Articles | Volume 20, issue 5
https://doi.org/10.5194/hess-20-1809-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-1809-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
10 May 2016
Research article |
| 10 May 2016
Accounting for three sources of uncertainty in ensemble hydrological forecasting
Antoine Thiboult
CORRESPONDING AUTHOR
Dept. of Civil and Water Engineering, Université Laval, 1065 avenue de la Médecine, Québec, Canada
François Anctil
Dept. of Civil and Water Engineering, Université Laval, 1065 avenue de la Médecine, Québec, Canada
Marie-Amélie Boucher
Dept. of Applied Sciences, Université du Québec à Chicoutimi, 555, boulevard de l'Université, Chicoutimi, Canada
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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
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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.
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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.
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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
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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.
Alireza Amani, Marie-Amélie Boucher, Alexandre R. Cabral, Vincent Vionnet, and Étienne Gaborit
EGUsphere, https://doi.org/10.5194/egusphere-2024-1277, https://doi.org/10.5194/egusphere-2024-1277, 2024
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Accurately estimating groundwater recharge using numerical models is particularly difficult in cold regions with snow and soil freezing. This study evaluated a physics-based model against high-resolution field measurements. Our findings highlight a need for a better representation of soil freezing processes, offering a roadmap for future model development. This leads to more accurate models to aid water resources management decisions in cold climates.
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-78, https://doi.org/10.5194/hess-2024-78, 2024
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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.
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
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The Cryosphere, 16, 3489–3506, https://doi.org/10.5194/tc-16-3489-2022, https://doi.org/10.5194/tc-16-3489-2022, 2022
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The research deals with the assimilation of in-situ local snow observations in a large-scale spatialized snow modeling framework over the province of Quebec (eastern Canada). The methodology is based on proposing multiple spatialized snow scenarios using the snow model and weighting them according to the available observations. The paper especially focuses on the spatial coherence of the snow scenario proposed in the framework.
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
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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
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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.
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
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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
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Simon Ricard, Philippe Lucas-Picher, and François Anctil
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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.
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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.
Konstantin F. F. Ntokas, Jean Odry, Marie-Amélie Boucher, and Camille Garnaud
Hydrol. Earth Syst. Sci., 25, 3017–3040, https://doi.org/10.5194/hess-25-3017-2021, https://doi.org/10.5194/hess-25-3017-2021, 2021
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This article shows a conversion model of snow depth into snow water equivalent (SWE) using an ensemble of artificial neural networks. The novelty is a direct estimation of SWE and the improvement of the estimation by in-depth analysis of network structures. The usage of an ensemble allows a probabilistic estimation and, therefore, a deeper insight. It is a follow-up study of a similar study over Quebec but extends it to the whole area of Canada and improves it further.
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
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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
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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.
Rachel Bazile, Marie-Amélie Boucher, Luc Perreault, and Robert Leconte
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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.
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
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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.
Simon Matte, Marie-Amélie Boucher, Vincent Boucher, and Thomas-Charles Fortier Filion
Hydrol. Earth Syst. Sci., 21, 2967–2986, https://doi.org/10.5194/hess-21-2967-2017, https://doi.org/10.5194/hess-21-2967-2017, 2017
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In this study we set the basis of an alternative framework to replace the popular cost-loss ratio for the economic assessment of flood forecasting systems. The C-L ratio implicitly considers the decision maker to be risk-neutral, whereas it is rarely the case in real-life emergency situations. Instead of the cost-loss ratio, we propose using a utility function. We show that the decision-maker’s level of risk aversion is a crucial factor in the assessment of the economic value of flood forecasts.
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
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Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
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Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)
Global total precipitable water variations and trends over the period 1958–2021
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Identification of compound drought and heatwave events on a daily scale and across four seasons
Potential for historically unprecedented Australian droughts from natural variability and climate change
Flood risk assessment for Indian sub-continental river basins
Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities
140-year daily ensemble streamflow reconstructions over 661 catchments in France
Divergent future drought projections in UK river flows and groundwater levels
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
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Accounting for hydroclimatic properties in flood frequency analysis procedures
An increase in the spatial extent of European floods over the last 70 years
Understanding the influence of “hot” models in climate impact studies: a hydrological perspective
A semi-parametric hourly space–time weather generator
A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics
Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model
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A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates
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Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia
The agricultural expansion in South America’s Dry Chaco: Regional hydroclimate effects
Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks
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Hybrid forecasting: blending climate predictions with AI models
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Local moisture recycling across the globe
How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?
Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany
The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting
Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System
Spatial distribution of oceanic moisture contributions to precipitation over the Tibetan Plateau
Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India
Declining water resources in response to global warming and changes in atmospheric circulation patterns over southern Mediterranean France
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A comparison of hydrological models with different level of complexity in Alpine regions in the context of climate change
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Hydrological concept formation inside long short-term memory (LSTM) networks
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Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
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We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
Hydrol. Earth Syst. Sci., 28, 2579–2601, https://doi.org/10.5194/hess-28-2579-2024, https://doi.org/10.5194/hess-28-2579-2024, 2024
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The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
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Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
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Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Kyungmin Sung, Max C. A. Torbenson, and James H. Stagge
Hydrol. Earth Syst. Sci., 28, 2047–2063, https://doi.org/10.5194/hess-28-2047-2024, https://doi.org/10.5194/hess-28-2047-2024, 2024
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This study examines centuries of nonstationary trends in meteorological drought and pluvial climatology. A novel approach merges tree-ring proxy data (North American Seasonal Precipitation Atlas – NASPA) with instrumental precipitation datasets by temporally downscaling proxy data, correcting biases, and analyzing shared trends in normal and extreme precipitation anomalies. We identify regions experiencing recent unprecedented shifts towards drier or wetter conditions and shifts in seasonality.
Baoying Shan, Niko E. C. Verhoest, and Bernard De Baets
Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, https://doi.org/10.5194/hess-28-2065-2024, 2024
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This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves (CDHW) across four seasons. Using this method, we could establish the start and/or end dates of drought (or heatwave) events. We found an increase in the frequency of heatwaves and CDHW events in Belgium caused by climate change. We also found that different months have different chances of CDHW events.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
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Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Urmin Vegad, Yadu Pokhrel, and Vimal Mishra
Hydrol. Earth Syst. Sci., 28, 1107–1126, https://doi.org/10.5194/hess-28-1107-2024, https://doi.org/10.5194/hess-28-1107-2024, 2024
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A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive damage of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
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Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-42, https://doi.org/10.5194/hess-2024-42, 2024
Revised manuscript accepted for HESS
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The recent development of the a new meteorological dataset providing precipitation and temperature over France – FYRE Climate – has been transformed to streamflow time series over 1871–2012 through the used of a hydrological model. This led to the creation of the daily hydrological reconstructions called HyDRE and HyDRE. These two reconstructions are evaluated allow to better understand the variability of past hydrology over France.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
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We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
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We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
EGUsphere, https://doi.org/10.5194/egusphere-2023-3040, https://doi.org/10.5194/egusphere-2023-3040, 2024
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Forecasting river flows months in advance is crucial for many water sectors and society. In N. America, snowmelt is a key driver of river flow. This study presents a statistical workflow using snow data to forecast flows months ahead in N. American snow-fed rivers. Variations in predictability across the continent are evident, raising concerns about future river flow predictability amid a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Alexander Gelfan, Andrey Panin, Andrey Kalugin, Polina Morozova, Vladimir Semenov, Alexey Sidorchuk, Vadim Ukraintsev, and Konstantin Ushakov
Hydrol. Earth Syst. Sci., 28, 241–259, https://doi.org/10.5194/hess-28-241-2024, https://doi.org/10.5194/hess-28-241-2024, 2024
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Paleogeographical data show that 17–13 ka BP, the Caspian Sea level was 80 m above the current level. There are large disagreements on the genesis of this “Great” Khvalynian transgression of the sea, and we tried to shed light on this issue. Using climate and hydrological models as well as the paleo-reconstructions, we proved that the transgression could be initiated solely by hydroclimatic factors within the deglaciation period in the absence of the glacial meltwater effect.
Joeri B. Reinders and Samuel E. Munoz
Hydrol. Earth Syst. Sci., 28, 217–227, https://doi.org/10.5194/hess-28-217-2024, https://doi.org/10.5194/hess-28-217-2024, 2024
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Flooding presents a major hazard for people and infrastructure along waterways; however, it is challenging to study the likelihood of a flood magnitude occurring regionally due to a lack of long discharge records. We show that hydroclimatic variables like Köppen climate regions and precipitation intensity explain part of the variance in flood frequency distributions and thus reduce the uncertainty of flood probability estimates. This gives water managers a tool to locally improve flood analysis.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
EGUsphere, https://doi.org/10.5194/egusphere-2023-2890, https://doi.org/10.5194/egusphere-2023-2890, 2024
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We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extents' trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault
Hydrol. Earth Syst. Sci., 27, 4355–4367, https://doi.org/10.5194/hess-27-4355-2023, https://doi.org/10.5194/hess-27-4355-2023, 2023
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Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
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Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
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Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
Kaltrina Maloku, Benoit Hingray, and Guillaume Evin
Hydrol. Earth Syst. Sci., 27, 3643–3661, https://doi.org/10.5194/hess-27-3643-2023, https://doi.org/10.5194/hess-27-3643-2023, 2023
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High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-214, https://doi.org/10.5194/hess-2023-214, 2023
Revised manuscript accepted for HESS
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The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the Cellular Automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-181, https://doi.org/10.5194/hess-2023-181, 2023
Revised manuscript accepted for HESS
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using 5 bias-corrected GCM outputs under three shared socioeconomic pathways, five hydrological models and a deep learning model.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
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In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
Yuanhong You, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu
Hydrol. Earth Syst. Sci., 27, 2919–2933, https://doi.org/10.5194/hess-27-2919-2023, https://doi.org/10.5194/hess-27-2919-2023, 2023
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This study aims to investigate the performance of a genetic particle filter which was used as a snow data assimilation scheme across different snow climates. The results demonstrated that the genetic algorithm can effectively solve the problem of particle degeneration and impoverishment in a particle filter algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground snow depth measurement.
Patricia Lawston-Parker, Joseph A. Santanello Jr., and Nathaniel W. Chaney
Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
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Irrigation has been shown to impact weather and climate, but it has only recently been considered in prediction models. Prescribing where (globally) irrigation takes place is important to accurately simulate its impacts on temperature, humidity, and precipitation. Here, we evaluated three different irrigation maps in a weather model and found that the extent and intensity of irrigated areas and their boundaries are important drivers of weather impacts resulting from human practices.
Marcos Julien Alexopoulos, Hannes Müller-Thomy, Patrick Nistahl, Mojca Šraj, and Nejc Bezak
Hydrol. Earth Syst. Sci., 27, 2559–2578, https://doi.org/10.5194/hess-27-2559-2023, https://doi.org/10.5194/hess-27-2559-2023, 2023
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For rainfall-runoff simulation of a certain area, hydrological models are used, which requires precipitation data and temperature data as input. Since these are often not available as observations, we have tested simulation results from atmospheric models. ERA5-Land and COSMO-REA6 were tested for Slovenian catchments. Both lead to good simulations results. Their usage enables the use of rainfall-runoff simulation in unobserved catchments as a requisite for, e.g., flood protection measures.
María Agostina Bracalenti, Omar Vicente Müller, Miguel Angel Lovino, and Ernesto Hugo Berbery
EGUsphere, https://doi.org/10.5194/egusphere-2023-1427, https://doi.org/10.5194/egusphere-2023-1427, 2023
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The Gran Chaco is a large dry forest in South America that has been heavily deforested, particularly in the Dry Chaco subregion. This deforestation has changed the land’s characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.
Tuantuan Zhang, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu, and Binquan Li
Hydrol. Earth Syst. Sci., 27, 1945–1960, https://doi.org/10.5194/hess-27-1945-2023, https://doi.org/10.5194/hess-27-1945-2023, 2023
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We use circulation classifications and spatiotemporal deep neural networks to correct raw daily forecast precipitation by combining large-scale circulation patterns with local spatiotemporal information. We find that the method not only captures the westward and northward movement of the western Pacific subtropical high but also shows substantially higher bias-correction capabilities than existing standard methods in terms of spatial scale, timescale, and intensity.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
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We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Zexuan Xu, Erica R. Siirila-Woodburn, Alan M. Rhoades, and Daniel Feldman
Hydrol. Earth Syst. Sci., 27, 1771–1789, https://doi.org/10.5194/hess-27-1771-2023, https://doi.org/10.5194/hess-27-1771-2023, 2023
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The goal of this study is to understand the uncertainties of different modeling configurations for simulating hydroclimate responses in the mountainous watershed. We run a group of climate models with various configurations and evaluate them against various reference datasets. This paper integrates a climate model and a hydrology model to have a full understanding of the atmospheric-through-bedrock hydrological processes.
Jolanda J. E. Theeuwen, Arie Staal, Obbe A. Tuinenburg, Bert V. M. Hamelers, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 27, 1457–1476, https://doi.org/10.5194/hess-27-1457-2023, https://doi.org/10.5194/hess-27-1457-2023, 2023
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Evaporation changes over land affect rainfall over land via moisture recycling. We calculated the local moisture recycling ratio globally, which describes the fraction of evaporated moisture that rains out within approx. 50 km of its source location. This recycling peaks in summer as well as over wet and elevated regions. Local moisture recycling provides insight into the local impacts of evaporation changes and can be used to study the influence of regreening on local rainfall.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
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Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 1109–1132, https://doi.org/10.5194/hess-27-1109-2023, https://doi.org/10.5194/hess-27-1109-2023, 2023
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Rainfall volumes at varying duration and frequencies are required for many engineering water works. These design volumes have been provided by KOSTRA-DWD in Germany. However, a revision of the KOSTRA-DWD is required, in order to consider the recent state-of-the-art and additional data. For this purpose, in our study, we investigate different methods and data available to achieve the best procedure that will serve as a basis for the development of the new KOSTRA-DWD product.
Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders
Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, https://doi.org/10.5194/hess-27-501-2023, 2023
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Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai
Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, https://doi.org/10.5194/hess-27-139-2023, 2023
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Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
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Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Ying Li, Chenghao Wang, Ru Huang, Denghua Yan, Hui Peng, and Shangbin Xiao
Hydrol. Earth Syst. Sci., 26, 6413–6426, https://doi.org/10.5194/hess-26-6413-2022, https://doi.org/10.5194/hess-26-6413-2022, 2022
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Spatial quantification of oceanic moisture contribution to the precipitation over the Tibetan Plateau (TP) contributes to the reliable assessments of regional water resources and the interpretation of paleo archives in the region. Based on atmospheric reanalysis datasets and numerical moisture tracking, this work reveals the previously underestimated oceanic moisture contributions brought by the westerlies in winter and the overestimated moisture contributions from the Indian Ocean in summer.
Urmin Vegad and Vimal Mishra
Hydrol. Earth Syst. Sci., 26, 6361–6378, https://doi.org/10.5194/hess-26-6361-2022, https://doi.org/10.5194/hess-26-6361-2022, 2022
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Floods cause enormous damage to infrastructure and agriculture in India. However, the utility of ensemble meteorological forecast for hydrologic prediction has not been examined. Moreover, Indian river basins have a considerable influence of reservoirs that alter the natural flow variability. We developed a hydrologic modelling-based streamflow prediction considering the influence of reservoirs in India.
Camille Labrousse, Wolfgang Ludwig, Sébastien Pinel, Mahrez Sadaoui, Andrea Toreti, and Guillaume Lacquement
Hydrol. Earth Syst. Sci., 26, 6055–6071, https://doi.org/10.5194/hess-26-6055-2022, https://doi.org/10.5194/hess-26-6055-2022, 2022
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The interest of this study is to demonstrate that we identify two zones in our study area whose hydroclimatic behaviours are uneven. By investigating relationships between the hydroclimatic conditions in both clusters for past observations with the overall atmospheric functioning, we show that the inequalities are mainly driven by a different control of the atmospheric teleconnection patterns over the area.
Daeha Kim, Minha Choi, and Jong Ahn Chun
Hydrol. Earth Syst. Sci., 26, 5955–5969, https://doi.org/10.5194/hess-26-5955-2022, https://doi.org/10.5194/hess-26-5955-2022, 2022
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We proposed a practical method that predicts the evaporation rates on land surfaces (ET) where only atmospheric data are available. Using a traditional equation that describes partitioning of precipitation into ET and streamflow, we could approximately identify the key parameter of the predicting formulation based on land–atmosphere interactions. The simple method conditioned by local climates outperformed sophisticated models in reproducing water-balance estimates across Australia.
Ruksana H. Rimi, Karsten Haustein, Emily J. Barbour, Sarah N. Sparrow, Sihan Li, David C. H. Wallom, and Myles R. Allen
Hydrol. Earth Syst. Sci., 26, 5737–5756, https://doi.org/10.5194/hess-26-5737-2022, https://doi.org/10.5194/hess-26-5737-2022, 2022
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Extreme rainfall events are major concerns in Bangladesh. Heavy downpours can cause flash floods and damage nearly harvestable crops in pre-monsoon season. While in monsoon season, the impacts can range from widespread agricultural loss, huge property damage, to loss of lives and livelihoods. This paper assesses the role of anthropogenic climate change drivers in changing risks of extreme rainfall events during pre-monsoon and monsoon seasons at local sub-regional-scale within Bangladesh.
Brigitta Simon-Gáspár, Gábor Soós, and Angela Anda
Hydrol. Earth Syst. Sci., 26, 4741–4756, https://doi.org/10.5194/hess-26-4741-2022, https://doi.org/10.5194/hess-26-4741-2022, 2022
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Due to climate change, it is extremely important to determine evaporation as accurately as possible. In nature, there are sediments and macrophytes in the open waters; thus, one of the aims was to investigate their effect on evaporation. The second aim of this paper was to estimate daily evaporation by using different models, which, according to results, have high priority in the evaporation prediction. Water management can obtain useful information from the results of the current research.
Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Hydrol. Earth Syst. Sci., 26, 4603–4618, https://doi.org/10.5194/hess-26-4603-2022, https://doi.org/10.5194/hess-26-4603-2022, 2022
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There have been many machine learning simulation studies based on eddy-covariance observations for water flux and evapotranspiration. We performed a meta-analysis of such studies to clarify the impact of different algorithms and predictors, etc., on the reported prediction accuracy. It can, to some extent, guide future global water flux modeling studies and help us better understand the terrestrial ecosystem water cycle.
Yaozhi Jiang, Kun Yang, Hua Yang, Hui Lu, Yingying Chen, Xu Zhou, Jing Sun, Yuan Yang, and Yan Wang
Hydrol. Earth Syst. Sci., 26, 4587–4601, https://doi.org/10.5194/hess-26-4587-2022, https://doi.org/10.5194/hess-26-4587-2022, 2022
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Our study quantified the altitudinal precipitation gradients (PGs) over the Third Pole (TP). Most sub-basins in the TP have positive PGs, and negative PGs are found in the Himalayas, the Hengduan Mountains and the western Kunlun. PGs are positively correlated with wind speed but negatively correlated with relative humidity. In addition, PGs tend to be positive at smaller spatial scales compared to those at larger scales. The findings can assist precipitation interpolation in the data-sparse TP.
Francesca Carletti, Adrien Michel, Francesca Casale, Alice Burri, Daniele Bocchiola, Mathias Bavay, and Michael Lehning
Hydrol. Earth Syst. Sci., 26, 3447–3475, https://doi.org/10.5194/hess-26-3447-2022, https://doi.org/10.5194/hess-26-3447-2022, 2022
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High Alpine catchments are dominated by the melting of seasonal snow cover and glaciers, whose amount and seasonality are expected to be modified by climate change. This paper compares the performances of different types of models in reproducing discharge among two catchments under present conditions and climate change. Despite many advantages, the use of simpler models for climate change applications is controversial as they do not fully represent the physics of the involved processes.
Ivan Vorobevskii, Thi Thanh Luong, Rico Kronenberg, Thomas Grünwald, and Christian Bernhofer
Hydrol. Earth Syst. Sci., 26, 3177–3239, https://doi.org/10.5194/hess-26-3177-2022, https://doi.org/10.5194/hess-26-3177-2022, 2022
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In the study we analysed the uncertainties of the meteorological data and model parameterization for evaporation modelling. We have taken a physically based lumped BROOK90 model and applied it in three different frameworks using global, regional and local datasets. Validating the simulations with eddy-covariance data from five stations in Germany, we found that the accuracy model parameterization plays a bigger role than the quality of the meteorological forcing.
Thomas Lees, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens De Bruijn, Reetik Kumar Sahu, Peter Greve, Louise Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, https://doi.org/10.5194/hess-26-3079-2022, 2022
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Despite the accuracy of deep learning rainfall-runoff models, we are currently uncertain of what these models have learned. In this study we explore the internals of one deep learning architecture and demonstrate that the model learns about intermediate hydrological stores of soil moisture and snow water, despite never having seen data about these processes during training. Therefore, we find evidence that the deep learning approach learns a physically realistic mapping from inputs to outputs.
Huajin Lei, Hongyu Zhao, and Tianqi Ao
Hydrol. Earth Syst. Sci., 26, 2969–2995, https://doi.org/10.5194/hess-26-2969-2022, https://doi.org/10.5194/hess-26-2969-2022, 2022
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How to combine multi-source precipitation data effectively is one of the hot topics in hydrometeorological research. This study presents a two-step merging strategy based on machine learning for multi-source precipitation merging over China. The results demonstrate that the proposed method effectively distinguishes the occurrence of precipitation events and reduces the error in precipitation estimation. This method is robust and may be successfully applied to other areas even with scarce data.
Alexane Lovat, Béatrice Vincendon, and Véronique Ducrocq
Hydrol. Earth Syst. Sci., 26, 2697–2714, https://doi.org/10.5194/hess-26-2697-2022, https://doi.org/10.5194/hess-26-2697-2022, 2022
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The hydrometeorological skills of two new nowcasting systems for forecasting Mediterranean intense rainfall events and floods are investigated. The results reveal that up to 75 or 90 min of forecast the performance of the nowcasting system blending numerical weather prediction and extrapolation of radar estimation is higher than the numerical weather model. For lead times up to 3 h the skills are equivalent in general. Using these nowcasting systems for flash flood forecasting is also promising.
Cited articles
Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: A comparison of the Canadian global and regional meteorological ensemble prediction systems for short-term hydrological forecasting (vol 141, pg 3462, 2013), Mon. Weather Rev., 142, 2561–2562, https://doi.org/10.1175/mwr-d-14-00018.1, 2014.
Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: Exploration of sequential streamflow assimilation in snow dominated watersheds, Adv. Water Resour., 80, 79–89, https://doi.org/10.1016/j.advwatres.2015.03.011, 2015.
Ajami, N. K., Duan, Q., Gao, X., and Sorooshian, S.: Multimodel combination techniques for analysis of hydrological simulations: Application to Distributed Model Intercomparison Project results, J. Hydrometeorol., 7, 755–768, https://doi.org/10.1175/jhm519.1, 2006.
Ajami, N. K., Duan, Q. Y., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005wr004745, 2007.
Bartholmes, J. C., Thielen, J., Ramos, M. H., and Gentilini, S.: The european flood alert system EFAS – Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts, Hydrol. Earth Syst. Sci., 13, 141–153, https://doi.org/10.5194/hess-13-141-2009, 2009.
Bergström, S. and Forsman, A.: Development of a conceptual deterministic rainfall-runoff model, Nord. Hydrol., 4, 147–170, 1973.
Beven, K. J.: On doing better hydrological science, Hydrol. Process., 22, 3549–3553, https://doi.org/10.1002/hyp.7108, 2008.
Beven, K. J. and Binley, A.: The future of distributed models – Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Beven, K. J. and Binley, A.: GLUE: 20 years on, Hydrol. Process., 28, 5897–5918, https://doi.org/10.1002/hyp.10082, 2014.
Beven, K. J., Kirkby, M. J., Schofield, N., and Tagg, A. F.: Testing a physically-based flood forecasting model (TOPMODEL) for 3 UK catchments, J. Hydrol., 69, 119–143, https://doi.org/10.1016/0022-1694(84)90159-8, 1984.
Boucher, M. A., Tremblay, D., Delorme, L., Perreault, L., and Anctil, F.: Hydro-economic assessment of hydrological forecasting systems, J. Hydrol., 416, 133–144, https://doi.org/10.1016/j.jhydrol.2011.11.042, 2012.
Bourdin, D. R., Fleming, S. W., and Stull, R. B.: Streamflow Modelling: A Primer on Applications, Approaches and Challenges, Atmos.-Ocean, 50, 507–536, https://doi.org/10.1080/07055900.2012.734276, 2012.
Breuer, L., Huisman, J. A., Willems, P., Bormann, H., Bronstert, A., Croke, B. F. W., Frede, H. G., Graff, T., Hubrechts, L., Jakeman, A. J., Kite, G., Lanini, J., Leavesley, G., Lettenmaier, D. P., Lindstrom, G., Seibert, J., Sivapalan, M., and Viney, N. R.: Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM). I: Model intercomparison with current land use, Adv. Water Resour., 32, 129–146, https://doi.org/10.1016/j.advwatres.2008.10.003, 2009.
Brochero, D., Anctil, F., and Gagne, C.: Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space, Hydrol. Earth Syst. Sci., 15, 3327–3341, https://doi.org/10.5194/hess-15-3327-2011, 2011.
Brochero, D., Gagne, C., and Anctil, F.: Evolutionary Multiobjective Optimization for Selecting Members of an Ensemble Streamflow Forecasting Model, Gecco'13: Proceedings of the 2013 Genetic and Evolutionary Computation Conference, Amsterdam, The Netherlands, 1221–1228, 2013.
Buizza, R., Miller, M., and Palmer, N.: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System, Q. J. Roy. Meteorol. Soc., 125, 2887–2908, 1999.
Burnash, R. J. C., Ferral, R. L., and McGuire, R. A.: A Generalized Streamflow Simulation System – Conceptual Modelling for Digital Computers, Technical Report, Joint Federal and State River Forecast Center, US National Weather Service and California Department of Water Resources, Sacramento, p. 204, 1973.
Chen, H., Yang, D. W., Hong, Y., Gourley, J. J., and Zhang, Y.: Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting, Adv. Water Resour., 59, 209–220, https://doi.org/10.1016/j.advwatres.2013.06.010, 2013.
Chiew, F. H. S., Peel, M. C., and Western, A. W.: Application and testing of the simple rainfall-runoff model SIMHYD, in Mathematical Models of Small Watershed Hydrology and Applications, Water Resources Publication, Littleton, Colorado, 335–367, 2002.
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.
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009.
Cormary, Y. and Guilbot, A.: Étude des relations pluie-débit sur trois bassins versants d'investigation, in: IAHS Madrid Symposium, Madrid, 265–279, 1973.
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.
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.
Demargne, J., Wu, L. M., Regonda, S. K., Brown, J. D., Lee, H., He, M. X., Seo, D. J., Hartman, R., Herr, H. D., Fresch, M., Schaake, J., and Zhu, Y. J.: 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.
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.
Fortin, V. and Turcotte, R.: Le modèle hydrologique MOHYSE, Note de cours pour SCA7420, Report, Département des sciences de la terre et de l'atmosphère, Université du Québec à Montreal, Montreal, 17 pp., 2007.
Fortin, V., Abaza, M., Anctil, F., and Turcotte, R.: Why should ensemble spread match the RMSE of the ensemble mean?, J. Hydrometeorol., 15, 1708–1713, https://doi.org/10.1175/JHM-D-14-0008.1, 2014.
Fraley, C., Raftery, A. E., and Gneiting, T.: Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging, Mon. Weather Rev., 138, 190–202, https://doi.org/10.1175/2009mwr3046.1, 2010.
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.
Garçon, R.: Modèle global Pluie-Débit pour la prévision et la prédétermination des crues, La Houille Blanche, 7/8, 88–95, https://doi.org/10.1051/lhb/1999088, 1999.
Girard, G., Morin, G., and Charbonneau, R.: Modèle précipitations-débits à discrétisation spatiale, Cahiers ORSTOM, Série Hydrologie, 9, 35–52, 1972.
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction, and estimation, J. Am. Stat. Assoc., 102, 359–378, https://doi.org/10.1198/016214506000001437, 2007.
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.
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.
Houtekamer, P. L., Mitchell, H. L., and Deng, X. X.: Model Error Representation in an Operational Ensemble Kalman Filter, Mon. Weather Rev., 137, 2126–2143, https://doi.org/10.1175/2008mwr2737.1, 2009.
Jakeman, A. J., Littlewood, I. G., and Whitehead, P. G.: 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.
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.
Lee, H., Seo, D. J., and Koren, V.: Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: Effects of uncertainties in the data and initial model soil moisture states, Adv. Water Resour., 34, 1597–1615, https://doi.org/10.1016/j.advwatres.2011.08.012, 2011.
Li, Y., Ryu, D., Wang, Q., Pagano, T., Western, A., Hapuarachchi, P., and Toscas, P.: Assimilation of streamflow discharge into a continuous flood forecasting model, in: The International Union of Geodesy and Geophysics XXV General Assembly, vol. 347, edited by: Bloschl, G., Takeuchi, K., Jain, S., Farnleitner, A., and Schumann, A., Internation Association of Hydrological Sciences Press, Melbourne, 107–113, 2011.
Liu, Y. Q. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework, Water Resour. Res., 43, W07401, https://doi.org/10.1029/2006wr005756, 2007.
Mandel, J.: Efficient Implementation of the Ensemble Kalman Filter, Report, University of Colorado at Denver and Health Sciences Center, Denber, 9 pp., 2006.
Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability distributions, Management Science, 22, 1087–1096, https://doi.org/10.1287/mnsc.22.10.1087, 1976.
Mazenc, B., Sanchez, M., and Thiery, D.: Analyse de l'influence de la physiographie d'un bassin versant sur les paramètres d'un modèle hydrologique global et sur les débits caractéristiques à l'exutoire, J. Hydrol., 69, 97–188, 1984.
McMillan, H. K., Hreinsson, E. O., Clark, M. P., Singh, S. K., Zammit, C., and Uddstrom, M. J.: Operational hydrological data assimilation with the recursive ensemble Kalman filter, Hydrol. Earth Syst. Sci., 17, 21–38, https://doi.org/10.5194/hess-17-21-2013, 2013.
Molteni, F., Buizza, R., Palmer, T. N., and Petroliagis, T.: The ECMWF ensemble prediction system: Methodology and validation, Q. J. Roy. Meteorol. Soc., 122, 73–119, https://doi.org/10.1002/qj.49712252905, 1996.
Moore, R. J. and Clarke, R. T.: A distribution function approach to rainfall runoff modeling, Water Resour. Res., 17, 1367–1382, https://doi.org/10.1029/WR017i005p01367, 1981.
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, 2005.
Nielsen, S. A. and Hansen, E.: Numerical simulation of the rainfall-runoff process on a daily basis, Nord. Hydrol., 4, 171–190, 1973.
Noh, S. J., Tachikawa, Y., Shiiba, M., and Kim, S.: Sequential data assimilation for streamflow forecasting using a distributed hydrologic model: particle filtering and ensemble Kalman filtering, in: Floods: from Risk to Opportunity, 357, IAHS Publications, Tokyo, 341–349, 2013.
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.
O'Connell, P. E., Nash, J. E., and Farrell, J. P.: River flow forecasting through conceptual models, Part II – The Brosna catchment at Ferbane, J. Hydrol., 10, 317–329, 1970.
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.
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.
Perrin, C.: Vers une amélioration d'un modèle global pluie-débit, Thesis, Institut National Polytechnique de Grenoble, Grenoble, 287 pp., 2000.
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.
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.
Rakovec, O., Weerts, A. H., Hazenberg, P., Torfs, P., 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.
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.
Salamon, P. and Feyen, L.: Disentangling uncertainties in distributed hydrological modeling using multiplicative error models and sequential data assimilation, Water Resour. Res., 46, W12501, https://doi.org/10.1029/2009wr009022, 2010.
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.
Stanski, H. R., Wilson, L. J., and Burrows, W. R.: Survey of common verification methods in meteorology, WMO World Weather Watch Tech Report 8, WMO, Downsview, Ontario, 8 pp., 1989.
Sugawara, M.: Automatic calibration of the tank model, Hydrolog. Sci., 24, 375–388, 1979.
Thiboult, A. and Anctil, F.: On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments, J. Hydrol., 529, 1147–1160, 2015a.
Thiboult, A. and Anctil, F.: Assessment of a multimodel ensemble against an operational hydrological forecasting system, Can. Water Resour. J., 40, 272–284, 2015b.
Thiery, D.: Utilisation d'un modèle global pour identifier sur un niveau piézométrique des influences multiples dues à diverses activités humaines, Improvement of methods of long term prediction of variations in groundwater resources and regimes due to human activity, 136, IAHS Publications, Exeter, 71–77, 1982.
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.
Thornthwaite, C. W. and Mather, J. R.: The water balance, Report, Drexel Institute of Climatology, Centerton, New Jersey, 86 pp., 1955.
Valery, A., Andreassian, 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.
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.
Verkade, J. S., Brown, J. D., Reggiani, P., and Weerts, A. H.: Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales, J. Hydrol., 501, 73–91, 2013.
Viney, N. R., Bormann, H., Breuer, L., Bronstert, A., Croke, B. F. W., Frede, H., Graeff, T., Hubrechts, L., Huisman, J. A., Jakeman, A. J., Kite, G. W., Lanini, J., Leavesley, G., Lettenmaier, D. P., Lindstroem, G., Seibert, J., Sivapalan, M., and Willems, P.: Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions, Adv. Water Resourc., 32, 147–158, https://doi.org/10.1016/j.advwatres.2008.05.006, 2009.
Vrugt, J. A. and Robinson, B. A.: Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging, Water Resour. Res., 43, W01411, https://doi.org/10.1029/2005wr004838, 2007.
Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, 1201, https://doi.org/10.1029/2002wr001642, 2003.
Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., and Sorooshian, S.: A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5, 13–26, https://doi.org/10.5194/hess-5-13-2001, 2001.
Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., and Krayer von Krauss, M. P.: Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support, Integrat. Assess., 4, 5–17, https://doi.org/10.1076/iaij.4.1.5.16466, 2003.
Warmerdam, P. M., Kole, J., and Chormanski, J.: Modelling rainfall-runoff processes in the Hupselse Beek research basin, in: IHP-V, Technical Documents in Hydrology, IHP/UNESCO – ERB – CEREG, Strasbourg, 155–160, 1997.
Weerts, A. H. and El Serafy, G. Y. H.: Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models, Water Resour. Res., 42, W09403, https://doi.org/10.1029/2005wr004093, 2006.
Wetterhall, F., Pappenberger, F., Alfieri, L., Cloke, H. L., Thielen-del Pozo, J., Balabanova, S., Danhelka, 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., Stankunavicius, G., Ceres, V., Sprokkereef, E., Stam, J., Anghel, E., Vladikovic, D., Eklund, C. A., Hjerdt, N., Djerv, H., Holmberg, F., Nilsson, J., Nystrom, K., Susnik, 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.
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.
Zhang, D., Madsen, H., Ridler, M., Refsgaard, J. C., and Jensen, K. H.: Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model, Adv. Water Resour., 86, 400–413, 2015.
Zhao, R. J., Zuang, Y. L., Fang, L. R., Liu, X. R., and Zhang, Q.: The Xinanjiang model, Hydrological Forecasting, IAHS Publ., 129, 351–356, 1980.
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.
Issuing a good hydrological forecast is challenging because of the numerous sources of...
