Articles | Volume 27, issue 18
https://doi.org/10.5194/hess-27-3329-2023
© Author(s) 2023. 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-27-3329-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume
Francesco Fatone
Department of Science and Engineering of Matter, Environment and Urban Planning (SIMAU), Polytechnic University of Marche Ancona, 60121 Ancona,
Italy
Bartosz Szeląg
CORRESPONDING AUTHOR
Institute of Environmental Engineering, Warsaw University of Life
Sciences (SGGW), 02-797 Warsaw, Poland
Przemysław Kowal
Faculty of Civil and Environmental Engineering, Gdańsk University of
Technology, 80-233 Gdańsk, Poland
Arthur McGarity
Department of Engineering, Swarthmore College, Swarthmore, PA 19081, USA
Adam Kiczko
Institute of Environmental Engineering, Warsaw University of Life
Sciences (SGGW), 02-797 Warsaw, Poland
Grzegorz Wałek
Institute of Geography and Environmental Sciences, Jan Kochanowski
University of Kielce, 25–406 Kielce, Poland
Ewa Wojciechowska
Faculty of Civil and Environmental Engineering, Gdańsk University of
Technology, 80-233 Gdańsk, Poland
Michał Stachura
Faculty of Law and Social Sciences, Jan Kochanowski University of Kielce, 25–406 Kielce, Poland
Nicolas Caradot
Kompetenzzentrum Wasser Berlin, 10709 Berlin, Germany
Related authors
Bartosz Szeląg, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Francesco Fatone
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-109, https://doi.org/10.5194/hess-2022-109, 2022
Manuscript not accepted for further review
Short summary
Short summary
A methodology for the development of a sewer network performance simulator and risk assesssment is given. The influence of catchment characteristics, sewer network and SWMM parameters on specific flood volume was taken into account in comparison with developed methods. The influence of spatial variability of catchment and sewer network characteristics on the relation between SWMM parameters and sewage flooding was determined, which can be used for spatial planning and urban catchment management.
Francesco Fatone, Bartosz Szeląg, Adam Kiczko, Dariusz Majerek, Monika Majewska, Jakub Drewnowski, and Grzegorz Łagód
Hydrol. Earth Syst. Sci., 25, 5493–5516, https://doi.org/10.5194/hess-25-5493-2021, https://doi.org/10.5194/hess-25-5493-2021, 2021
Short summary
Short summary
A sensitivity analysis based on a simulator of hydrograph parameters (volume, maximum flow) is shown. The method allows us to analyze the impact of calibrated hydrodynamic model parameters, including rainfall distribution and intensity, on the hydrograph. A sensitivity coefficient and the effect of the simulator uncertainty on calculation results are presented. This approach can be used to select hydrographs for calibration and validation of models, which has not been taken into account so far.
Adam P. Kozioł, Adam Kiczko, Marcin Krukowski, Elżbieta Kubrak, Janusz Kubrak, Grzegorz Majewski, and Andrzej Brandyk
Hydrol. Earth Syst. Sci., 29, 535–545, https://doi.org/10.5194/hess-29-535-2025, https://doi.org/10.5194/hess-29-535-2025, 2025
Short summary
Short summary
Floodplain trees play a crucial role in increasing flow resistance. Their impact extends beyond floodplains to affect the main channel. The experiments reveal the influence of floodplain trees on the discharge capacity of channels with varying roughness. We determine resistance coefficients for different roughness levels of the main channel bottom. The research contributes to a deeper understanding of open-channel flow dynamics and has practical implications for river engineering.
Monika Barbara Kalinowska, Kaisa Västilä, Michael Nones, Adam Kiczko, Emilia Karamuz, Andrzej Brandyk, Adam Kozioł, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 27, 953–968, https://doi.org/10.5194/hess-27-953-2023, https://doi.org/10.5194/hess-27-953-2023, 2023
Short summary
Short summary
Vegetation is commonly found in rivers and channels. Using field investigations, we evaluated the influence of different vegetation coverages on the flow and mixing in the small naturally vegetated channel. The obtained results are expected to be helpful for practitioners, enlarge our still limited knowledge, and show the further required scientific directions for a better understanding of the influence of vegetation on the flow and mixing of dissolved substances in real natural conditions.
Bartosz Szeląg, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Francesco Fatone
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-109, https://doi.org/10.5194/hess-2022-109, 2022
Manuscript not accepted for further review
Short summary
Short summary
A methodology for the development of a sewer network performance simulator and risk assesssment is given. The influence of catchment characteristics, sewer network and SWMM parameters on specific flood volume was taken into account in comparison with developed methods. The influence of spatial variability of catchment and sewer network characteristics on the relation between SWMM parameters and sewage flooding was determined, which can be used for spatial planning and urban catchment management.
Francesco Fatone, Bartosz Szeląg, Adam Kiczko, Dariusz Majerek, Monika Majewska, Jakub Drewnowski, and Grzegorz Łagód
Hydrol. Earth Syst. Sci., 25, 5493–5516, https://doi.org/10.5194/hess-25-5493-2021, https://doi.org/10.5194/hess-25-5493-2021, 2021
Short summary
Short summary
A sensitivity analysis based on a simulator of hydrograph parameters (volume, maximum flow) is shown. The method allows us to analyze the impact of calibrated hydrodynamic model parameters, including rainfall distribution and intensity, on the hydrograph. A sensitivity coefficient and the effect of the simulator uncertainty on calculation results are presented. This approach can be used to select hydrographs for calibration and validation of models, which has not been taken into account so far.
Adam Kiczko, Kaisa Västilä, Adam Kozioł, Janusz Kubrak, Elżbieta Kubrak, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 24, 4135–4167, https://doi.org/10.5194/hess-24-4135-2020, https://doi.org/10.5194/hess-24-4135-2020, 2020
Short summary
Short summary
The study compares the uncertainty of discharge curves for vegetated channels, calculated using several methods, including the simplest ones, based on the Manning formula and advanced approaches, providing a detailed physical representation of the channel flow processes. Parameters of each method were identified for the same data sets. The outcomes of the study include the widths of confidence intervals, showing which method was the most successful in explaining observations.
Bartosz Szeląg, Roman Suligowski, Jan Studziński, and Francesco De Paola
Hydrol. Earth Syst. Sci., 24, 595–614, https://doi.org/10.5194/hess-24-595-2020, https://doi.org/10.5194/hess-24-595-2020, 2020
Short summary
Short summary
A method for linking releases of a storm overflow with the precipitation-forming mechanism, depending on air circulation, was presented. The logit model was used to simulate overflow releases, and a rainfall generator accounting for a forming mechanism was used for forecasting. It was found that the logit model is universal and can be applied to a catchment with diverse geographical characteristics and that the precipitation-forming mechanism has an impact on the operation of the storm overflow.
A. Kiczko, R. J. Romanowicz, M. Osuch, and E. Karamuz
Nat. Hazards Earth Syst. Sci., 13, 3443–3455, https://doi.org/10.5194/nhess-13-3443-2013, https://doi.org/10.5194/nhess-13-3443-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models
Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations
Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
Learning landscape features from streamflow with autoencoders
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
Projections of streamflow intermittence under climate change in European drying river networks
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Extended range forecasting of stream water temperature with deep learning models
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
Analyzing the generalization capabilities of hybrid hydrological models for extrapolation to extreme events
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
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?
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric-hydrological model
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
HESS Opinions: The sword of Damocles of the impossible flood
Scale-dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
A diversity centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Metamorphic testing of machine learning and conceptual hydrologic models
The influence of human activities on streamflow reductions during the megadrought in central Chile
Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA
Lack of robustness of hydrological models: A large-sample diagnosis and an attempt to identify the hydrological and climatic drivers
Exploring the Potential Processes Controls for Changes of Precipitation-Runoff Relationships in Non-stationary Environments
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Long Short-Term Memory Networks for Real-time Flood Forecast Correction: A Case Study for an Underperforming Hydrologic Model
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
Short summary
Short summary
Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci., 29, 335–360, https://doi.org/10.5194/hess-29-335-2025, https://doi.org/10.5194/hess-29-335-2025, 2025
Short summary
Short summary
Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping to better prepare for and respond to floods.
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
Short summary
Short summary
To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025, https://doi.org/10.5194/hess-29-27-2025, 2025
Short summary
Short summary
The influence of watershed disturbances has proved challenging to disentangle from natural streamflow variability. This study evaluates the influence of time-varying hydrologic controls on rainfall–runoff in undisturbed and wildfire-disturbed watersheds using a novel time-series event separation method. Across watersheds, water year type and season influenced rainfall–runoff patterns. Accounting for these controls enabled clearer isolation of wildfire effects.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, https://doi.org/10.5194/hess-28-5511-2024, 2024
Short summary
Short summary
Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
Short summary
Short summary
Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
Short summary
Short summary
Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
Short summary
Short summary
We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
Short summary
Short summary
Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
Short summary
Short summary
The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
Short summary
Short summary
We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
Short summary
Short summary
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
Short summary
Short summary
We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
Short summary
Short summary
This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
Short summary
Short summary
A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
Short summary
Short summary
Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
Short summary
Short summary
We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
Short summary
Short summary
Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
Short summary
Short summary
Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2591, https://doi.org/10.5194/egusphere-2024-2591, 2024
Short summary
Short summary
We generate operational forecasts of daily maximum stream water temperature for the next month at 54 stations in Switzerland with our best performing data-driven model. The average forecast error is 0.38 °C for 1 day ahead and increases to 0.90 °C for 1 month ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
Short summary
Short summary
An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
Short summary
Short summary
The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-230, https://doi.org/10.5194/hess-2024-230, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Water budget non-closure is a widespread phenomenon among multisource datasets, which undermines the robustness of hydrological inferences. This study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset, and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Eduardo Acuna Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2024-2147, https://doi.org/10.5194/egusphere-2024-2147, 2024
Short summary
Short summary
Data-driven techniques have shown the potential to outperform process-based models in rainfall-runoff simulations. Hybrid models, combining both approaches, aim to enhance accuracy and maintain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions we test their generalization capabilities for extreme hydrological events.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
Short summary
Short summary
The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
Short summary
Short summary
By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
Short summary
Short summary
Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
Short summary
Short summary
Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
Short summary
Short summary
Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1464, https://doi.org/10.5194/egusphere-2024-1464, 2024
Short summary
Short summary
Our study conducted a detailed analysis of runoff component and future trend in the Yarlung Tsangpo River basin owing to the existed differences in the published results, and find that the contributions of snowmelt and glacier melt runoff to streamflow were limited, both for ~5 % which were much lower than previous results. The streamflow there will continuously increase in the future, but the overestimated contribution from glacier melt would lead to an underestimation on such increasing trend.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
Short summary
Short summary
Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
Short summary
Short summary
Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
Short summary
Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
Short summary
Short summary
A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
Short summary
Short summary
Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
Short summary
Short summary
Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
Short summary
Short summary
Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart Lane, and Francesco Comiti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1687, https://doi.org/10.5194/egusphere-2024-1687, 2024
Short summary
Short summary
In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until an overparametrization limit is reached.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
Short summary
Short summary
We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
Short summary
Short summary
In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
Short summary
Short summary
Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-118, https://doi.org/10.5194/hess-2024-118, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This study develops an integrated framework based on the novel Driving index for changes in Precipitation-Runoff Relationships (DPRR) to explore the controls for changes in precipitation-runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation-runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2024-1030, https://doi.org/10.5194/egusphere-2024-1030, 2024
Short summary
Short summary
Accurate early warning systems are crucial for reducing damages caused by flooding events. In this study, we demonstrate the potential of Long Short-Term Memory Networks for enhancing the forecast accuracy of hydrologic models employed in operational flood forecasting. The presented approach elevated the investigated hydrologic model’s forecast accuracy for further ahead predictions and at flood event runoff.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
Short summary
Short summary
Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Cited articles
Babovic, F., Mijic, A., and Madani, K.: Decision making under deep uncertainty
for adapting urban drainage systems to change, Urban Water J., 15, 552–560, https://doi.org/10.1080/1573062X.2018.1529803, 2018.
Ball, J. E.: An Assessment of Continuous Modeling for Robust Design Flood
Estimation in Urban Environments, Front. Earth Sci., 8, 1–10,
https://doi.org/10.3389/feart.2020.00124, 2020.
Bui, D. T., Hoang, N. D., Martínez-Álvarez, F., Ngo, P. T. T., Hoa,
P. V., Pham, T. D., Samui, P., and Costache, R.: A novel deep learning neural
network approach for predicting flash flood susceptibility: A case study at
a high frequency tropical storm area, Sci. Total Environ., 701, 134413,
https://doi.org/10.1016/j.scitotenv.2019.134413, 2018.
Cea, L. and Costabile, P.: Flood Risk in Urban Areas: Modelling, Management and
Adaptation to Climate Change. A Review, Hydrology, 9, 50,
https://doi.org/10.3390/hydrology9030050, 2022.
Chang, H., Pallathadka, A., Sauer, J., Grimm, N. B., Zimmerman, R., Cheng,
C., Iwaniec, D. M., Kim, Y., Lloyd, R., McPhearson, T., Rosenzweig, B.,
Troxler, T., Welty, C., Brenner, R., and Herreros-Cantis, P.: Assessment of
urban flood vulnerability using the social-ecological-technological systems
framework in six US cities, Sustain. Cities Soc., 68, 102786,
https://doi.org/10.1016/j.scs.2021.102786, 2020.
Chen, L., Li, S., Zhong, Y., and Shen, Z.: Improvement of model evaluation by incorporating prediction and measurement uncertainty, Hydrol. Earth Syst. Sci., 22, 4145–4154, https://doi.org/10.5194/hess-22-4145-2018, 2018.
Chen, S., Garambois, P.-A., Finaud-Guyot, P., Dellinger, G., Mosé, R., Terfous, A., and Ghenaim, A.: Variance based sensitivity analysis of 1D and 2D hydraulic models: An experimental urban flood case, Environ. Model. Softw., 109, 167181, https://doi.org/10.1016/j.envsoft.2018.08.008, 2018.
Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, X., Bian, H.,
Zhang, S., Pradhan, B., and Bin Ahmad, B.: Modeling flood susceptibility using
data-driven approaches of naïve Bayes tree, alternating decision tree,
and random forest methods, Sci. Total Environ., 701, 134979, https://doi.org/10.1016/j.scitotenv.2019.134979, 2019.
Cristiano, E., ten Veldhuis, M. C., Wright, D. B., Smith, J. A., and van de
Giesen, N.: The Influence of Rainfall and Catchment Critical Scales on Urban
Hydrological Response Sensitivity, Water Resour. Res., 55, 3375–3390,
https://doi.org/10.1029/2018WR024143, 2019.
Del Giudice, D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., and Rieckermann, J.: Improving uncertainty estimation in urban hydrological modeling by statistically describing bias, Hydrol. Earth Syst. Sci., 17, 4209–4225, https://doi.org/10.5194/hess-17-4209-2013, 2013.
Dotto, C. B. S., Kleidorfer, M., Deletic, A., Rauch, W., and McCarthy, D.
T.: Impacts of measured data uncertainty on urban stormwater models, J.
Hydrol., 508, 28–42, https://doi.org/10.1016/j.jhydrol.2013.10.025, 2014.
Duncan, A. P., Chen, A. S., Keedwell, E. C., Djordjević, S., and Savić, D. A.: Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks, Weather Radar and Hydrology: IAHS Red Book Proceedings, 18–21 April 2011, University of Exeter, UK, 2012.
DWA-A118E: Hydraulic Dimensioning and Verification of Drain and Sewer
Systems, Ger. Assoc. Water Wastewater Waste, 2006.
Fatone, F., Szeląg, B., Kiczko, A., Majerek, D., Majewska, M., Drewnowski, J., and Łagód, G.: Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments, Hydrol. Earth Syst. Sci., 25, 5493–5516, https://doi.org/10.5194/hess-25-5493-2021, 2021.
Fraga, I., Cea, L., Puertas, J., Suárez, J., Jiménez, V., and
Jácome, A.: Global sensitivity and GLUE-based uncertainty analysis of a
2D-1D dual urban drainage model, J Hydrol Eng., 21, 04016004,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001335, 2016.
Freni, G. and Oliveri, O.: Mitigation of urban flooding: a simplified approach for distributed stormwater management practices selection and planning, Urban Water J., 2, 215226, https://doi.org/10.1080/15730620500386461, 2005.
Fu, G. and Butler, D.: Copula-based frequency analysis of overflow and flooding
in urban drainage systems, J. Hydrol., 510, 49–58,
https://doi.org/10.1016/j.jhydrol.2013.12.006, 2014.
Fu, G., Butler, D., Khu, S.-T., and Sun, S.: Imprecise probabilistic evaluation
of sewer flooding in urban drainage systems using random set theory, Water
Resour. Res., 47, 1–13, https://doi.org/10.1029/2009WR008944, 2011.
Guo, K., Guan, M., and Yu, D.: Urban surface water flood modelling – a comprehensive review of current models and future challenges, Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, 2021.
Harrell, F. E.: Regression Modeling Strategies: With Applications to Linear
Models, Logistic Regression, and Survival Analysis, Springer Series in
Statistics, New York, ISBN 9781475734621, 2001.
Hettiarachchi, S., Wasko, C., and Sharma, A.: Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns, Hydrol. Earth Syst. Sci., 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018, 2018.
Huang, H., Chen, X., Zhu, Z., Xie, Y., Liu, L., Wang, X., Wang, X., and Liu, K.: The changing pattern of urban flooding in Guangzhou, China, Sci. Total Environ., 622623, 394401, https://doi.org/10.1016/j.scitotenv.2017.11.358, 2018.
Hung, W. and Hobbs, F. B.: How can learning-by-doing improve decisions in
stormwater management? A Bayesian-based optimization model for planning
urban green infrastructure investments, Environ. Modell. Softw., 113, 59–72,
https://doi.org/10.1016/j.envsoft.2018.12.005, 2019.
Jato-Espino, D., Sillanpää, N., Andrés-Doménech, I.,
and Rodriguez-Hernandez, J.: Flood Risk Assessment in Urban Catchments Using
Multiple Regression Analysis, J. Water Resour. Plan. Manag., 144, 04017085,
https://doi.org/10.1061/(asce)wr.1943-5452.0000874, 2018.
Jato-Espino, D., Sillanpää, N., and Pathak, S.: Flood modelling in sewer networks using dependence measures and learning classifier systems, J. Hydrol., 578, 124013, https://doi.org/10.1016/j.jhydrol.2019.124013, 2019.
Jiang, Y., Zevenbergen, C., and Mab, Y.: Urban pluvial flooding and stormwater
management: A contemporary review of China's challenges and “sponge
cities” strategy, Environ. Sci. Policy, 80, 132–143,
https://doi.org/10.1016/j.envsci.2017.11.016, 2018.
Karamouz, M. and Nazif, S.: Reliability-based flood management in
urban watersheds considering climate change impacts, J. Water Resour.
Plann. Manage., 139, 520–533, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000345, 2013.
Ke, Q., Bricker, J., Tian, Z., Guan, G., Cai, H., Huang, X., Yang, H., and Liu,
J.: Urban pluvial flooding prediction by machine learning approaches – a
case study of Shenzen city, China, Adv. Water Resour., 145, 103719,
https://doi.org/10.1016/j.advwatres.2020.103719, 2020.
Kelleher, C., McGlynn, B., and Wagener, T.: Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding, Hydrol. Earth Syst. Sci., 21, 3325–3352, https://doi.org/10.5194/hess-21-3325-2017, 2017.
Khan, M. P., Hubacek, K., Brubaker, K. L., Sun, L., and Moglen, G. E.: Stormwater
Management Adaptation Pathways under Climate Change and Urbanization, J.
Sustain. Water Built Environ., 8, 04022009,
https://doi.org/10.1061/JSWBAY.0000992, 2022.
Kiczko, A., Szeląg, B., Kozioł, A. P., Krukowski, M., Kubrak, E.,
Kubrak, J., and Romanowicz, R. J.: Optimal capacity of a stormwater reservoir for
flood peak reduction, J. Hydrol. Eng., 23, 04018008,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001636, 2018.
Kim, Y., Eisenberg, D. A., Bondank, E. N., Chester, M. V., Mascaro, G.,
and Underwood, S.: Fail-safe and safe-to-fail adaptation: decision-making for
urban flooding under climate change, Clim. Change, 145, 397–412,
https://doi.org/10.1007/s10584-017-2090-1, 2015.
Kirshen, P., Caputo, L., Vogel, R. M., Mathisen, P., Rosner, A., and Renaud, T.:
Adapting urban infrastructure to climate change: a drainage case study, J.
Water Resour. Plan. Manag., 141, 04014064,
https://doi.org/10.1061/(ASCE)WR.1943-5452.0000443, 2015.
Knighton, J., Lennon, E., Bastidas, L., and White, E.: Stormwater detention
system parameter sensitivity and uncertainty analysis using SWMM, J. Hydrol.
Eng., 21, 05016014, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001382,
2016.
Kobarfard, M., Fazloula, R., Zarghami M., and Akbarpour: Evaluating the
uncertainty of urban flood model using glue approach, Urban Water J., 19, 600–615, https://doi.org/10.1080/1573062X.2022.2053865, 2022.
Kotowski, A., Kaźmierczak, B., and Nowakowska, M. A.: Analysis of the drainage system load in case of the predicted increase in frequency and intensity of rain due to climate change, Ochrona Środowiska, 35, 25–32, 2013.
Lama, G. F. C., Crimaldi, M., De Vivo, A., Chirico, G. B., and Sarghini, F.:
Eco-hydrodynamic characterization of vegetated flows derived by UAV-based
imagery, 2021 IEEE International Workshop on Metrology for Agriculture and
Forestry (MetroAgriFor), 273–278,
https://doi.org/10.1109/MetroAgriFor52389.2021.9628749, 2021a.
Lama, G. F. C., Rillo Migliorini Giovannini, M., Errico, A., Mirzaei, S.,
Chirico, G. B., and Preti, F.: The impacts of Nature Based Solutions (NBS) on
vegetated flows' dynamics in urban areas, 2021 IEEE International Workshop
on Metrology for Agriculture and Forestry (MetroAgriFor), 3–5 November 2021, Trento-Bolzano, Italy, 58–63,
https://doi.org/10.1109/MetroAgriFor52389.2021.9628438), 2021b.
Lei, X., Chen, W., Panahi, M., Falah, F., Rahmati, O., Uuemaa, E.,
Kalantari, Z., Ferreira, C. S. S., Rezaie, F., Tiefenbacher, J. P., Lee, S.,
and Bian, H.: Urban flood modeling using deep-learning approaches in Seoul,
South Korea, J. Hydrol., 601, 126684,
https://doi.org/10.1016/j.jhydrol.2021.126684, 2021.
Lense, G. H. E., Lämmle, L., Ayer, J. E. B., Lama, G. F. C., Rubira, F. G., and
Mincato, R. L.: Modeling of Soil Loss by Water Erosion and Its Impacts on the
Cantareira System, Brazil. Water, 15, 1490,
https://doi.org/10.3390/w15081490, 2023.
Li, X. and Willems, P.: A Hybrid Model for Fast and Probabilistic Urban Pluvial
Flood Prediction, Water Resour. Res., 56, e2019WR025128,
https://doi.org/10.1029/2019WR025128, 2020.
Ma, B., Wu, Z., Hu, C., Wang, H., Xu, H., Yan, D., and Soomro, S.:
Process-oriented SWMM real-time correction and urban flood dynamic
simulation, J. Hydrol., 605, 127269,
https://doi.org/10.1016/j.jhydrol.2021.127269, 2022.
Martins, R., Leandro, J., and Djordjević, S.: Influence of sewer
network models on urban flood damage assessment based on coupled 1D/2D
models, J. Flood Risk Manag., 11, 717–728,
https://doi.org/10.1111/jfr3.12244, 2018.
Mignot, E., Li, X., and Dewals, B.: Experimental modelling of urban flooding: A
review, J. Hydrol., 568, 334–342,
https://doi.org/10.1016/j.jhydrol.2018.11.001, 2019.
Miller, J., Kim, H., Kjeldsen, T. R., Packman, J., Grebby, S., and Dearden, R.:
Assessing the impact of urbanization on storm runoff in a peri-urban
catchment using historical change in impervious cover, J. Hydrol., 515, 59–70, https://doi.org/10.1016/j.jhydrol.2014.04.011, 2014.
Mohammad, L., Bandyopadhyay, L., Sk, R., Mondal, I., Nguyen, T. T., Lama,
G. F. C., and Ahn, D. T.: Estimation of agricultural burned affected area using
NDVI and dNBR satellite-based empirical models, J. Environ. Manage., 343,
118226, https://doi.org/10.1016/j.jenvman.2023.118226, 2023.
Morio, J.: Global and local sensitivity analysis methods for a physical
system, Eur. J. Phys., 32, 1577–1583,
https://doi.org/10.1088/0143-0807/32/6/011, 2011.
Petersen, B., Gernaey, K., Henze, M., and Vanrolleghem, P. A.: Evaluation of an ASM1 model calibration procedure on a municipal industrial wastewater treatment plant, J. Hydroinf., 4, 1538, https://doi.org/10.2166/hydro.2002.0003, 2002.
Prodanovic, V., Jamali, B., Kuller, M., Wang, Y., Bach, P. M., Coleman, R. A., Metzeling, L., McCarthy, D. T., Shi, B., Deletic, A.: Calibration and sensitivity analysis of a novel water flow and pollution model for future city planning: Future Urban Stormwater Simulation (FUSS), Water Sci. Technol., 85, 961969, https://doi.org/10.2166/wst.2022.046, 2022.
Ray, R., Das, A., Hasan, M. S. U., Aldrees, A., Islam, S., Khan, M. A., and Lama,
G. F. C.: Quantitative Analysis of Land Use and Land Cover Dynamics using
Geoinformatics Techniques: A Case Study on Kolkata Metropolitan Development
Authority (KMDA) in West Bengal, India, Remote Sens., 15, 959,
https://doi.org/10.3390/rs15040959, 2023.
Razavi, S. and Gupta, H. V.: A multi-method Generalized Global Sensitivity
Matrix approach to accounting for the dynamical nature of earth and
environmental systems models, Environ. Model. Softw., 114, 1–11,
https://doi.org/10.1016/j.envsoft.2018.12.002, 2019.
Reyes-Silva, J. D., Bangura, E., Helm, B., Benisch, J., and Krebs, P.: The Role of Sewer Network Structure on the Occurrence and Magnitude of Combined Sewer Overflows (CSOs), Water, 12, 2675, https://doi.org/10.3390/w12102675, 2020.
Rosenzweig, B. R., Cantis, H., Kim, Y., Cohn, A., Grove, K., Brock, J.,
Yesuf, J., Mistry, P., Welty, C., McPhearson, T., Sauer, J., and Chang, H.: The
value of urban flood modeling, Earth's Future, 9, e2020EF001739,
https://doi.org/10.1029/2020EF001739, 2021.
Salman, B. and Salem, O.: Modeling Failure of Wastewater Collection Lines Using Various Section-Level Regression Models, J. Infrastruct. Syst., 18, 146–154, https://doi.org/10.1061/(ASCE)IS.1943-555X.0000075, 2012.
Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., and Shirzadi, A.:
Novel forecasting approaches using combination of machine learning and
statistical models for flood susceptibility mapping, J. Environ. Manage., 217,
1–11, https://doi.org/10.1016/j.jenvman.2018.03.089, 2018.
Shrestha, A., Mascaro, G., and Garcia, M.: Effects of stormwater infrastructure
data completeness and model resolution on urban flood modeling, J. Hydrol.,
607, 127498, https://doi.org/10.1016/j.jhydrol.2022.127498, 2022.
Siekmann, M. and Pinnekamp, J.: Indicator based strategy to adapt urban drainage systems in regard to the consequences caused by climate change. 12th International Conference on Urban Drainage, 11–16 September 2011, Porto Alegre/Brazil, 2011.
Siekmann, M., Vomberg, N., Mirgartz, M., Pinnekamp, J., and Mühle, S.: Multifunctional Land Use in Urban Spaces to Adapt Urban Infrastructure, in: Climate Change and the Sustainable Use of Water Resources. Climate Change Management, edited by: Leal Filho, W., Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-22266-5_37, 2011.
Sonavane N., Rangari, V. A., Waikar, M. L., and Patil, M.: Urban storm-water
modeling using EPA SWMM – a case study of Pune city, 2020 IEEE Bangalore
Humanitarian Technology Conference (B-HTC), 8–10 October 2020, Vijiyapur, India, https://doi.org/10.1109/B-HTC50970.2020.9297900,
2020.
Sun, Y., Liu, Ch., Du, X., Yang, F., Yao, Y., Soomro, S., and Hu, C.: Urban
storm flood simulation using improved SWMM based on K-means clustering of
parameter samples, J. Flood Risk Manag., 15, e12826,
https://doi.org/10.1111/jfr3.12826, 2022.
Szeląg, B.: Influence of the Hydrogramme Shape on the Capacity and Selection of Drains of a Small Retention Reservoir, PhD thesis, University of Technology, Kielce, 2013.
Szeląg, B., Kiczko, A., and Dąbek, L.: Sensitivity and uncertainty analysis of hydrodynamic model (SWMM) for storm water runoff forecasting in an urban basin – a case study, Ochr. Sr., 38, 15–22, 2016.
Szeląg, B., Suligowski, R., Studziński, J., and De Paola, F.: Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach, Hydrol. Earth Syst. Sci., 24, 595–614, https://doi.org/10.5194/hess-24-595-2020, 2020.
Szeląg, B., Kiczko, A., Łagód, G., and De Paola, F.: Relationship
between rainfall duration and sewer system performance measures within the
context of uncertainty, Water Res Manage., 35, 5073–5087,
https://doi.org/10.1007/s11269-021-02998-x, 2021a.
Szeląg, B., Suligowski, R., Drewnowski, J., De Paola, F., Fernandez-Morales, F. J., and Bąk, Ł.: Simulation of the number of storm overflows
considering changes in precipitation dynamics and the urbanisation of the
catchment area: A probabilistic approach, J. Hydrol., 598, 126275,
https://doi.org/10.1016/j.jhydrol.2021.126275, 2021b.
Szeląg, B., Majerek, D., Kiczko, A., Łagód, G., Fatone, F.,
and McGarity, A.: Analysis of sewer network performance in context of
modernization: modeling, sensitivity, uncertainty analysis, 12, 148,
https://doi.org/10.1061/(ASCE)WR.1943-5452.0001610, 2022a.
Szeląg, B., Suligowski, R., De Paola, F., Siwicki, P., Majerek, D., and Łagód, G.: Influence of urban catchment characteristics and rainfall
origins on the phenomenon of stormwater flooding: Case study, Environ.
Model. Softw., 150, 105335, https://doi.org/10.1016/j.envsoft.2022.105335,
2022b.
Taromideh, F., Fazloula, R., Choubin, B., Emadi, A., and Berndtsson, R.: Urban
Flood-Risk Assessment: Integration of Decision-Making and Machine Learning,
Sustainability, 14, 4483, https://doi.org/10.3390/su14084483, 2022.
Thorndahl, S.: Stochastic long term modelling of a drainage system with
estimation of return period uncertainty, Water Sci. Technol., 59, 2331–2339,
https://doi.org/10.2166/wst.2009.305, 2009.
Thorndahl, S., Schaarup-Jensen, K., and Jensen, J. B.: Probabilistic modelling of combined sewer overflow using the First Order Reliability Method, Water Sci. Technol., 57, 1337–1344,
https://doi.org/10.2166/wst.2008.301, 2008.
Ursino, N.: Reliability analysis of sustainable storm water drainage
systems, WIT Transactions on The Built Environment, 139, 149–157,
https://doi.org/10.2495/UW140131, 2014.
Venvik, G., Bang-Kittilsen, A., and Boogaard, F. C.: Risk assessment for
areas prone to flooding and subsidence: a case study from Bergen, Western
Norway, Hydrol. Res., 51, 322-338,
https://doi.org/10.2166/nh.2019.030, 2021.
Vorobevskii, I., Al Janabi, F., Schneebeck, F., Bellera, J., and Krebs, P.: Urban Floods: Linking the Overloading of a Storm Water Sewer System to Precipitation Parameters, Hydrology, 7, 35, https://doi.org/10.3390/hydrology7020035, 2020.
Wałek, G.: Wpływ dróg na kształtowanie spływu powierzchniowego w
obszarze zurbanizowanym na przykładzie zlewni rzeki Silnicy w Kielcach,
Jan Kochanowski University Press, Kielce, 2019 (in Polish).
Wu, J. Y., Thompson, J. R., Kolka, R. K., Franz, K. J., and Stewart, T. W.: Using the Storm Water Management Model to predict urban headwater stream hydrological response to climate and land cover change, Hydrol. Earth Syst. Sci., 17, 4743–4758, https://doi.org/10.5194/hess-17-4743-2013, 2013.
Xing, Y., Shao, D., Yang, Y., Ma, X., and Zhang, S.: Influence and interactions of input factors in urban flood inundation modeling: An examination with variance-based global sensitivity analysis, J. Hydrol., 600, 126524, https://doi.org/10.1016/j.jhydrol.2021.126524, 2021.
Yang, Q., Ma, Z., and Zhang, S.: Urban Pluvial Flood Modeling by Coupling
Raster-Based Two-Dimensional Hydrodynamic Model and SWMM, Water, 14, 1760,
https://doi.org/10.3390/w14111760, 2022.
Yang, Y. and Chui, T. F. M.: Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods, Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, 2021.
Yao, Y., Hu, C., Liu, C., Yang, F., Ma, B. Wu, O. Li, X., and Soomro, S.: Comprehensive performance evaluation of stormwater management measures for sponge city construction: A case study in Gui'an New District, China, J. Flood Risk Manage., 15, e12834, https://doi.org/10.1111/jfr3.12834, 2022.
Zhou, Y., Shen, D., Huang, N., Guo, Y., Zhang, T., and Zhang, Y.: Urban flood
risk assessment using storm characteristic parameters sensitive to
catchment-specific drainage system, Sci. Total Environ., 659, 1362–1369,
https://doi.org/10.1016/j.scitotenv.2019.01.004, 2019.
Short summary
A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
A novel methodology for the development of a stormwater network performance simulator including...