://doi.org/10.5194/hess-18-2033-2014, 2014
M. H. Ramos, S. J. van Andel, and F. Pappenberger
Hydrol. Earth Syst. Sci., 17, 2219–2232, https://doi.org/10.5194/hess-17-2219-2013, https://doi.org/10.5194/hess-17-2219-2013, 2013
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Uncertainty analysis
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
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
A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context
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
Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China
Estimating the water needed to end the drought or reduce the drought severity in the Carpathian region
Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: a comparison
Comparison of drought indicators derived from multiple data sets over Africa
The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps
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).
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.
Lionel Berthet, François Bourgin, Charles Perrin, Julie Viatgé, Renaud Marty, and Olivier Piotte
Hydrol. Earth Syst. Sci., 24, 2017–2041, https://doi.org/10.5194/hess-24-2017-2020, https://doi.org/10.5194/hess-24-2017-2020, 2020
Short summary
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.
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.
C. H. Wu, G. R. Huang, and H. J. Yu
Hydrol. Earth Syst. Sci., 19, 1385–1399, https://doi.org/10.5194/hess-19-1385-2015, https://doi.org/10.5194/hess-19-1385-2015, 2015
T. Antofie, G. Naumann, J. Spinoni, and J. Vogt
Hydrol. Earth Syst. Sci., 19, 177–193, https://doi.org/10.5194/hess-19-177-2015, https://doi.org/10.5194/hess-19-177-2015, 2015
P. López López, J. S. Verkade, A. H. Weerts, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 18, 3411–3428, https://doi.org/10.5194/hess-18-3411-2014, https://doi.org/10.5194/hess-18-3411-2014, 2014
G. Naumann, E. Dutra, P. Barbosa, F. Pappenberger, F. Wetterhall, and J. V. Vogt
Hydrol. Earth Syst. Sci., 18, 1625–1640, https://doi.org/10.5194/hess-18-1625-2014, https://doi.org/10.5194/hess-18-1625-2014, 2014
K. Liechti, L. Panziera, U. Germann, and M. Zappa
Hydrol. Earth Syst. Sci., 17, 3853–3869, https://doi.org/10.5194/hess-17-3853-2013, https://doi.org/10.5194/hess-17-3853-2013, 2013
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
Addor, N., Jaun, S., Fundel, F., and Zappa, M.: An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios, Hydrol. Earth Syst. Sci., 15, 2327–2347, https://doi.org/10.5194/hess-15-2327-2011, 2011. a
Alfieri, L., Pappenberger, F., Wetterhall, F., Haiden, T., Richardson, D., and
Salamon, P.: Evaluation of ensemble streamflow predictions in Europe, J. Hydrol., 517, 913–922,
https://doi.org/10.1016/j.jhydrol.2014.06.035, 2014. a
Aminyavari, S. and Saghafian, B.: Probabilistic streamflow forecast based on
spatial post-processing of TIGGE precipitation forecasts, Stoch.
Env. Res. Risk A., 33, 1939–1950, 2019. a
Anctil, F. and Ramos, M.-H.: Verification Metrics for Hydrological Ensemble
Forecasts, in: Handbook of Hydrometeorological Ensemble Forecasting, edited by: Duan, Q., Pappenberger, F., Wood, A., and Cloke, H. L., and Schaake, J. C., Springer Berlin Heidelberg,
1–30, https://doi.org/10.1007/978-3-642-39925-1_3, 2019. a, b
Andréassian, V., Oddos, A., Michel, C., Anctil, F., Perrin, C., and Loumagne,
C.: Impact of spatial aggregation of inputs and parameters on the efficiency
of rainfall-runoff models: A theoretical study using chimera watersheds,
Water Resour. Res., 40, W05209,
https://doi.org/10.1029/2003WR002854, 2004. a
Anghileri, D., Monhart, S., Zhou, C., Bogner, K., Castelletti, A., Burlando,
P., and Zappa, M.: The Value of Subseasonal Hydrometeorological Forecasts to
Hydropower Operations: How Much Does Preprocessing Matter?, Water Resour.
Res., 55, 10159–10178, https://doi.org/10.1029/2019WR025280,
2019. a
Arsenault, R., Poulin, A., Côté, P., and Brissette, F.: Comparison of
stochastic optimization algorithms in hydrological model calibration, J. Hydrol. Eng., 19, 1374–1384,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000938, 2014. a
Bellier, J., Bontron, G., and Zin, I.: Using Meteorological Analogues for
Reordering Postprocessed Precipitation Ensembles in Hydrological Forecasting,
Water Resour. Res., 53, 10085–10107,
https://doi.org/10.1002/2017WR021245, 2017. a, b
Bergeron, O.: Guide d'utilisation 2016 – Grilles climatiques quotidiennes du
Programme de surveillance du climat du Québec, version 1.2, Québec,
Ministère du Développement durable, de l’Environnement et de la
Lutte contre les changements climatiques, Direction du suivi de l’état
de l’environnement, 33 pp., ISBN 978-2-550-74872-4, 2016. a
Bergström, S. and Forsman, A.: Development of a conceptual deterministic
rainfall-runoff model, Hydrol. Res., 4, 147–170,
https://doi.org/10.2166/nh.1973.0012, 1973. a
Beven, K.: Causal models as multiple working hypotheses about environmental
processes, C. R. Geosci., 344, 77–88,
https://doi.org/10.1016/j.crte.2012.01.005, 2012. a
Beven, K.: Facets of uncertainty: epistemic uncertainty, non-stationarity,
likelihood, hypothesis testing, and communication, Hydrolog. Sci.
J., 61, 1652–1665, https://doi.org/10.1080/02626667.2015.1031761, 2016. a
Beven, K. J. and Alcock, R. E.: Modelling everything everywhere: a new approach
to decision-making for water management under uncertainty, Freshwater
Biol., 57, 124–132, 2012. a
Biondi, D. and Todini, E.: Comparing Hydrological Postprocessors Including
Ensemble Predictions Into Full Predictive Probability Distribution of
Streamflow, Water Resour. Res., 54, 9860–9882,
https://doi.org/10.1029/2017WR022432, 2018. a
Boelee, L., Lumbroso, D. M., Samuels, P. G., and Cloke, H. L.: Estimation of
uncertainty in flood forecasts – A comparison of methods, J. Flood
Risk Manag., 12, e12516, https://doi.org/10.1111/jfr3.12516, 2019. a, b, c, d
Bogner, K., Liechti, K., Bernhard, L., Monhart, S., and Zappa, M.: Skill of
Hydrological Extended Range Forecasts for Water Resources Management in
Switzerland, Water Resour. Manag., 32, 969–984, https://doi.org/10.1007/s11269-017-1849-5,
2018. a
Boucher, M. A., Tremblay, D., Delorme, L., Perreault, L., and Anctil, F.:
Hydro-economic assessment of hydrological forecasting systems, J.
Hydrol., 416-417, 133–144, https://doi.org/10.1016/j.jhydrol.2011.11.042, 2012. a
Boucher, M.-A., Perreault, L., Anctil, F., and Favre, A.-C.: Exploratory
analysis of statistical post-processing methods for hydrological ensemble
forecasts, Hydrol. Process., 29, 1141–1155,
https://doi.org/10.1002/hyp.10234, 2015. a
Bougeault, P., Toth, Z., Bishop, C., Brown, B., Burridge, D., Chen, D. H., Ebert, B., Fuentes, M., Hamill, T. M., Mylne, K., Nicolau, J., Paccagnella, T., Park, Y.-Y., Parsons, D., Raoult, B., Schuster, D., Silva Dias, P., Swinbank, R., Takeuchi, Y., Tennant, W., Wilson, L., and Worley, S.: The THORPEX interactive grand global ensemble, B. Am. Meteorol. Soc., 91, 1059–1072, https://doi.org/10.1175/2010BAMS2853.1, 2010 (data available at: https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/, last access: 11 January 2022). a
Bourgin, F., Ramos, M. H., Thirel, G., and Andréassian, V.:
Investigating the interactions between data assimilation and post-processing
in hydrological ensemble forecasting, J. Hydrol., 519, 2775–2784,
https://doi.org/10.1016/j.jhydrol.2014.07.054, 2014. a, b
Bröcker, J.: Evaluating raw ensembles with the continuous ranked probability
score, Q. J. Roy. Meteor. Soc., 138,
1611–1617, https://doi.org/10.1002/qj.1891, 2012. a
Brown, J. D. and Seo, D. J.: Evaluation of a nonparametric post-processor for
bias correction and uncertainty estimation of hydrologic predictions,
Hydrol. Process., 27, 83–105, https://doi.org/10.1002/hyp.9263, 2013. a
Brown, J. D., Demargne, J., Seo, D.-J., and Liu, Y.: Environmental Modelling
& Software The Ensemble Verification System (EVS): A software tool for
verifying ensemble forecasts of hydrometeorological and hydrologic variables
at discrete locations, Environmental Modelling and Software, 25, 854–872,
https://doi.org/10.1016/j.envsoft.2010.01.009, 2010. a
Buizza, R.: Introduction to the special issue on “25 years of ensemble
forecasting”, Q. J. Roy. Meteor. Soc., 145,
1–11, https://doi.org/10.1002/qj.3370, 2019. a
Buizza, R. and Leutbecher, M.: The forecast skill horizon, Q. J.
Roy. Meteor. Soc., 141, 3366–3382,
https://doi.org/10.1002/qj.2619, 2015. a
Buizza, R. and Palmer, T.: The singular-vector structure of the atmospheric
general circulation, Tech. Rep., 208, Shinfield Park, Reading,
https://doi.org/10.21957/5k3hq6zqq, 1995. a
Buizza, R., Houtekamer, P., Pellerin, G., Toth, Z., Zhu, Y., and Wei, M.: A
comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems,
Mon. Weather Rev., 133, 1076–1097,
https://doi.org/10.1175/MWR2905.1, 2005. a
Buizza, R., Leutbecher, M., and Isaksen, L.: Potential use of an ensemble of
analyses in the ECMWF Ensemble Prediction System, Q. J.
Roy. Meteor. Soc., 134, 2051–2066,
https://doi.org/10.1002/qj.346, 2008. a
Burnash, R. J., Ferral, R. L., and McGuire, R. A.: A generalized streamflow
simulation system, conceptual modeling for digital computers, US Department of Commerce, National Weather Service, and State of California, Department of Water Resources, 1973. a
Cane, D., Ghigo, S., Rabuffetti, D., and Milelli, M.: Real-time flood forecasting coupling different postprocessing techniques of precipitation forecast ensembles with a distributed hydrological model. The case study of may 2008 flood in western Piemonte, Italy, Nat. Hazards Earth Syst. Sci., 13, 211–220, https://doi.org/10.5194/nhess-13-211-2013, 2013. a
Cassagnole, M., Ramos, M.-H., Zalachori, I., Thirel, G., Garçon, R., Gailhard, J., and Ouillon, T.: Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach, Hydrol. Earth Syst. Sci., 25, 1033–1052, https://doi.org/10.5194/hess-25-1033-2021, 2021. a, b
Choi, J., Won, J., Lee, O., and Kim, S.: Usefulness of Global Root Zone Soil
Moisture Product for Streamflow Prediction of Ungauged Basins, Remote
Sensing, 13, 756, https://doi.org/10.3390/rs13040756, 2021. a
Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R.: The
Schaake shuffle: A method for reconstructing space–time variability in
forecasted precipitation and temperature fields, J. Hydrometeorol.,
5, 243–262,
https://doi.org/10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2, 2004. a
Clark, M. P., Rupp, D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., Slater,
A. G., Schmidt, J., and Uddstrom, M. J.: Hydrological data assimilation with
the ensemble Kalman filter: Use of streamflow observations to update states
in a distributed hydrological model, Adv. Water Resour., 31,
1309–1324, https://doi.org/10.1016/j.advwatres.2008.06.005, 2008. a
Cloke, H. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626,
https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009. a
Coustau, M., Rousset-Regimbeau, F., Thirel, G., Habets, F., Janet, B., Martin,
E., de Saint-Aubin, C., and Soubeyroux, J.-M.: Impact of improved
meteorological forcing, profile of soil hydraulic conductivity and data
assimilation on an operational Hydrological Ensemble Forecast System over
France, J. Hydrol., 525, 781–792,
https://doi.org/10.1016/j.jhydrol.2015.04.022, 2015. a
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016. a
DeChant, C. M. and Moradkhani, H.: Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation, Hydrol. Earth Syst. Sci., 15, 3399–3410, https://doi.org/10.5194/hess-15-3399-2011, 2011. a
DeChant, C. M. and Moradkhani, H.: Examining the effectiveness and robustness
of sequential data assimilation methods for quantification of uncertainty in
hydrologic forecasting, Water Resour. Res., 48, W04518,
https://doi.org/10.1029/2011WR011011, 2012. a
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
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Effect of different
uncertainty sources on the skill of 10 day ensemble low flow forecasts for
two hydrological models, Water Resour. Res., 49, 4035–4053,
https://doi.org/10.1002/wrcr.20294, 2013. a, b
Donnelly, C., Andersson, J. C., and Arheimer, B.: Using flow signatures and
catchment similarities to evaluate the E-HYPE multi-basin model across
Europe, Hydrolog. Sci. J., 61, 255–273,
https://doi.org/10.1080/02626667.2015.1027710, 2016. a
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA global
optimization method for calibrating watershed models, J. Hydrol.,
158, 265–284, https://doi.org/10.1016/0022-1694(94)90057-4, 1994. a
Emerton, R. E., Stephens, E. M., Pappenberger, F., Pagano, T. C., Weerts,
A. H., Wood, A. W., Salamon, P., Brown, J. D., Hjerdt, N., Donnelly, C.,
Baugh, C. A., and Cloke, H. L.: Continental and global scale flood
forecasting systems, WIRES Water, 3, 391–418,
https://doi.org/10.1002/wat2.1137, 2016. a
Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical
implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Ferro, C. A., Richardson, D. S., and Weigel, A. P.: On the effect of ensemble
size on the discrete and continuous ranked probability scores, Meteorol.
Appl., 15, 19–24, https://doi.org/10.1002/met.45,
2008. a, b, c, d
Gaborit, É., Anctil, F., Fortin, V., and Pelletier, G.: On the reliability
of spatially disaggregated global ensemble rainfall forecasts, Hydrol.
Process., 27, 45–56, https://doi.org/10.1002/hyp.9509, 2013. a
Garçon, R.: Overall rain-flow model for flood forecasting and
pre-determination, Houille Blanche, 85, 88–95, https://doi.org/10.1051/lhb/1999088,
1999. a
Ghazvinian, M., Zhang, Y., and Seo, D.-J.: A Nonhomogeneous Regression-Based
Statistical Postprocessing Scheme for Generating Probabilistic Quantitative
Precipitation Forecast, J. Hydrometeorol., 21, 2275–2291,
https://doi.org/10.1175/JHM-D-20-0019.1, 2020. a
Girard, G., Morin, G., and Charbonneau, R.: Modèle
précipitations-débits à discrétisation spatiale, Cahiers
ORSTOM, série hydrologie, 9, 35–52, 1972. a
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts,
calibration and sharpness, J. R. Stat. Soc. B, 69, 243–268,
https://doi.org/10.1111/j.1467-9868.2007.00587.x, 2007. a
Gourley, J. J. and Vieux, B. E.: A method for identifying sources of model
uncertainty in rainfall-runoff simulations, J. Hydrol., 327,
68–80, https://doi.org/10.1016/j.jhydrol.2005.11.036, 2006. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hagedorn, R., Hamill, T. M., and Whitaker, J. S.: Probabilistic forecast
calibration using ECMWF and GFS ensemble reforecasts. Part I: Two-meter
temperatures, Mon. Weather Rev., 136, 2608–2619,
https://doi.org/10.1175/2007MWR2410.1, 2008. a
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
Houtekamer, P. L., Buehner, M., and De La Chevrotière, M.: Using the hybrid
gain algorithm to sample data assimilation uncertainty, Q. J. Roy. Meteor. Soc., 145,
35–56, https://doi.org/10.1002/qj.3426, 2019. a
Jakeman, A., Littlewood, I., and Whitehead, P.: Computation of the
instantaneous unit hydrograph and identifiable component flows with
application to two small upland catchments, J. Hydrol., 117,
275–300, https://doi.org/10.1016/0022-1694(90)90097-H, 1990. a
Kang, T.-H., Kim, Y.-O., and Hong, I.-P.: Comparison of pre- and
post-processors for ensemble streamflow prediction, Atmos. Sci.
Lett., 11, 153–159, https://doi.org/10.1002/asl.276, 2010. a
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input
uncertainty in hydrological modeling: 2. Application, Water Resour.
Res., 42, W03408, https://doi.org/10.1029/2005WR004376, 2006. a, b
Klemeš, V.: Operational testing of hydrological simulation models,
Hydrolog. Sci. J., 31, 13–24, https://doi.org/10.1080/02626668609491024,
1986. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol.,
424, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a
Kollet, S., Sulis, M., Maxwell, R. M., Paniconi, C., Putti, M., Bertoldi, G.,
Coon, E. T., Cordano, E., Endrizzi, S., Kikinzon, E., Mouche, E., Mügler, C., Park, Y.-J., Refsgaard, J. C., Stisen, S., and Sudicky, E.: The integrated
hydrologic model intercomparison project, IH-MIP2: A second set of benchmark
results to diagnose integrated hydrology and feedbacks, Water Resour.
Res., 53, 867–890, 2017. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of the
Köppen-Geiger climate classification updated, Meteorol.
Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a
Kwon, M., Kwon, H.-H., and Han, D.: A Hybrid Approach Combining Conceptual
Hydrological Models, Support Vector Machines and Remote Sensing Data for
Rainfall-Runoff Modeling, Remote Sensing, 12, 1801, https://doi.org/10.3390/rs12111801, 2020. a
L'hôte, Y., Chevallier, P., Coudrain, A., Lejeune, Y., and Etchevers, P.:
Relationship between precipitation phase and air temperature: comparison
between the Bolivian Andes and the Swiss Alps/Relation entre phase de
précipitation et température de l'air: comparaison entre les Andes
Boliviennes et les Alpes Suisses, Hydrolog. Sci. J., 50,
null–997, https://doi.org/10.1623/hysj.2005.50.6.989, 2005. a
Li, W., Duan, Q., Miao, C., Ye, A., Gong, W., and Di, Z.: A review on
statistical postprocessing methods for hydrometeorological ensemble
forecasting, WIRES Water, 4, e1246,
https://doi.org/10.1002/wat2.1246, 2017. a, b
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He, M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012. a
Lucatero, D., Madsen, H., Refsgaard, J. C., Kidmose, J., and Jensen, K. H.: On the skill of raw and post-processed ensemble seasonal meteorological forecasts in Denmark, Hydrol. Earth Syst. Sci., 22, 6591–6609, https://doi.org/10.5194/hess-22-6591-2018, 2018. a
Mendoza, P. A., Wood, A. W., Clark, E., Rothwell, E., Clark, M. P., Nijssen, B., Brekke, L. D., and Arnold, J. R.: An intercomparison of approaches for improving operational seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 21, 3915–3935, https://doi.org/10.5194/hess-21-3915-2017, 2017. a
Monhart, S., Zappa, M., Spirig, C., Schär, C., and Bogner, K.: Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach, Hydrol. Earth Syst. Sci., 23, 493–513, https://doi.org/10.5194/hess-23-493-2019, 2019. a
Moore, R. and Clarke, R.: A distribution function approach to rainfall runoff
modeling, Water Resour. Res., 17, 1367–1382,
https://doi.org/10.1029/WR017i005p01367, 1981. a
Noh, S. J., Rakovec, O., Weerts, A. H., and Tachikawa, Y.: On noise
specification in data assimilation schemes for improved flood forecasting
using distributed hydrological models, J. Hydrol., 519, 2707–2721,
https://doi.org/10.1016/j.jhydrol.2014.07.049, 2014. a
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, 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, b
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, b
Palmer, T.: The ECMWF ensemble prediction system: Looking back (more than) 25
years and projecting forward 25 years, Q. J. Roy.
Meteorol. Soc., 145, 12–24, https://doi.org/10.1002/qj.3383,
2019. a
Pappenberger, F., Beven, K. J., Hunter, N. M., Bates, P. D., Gouweleeuw, B. T., Thielen, J., and de Roo, A. P. J.: Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS), Hydrol. Earth Syst. Sci., 9, 381–393, https://doi.org/10.5194/hess-9-381-2005, 2005. a
Pappenberger, F., Ramos, M.-H., Cloke, H. L., Wetterhall, F., Alfieri, L.,
Bogner, K., Mueller, A., and Salamon, P.: How do I know if my forecasts are
better? Using benchmarks in hydrological ensemble prediction, J.
Hydrol., 522, 697–713, 2015. 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, edited by: Duan, Q., Pappenberger, F., Wood, A., Cloke, H. L., and Schaake, J. C., Springer Berlin Heidelberg, Berlin,
Heidelberg, 1187–1221, https://doi.org/10.1007/978-3-642-39925-1_47, 2019. a
Parker, W. S.: Model Evaluation: An Adequacy-for-Purpose View, Philos.
Sci., 87, 457–477, https://doi.org/10.1086/708691, 2020. a
Perrin, C.: Vers une amélioration d'un modèle global pluie-débit, PhD
thesis, Institut National Polytechnique de Grenoble-INPG, Grenoble, France, 287 pp., 2000. a
Poulin, A., Brissette, F., Leconte, R., Arsenault, R., and Malo, J.-S.:
Uncertainty of hydrological modelling in climate change impact studies in a
Canadian, snow-dominated river basin, J. Hydrol., 409, 626–636,
https://doi.org/10.1016/j.jhydrol.2011.08.057, 2011. a
Rakovec, O., Weerts, A. H., Hazenberg, P., Torfs, P. J. J. F., and Uijlenhoet, R.: State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy, Hydrol. Earth Syst. Sci., 16, 3435–3449, https://doi.org/10.5194/hess-16-3435-2012, 2012. a
Roulin, E. and Vannitsem, S.: Post-processing of medium-range probabilistic
hydrological forecasting: Impact of forcing, initial conditions and model
errors, Hydrol. Process., 29, 1434–1449, https://doi.org/10.1002/hyp.10259,
2015. a, b
Schaake, J. C., Hamill, T. M., Buizza, R., and Clark, M.: HEPEX: the
hydrological ensemble prediction experiment, B. Am.
Meteorol. Soc., 88, 1541–1548,
https://doi.org/10.1175/BAMS-88-10-1541, 2007. a, b
Schefzik, R., Thorarinsdottir, T. L., and Gneiting, T.: Uncertainty
Quantification in Complex Simulation Models Using Ensemble Copula Coupling,
Stat. Sci., 28, 616–640, https://doi.org/10.1214/13-STS443, 2013. a, b, c
Scheuerer, M., Hamill, T. M., Whitin, B., He, M., and Henkel, A.: A method for
preferential selection of dates in the Schaake shuffle approach to
constructing spatiotemporal forecast fields of temperature and precipitation,
Water Resour. Res., 53, 3029–3046,
https://doi.org/10.1002/2016WR020133, 2017. a, b
Seiller, G., Anctil, F., and Perrin, C.: Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions, Hydrol. Earth Syst. Sci., 16, 1171–1189, https://doi.org/10.5194/hess-16-1171-2012, 2012. a, b, c
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, b
Sharma, S., Siddique, R., Reed, S., Ahnert, P., and Mejia, A.: Hydrological
Model Diversity Enhances Streamflow Forecast Skill at Short-to Medium-Range
Timescales, Water Resour. Res., 55, 1510–1530,
https://doi.org/10.1029/2018WR023197, 2019. a, b
Sivapalan, M.: Process complexity at hillslope scale, process simplicity at the
watershed scale: is there a connection?, Hydrol. Process., 17,
1037–1041, https://doi.org/10.1002/hyp.5109, 2003. a
Slater, L. J. and Villarini, G.: Enhancing the Predictability of Seasonal
Streamflow With a Statistical-Dynamical Approach, Geophys. Res.
Lett., 45, 6504–6513, https://doi.org/10.1029/2018GL077945, 2018. a
Thiboult, A., Anctil, F., and Ramos, M.: How does the quantification of
uncertainties affect the quality and value of flood early warning systems?,
J. Hydrol., 551, 365–373, https://doi.org/10.1016/j.jhydrol.2017.05.014,
2017. a, b
Thiboult, A., Seiller, G., Poncelet, C., and Anctil, F.: The hoopla toolbox: a
hydrological prediction laboratory,
Environ. Modell. Softw., submitted, 2018. a
Thiboult, A., Seiller, G., and Anctil, F.: HOOPLA, GitHub [code], available at: https://github.com/AntoineThiboult/HOOPLA, 2019. a
Thirel, G., Salamon, P., Burek, P., and Kalas, M.: Assimilation of MODIS Snow
Cover Area Data in a Distributed Hydrological Model Using the Particle
Filter, Remote Sensing, 5, 5825–5850, https://doi.org/10.3390/rs5115825, 2013. 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
Vannitsem, S., Bremnes, J. B., Demaeyer, J., Evans, G. R., Flowerdew, J.,
Hemri, S., Lerch, S., Roberts, N., Theis, S., Atencia, A., Bouallègue,
Z. B., Bhend, J., Dabernig, M., Cruz, L. D., Hieta, L., Mestre, O., Moret,
L., Plenković, I. O., Schmeits, M., Taillardat, M., den Bergh, J. V.,
Schaeybroeck, B. V., Whan, K., and Ylhaisi, J.: Statistical Postprocessing
for Weather Forecasts – Review, Challenges and Avenues in a Big Data World,
B. Am. Meteorol. Soc., 102, E681–E699,
https://doi.org/10.1175/BAMS-D-19-0308.1, 2020. a
Velázquez, J. A., Anctil, F., Ramos, M. H., and Perrin, C.: Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures, Adv. Geosci., 29, 33–42, https://doi.org/10.5194/adgeo-29-33-2011, 2011. a
Verkade, J., Brown, J., Reggiani, P., and Weerts, A.: Post-processing ECMWF
precipitation and temperature ensemble reforecasts for operational hydrologic
forecasting at various spatial scales, J. Hydrol., 501, 73–91,
https://doi.org/10.1016/j.jhydrol.2013.07.039, 2013. a, b
Wetterhall, F., Pappenberger, F., Alfieri, L., Cloke, H. L., Thielen-del Pozo, J., Balabanova, S., Daňhelka, J., Vogelbacher, A., Salamon, P., Carrasco, I., Cabrera-Tordera, A. J., Corzo-Toscano, M., Garcia-Padilla, M., Garcia-Sanchez, R. J., Ardilouze, C., Jurela, S., Terek, B., Csik, A., Casey, J., Stankūnavičius, G., Ceres, V., Sprokkereef, E., Stam, J., Anghel, E., Vladikovic, D., Alionte Eklund, C., Hjerdt, N., Djerv, H., Holmberg, F., Nilsson, J., Nyström, K., Sušnik, M., Hazlinger, M., and Holubecka, M.: HESS Opinions “Forecaster priorities for improving probabilistic flood forecasts”, Hydrol. Earth Syst. Sci., 17, 4389–4399, https://doi.org/10.5194/hess-17-4389-2013, 2013. a
Wilks, D. S.: Statistical methods in the atmospheric sciences, vol. 100,
Elsevier, ISBN 978-0-12-815823-4, https://doi.org/10.1016/C2017-0-03921-6, 2011. a
Wu, W., Emerton, R., Duan, Q., Wood, A. W., Wetterhall, F., and Robertson,
D. E.: Ensemble flood forecasting: Current status and future opportunities,
WIRES Water, 7, e1432, https://doi.org/10.1002/wat2.1432, 2020. a
Yu, W. and Kim, S.: Accuracy improvement of flood forecasting using
pre-processing of ensemble numerical weather prediction rainfall fields,
Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 70, 151–156,
https://doi.org/10.2208/jscejhe.70.I_151, 2014. a, b
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, b
Zappa, M., Beven, K. J., Bruen, M., Cofi, A. S., Kok, K., and Martin, E.:
Propagation of uncertainty from observing systems and NWP into hydrological
models : COST-731 Working Group 2, Atmos. Sci. Lett., 11, 83–91,
https://doi.org/10.1002/asl.248, 2010.
a
Zappa, M., van Andel, S. J., and Cloke, H. L.: Introduction to Ensemble
Forecast Applications and Showcases, Springer Berlin
Heidelberg, Berlin, Heidelberg, 1181–1185, https://doi.org/10.1007/978-3-642-39925-1_45, 2019. a
Zeng, T., Wang, L., Li, X., Song, L., Zhang, X., Zhou, J., Gao, B., and Liu,
R.: A New and Simplified Approach for Estimating the Daily River Discharge of
the Tibetan Plateau Using Satellite Precipitation: An Initial Study on the
Upper Brahmaputra River, Remote Sensing, 12, 2103, https://doi.org/10.3390/rs12132103, 2020. a
Zhang, Y., Wu, L., Scheuerer, M., Schaake, J., and Kongoli, C.: Comparison of
probabilistic quantitative precipitation forecasts from two postprocessing
mechanisms, J. Hydrometeorol., 18, 2873–2891,
https://doi.org/10.1175/JHM-D-16-0293.1, 2017. a
Zhao, R., Zuang, Y., Fang, L., Liu, X., and Zhang, Q.: The Xinanjiang model,
IAHS Publications, 129, 351–356, 1980. a
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.
We investigated how a precipitation post-processor interacts with other tools for uncertainty...
