Articles | Volume 21, issue 10
https://doi.org/10.5194/hess-21-5273-2017
© Author(s) 2017. 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-21-5273-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Oct 2017
Research article |
| 19 Oct 2017
Performance of ensemble streamflow forecasts under varied hydrometeorological conditions
Harm-Jan F. Benninga
CORRESPONDING AUTHOR
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, the Netherlands
present address: Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the Netherlands
Martijn J. Booij
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, the Netherlands
Renata J. Romanowicz
Institute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland
Tom H. M. Rientjes
Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the Netherlands
Related authors
Rogier van der Velde, Harm-Jan F. Benninga, Bas Retsios, Paul C. Vermunt, and M. Suhyb Salama
Earth Syst. Sci. Data, 15, 1889–1910, https://doi.org/10.5194/essd-15-1889-2023, https://doi.org/10.5194/essd-15-1889-2023, 2023
Short summary
Short summary
From 2009, a network of 20 profile soil moisture and temperature monitoring stations has been operational in the Twente region, east of the Netherlands. In addition, field campaigns have been conducted covering four growing seasons during which soil moisture was measured near 12 monitoring stations. We describe the monitoring network and field campaigns, and we provide an overview of open third-party datasets that may support the use of the Twente datasets.
Rogier van der Velde, Andreas Colliander, Michiel Pezij, Harm-Jan F. Benninga, Rajat Bindlish, Steven K. Chan, Thomas J. Jackson, Dimmie M. D. Hendriks, Denie C. M. Augustijn, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 473–495, https://doi.org/10.5194/hess-25-473-2021, https://doi.org/10.5194/hess-25-473-2021, 2021
Short summary
Short summary
NASA’s SMAP satellite provides estimates of the amount of water in the soil. With measurements from a network of 20 monitoring stations, the accuracy of these estimates has been studied for a 4-year period. We found an agreement between satellite and in situ estimates in line with the mission requirements once the large mismatches associated with rapidly changing water contents, e.g. soil freezing and rainfall, are excluded.
Joost Buitink, Anne M. Swank, Martine van der Ploeg, Naomi E. Smith, Harm-Jan F. Benninga, Frank van der Bolt, Coleen D. U. Carranza, Gerbrand Koren, Rogier van der Velde, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 24, 6021–6031, https://doi.org/10.5194/hess-24-6021-2020, https://doi.org/10.5194/hess-24-6021-2020, 2020
Short summary
Short summary
The amount of water stored in the soil is critical for the productivity of plants. Plant productivity is either limited by the available water or by the available energy. In this study, we infer this transition point by comparing local observations of water stored in the soil with satellite observations of vegetation productivity. We show that the transition point is not constant with soil depth, indicating that plants use water from deeper layers when the soil gets drier.
Harm-Jan F. Benninga, Coleen D. U. Carranza, Michiel Pezij, Pim van Santen, Martine J. van der Ploeg, Denie C. M. Augustijn, and Rogier van der Velde
Earth Syst. Sci. Data, 10, 61–79, https://doi.org/10.5194/essd-10-61-2018, https://doi.org/10.5194/essd-10-61-2018, 2018
Short summary
Short summary
Soil moisture is a central hydrological state variable. We set up a soil moisture and soil temperature profile monitoring network of 15 stations, distributed over the 495 km2 Raam region. The Raam catchment faces dry and wet periods, which both have implications for agricultural and regional water management. The measurements at 5 cm depth provide a reference for soil moisture retrievals from earth observations, while the measurements at deeper layers enable investigation of root zone processes.
Rogier van der Velde, Harm-Jan F. Benninga, Bas Retsios, Paul C. Vermunt, and M. Suhyb Salama
Earth Syst. Sci. Data, 15, 1889–1910, https://doi.org/10.5194/essd-15-1889-2023, https://doi.org/10.5194/essd-15-1889-2023, 2023
Short summary
Short summary
From 2009, a network of 20 profile soil moisture and temperature monitoring stations has been operational in the Twente region, east of the Netherlands. In addition, field campaigns have been conducted covering four growing seasons during which soil moisture was measured near 12 monitoring stations. We describe the monitoring network and field campaigns, and we provide an overview of open third-party datasets that may support the use of the Twente datasets.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Oleksandr Mialyk, Joep F. Schyns, Martijn J. Booij, and Rick J. Hogeboom
Hydrol. Earth Syst. Sci., 26, 923–940, https://doi.org/10.5194/hess-26-923-2022, https://doi.org/10.5194/hess-26-923-2022, 2022
Short summary
Short summary
As the global demand for crops is increasing, it is vital to understand spatial and temporal patterns of crop water footprints (WFs). Previous studies looked into spatial patterns but not into temporal ones. Here, we present a new process-based gridded crop model to simulate WFs and apply it for maize in 1986–2016. We show that despite the average unit WF reduction (−35 %), the global WF of maize production has increased (+50 %), which might harm ecosystems and human livelihoods in some regions.
Rogier van der Velde, Andreas Colliander, Michiel Pezij, Harm-Jan F. Benninga, Rajat Bindlish, Steven K. Chan, Thomas J. Jackson, Dimmie M. D. Hendriks, Denie C. M. Augustijn, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 473–495, https://doi.org/10.5194/hess-25-473-2021, https://doi.org/10.5194/hess-25-473-2021, 2021
Short summary
Short summary
NASA’s SMAP satellite provides estimates of the amount of water in the soil. With measurements from a network of 20 monitoring stations, the accuracy of these estimates has been studied for a 4-year period. We found an agreement between satellite and in situ estimates in line with the mission requirements once the large mismatches associated with rapidly changing water contents, e.g. soil freezing and rainfall, are excluded.
Joost Buitink, Anne M. Swank, Martine van der Ploeg, Naomi E. Smith, Harm-Jan F. Benninga, Frank van der Bolt, Coleen D. U. Carranza, Gerbrand Koren, Rogier van der Velde, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 24, 6021–6031, https://doi.org/10.5194/hess-24-6021-2020, https://doi.org/10.5194/hess-24-6021-2020, 2020
Short summary
Short summary
The amount of water stored in the soil is critical for the productivity of plants. Plant productivity is either limited by the available water or by the available energy. In this study, we infer this transition point by comparing local observations of water stored in the soil with satellite observations of vegetation productivity. We show that the transition point is not constant with soil depth, indicating that plants use water from deeper layers when the soil gets drier.
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.
Webster Gumindoga, Tom H. M. Rientjes, Alemseged Tamiru Haile, Hodson Makurira, and Paolo Reggiani
Hydrol. Earth Syst. Sci., 23, 2915–2938, https://doi.org/10.5194/hess-23-2915-2019, https://doi.org/10.5194/hess-23-2915-2019, 2019
Short summary
Short summary
We evaluate the influence of elevation and distance from large-scale open water bodies on bias for CMORPH satellite rainfall in the Zambezi basin. Effects of distance > 10 km from water bodies are minimal, whereas the effects at shorter distances are indicated but are not conclusive for lack of rain gauges. Taylor diagrams show station elevation influencing CMORPH performance. The
spatio-temporaland newly developed
elevation zonebias schemes proved more effective in removing CMORPH bias.
Dagnenet Fenta Mekonnen, Zheng Duan, Tom Rientjes, and Markus Disse
Hydrol. Earth Syst. Sci., 22, 6187–6207, https://doi.org/10.5194/hess-22-6187-2018, https://doi.org/10.5194/hess-22-6187-2018, 2018
Short summary
Short summary
Understanding responses by changes in land use and land cover (LULC) and climate over the past decades on streamflow in the upper Blue Nile River basin is important for water management and water resource planning. Streamflow in the UBNRB has shown an increasing trend over the last 40 years, while rainfall has shown no trend change. LULC change detection findings indicate increases in cultivated land and decreases in forest coverage prior to 1995.
Harm-Jan F. Benninga, Coleen D. U. Carranza, Michiel Pezij, Pim van Santen, Martine J. van der Ploeg, Denie C. M. Augustijn, and Rogier van der Velde
Earth Syst. Sci. Data, 10, 61–79, https://doi.org/10.5194/essd-10-61-2018, https://doi.org/10.5194/essd-10-61-2018, 2018
Short summary
Short summary
Soil moisture is a central hydrological state variable. We set up a soil moisture and soil temperature profile monitoring network of 15 stations, distributed over the 495 km2 Raam region. The Raam catchment faces dry and wet periods, which both have implications for agricultural and regional water management. The measurements at 5 cm depth provide a reference for soil moisture retrievals from earth observations, while the measurements at deeper layers enable investigation of root zone processes.
Hadush K. Meresa and Renata J. Romanowicz
Hydrol. Earth Syst. Sci., 21, 4245–4258, https://doi.org/10.5194/hess-21-4245-2017, https://doi.org/10.5194/hess-21-4245-2017, 2017
Short summary
Short summary
Evaluation of the uncertainty in projections of future hydrological extremes in the mountainous catchment was performed. The uncertainty of the estimate of 1-in-100-year return maximum flow based on the 1971–2100 time series exceeds 200 % of its median value with the largest influence of the climate model uncertainty, while the uncertainty of the 1-in-100-year return minimum flow is of the same order (i.e. exceeds 200 %) but it is mainly influenced by the hydrological model parameter uncertainty.
Gregor Laaha, Tobias Gauster, Lena M. Tallaksen, Jean-Philippe Vidal, Kerstin Stahl, Christel Prudhomme, Benedikt Heudorfer, Radek Vlnas, Monica Ionita, Henny A. J. Van Lanen, Mary-Jeanne Adler, Laurie Caillouet, Claire Delus, Miriam Fendekova, Sebastien Gailliez, Jamie Hannaford, Daniel Kingston, Anne F. Van Loon, Luis Mediero, Marzena Osuch, Renata Romanowicz, Eric Sauquet, James H. Stagge, and Wai K. Wong
Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, https://doi.org/10.5194/hess-21-3001-2017, 2017
Short summary
Short summary
In 2015 large parts of Europe were affected by a drought. In terms of low flow magnitude, a region around the Czech Republic was most affected, with return periods > 100 yr. In terms of deficit volumes, the drought was particularly severe around S. Germany where the event lasted notably long. Meteorological and hydrological events developed differently in space and time. For an assessment of drought impacts on water resources, hydrological data are required in addition to meteorological indices.
Tom Brouwer, Dirk Eilander, Arnejan van Loenen, Martijn J. Booij, Kathelijne M. Wijnberg, Jan S. Verkade, and Jurjen Wagemaker
Nat. Hazards Earth Syst. Sci., 17, 735–747, https://doi.org/10.5194/nhess-17-735-2017, https://doi.org/10.5194/nhess-17-735-2017, 2017
Short summary
Short summary
The increasing number and severity of floods, driven by e.g. urbanization, subsidence and climate change, create a growing need for accurate and timely flood maps. At the same time social media is a source of much real-time data that is still largely untapped in flood disaster management. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.
Marzena Osuch, Renata J. Romanowicz, Deborah Lawrence, and Wai K. Wong
Hydrol. Earth Syst. Sci., 20, 1947–1969, https://doi.org/10.5194/hess-20-1947-2016, https://doi.org/10.5194/hess-20-1947-2016, 2016
Short summary
Short summary
Possible future climate change effects on dryness conditions in Poland are estimated for six climate projections using the standardized precipitation index. The time series of precipitation represent six different climate model runs under the A1B SRES scenario for the period 1971–2099. Monthly precipitation values were used to estimate the standardized precipitation index for multiple timescales (1, 3, 6, 12 and 24 months) for a spatial resolution of 25 km for the whole country.
W. Gumindoga, T. H. M. Rientjes, A. T. Haile, H. Makurira, and P. Reggiani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-33, https://doi.org/10.5194/hess-2016-33, 2016
Manuscript not accepted for further review
J. F. Schyns, A. Y. Hoekstra, and M. J. Booij
Hydrol. Earth Syst. Sci., 19, 4581–4608, https://doi.org/10.5194/hess-19-4581-2015, https://doi.org/10.5194/hess-19-4581-2015, 2015
Short summary
Short summary
The paper draws attention to the fact that green water (soil moisture returning to the atmosphere through evaporation) is a scarce resource, because its availability is limited and there are competing demands for green water. Around 80 indicators of green water availability and scarcity are reviewed and classified based on their scope and purpose of measurement. This is useful in order to properly include limitations in green water availability in water scarcity assessments.
A. W. Worqlul, A. S. Collick, S. A. Tilahun, S. Langan, T. H. M. Rientjes, and T. S. Steenhuis
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-2081-2015, https://doi.org/10.5194/hessd-12-2081-2015, 2015
Revised manuscript not accepted
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 19, 275–291, https://doi.org/10.5194/hess-19-275-2015, https://doi.org/10.5194/hess-19-275-2015, 2015
Short summary
Short summary
This paper investigates the skill of 90-day low-flow forecasts using three models. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
A. Kiczko, R. J. Romanowicz, M. Osuch, and E. Karamuz
Nat. Hazards Earth Syst. Sci., 13, 3443–3455, https://doi.org/10.5194/nhess-13-3443-2013, https://doi.org/10.5194/nhess-13-3443-2013, 2013
W. R. van Esse, C. Perrin, M. J. Booij, D. C. M. Augustijn, F. Fenicia, D. Kavetski, and F. Lobligeois
Hydrol. Earth Syst. Sci., 17, 4227–4239, https://doi.org/10.5194/hess-17-4227-2013, https://doi.org/10.5194/hess-17-4227-2013, 2013
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 17, 4241–4257, https://doi.org/10.5194/hess-17-4241-2013, https://doi.org/10.5194/hess-17-4241-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations
Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
Learning landscape features from streamflow with autoencoders
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
Projections of streamflow intermittence under climate change in European drying river networks
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China
On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?
Achieving water budget closure through physical hydrological processes modelling: insights from a large-sample study
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Heavy-tailed flood peak distributions: What is the effect of the spatial variability of rainfall and runoff generation?
Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
State updating in the Xin'anjiang Model: Joint assimilating streamflow and multi-source soil moisture data via Asynchronous Ensemble Kalman Filter with enhanced Error Models
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric-hydrological model
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
HESS Opinions: The sword of Damocles of the impossible flood
A diversity centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Metamorphic testing of machine learning and conceptual hydrologic models
The influence of human activities on streamflow reductions during the megadrought in central Chile
Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA
Lack of robustness of hydrological models: A large-sample diagnosis and an attempt to identify the hydrological and climatic drivers
Exploring the Potential Processes Controls for Changes of Precipitation-Runoff Relationships in Non-stationary Environments
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
CH-RUN: A data-driven spatially contiguous runoff monitoring product for Switzerland
Simulating the Tone River Eastward Diversion Project in Japan Carried Out Four Centuries Ago
Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China
A network approach for multiscale catchment classification using traits
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
Short summary
Short summary
To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025, https://doi.org/10.5194/hess-29-27-2025, 2025
Short summary
Short summary
The influence of watershed disturbances has proved challenging to disentangle from natural streamflow variability. This study evaluates the influence of time-varying hydrologic controls on rainfall–runoff in undisturbed and wildfire-disturbed watersheds using a novel time-series event separation method. Across watersheds, water year type and season influenced rainfall–runoff patterns. Accounting for these controls enabled clearer isolation of wildfire effects.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, https://doi.org/10.5194/hess-28-5511-2024, 2024
Short summary
Short summary
Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
Short summary
Short summary
Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
Short summary
Short summary
Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
Short summary
Short summary
We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
Short summary
Short summary
Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
Short summary
Short summary
The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
Short summary
Short summary
We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
Short summary
Short summary
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
Short summary
Short summary
We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
Short summary
Short summary
This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
Short summary
Short summary
A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
Short summary
Short summary
Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
Short summary
Short summary
We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
Short summary
Short summary
Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
Preprint under review for HESS
Short summary
Short summary
Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
Short summary
Short summary
Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
Short summary
Short summary
An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
Short summary
Short summary
The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-230, https://doi.org/10.5194/hess-2024-230, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Water budget non-closure is a widespread phenomenon among multisource datasets, which undermines the robustness of hydrological inferences. This study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset, and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
Short summary
Short summary
The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-181, https://doi.org/10.5194/hess-2024-181, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small compared to large catchments, and that spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show an effect. The results can improve estimations of occurrence probabilities of extreme floods.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
Short summary
Short summary
By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
Short summary
Short summary
Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
Short summary
Short summary
Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-211, https://doi.org/10.5194/hess-2024-211, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping better prepare for and respond to floods.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
Short summary
Short summary
Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1464, https://doi.org/10.5194/egusphere-2024-1464, 2024
Short summary
Short summary
Our study conducted a detailed analysis of runoff component and future trend in the Yarlung Tsangpo River basin owing to the existed differences in the published results, and find that the contributions of snowmelt and glacier melt runoff to streamflow were limited, both for ~5 % which were much lower than previous results. The streamflow there will continuously increase in the future, but the overestimated contribution from glacier melt would lead to an underestimation on such increasing trend.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
Short summary
Short summary
Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
Short summary
Short summary
Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
Short summary
Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
Short summary
Short summary
A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
Short summary
Short summary
Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
Short summary
Short summary
Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
Short summary
Short summary
Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
Short summary
Short summary
We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
Short summary
Short summary
In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
Short summary
Short summary
Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-118, https://doi.org/10.5194/hess-2024-118, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This study develops an integrated framework based on the novel Driving index for changes in Precipitation-Runoff Relationships (DPRR) to explore the controls for changes in precipitation-runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation-runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
Short summary
Short summary
Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
EGUsphere, https://doi.org/10.5194/egusphere-2024-993, https://doi.org/10.5194/egusphere-2024-993, 2024
Short summary
Short summary
This study uses deep learning to predict spatially contiguous water runoff in Switzerland from 1962–2023. It outperforms traditional models, requiring less data and computational power. Key findings include increased dry years and summer water scarcity. This method offers significant advancements in water monitoring.
Joško Trošelj and Naota Hanasaki
EGUsphere, https://doi.org/10.5194/egusphere-2024-595, https://doi.org/10.5194/egusphere-2024-595, 2024
Short summary
Short summary
This study presents the first distributed hydrological simulation which confirms the claims raised by historians that the Eastward Diversion Project of the Tone River in Japan was conducted four centuries ago to increase low flows and subsequent travelling possibilities surrounding the Capitol Edo (Tokyo) using inland navigation. We reconstructed six historical river maps and indirectly validated the historical simulations with reachable ancient river ports via increased low-flow water levels.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
Short summary
Short summary
Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
Short summary
Short summary
We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cited articles
Akhtar, M., Ahmad, N., and Booij, M. J.: Use of regional climate model simulations as input for hydrological models for the Hindukush-Karakorum-Himalaya region, Hydrol. Earth Syst. Sci., 13, 1075–1089, https://doi.org/10.5194/hess-13-1075-2009, 2009.
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.
Bennett, J. C., Robertson, D. E., Shrestha, D. L., and Wang, Q. J.: Selecting reference streamflow forecasts to demonstrate the performance of NWP-forced streamflow forecasts, in: MODSIM 2013, 20th International Congress on Modelling and Simulation, edited by: Piantadosi, J., Anderssen, R. S., and Boland, J., Modelling and Simulation Society of Australia and New Zealand, Adelaide, Australia, 1–6 December 2013, available at: http://www.mssanz.org.au/modsim2013/L8/bennett.pdf (last access: 9 October 2017), 2013.
Bennett, J. C., Robertson, D. E., Shrestha, D. L., Wang, Q. J., Enever, D., Hapuarachchi, P., and Tuteja, N. K.: A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days, J. Hydrol., 519, 2832–2846, https://doi.org/10.1016/j.jhydrol.2014.08.010, 2014.
Boé, J., Terray, L., Habets, F., and Martin, E.: Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies, Int. J. Climatol., 27, 1643–1655, https://doi.org/10.1002/joc.1602, 2007.
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., Dias, P. S., 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.
Bourdin, D. R. and Stull, R. B.: Bias-corrected short-range Member-to-Member ensemble forecasts of reservoir inflow, J. Hydrol., 502, 77–88, https://doi.org/10.1016/j.jhydrol.2013.08.028, 2013.
Bouwer, L. M., Bubeck, P., and Aerts, J. C. J. H.: Changes in future flood risk due to climate and development in a Dutch polder area, Global Environ. Chang., 20, 463–471, https://doi.org/10.1016/j.gloenvcha.2010.04.002, 2010.
Bröcker, J. and Smith, L. A.: Increasing the Reliability of Reliability Diagrams, Weather Forecast., 22, 651–661, https://doi.org/10.1175/WAF993.1, 2007.
Buizza, R., Hollingsworth, A., Lalaurette, F., and Ghelli, A.: Probabilistic Predictions of Precipitation Using the ECMWF Ensemble Prediction System, Weather Forecast., 14, 168–189, https://doi.org/10.1175/1520-0434(1999)014<0168:PPOPUT>2.0.CO;2, 1999.
Buizza, R., Houtekamer, P. L., Toth, Z., Pellerin, G., Wei, M., and Zhu, Y.: 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.
Bürger, G., Reusser, D., and Kneis, D.: Early flood warnings from empirical (expanded) downscaling of the full ECMWF Ensemble Prediction System, Water Resour. Res., 45, W10443, https://doi.org/10.1029/2009WR007779, 2009.
Candille, G. and Talagrand, O.: Evaluation of probabilistic prediction systems for a scalar variable, Q. J. Roy. Meteor. Soc., 131, 2131–2150, https://doi.org/10.1256/qj.04.71, 2005.
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.
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.
Das, S., Abraham, A., Chakraborty, U. K., and Konar, A.: Differential Evolution Using a Neighborhood-Based Mutation Operator, IEEE T. Evolut. Comput., 13, 526–553, https://doi.org/10.1109/TEVC.2008.2009457, 2009.
Demargne, J., Brown, J., Liu, Y., Seo, D. J., Wu, L., Toth, Z., and Zhu, Y.: Diagnostic verification of hydrometeorological and hydrologic ensembles, Atmos. Sci. Lett., 11, 114–122, https://doi.org/10.1002/asl.261, 2010.
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, 2013a.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times, Hydrol. Process., 27, 2742–2758, https://doi.org/10.1002/hyp.9402, 2013b.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models, Hydrol. Earth Syst. Sci., 19, 275–291, https://doi.org/10.5194/hess-19-275-2015, 2015.
Déqué, M.: Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values, Global Planet. Change, 57, 16–26, https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007.
ECMWF: Describing ECMWF's forecasts and forecasting system, ECMWF Newsl., 133, 11–13, available at: https://www.ecmwf.int/sites/default/files/elibrary/2012/14576-newsletter-no133-autumn-2012.pdf (last access: 9 October 2017), 2012.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Fleming, S. W.: Demand modulation of water scarcity sensitivities to secular climatic variation: theoretical insights from a computational maquette, Hydrolog. Sci. J., 61, 2849–2859, https://doi.org/10.1080/02626667.2016.1164316, 2016.
Fleming, S. W., Bourdin, D. R., Campbell, D., Stull, R. B., and Gardner, T.: Development and Operational Testing of a Super-Ensemble Artificial Intelligence Flood-Forecast Model for a Pacific Northwest River, J. Am. Water Resour. As., 51, 502–512, https://doi.org/10.1111/jawr.12259, 2015.
Fundel, F., Jörg-Hess, S., and Zappa, M.: Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices, Hydrol. Earth Syst. Sci., 17, 395–407, https://doi.org/10.5194/hess-17-395-2013, 2013.
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.
Hagedorn, R.: Using the ECMWF reforecast dataset to calibrate EPS forecasts, ECMWF Newsl., 117, 8–13, available at: https://www.ecmwf.int/sites/default/files/elibrary/2008/14608-newsletter-no117-autumn-2008.pdf (last access: 9 October 2017), 2008.
Hamill, T. M.: Interpretation of Rank Histograms for Verifying Ensemble Forecasts, Mon. Weather Rev., 129, 550–560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2, 2001.
Hamill, T. M. and Colucci, S. J.: Evaluation of Eta–RSM Ensemble Probabilistic Precipitation Forecasts, Mon. Weather Rev., 126, 711–724, https://doi.org/10.1175/1520-0493(1998)126<0711:EOEREP>2.0.CO;2, 1998.
Hamill, T. M., Mullen, S. L., Snyder, C., Toth, Z., and Baumhefner, D. P.: Ensemble Forecasting in the Short to Medium Range: Report from a Workshop, B. Am. Meteorol. Soc., 81, 2653–2664, https://doi.org/10.1175/1520-0477(2000)081<2653:EFITST>2.3.CO;2, 2000.
Hashino, T., Bradley, A. A., and Schwartz, S. S.: Evaluation of bias-correction methods for ensemble streamflow volume forecasts, Hydrol. Earth Syst. Sci., 11, 939–950, https://doi.org/10.5194/hess-11-939-2007, 2007.
He, Y., Wetterhall, F., Cloke, H. L., Pappenberger, F., Wilson, M., Freer, J., and McGregor, G.: Tracking the uncertainty in flood alerts driven by grand ensemble weather predictions, Meteorol. Appl., 16, 91–101, https://doi.org/10.1002/met.132, 2009.
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.
Houser, P. R., De Lannoy, G. J. M., and Walker, J. P.: Hydrologic Data Assimilation, in: Approaches to Managing Disaster – Assessing Hazards, Emergencies Disaster Impacts, edited by: Tiefenbacher, J., chap. 3, InTech, 41–64, https://doi.org/10.5772/31246, 2012.
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Pachauri, R. K., and Meyer, L. A., Tech. rep., IPCC, Geneva, Zwitzerland, available at: http://www.ipcc.ch/pdf/assessment-report/ar5/syr/SYR_AR5_FINAL_full.pdf (last access: 9 October 2017), 2014.
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.
Khajehei, S. and Moradkhani, H.: Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach, J. Hydrol., 546, 476–489, https://doi.org/10.1016/j.jhydrol.2017.01.026, 2017.
Kiczko, A., Romanowicz, R. J., Osuch, M., and Pappenberger, F.: Adaptation of the Integrated Catchment System to On-line Assimilation of ECMWF Forecasts, in: Stochastic Flood Forecasting System, edited by: Romanowicz, R. J. and Osuch, M., chap. 11, Springer International Publishing, Cham, Switzerland, 173–186, https://doi.org/10.1007/978-3-319-18854-6_11, 2015.
Komma, J., Reszler, C., Blöschl, G., and Haiden, T.: Ensemble prediction of floods – catchment non-linearity and forecast probabilities, Nat. Hazards Earth Syst. Sci., 7, 431–444, https://doi.org/10.5194/nhess-7-431-2007, 2007.
Krzysztofowicz, R.: The case for probabilistic forecasting in hydrology, J. Hydrol., 249, 2–9, https://doi.org/10.1016/S0022-1694(01)00420-6, 2001.
Leutbecher, M. and Palmer, T. N.: Ensemble forecasting, J. Comput. Phys., 227, 3515–3539, https://doi.org/10.1016/j.jcp.2007.02.014, 2008.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997.
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.
Lu, J., Sun, G., McNulty, S. G., and Amatya, D. M.: A comparison of six potential evapotranspiration methods for regional use in the Southeastern United States, J. Am. Water Resour. As., 41, 621–633, https://doi.org/10.1111/j.1752-1688.2005.tb03759.x, 2005.
Madadgar, S., Moradkhani, H., and Garen, D.: Towards improved post-processing of hydrologic forecast ensembles, Hydrol. Process., 28, 104–122, https://doi.org/10.1002/hyp.9562, 2014.
Martina, M. L. V., Todini, E., and Libralon, A.: A Bayesian decision approach to rainfall thresholds based flood warning, Hydrol. Earth Syst. Sci., 10, 413–426, https://doi.org/10.5194/hess-10-413-2006, 2006.
Merz, R. and Blöschl, G.: Regional flood risk – what are the driving processes?, in: Water Resources Systems - Hydrological Risk, Management and Development, edited by: Blöschl, G., Franks, S., Kumagai, M., Musiake, K., and Rosbjerg, D., International Association of Hydrological Sciences Press, Sapporo, Japan, 30 June–11 July 2003, 281, 49–58, available at: http://hydrologie.org/redbooks/a281/iahs_281_049.pdf (last access: 9 October 2017), 2003.
Mladek, R.: TIGGE, Model upgrades, available at: https://software.ecmwf.int/wiki/display/TIGGE/Model+upgrades#Modelupgrades-ECMWF (last access: 7 March 2017), 2016.
Napiorkowski, M. J., Piotrowski, A. P., and Napiorkowski, J. J.: Stream temperature forecasting by means of ensemble of neural networks: Importance of input variables and ensemble size, in: River Flow 2014, edited by: Schleiss, A. J., De Cesare, G., Franca, M. J., and Pfister, M., Lausanne, Zwitzerland, 3–5 September 2014, 2017–2025, https://doi.org/10.1201/b17133-269, 2014.
Olsson, J. and Lindström, G.: Evaluation and calibration of operational hydrological ensemble forecasts in Sweden, J. Hydrol., 350, 14–24, https://doi.org/10.1016/j.jhydrol.2007.11.010, 2008.
Osuch, M., Romanowicz, R. J., and Booij, M. J.: The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics, Hydrolog. Sci. J., 60, 1299–1316, https://doi.org/10.1080/02626667.2014.967694, 2015.
Pagano, T. C., Shrestha, D. L., Wang, Q. J., Robertson, D., and Hapuarachchi, P.: Ensemble dressing for hydrological applications, Hydrol. Process., 27, 106–116, https://doi.org/10.1002/hyp.9313, 2013.
Panagoulia, D.: Assessment of daily catchment precipitation in mountainous regions for climate change interpretation, Hydrolog. Sci. J., 40, 331–350, https://doi.org/10.1080/02626669509491419, 1995.
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 predictions, J. Hydrol., 522, 697–713, https://doi.org/10.1016/j.jhydrol.2015.01.024, 2015.
Penning-Rowsell, E. C., Tunstall, S. M., Tapsell, S. M., and Parker, D. J.: The Benefits of Flood Warnings: Real But Elusive, and Politically Significant, Water Environ. J., 14, 7–14, https://doi.org/10.1111/j.1747-6593.2000.tb00219.x, 2000.
Persson, A. and Andersson, E.: User guide to ECMWF forecast products, Tech. rep., ECMWF, available at: https://www.ecmwf.int/sites/default/files/elibrary/2015/16559-user-guide-ecmwf-forecast-products.pdf (last access: 9 October 2017), 2013.
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010.
Ranjan, R.: Combining and Evaluating Probabilistic Forecasts, PhD thesis, University of Washington, Seattle, Washington USA, 2009.
Renner, M., Werner, M. G. F., Rademacher, S., and Sprokkereef, E.: Verification of ensemble flow forecasts for the River Rhine, J. Hydrol., 376, 463–475, https://doi.org/10.1016/j.jhydrol.2009.07.059, 2009.
Rientjes, T. H. M., Muthuwatta, L. P., Bos, M. G., Booij, M. J., and Bhatti, H. A.: Multi-variable calibration of a semi-distributed hydrological model using streamflow data and satellite-based evapotranspiration, J. Hydrol., 505, 276–290, https://doi.org/10.1016/j.jhydrol.2013.10.006, 2013.
Rojas, R., Feyen, L., and Watkiss, P.: Climate change and river floods in the European Union: Socio-economic consequences and the costs and benefits of adaptation, Global Environ. Chang., 23, 1737–1751, https://doi.org/10.1016/j.gloenvcha.2013.08.006, 2013.
Roulin, E. and Vannitsem, S.: Skill of Medium-Range Hydrological Ensemble Predictions, J. Hydrometeorol., 6, 729–744, https://doi.org/10.1175/JHM436.1, 2005.
Sevruk, B.: Regional dependency of precipitation-altitude relationship in the Swiss Alps, Climatic Change, 36, 355–369, https://doi.org/10.1023/A:1005302626066, 1997.
Shi, X., Wood, A. W., and Lettenmaier, D. P.: How Essential is Hydrologic Model Calibration to Seasonal Streamflow Forecasting?, J. Hydrometeorol., 9, 1350–1363, https://doi.org/10.1175/2008JHM1001.1, 2008.
Tao, Y., Duan, Q., Ye, A., Gong, W., Di, Z., Xiao, M., and Hsu, K.: An evaluation of post-processed TIGGE multimodel ensemble precipitation forecast in the Huai river basin, J. Hydrol., 519, 2890–2905, https://doi.org/10.1016/j.jhydrol.2014.04.040, 2014.
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125-140, https://doi.org/10.5194/hess-13-125-2009, 2009.
Vannitsem, S. and Hagedorn, R.: Ensemble forecast post-processing over Belgium: comparison of deterministic-like and ensemble regression methods, Meteorol. Appl., 18, 94–104, https://doi.org/10.1002/met.217, 2011.
Velázquez, J. A., Anctil, F., and Perrin, C.: Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments, Hydrol. Earth Syst. Sci., 14, 2303–2317, https://doi.org/10.5194/hess-14-2303-2010, 2010.
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, https://doi.org/10.1016/j.jhydrol.2013.07.039, 2013.
Vitart, F.: S2S, ECMWF Model, available at: https://software.ecmwf.int/wiki/display/S2S/ECMWF+Model, last access: 3 July 2017.
Werner, M. G. F., Schellekens, J., and Kwadijk, J. C. J.: Flood early warning systems for hydrological (sub) catchments, in: Encyclopedia of Hydrological Sciences, edited by: Anderson, M. G. and McDonnell, J. J., chap. 2:23, John Wiley & Sons, https://doi.org/10.1002/0470848944.hsa022, 2005.
Wetterhall, F., Pappenberger, F., He, Y., Freer, J., and Cloke, H. L.: Conditioning model output statistics of regional climate model precipitation on circulation patterns, Nonlin. Processes Geophys., 19, 623–633, https://doi.org/10.5194/npg-19-623-2012, 2012.
Wheater, H. S. and Gober, P.: Water security and the science agenda, Water Resour. Res., 51, 5406–5424, https://doi.org/10.1002/2015WR016892, 2015.
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Elsevier, Oxford, UK, 2nd Edn., 2006.
WMO: Forecast Verification: Issues, Methods and FAQ, available at: http://www.cawcr.gov.au/projects/verification/, last access: 12 March 2015.
Wöhling, Th., Lennartz, F., and Zappa, M.: Technical Note: Updating procedure for flood forecasting with conceptual HBV-type models, Hydrol. Earth Syst. Sci., 10, 783–788, https://doi.org/10.5194/hess-10-783-2006, 2006.
Ye, J., He, Y., Pappenberger, F., Cloke, H. L., Manful, D. Y., and Li, Z.: Evaluation of ECMWF medium-range ensemble forecasts of precipitation for river basins, Q. J. Roy. Meteor. Soc., 140, 1615–1628, https://doi.org/10.1002/qj.2243, 2014.
Yossef, N. C., Winsemius, H., Weerts, A., Van Beek, R., and Bierkens, M. F. P.: Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing, Water Resour. Res., 49, 4687–4699, https://doi.org/10.1002/wrcr.20350, 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.
Zappa, M., Jaun, S., Germann, U., Walser, A., and Fundel, F.: Superposition of three sources of uncertainties in operational flood forecasting chains, Atmos. Res., 100, 246–262, https://doi.org/10.1016/j.atmosres.2010.12.005, 2011.
Short summary
Accurate flood and low-streamflow forecasting are important. The paper presents a methodology to evaluate ensemble streamflow-forecasting systems for different lead times; low, medium and high streamflow; and related runoff-generating processes. We applied the methodology to a study forecasting system of the Biała Tarnowska River in Poland. The results provide valuable information about the forecasting system: in which conditions it can be used and how the system can be improved effectively.
Accurate flood and low-streamflow forecasting are important. The paper presents a methodology to...
