Articles | Volume 20, issue 6
https://doi.org/10.5194/hess-20-2383-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-2383-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
20 Jun 2016
Research article |
| 20 Jun 2016
Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions
Department DICAM, School of Engineering, University of Bologna, Bologna, Italy
Related authors
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.
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
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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.
Mattia Neri, Juraj Parajka, and Elena Toth
Hydrol. Earth Syst. Sci., 24, 5149–5171, https://doi.org/10.5194/hess-24-5149-2020, https://doi.org/10.5194/hess-24-5149-2020, 2020
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One of the most informative ways to gain information on ungauged river sections is through the implementation of a rainfall-runoff model, exploiting the information collected in gauged catchments in the study area. This study analyses how the performances of different model regionalisation approaches are influenced by the informative content of the available regional data set, in order to identify the methods that are more suitable for the data availability in the region.
S. Ceola, B. Arheimer, E. Baratti, G. Blöschl, R. Capell, A. Castellarin, J. Freer, D. Han, M. Hrachowitz, Y. Hundecha, C. Hutton, G. Lindström, A. Montanari, R. Nijzink, J. Parajka, E. Toth, A. Viglione, and T. Wagener
Hydrol. Earth Syst. Sci., 19, 2101–2117, https://doi.org/10.5194/hess-19-2101-2015, https://doi.org/10.5194/hess-19-2101-2015, 2015
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We present the outcomes of a collaborative hydrological experiment undertaken by five different international research groups in a virtual laboratory. Moving from the definition of accurate protocols, a rainfall-runoff model was independently applied by the research groups, which then engaged in a comparative discussion. The results revealed that sharing protocols and running the experiment within a controlled environment is fundamental for ensuring experiment repeatability and reproducibility.
E. Toth
Hydrol. Earth Syst. Sci., 17, 1149–1159, https://doi.org/10.5194/hess-17-1149-2013, https://doi.org/10.5194/hess-17-1149-2013, 2013
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.
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
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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.
Mattia Neri, Juraj Parajka, and Elena Toth
Hydrol. Earth Syst. Sci., 24, 5149–5171, https://doi.org/10.5194/hess-24-5149-2020, https://doi.org/10.5194/hess-24-5149-2020, 2020
Short summary
Short summary
One of the most informative ways to gain information on ungauged river sections is through the implementation of a rainfall-runoff model, exploiting the information collected in gauged catchments in the study area. This study analyses how the performances of different model regionalisation approaches are influenced by the informative content of the available regional data set, in order to identify the methods that are more suitable for the data availability in the region.
S. Ceola, B. Arheimer, E. Baratti, G. Blöschl, R. Capell, A. Castellarin, J. Freer, D. Han, M. Hrachowitz, Y. Hundecha, C. Hutton, G. Lindström, A. Montanari, R. Nijzink, J. Parajka, E. Toth, A. Viglione, and T. Wagener
Hydrol. Earth Syst. Sci., 19, 2101–2117, https://doi.org/10.5194/hess-19-2101-2015, https://doi.org/10.5194/hess-19-2101-2015, 2015
Short summary
Short summary
We present the outcomes of a collaborative hydrological experiment undertaken by five different international research groups in a virtual laboratory. Moving from the definition of accurate protocols, a rainfall-runoff model was independently applied by the research groups, which then engaged in a comparative discussion. The results revealed that sharing protocols and running the experiment within a controlled environment is fundamental for ensuring experiment repeatability and reproducibility.
E. Toth
Hydrol. Earth Syst. Sci., 17, 1149–1159, https://doi.org/10.5194/hess-17-1149-2013, https://doi.org/10.5194/hess-17-1149-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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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.
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
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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
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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.
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
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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
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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.
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
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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
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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
Revised manuscript accepted 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.
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
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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
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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.
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
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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
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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.
Cited articles
Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geogr., 36, 480–513, https://doi.org/10.1177/0309133312444943, 2012.
Archfield, S. A., Pugliese, A., Castellarin, A., Skøien, J. O., and Kiang, J. E.: Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?, Hydrol. Earth Syst. Sci., 17, 1575–1588, https://doi.org/10.5194/hess-17-1575-2013, 2013.
Aziz, K., Rahman, A., Fang, G., and Shreshtha, S.: Application of Artificial Neural Networks in Regional Flood Frequency Analysis: A Case Study for Australia, Stoch. Environ. Res. Risk A., 28, 541–554, https://doi.org/10.1007/s00477-013-0771-5, 2013.
Bloeschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H. (Eds.): Runff prediction in ungauged basins: Synthesis across processes, places and scales, Cambridge University Press, New York, USA, 490 pp., 2013.
Bocchiola, D., De Michele, C., and Rosso, R.: Review of recent advances in index flood estimation, Hydrol. Earth Syst. Sci., 7, 283–296, https://doi.org/10.5194/hess-7-283-2003, 2003.
Bowden, G. J., Maier, H. R., and Dandy, G. C.: Optimal division of data for neural network models in water resources applications, Water Resour. Res., 38, 1010, https://doi.org/10.1029/2001WR000266, 2002.
Brath, A., Castellarin, A., Franchini, M., and Galeati, G.: Estimating the index flood using indirect methods, Hydrolog. Sci. J., 46, 399–418, 2001.
Carpenter, T. M., Sperfslage, J. A., Georgakakos, K. P., Sweeney, T., and Fread, D. L.: National threshold runoff estimation utilizing GIS in support of operational flash flood warning systems, J. Hydrol., 224, 21–44, 1999.
Chang, F. J., Tsai, M. J., Tsai, W. P., and Herricks, E. E.: Assessing the Ecological Hydrology of Natural Flow Conditions in Taiwan, J. Hydrol., 354, 75–89, 2008.
Christoffersen, P. F. and Diebold, F. X.: Further results on forecasting and model selection under asymmetric loss, J. Appl. Econ., 11, 561–571, 1996.
Claps and the CUBIST Team: Development of an Information System of the Italian basins for the CUBIST project, Geophys. Res. Abstr., 10, EGU2008-A-12048, 2008.
Coulibaly, P., Anctil, F., and Bobee, B.: Daily reservoir inflow forecasting using artificial neural networks with stopped Training Approach, J. Hydrol., 230, 244–257, 2000.
Crone, S.F.: Training Artificial Neural Networks using Asymmetric Cost Functions, in: Vol. 5, IEEE Proceedings of the 9th International Conference on Neural Infomation Processing (ICONIP'OZ), 18–22 November 2002, Singapore, 2374–2380, 2002.
Cunha, L. K., Krajewski, W. F., and Mantilla, R.: A framework for flood risk assessment under nonstationary conditions or in the absence of historical data, J. Flood Risk Manage., 4, 3–22, 2011.
Dalrymple, T.: Flood frequency analyses, Water Supply Paper 1543-A, US Geological Survey, Reston, Virginia, USA, 80 pp., 1960.
Daňhelka, J. and Vlasák, T: Evaluation of Real-time Flood Forecasts in the Czech Republic, 2002–2012, Czech Hydrometeorological Institute Report, http://www.chmi.cz/files/portal/docs/poboc/CB/pruvodce/vyhodnoceni_en.html (last access: 17 June 2015), 2013.
Dawson, C. W. and Wilby, R.: Hydrological modelling using artificial neural networks, Prog. Phys. Geogr., 25, 80–108, 2001.
Dawson, C. W., Abrahart, R. J., Shamseldin, A. Y., and Wilby, R. L.: Flood estimation at ungauged sites using artificial neural networks, J. Hydrol., 319, 391–409, 2006.
Diebold, F. X. and Lopez, J. A.: Forecast Evaluation and Combination, in: Vol. 14, Handbook of Statistics, edited by: Maddala, G. S. and Rao, C. R., North-Holland, Amsterdam, 241–268, 1996.
Di Prinzio, M., Castellarin, A., and Toth, E.: Data-driven catchment classification: application to the pub problem, Hydrol. Earth Syst. Sci., 15, 1921–1935, https://doi.org/10.5194/hess-15-1921-2011, 2011.
Elliott, G., Komunjer, I., and Timmermann, A.: Estimation and Testing of Forecast Rationality under Flexible Loss, Rev. Econ. Stud., 72, 1107–1125, 2005.
Granger, C. W. J.: Outline of Forecast Theory Using Generalized Cost Functions, Spanish Econ. Rev., 1, 161–173, 1999.
Granger, C. W. J. and Pesaran, M. H.: A Decision Theoretic Approach to Forecast Evaluation, in: Statistics and Finance: An Interface, edited by: Chan, W. S., Li, W. K., and Tong, H., Imperial College Press, London, 261–278, 2000.
GREHYS – Groupe de recherche en hydrologie statistique: Presentation and review of some methods for regional flood frequency analysis, J. Hydrol., 186, 63–84, 1996.
Griffis, V. W. and Stedinger, J. R.: The use of GLS regression in regional hydrologic analyses, J. Hydrol., 344, 82–95, 2007.
Hagan, M. T. and Menhaj, M.: Training feedforward networks with the Marquardt algorithm, IEEE T. Neural Netw., 5, 989–993, 1994.
Hall, M. J. and Minns, A. W.: The classification of hydrologically homogeneous regions, Hydrolog. Sci. J., 44, 693–704, 1999.
Hall, M. J., Minns, A. W., and Ashrafuzzaman, A. K. M.: The application of data mining techniques for the regionalisation of hydrological variables, Hydrol. Earth Syst. Sci., 6, 685–694, https://doi.org/10.5194/hess-6-685-2002, 2002.
Hapuarachchi, H. A. P., Wang, Q. J., and Pagano, T. C.: A review of advances in flash flood forecasting, Hydrol. Process., 25, 2771–2784, 2011.
Harman, C., Stewardson, M., and DeRose, R.: Variability and uncertainty in reach bankfull hydraulic geometry, J. Hydrol., 351, 13–25, 2008.
Javelle, P., Demargne, J., Defrance, D., Pansu, J., and Arnaud, P.: Evaluating flash flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system, Hydrolog. Sci. J., 59, 1390–1402, 2014.
Kalteh, A. M., Hjorth, P., and Berndtsson, R.: Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application, Environ. Model. Softw., 23, 835–845, 2008.
Kjeldsen, T. R., Smithers, J. C., and Schulze, R. E.: Flood frequency analysis at ungauged sites in the KwaZulu-Natal Province, South Africa, Water SA, 27, 315–324, 2001.
Kjeldsen, T. R., Jones, D. A., and Morris, D. G.: Using multiple donor sites for enhanced flood estimation in ungauged catchments, Water Resour. Res., 50, 6646–6657, 2014.
Kocjancic, R. and Zupan, J.: Modeling of the river flowrate: the influence of the training set selection, Chemom. Intell. Lab. Syst., 54, 21–34, 2000.
Kohonen, T.: Self-Organizing Maps, 2nd Edn., Springer, Berlin, 362 pp., 1997.
Ley, R., Casper, M. C., Hellebrand, H., and Merz, R.: Catchment classification by runoff behaviour with self-organizing maps (SOM), Hydrol. Earth Syst. Sci., 15, 2947–2962, https://doi.org/10.5194/hess-15-2947-2011, 2011.
Maier, H. R., Jain, A., Dandy, G. C., and Sudheer, K. P.: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions, Environ. Model. Softw., 25, 891–909, https://doi.org/10.1016/j.envsoft.2010.02.003, 2010.
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, B., Elmer, F., and Thieken, A. H.: Significance of "high probability/low damage" versus "low probability/high damage" flood events, Nat. Hazards Earth Syst. Sci., 9, 1033–1046, https://doi.org/10.5194/nhess-9-1033-2009, 2009.
Merz, R. and Bloschl, G.: Flood frequency regionalisation – Spatial proximity vs. catchment attributes, J. Hydrol., 302, 283–306, 2005.
Minns, A. W. and Hall, M. J.: Artificial neural network concepts in hydrology. in: Encyclopedia of Hydrological Sciences, edited by: Anderson, M. G. and McDonnell, J. J., John Wiley and Sons, Chichester, UK, 307–320, 2005.
Montanari, A., Young, G., Savenije, H. H. G., Hughes, D., Wagener, T., Ren, L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Bloschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S. J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D. A., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V.: Panta Rhei-Everything Flows: Change in hydrology and society-The IAHS Scientific Decade 2013–2022, Hydrolog. Sci. J., 58, 1256–1275, 2013.
Montesarchio, V., Ridolfi, E., Russo, F., and Napolitano, F.: Rainfall threshold definition using an entropy decision approach and radar data, Nat. Hazards Earth Syst. Sci., 11, 2061–2074, https://doi.org/10.5194/nhess-11-2061-2011, 2011.
Mount, N. J., Maier, H. R., Toth, E., Elshorbagy, A., Solomatine, D., Chang F.-J., and Abrahart, R. J.: Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan, Hydrolog. Sci. J., 61, 1192–1208, https://doi.org/10.1080/02626667.2016.1159683, 2016.
Muttiah, R. S., Srinivasan, R., and Allen, P. M.: Prediction of two year peak stream discharges using neural networks, J. Am. Water Resour. Assoc., 33, 625–630, 1997.
Norbiato, D., Borga, M., and Dinale, R.: Flash flood warning in ungauged basins by use of the flash flood guidance and model-based runoff thresholds, Meteorol. Appl., 16, 65–75, https://doi.org/10.1002/met.126, 2009.
Ntelekos, A. A., Georgakakos, K. P., and Krajewski, W. F.: On the uncertainties of flash flood guidance: Towards probabilistic forecasting of flash floods, J. Hydrometeorol., 7, 896–915, https://doi.org/10.1175/JHM529.1, 2006.
Pandey, G. R. and Nguyen, V.-T.-V.: A comparative study of regression based methods in regional flood frequency analysis, J. Hydrol., 225, 92–101, 1999.
Pappenberger, F., Bartholmes, J., Thielen, J., Cloke, H., Buizza, R., and de Roo, A.: New dimensions in early flood warning across the globe using grand-ensemble weather predictions, Geophys. Res. Lett., 35, L10404, https://doi.org/10.1029/2008GL033837, 2008.
Patton, A. J. and Timmermann, A.: Properties of Optimal Forecasts under Asymmetric Loss and Nonlinearity, J. Econometr., 140, 884–918, 2007.
Reed, S., Schaake, J., and Zhang, Z.: A distributed hydrologic model and threshold frequency based method for flash flood forecasting at ungauged locations, J. Hydrol., 337, 402–420, 2007.
Rumelhart, D. E., Widrow, B., and Lehr, M. A.: The basic ideas in neural networks, Commun. ACM, 37, 87–92, 1994.
Salinas, J. L., Laaha, G., Rogger, M., Parajka, J., Viglione, A., Sivapalan, M., and Blöschl, G.: Comparative assessment of predictions in ungauged basins – Part 2: Flood and low flow studies, Hydrol. Earth Syst. Sci., 17, 2637–2652, https://doi.org/10.5194/hess-17-2637-2013, 2013.
Sene, K.: Flash floods: forecasting and warning, Springer, Dordrecht, p. 385, 2013.
Shahin, M., Maier, H., and Jaksa, M.: Data Division for Developing Neural Networks Applied to Geotechnical Engineering, J. Comput. Civ. Eng., 18, 105–114, 2004.
Shu, C. and Burn, D. H.: Artificial neural network ensembles and their application in pooled flood frequency analysis, Water Resour. Res., 40, W09301, https://doi.org/10.1029/2003WR002816, 2004.
Shu, C. and Ouarda, T. B. M. J.: Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system, J. Hydrol., 349, 31–43, 2008.
Silva, D. G. E., Jino, M., and de Abreu, B. T.: Machine learning methods and asymmetric cost function to estimate execution effort of software testing, in: IEEE Proc. Third International Conference on Software Testing, Verification and Validation (ICST), 7–9 April 2010, Paris, 275–284, 2010.
Simor, V., Hlavcova, K., Silvia Kohnova, S., and Szolgay, J.: Application of Artificial Neural Networks for estimating index floods, Contrib. Geophys. Geodesy, 42/4, 295–311, 2012.
Singh, K. K., Pal, M., and Singh, V. P.: Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree, Water Resour. Manage., 24, 2007–2019, https://doi.org/10.1007/s11269-009-9535-x, 2010.
Smith, A., Sampson, C., and Bates, P.: Regional flood frequency analysis at the global scale, Water Resour. Res., 51, 539–553, https://doi.org/10.1002/2014WR015814, 2015.
Srinivas, V. V., Tripathi, S., Rao, A. R., and Govindaraju, R. S.: Regional flood frequency analysis by combining self-organizing feature maps and fuzzy clustering, J. Hydrol., 348, 148–166, 2008.
Stedinger, J. R. and Lu, L.: Appraisal of regional and index flood quantile estimators, Stoch. Hydrol. Hydraul., 9, 49–75, 1995
Stedinger, J. R. and Tasker, G. D.: Regional hydrologic analysis 1. Ordinary, weighted, and generalized least squares compared, Water Resour. Res., 21, 1421–1432, 1985.
Toth, E.: Catchment classification based on characterisation of streamflow and precipitation time series, Hydrol. Earth Syst. Sci., 17, 1149–1159, https://doi.org/10.5194/hess-17-1149-2013, 2013.
Toth, E.: Asymmetric Error Functions for Reducing the Underestimation of Local Scour around Bridge Piers: Application to Neural Networks Models, J. Hydraul. Eng., 141, 04015011, https://doi.org/10.1061/(ASCE)HY.1943-7900.0000981, 2015.
UCAR – University Corporation for Atmospheric Research: Flash Flood Early Warning System Reference Guide 2010, http://www.meted.ucar.edu/communities/hazwarnsys/ffewsrg/FF_EWS.pdf (last access: 17 June 2015), 2010.
Verkade, J. S. and Werner, M. G. F.: Estimating the benefits of single value and probability forecasting for flood warning, Hydrol. Earth Syst. Sci., 15, 3751–3765, https://doi.org/10.5194/hess-15-3751-2011, 2011.
Ward, P. J., Jongman, B., Sperna Weiland, F. C., Bouwman, A., van Beek, R., Bierkens, M. F. P., Ligtvoet, W., and Winsemius, H. C.: Assessing flood risk at the global scale: Model setup, results, and sensitivity, Environ. Res. Lett., 8, 44019, https://doi.org/10.1088/1748-9326/8/4/044019, 2013.
Wilkerson, G. V.: Improved bankfull discharge prediction using 2-year recurrence-period discharge, J. Am. Water Resour. Assoc., 44, 243–258, https://doi.org/10.1111/j.1752-1688.2007.00151.x, 2008.
WMO: Manual on flood forecasting and warning, WMO Series No. 1072, 142 pp., http://www.wmo.int/pages/prog/hwrp/publications.php (last access: 17 June 2015), 2011.
Short summary
Runoff thresholds for activating flood warnings might be estimated with regionally derived relationships between catchment descriptors and assigned flood quantiles. Since the consequences of overestimated thresholds (leading to missing alarms) are generally more severe than those of an underestimation (leading to false alarms), the work proposes to parameterise the regression model with an asymmetric error function, instead of using a traditional, symmetric square errors sum.
Runoff thresholds for activating flood warnings might be estimated with regionally derived...
