Articles | Volume 28, issue 14
https://doi.org/10.5194/hess-28-3367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-3367-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jul 2024
Research article |
| 29 Jul 2024
| 29 Jul 2024
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Louise Akemi Kuana
Programa de Pós-Graduação em Engenharia Ambiental, Universidade Federal do Paraná, Curitiba, Brazil
Arlan Scortegagna Almeida
Sistema de Tecnologia e Monitoramento Ambiental do Paraná (Simepar), Curitiba, Brazil
Emílio Graciliano Ferreira Mercuri
CORRESPONDING AUTHOR
Departamento de Engenharia Ambiental, Universidade Federal do Paraná, Curitiba, Brazil
Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia
Steffen Manfred Noe
Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia
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Piaopiao Ke, Anna Lintunen, Pasi Kolari, Annalea Lohila, Santeri Tuovinen, Janne Lampilahti, Roseline Thakur, Maija Peltola, Otso Peräkylä, Tuomo Nieminen, Ekaterina Ezhova, Mari Pihlatie, Asta Laasonen, Markku Koskinen, Helena Rautakoski, Laura Heimsch, Tom Kokkonen, Aki Vähä, Ivan Mammarella, Steffen Noe, Jaana Bäck, Veli-Matti Kerminen, and Markku Kulmala
EGUsphere, https://doi.org/10.5194/egusphere-2024-1967, https://doi.org/10.5194/egusphere-2024-1967, 2024
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Our research explores diverse ecosystems’ role in climate cooling via the concept of CarbonSink+ Potential. We measured CO2 uptake and loaal aerosol production in forests, farms, peatlands, urban gardens, and coastal areas across Finland and Estonia. The long-term data reveal that while forests are vital regarding CarbonSink+ Potential, farms and urban gardens also play significant roles. These insights can help optimize management policy of natural resource to mitigate global warming.
Rostislav Kouznetsov, Risto Hänninen, Andreas Uppstu, Evgeny Kadantsev, Yalda Fatahi, Marje Prank, Dmitrii Kouznetsov, Steffen Manfred Noe, Heikki Junninen, and Mikhail Sofiev
Atmos. Chem. Phys., 24, 4675–4691, https://doi.org/10.5194/acp-24-4675-2024, https://doi.org/10.5194/acp-24-4675-2024, 2024
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By relying solely on publicly available media reports, we were able to infer the temporal evolution and the injection height for the Nord Stream gas leaks in September 2022. The inventory specifies locations, vertical distributions, and temporal evolution of the methane sources. The inventory can be used to simulate the event with atmospheric transport models. The inventory is supplemented with a set of observational data tailored to evaluate the results of the simulated atmospheric dispersion.
Ville Leinonen, Harri Kokkola, Taina Yli-Juuti, Tero Mielonen, Thomas Kühn, Tuomo Nieminen, Simo Heikkinen, Tuuli Miinalainen, Tommi Bergman, Ken Carslaw, Stefano Decesari, Markus Fiebig, Tareq Hussein, Niku Kivekäs, Radovan Krejci, Markku Kulmala, Ari Leskinen, Andreas Massling, Nikos Mihalopoulos, Jane P. Mulcahy, Steffen M. Noe, Twan van Noije, Fiona M. O'Connor, Colin O'Dowd, Dirk Olivie, Jakob B. Pernov, Tuukka Petäjä, Øyvind Seland, Michael Schulz, Catherine E. Scott, Henrik Skov, Erik Swietlicki, Thomas Tuch, Alfred Wiedensohler, Annele Virtanen, and Santtu Mikkonen
Atmos. Chem. Phys., 22, 12873–12905, https://doi.org/10.5194/acp-22-12873-2022, https://doi.org/10.5194/acp-22-12873-2022, 2022
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We provide the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five different earth system models. We investigated aerosol modes (nucleation, Aitken, and accumulation) separately and were able to show the differences between measured and modeled trends and especially their seasonal patterns. The differences in model results are likely due to complex effects of several processes instead of certain specific model features.
Alisa Krasnova, Dmitrii Krasnov, Hans Peter Ernst Cordey, and Steffen M. Noe
EGUsphere, https://doi.org/10.5194/egusphere-2022-384, https://doi.org/10.5194/egusphere-2022-384, 2022
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Forests play important role in global carbon cycle. Hemiboreal forests are located in a transitional zone between boreal and temperate zones and are characterised by higher variability in dominating tree species. We analysed carbon exchange of a hemiboreal forest using two measurement systems located on the same tower. The lower system could detect the influence of clearcuts and tower guywire tunnels, while the higher system allowed to assess the carbon balance of the full landscape.
Luis M. F. Barreira, Arttu Ylisirniö, Iida Pullinen, Angela Buchholz, Zijun Li, Helina Lipp, Heikki Junninen, Urmas Hõrrak, Steffen M. Noe, Alisa Krasnova, Dmitrii Krasnov, Kaia Kask, Eero Talts, Ülo Niinemets, Jose Ruiz-Jimenez, and Siegfried Schobesberger
Atmos. Chem. Phys., 21, 11781–11800, https://doi.org/10.5194/acp-21-11781-2021, https://doi.org/10.5194/acp-21-11781-2021, 2021
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We present results from PM1 atmospheric composition and concentration measurements performed in a springtime hemiboreal forest. Sesquiterpene mixing ratios and particle-phase concentrations of corresponding oxidation products were rapidly increasing on some early mornings. The particle volatility suggested that condensable sesquiterpene oxidation products are rapidly formed in the atmosphere. The results revealed the importance of sesquiterpenes for secondary organic aerosol particulate mass.
Tuukka Petäjä, Ella-Maria Duplissy, Ksenia Tabakova, Julia Schmale, Barbara Altstädter, Gerard Ancellet, Mikhail Arshinov, Yurii Balin, Urs Baltensperger, Jens Bange, Alison Beamish, Boris Belan, Antoine Berchet, Rossana Bossi, Warren R. L. Cairns, Ralf Ebinghaus, Imad El Haddad, Beatriz Ferreira-Araujo, Anna Franck, Lin Huang, Antti Hyvärinen, Angelika Humbert, Athina-Cerise Kalogridis, Pavel Konstantinov, Astrid Lampert, Matthew MacLeod, Olivier Magand, Alexander Mahura, Louis Marelle, Vladimir Masloboev, Dmitri Moisseev, Vaios Moschos, Niklas Neckel, Tatsuo Onishi, Stefan Osterwalder, Aino Ovaska, Pauli Paasonen, Mikhail Panchenko, Fidel Pankratov, Jakob B. Pernov, Andreas Platis, Olga Popovicheva, Jean-Christophe Raut, Aurélie Riandet, Torsten Sachs, Rosamaria Salvatori, Roberto Salzano, Ludwig Schröder, Martin Schön, Vladimir Shevchenko, Henrik Skov, Jeroen E. Sonke, Andrea Spolaor, Vasileios K. Stathopoulos, Mikko Strahlendorff, Jennie L. Thomas, Vito Vitale, Sterios Vratolis, Carlo Barbante, Sabine Chabrillat, Aurélien Dommergue, Konstantinos Eleftheriadis, Jyri Heilimo, Kathy S. Law, Andreas Massling, Steffen M. Noe, Jean-Daniel Paris, André S. H. Prévôt, Ilona Riipinen, Birgit Wehner, Zhiyong Xie, and Hanna K. Lappalainen
Atmos. Chem. Phys., 20, 8551–8592, https://doi.org/10.5194/acp-20-8551-2020, https://doi.org/10.5194/acp-20-8551-2020, 2020
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The role of polar regions is increasing in terms of megatrends such as globalization, new transport routes, demography, and the use of natural resources with consequent effects on regional and transported pollutant concentrations. Here we summarize initial results from our integrative project exploring the Arctic environment and pollution to deliver data products, metrics, and indicators for stakeholders.
Ekaterina Ezhova, Ilona Ylivinkka, Joel Kuusk, Kaupo Komsaare, Marko Vana, Alisa Krasnova, Steffen Noe, Mikhail Arshinov, Boris Belan, Sung-Bin Park, Jošt Valentin Lavrič, Martin Heimann, Tuukka Petäjä, Timo Vesala, Ivan Mammarella, Pasi Kolari, Jaana Bäck, Üllar Rannik, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 18, 17863–17881, https://doi.org/10.5194/acp-18-17863-2018, https://doi.org/10.5194/acp-18-17863-2018, 2018
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Understanding the connections between aerosols, solar radiation and photosynthesis in terrestrial ecosystems is important for estimates of the CO2 balance in the atmosphere. Atmospheric aerosols and clouds influence solar radiation. In this study, we quantify the aerosol effect on solar radiation in boreal forests and study forest ecosystems response to this change in the radiation conditions. The analysis is based on atmospheric observations from several remote stations in Eurasian forests.
Pertti Hari, Steffen Noe, Sigrid Dengel, Jan Elbers, Bert Gielen, Veli-Matti Kerminen, Bart Kruijt, Liisa Kulmala, Anders Lindroth, Ivan Mammarella, Tuukka Petäjä, Guy Schurgers, Anni Vanhatalo, Markku Kulmala, and Jaana Bäck
Atmos. Chem. Phys., 18, 13321–13328, https://doi.org/10.5194/acp-18-13321-2018, https://doi.org/10.5194/acp-18-13321-2018, 2018
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The development of eddy-covariance measurements of ecosystem CO2 fluxes began a new era in the field studies of photosynthesis. The interpretation of the very variable CO2 fluxes in evergreen forests has been problematic especially in seasonal transition times. We apply two theoretical needle-level equations and show they can predict photosynthetic CO2 flux between the atmosphere and Scots pine forests. This has strong implications for the interpretation of the global change and boreal forests.
Pertti Hari, Veli-Matti Kerminen, Liisa Kulmala, Markku Kulmala, Steffen Noe, Tuukka Petäjä, Anni Vanhatalo, and Jaana Bäck
Atmos. Chem. Phys., 17, 15045–15053, https://doi.org/10.5194/acp-17-15045-2017, https://doi.org/10.5194/acp-17-15045-2017, 2017
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We developed a theory on the seasonal behaviour of photosynthesis in natural conditions and tested the theory with intensive measurements. Light, temperature, water vapor and CO2 concentration explained the daily variation in photosynthesis, and the physiological state of the photosynthetic machinery explained the annual pattern of photosynthesis. The theory explained about 95 % of the variance of photosynthesis measured with chambers in the field in northern Finland.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
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After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
A. M. Yáñez-Serrano, A. C. Nölscher, E. Bourtsoukidis, B. Derstroff, N. Zannoni, V. Gros, M. Lanza, J. Brito, S. M. Noe, E. House, C. N. Hewitt, B. Langford, E. Nemitz, T. Behrendt, J. Williams, P. Artaxo, M. O. Andreae, and J. Kesselmeier
Atmos. Chem. Phys., 16, 10965–10984, https://doi.org/10.5194/acp-16-10965-2016, https://doi.org/10.5194/acp-16-10965-2016, 2016
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This paper provides a general overview of methyl ethyl ketone (MEK) ambient observations in different ecosystems around the world in order to provide insights into the sources, sink and role of MEK in the atmosphere.
S. Smolander, Q. He, D. Mogensen, L. Zhou, J. Bäck, T. Ruuskanen, S. Noe, A. Guenther, H. Aaltonen, M. Kulmala, and M. Boy
Biogeosciences, 11, 5425–5443, https://doi.org/10.5194/bg-11-5425-2014, https://doi.org/10.5194/bg-11-5425-2014, 2014
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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)
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
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
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
The Significance of the Leaf-Area-Index on the Evapotranspiration Estimation in SWAT-T for Characteristic Land Cover Types of Western Africa
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
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
Multi-model approach in a variable spatial framework for streamflow simulation
Advancing understanding of lake–watershed hydrology: a fully coupled numerical model illustrated by Qinghai Lake
Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins
Empirical stream thermal sensitivity cluster on the landscape according to geology and climate
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow
Toward interpretable LSTM-based modeling of hydrological systems
Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model
What controls the tail behaviour of flood series: rainfall or runoff generation?
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
Preprint under review for HESS
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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
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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
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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
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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
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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.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-131, https://doi.org/10.5194/hess-2024-131, 2024
Revised manuscript accepted for HESS
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ET is computed from vegetation (plant transpiration) and soil (soil evaporation). In Western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented with the leaf-area-index (LAI). In this study, we evaluate the importance of LAI for the ET calculation. We take a close look at the LAI-ET interaction and show the relevance to consider both, LAI and ET. Our work contributes to the understanding of the processes of the terrestrial water cycle.
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
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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
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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.
Joško Trošelj and Naota Hanasaki
EGUsphere, https://doi.org/10.5194/egusphere-2024-595, https://doi.org/10.5194/egusphere-2024-595, 2024
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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
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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
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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.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-57, https://doi.org/10.5194/hess-2024-57, 2024
Revised manuscript accepted for HESS
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Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. In this work we investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analysis indicate that adding two vegetation is enough to improve the representation of evaporation, and the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Cited articles
AGUASPARANÁ: Manual técnico de outorgas, i Edn., Estado do Paraná, https://www.iat.pr.gov.br/sites/agua-terra/arquivos_restritos/files/documento/2020-10/manual_outorgas_suderhsa_2006.pdf (last access: 17 July 2024), 2010. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration: guidelines for computing crop water requirements, Food and Agriculture Organization of the United Nations, Rome, ISBN 9251042195, 1998. a
Almagro, A., Oliveira, P. T. S., Neto, A. A. M., Roy, T., and Troch, P.: CABra: a novel large-sample dataset for Brazilian catchments, Hydrol. Earth Syst. Sci., 25, 3105–3135, https://doi.org/10.5194/hess-25-3105-2021, 2021. a
Arsenault, R., Breton-Dufour, M., Poulin, A., Dallaire, G., and Romero-Lopez, R.: Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico, Hydrolog. Sci. J., 64, 1297–1311, https://doi.org/10.1080/02626667.2019.1639716, 2019. a, b, c, d
Ayzel, G., Varentsova, N., Erina, O., Sokolov, D., Kurochkina, L., and Moreydo, V.: OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia, Water, 11, 1546, https://doi.org/10.3390/w11081546, 2019. a
Barbieri, G. M. L., Costa, A. B. F., Olivieira, C., Jusevicius, M., and D'Ávila, V. C.: Atlas Solarimétrico Do Estado Do Paraná, Manuscrito não publicado, https://solar.copel.com/solar/atlas-solarimetrico-copel.pdf (last access: 17 July 2024), 2017. a
Bartiko, D., Oliveira, D., Bonumá, N., and Chaffe, P.: Spatial and seasonal patterns of flood change across Brazil, Hydrolog. Sci. J., 64, 1071–1079, 2019. a
Bazzo, J. P. V. and Almeida, R. C. d.: Regionalização de Vazões com o Emprego de Redes Neurais Artificiais RBF, in: I Simpósio de Métodos Numéricos em Engenharia, 30 November 2016, Curitiba, 2016. a
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H.: Runoff Prediction in Ungauged Basins, Cambridge University Press, https://doi.org/10.1017/cbo9781139235761, 2013. a, b, c
Boutsidis, C., Zouzias, A., Mahoney, M. W., and Drineas, P.: Randomized Dimensionality Reduction for k-Means Clustering, IEEE T. Inf. Theory, 61, 1045–1062, 2014. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2001. a
Burn, D. H., Zrinji, Z., and Kowalchuk, M.: Regionalization of catchments for regional flood frequency analysis, J. Hydrol. Eng., 2, 76–82, 1997. a
Burnash, R. J. C.: The NWS River Forecast System-catchment modeling, in: Computer models of watershed hydrology, 311–366, https://www.cabidigitallibrary.org/doi/full/10.5555/19961904770 (last access: 1 February 2020), 1995. a
Burt, T. P. and McDonnell, J. J.: Whither field hydrology? The need for discovery science and outrageous hydrological hypotheses, Water Resour. Res., 51, 5919–5928, 2015. a
Calvetti, L., Beneti, C., Neundorf, R. L. A., Inouye, R. T., dos Santos, T. N., Gomes, A. M., Herdies, D. L., and de Gonçalves, L. G. G.: Quantitative Precipitation Estimation Integrated by Poisson's Equation Using Radar Mosaic, Satellite, and Rain Gauge Network, J. Hydrol. Eng., 22, E5016003, https://doi.org/10.1061/(asce)he.1943-5584.0001432, 2017. a
Carneiro, L., Ostroski, A., and Mercuri, E. G. F.: Trophic state index for heavily impacted watersheds: modeling the influence of diffuse pollution in water bodies, Hydrolog. Sci. J., 65, 2548–2560, 2020. a
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020. a
Cunha, A. P. M. A., Zeri, M., Leal, K. D., Costa, L., Cuartas, L. A., Marengo, J. A., Tomasella, J., Vieira, R. M., Barbosa, A. A., Cunningham, C., Garcia, J. V. C., Broedel, E., Alvalá, R., and Ribeiro-Neto, G.: Extreme drought events over Brazil from 2011 to 2019, Atmosphere, 10, 642, https://doi.org/10.3390/atmos10110642, 2019. a
Daggupati, P., Pai, N., Ale, S., Douglas-Mankin, K. R., andJ. Jeong, R. W. Z., Parajuli, P. B., Saraswat, D., and Youssef, M. A.: A Recommended Calibration and Validation Strategy for Hydrologic and Water Quality Models, Am. Soc. Agricult. Biol. Eng., 58, 1705–1719, https://doi.org/10.13031/trans.58.10712, 2015. a
Embrapa: Mapa de solos do estado do Paraná, http://geoinfo.cnps.embrapa.br/layers/geonode:parana_solos_20201105, (last access: 5 July 2021), 2020. a
Guo, Y., Zhang, Y., Zhang, L., and Wang, Z.: Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review, Wires Water, 8, e1487, https://doi.org/10.1002/wat2.1487, 2020. a, b
He, Y., Bárdossy, A., and Zehe, E.: A review of regionalisation for continuous streamflow simulation, Hydrol. Earth Syst. Sci., 15, 3539–3553, https://doi.org/10.5194/hess-15-3539-2011, 2011. a
Hengl, T., Heuvelink, G. B. M., and Rossiter, G. D.: About regression-kriging: From equations to case studies, Comput. Geosci., 33, 1301–1315, 2007. a
Hirata, R. and Foster, S.: The Guarani Aquifer System – from regional reserves to local use, Q. J. Eng. Geol. Hydrogeol., 54, qjegh2020-091, https://doi.org/10.1144/qjegh2020-091, 2021. a
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut, R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S., Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1–58, 2013. a
IAT: Mapas e Dados Espaciais, http://www.iat.pr.gov.br/Pagina/Mapas-e-Dados-Espaciais (last access: 5 July 2021), 2020. a
Juliani, B. H. T., de Campos, A. L., Almeida, A. S., and Leite, E. A.: Estatísticas meteorológicas da seca de 2020 no estado do Paraná, in: Anais do II END – Encontro Nacional de Desastres da ABRHidro, ABRHidro, https://anais.abrhidro.org.br/job.php?Job=7358 (last access: 17 July 2024), 2020. a
Kaviski, E., Rohn, M. d. C., and Mazer, W.: Projeto HG-171: Consistência e regionalização de dados hidrológicos, Centro de Hidráulica e Hidrologia Prof. Parigot de Souza, 2002. a
Ketchen Junior, D. J. and Shook, C. L.: The application of cluster analysis in strategic management research: an analysis and critique, Strat. Manage. J., 17, 441–458, 1996. a
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005. a
Kuentz, A., Arheimer, B., Hundecha, Y., and Wagener, T.: Understanding hydrologic variability across Europe through catchment classification, Hydrol. Earth Syst. Sci., 21, 2863–2879, https://doi.org/10.5194/hess-21-2863-2017, 2017. a
Llabrés-Brustenga, A., Rius, A., Rodríguez-Sol, R., Casas-Castillo, M. C., and Redaño, A.: Quality control process of the daily rainfall series available in Catalonia from 1855 to the present, Theor. Appl. Climatol., 137, 2715–2729, 2019. a
Matallo Junior, H.: Indicadores de desertificação: histórico e perspectivas, Edições UNESCO Brasil, Brasília, DF, Brasil, ISBN 8587853279, 2001. a
Melo, D., Ramos, G., Ferreira, G., Schwamback, D., Siqueira, J., Duarte-Carvajalino, J., Jhunior, H., Nóbrega, J., Morita, A., Almeida, C., Coutinho, J., Leite, C., Guedes, A., Coelho, V. H., Anache, J., Pelinson, N., Rosalem, L., Calixto, K. G., and Wendland, E.: The big picture of field hydrology studies in Brazil, Hydrolog. Sci. J., 65, 1262–1280, 2020. a
Melo, D. D. C. D., Scanlon, B. R., Zhang, Z., Wendland, E., and Yin, L.: Reservoir storage and hydrologic responses to droughts in the Paraná River basin, south-eastern Brazil, Hydrol. Earth Syst. Sci., 20, 4673–4688, https://doi.org/10.5194/hess-20-4673-2016, 2016. a
Mohamed, S., Ludovic, O., and Ribstein, P.: Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters, Water, 11, 8, https://doi.org/10.3390/w11081540, 2019. a
Muleta, M. K.: Model Performance Sensitivity to Objective Function during Automated Calibrations, J. Hydrol. Eng., 17, 756–767, https://doi.org/10.1061/(asce)he.1943-5584.0000497, 2012. a
Musy, A., Hingray, B., and Picouet, C.: Hydrology: a science for engineers, CRC Press, https://doi.org/10.1201/b17169, 2014. a
Neto, W. M. P., Vieira, F. R., and Matosinhos, C. C.: Avaliação da perfomance dos modelos GR4J, GR5J e GR6J na bacia hidrográfica do ribeirão São João, Minas Gerais, in: Base de Conhecimentos Gerados na Engenharia Ambiental e Sanitária 3, Atena, https://doi.org/10.22533/at.ed.74521080423, 2021. a
Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Moine, N. L.: Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments, Water Resour. Res., 44, W03413, https://doi.org/10.1029/2007wr006240, 2008. a, b, c, d
Oudin, L., Kay, A., Andréassian, V., and Perrin, C.: Are seemingly physically similar catchments truly hydrologically similar?, Water Resour. Res., 46, W11558, https://doi.org/10.1029/2009wr008887, 2010. a, b, c
Pagano, T., Hapuarachchi, P., and Wang, Q. J.: Continuous rainfall-runoff model comparison and short-term daily streamflow forecast skill evaluation, Tech. Rep., CSIRO, EP103545, https://doi.org/10.4225/08/58542C672DD2C, 2010. a
Parajka, J., Merz, R., and Blöschl, G.: A comparison of regionalisation methods for catchment model parameters, Hydrol. Earth Syst. Sci., 9, 157–171, https://doi.org/10.5194/hess-9-157-2005, 2005. a, b
Parajka, J., Kohnová, S., Bálint, G., Barbuc, M., Borga, M., Claps, P., Cheval, S., Dumitrescu, A., Gaume, E., Hlavčová, K., Merz, R., Pfaundler, M., Stancalie, G., Szolgay, J., and Blöschl, G.: Seasonal characteristics of flood regimes across the Alpine–Carpathian range, J. Hydrol., 394, 78–89, 2010. a
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, 2003. a
Pettitt, A. N.: A Non-Parametric Approach to the Change-Point Problem, Appl. Stat., 28, 126, https://doi.org/10.2307/2346729, 1979. a
Pfafstetter, O.: Classificação de bacias hidrográficas, manuscrito não publicado, DNOS – Departamento Nacional de Obras de Saneamento, 1989. a
Pugliese, A., Castellarin, A., and Brath, A.: Geostatistical prediction of flow–duration curves in an index-flow framework, Hydrol. Earth Syst. Sci., 18, 3801–3816, https://doi.org/10.5194/hess-18-3801-2014, 2014. a
Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andréassian, V.: A downward structural sensitivity analysis of hydrological models to improve low-flow simulation, J. Hydrol., 411, 66–76, 2011. a
Razavi, T. and Coulibaly, P.: Streamflow Prediction in Ungauged Basins: Review of Regionalization Methods, J. Hydrol. Eng., 18, 958–975, https://doi.org/10.1061/(asce)he.1943-5584.0000690, 2013. a
Rousseeuw, P. J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987. a
Shin, M.-J. and Kim, C.-S.: Assessment of the suitability of rainfall–runoff models by coupling performance statistics and sensitivity analysis, Hydrol. Res., 48, 1192–1213, https://doi.org/10.2166/nh.2016.129, 2016. a
Soil Conservation Service: National engineering handbook, in: Chap. Seção 4, Hydrology, Department ofAgriculture, Washington, p. 762, https://books.google.com.br/books?id=sjOEf-5zjXgC (last access: 1 December 2023), 1972. a
Sousa, F. M. L., Neto, V. S. C., Pacheco, W. E., and Barbosa, S. A.: Sistema Nacional De Informações Sobre Recursos Hídricos: Sistematização Conceitual E Modelagem Funcional, in: Anais do XVIII Simpósio Brasileiro de Recursos Hídricos, Associação Brasileira de Recursos Hídricos, Campo Grande, https://anais.abrhidro.org.br/job.php?Job=10334 (last access: 1 December 2023), 2009. a
Souza, C. M., Shimbo, J. Z., Rosa, M. R., Parente, L. L., Alencar, A. A., Rudorff, B. F. T., Hasenack, H., Matsumoto, M., Ferreira, L. G., Souza-Filho, P. W. M., de Oliveira, S. W., Rocha, W. F., Fonseca, A. V., Marques, C. B., Diniz, C. G., Costa, D., Monteiro, D., Rosa, E. R., Vélez-Martin, E., Weber, E. J., Lenti, F. E. B., Paternost, F. F., Pareyn, F. G. C., Siqueira, J. V., Viera, J. L., Neto, L. C. F., Saraiva, M. M., Sales, M. H., Salgado, M. P. G., Vasconcelos, R., Galano, S., Mesquita, V. V., and Azevedo, T.: Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine, Remote Sens., 12, 2735, https://doi.org/10.3390/rs12172735, 2020. a
Storn, R. and Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, J. Global Optimiz., 11, 341–359, https://doi.org/10.1023/a:1008202821328, 1997. a
Viviroli, D., Mittelbach, H., Gurtz, J., and Weingartner, R.: Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland – Part II: Parameter regionalisation and flood estimation results, J. Hydrol., 377, 208–225, https://doi.org/10.1016/j.jhydrol.2009.08.022, 2009. a
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Academic Press, ISBN 0123850223, https://www.ebook.de/de/product/14751307/daniel_s_wilks_statistical_methods_in_the_atmospheric_sciences_100.html (last access: 1 December 2023), 2011. a
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.
The authors compared regionalization methods for river flow prediction in 126 catchments from...
