Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5981-2021
© Author(s) 2021. 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-25-5981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Nov 2021
Research article |
| 22 Nov 2021
| 22 Nov 2021
Design flood estimation for global river networks based on machine learning models
School of Geographical Sciences, University of Bristol, Bristol, UK
Paul Bates
School of Geographical Sciences, University of Bristol, Bristol, UK
Fathom, Engine Shed, Station Approach, Bristol, UK
Jeffrey Neal
School of Geographical Sciences, University of Bristol, Bristol, UK
Fathom, Engine Shed, Station Approach, Bristol, UK
Bo Pang
College of Water Sciences, Beijing Normal University, Beijing, China
Related authors
No articles found.
Joshua Green, Ivan Haigh, Niall Quinn, Jeff Neal, Thomas Wahl, Melissa Wood, Dirk Eilander, Marleen de Ruiter, Philip Ward, and Paula Camus
EGUsphere, https://doi.org/10.5194/egusphere-2024-2247, https://doi.org/10.5194/egusphere-2024-2247, 2024
Short summary
Short summary
Compound flooding, involving the combination or successive occurrence of two or more flood drivers, can amplify flood impacts in coastal/estuarine regions. This paper reviews the practices, trends, methodologies, applications, and findings of coastal compound flooding literature at regional to global scales. We explore the types of compound flood events, their mechanistic processes, and the range of terminology. Lastly, this review highlights knowledge gaps and implications for future practices.
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
Short summary
Short summary
This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
Thomas P. Collings, Niall D. Quinn, Ivan D. Haigh, Joshua Green, Izzy Probyn, Hamish Wilkinson, Sanne Muis, William V. Sweet, and Paul D. Bates
Nat. Hazards Earth Syst. Sci., 24, 2403–2423, https://doi.org/10.5194/nhess-24-2403-2024, https://doi.org/10.5194/nhess-24-2403-2024, 2024
Short summary
Short summary
Coastal areas are at risk of flooding from rising sea levels and extreme weather events. This study applies a new approach to estimating the likelihood of coastal flooding around the world. The method uses data from observations and computer models to create a detailed map of where these coastal floods might occur. The approach can predict flooding in areas for which there are few or no data available. The results can be used to help prepare for and prevent this type of flooding.
Laurence Hawker, Jeffrey Neal, James Savage, Thomas Kirkpatrick, Rachel Lord, Yanos Zylberberg, Andre Groeger, Truong Dang Thuy, Sean Fox, Felix Agyemang, and Pham Khanh Nam
Nat. Hazards Earth Syst. Sci., 24, 539–566, https://doi.org/10.5194/nhess-24-539-2024, https://doi.org/10.5194/nhess-24-539-2024, 2024
Short summary
Short summary
We present a global flood model built using a new terrain data set and evaluated in the Central Highlands of Vietnam.
Leanne Archer, Jeffrey Neal, Paul Bates, Emily Vosper, Dereka Carroll, Jeison Sosa, and Daniel Mitchell
Nat. Hazards Earth Syst. Sci., 24, 375–396, https://doi.org/10.5194/nhess-24-375-2024, https://doi.org/10.5194/nhess-24-375-2024, 2024
Short summary
Short summary
We model hurricane-rainfall-driven flooding to assess how the number of people exposed to flooding changes in Puerto Rico under the 1.5 and 2 °C Paris Agreement goals. Our analysis suggests 8 %–10 % of the population is currently exposed to flooding on average every 5 years, increasing by 2 %–15 % and 1 %–20 % at 1.5 and 2 °C. This has implications for adaptation to more extreme flooding in Puerto Rico and demonstrates that 1.5 °C climate change carries a significant increase in risk.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
Short summary
Short summary
A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
Short summary
Short summary
This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Paul D. Bates, James Savage, Oliver Wing, Niall Quinn, Christopher Sampson, Jeffrey Neal, and Andrew Smith
Nat. Hazards Earth Syst. Sci., 23, 891–908, https://doi.org/10.5194/nhess-23-891-2023, https://doi.org/10.5194/nhess-23-891-2023, 2023
Short summary
Short summary
We present and validate a model that simulates current and future flood risk for the UK at high resolution (~ 20–25 m). We show that UK flood losses were ~ 6 % greater in the climate of 2020 compared to recent historical values. The UK can keep any future increase to ~ 8 % if all countries implement their COP26 pledges and net-zero ambitions in full. However, if only the COP26 pledges are fulfilled, then UK flood losses increase by ~ 23 %; and potentially by ~ 37 % in a worst-case scenario.
Yinxue Liu, Paul D. Bates, and Jeffery C. Neal
Nat. Hazards Earth Syst. Sci., 23, 375–391, https://doi.org/10.5194/nhess-23-375-2023, https://doi.org/10.5194/nhess-23-375-2023, 2023
Short summary
Short summary
In this paper, we test two approaches for removing buildings and other above-ground objects from a state-of-the-art satellite photogrammetry topography product, ArcticDEM. Our best technique gives a 70 % reduction in vertical error, with an average difference of 1.02 m from a benchmark lidar for the city of Helsinki, Finland. When used in a simulation of rainfall-driven flooding, the bare-earth version of ArcticDEM yields a significant improvement in predicted inundation extent and water depth.
Maria Pregnolato, Andrew O. Winter, Dakota Mascarenas, Andrew D. Sen, Paul Bates, and Michael R. Motley
Nat. Hazards Earth Syst. Sci., 22, 1559–1576, https://doi.org/10.5194/nhess-22-1559-2022, https://doi.org/10.5194/nhess-22-1559-2022, 2022
Short summary
Short summary
The interaction of flow, structure and network is complex, and yet to be fully understood. This study aims to establish rigorous practices of computational fluid dynamics (CFD) for modelling hydrodynamic forces on inundated bridges, and understanding the consequences of such impacts on the surrounding network. The objectives of this study are to model hydrodynamic forces as the demand on the bridge structure, to advance a structural reliability and network-level analysis.
Peter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, and Hylke E. Beck
Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, https://doi.org/10.5194/gmd-14-4865-2021, 2021
Short summary
Short summary
We present a cascade of models to compute high-resolution river flooding. This takes meteorological inputs, e.g., rainfall and temperature from observations or climate models, and takes them through a series of modeling steps. This is relevant to evaluating current day and future flood risk and impacts. The model framework uses global data sets, allowing it to be applied anywhere in the world.
James Shaw, Georges Kesserwani, Jeffrey Neal, Paul Bates, and Mohammad Kazem Sharifian
Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021, https://doi.org/10.5194/gmd-14-3577-2021, 2021
Short summary
Short summary
LISFLOOD-FP has been extended with new shallow-water solvers – DG2 and FV1 – for modelling all types of slow- or fast-moving waves over any smooth or rough surface. Using GPU parallelisation, FV1 is faster than the simpler ACC solver on grids with millions of elements. The DG2 solver is notably effective on coarse grids where river channels are hard to capture, improving predicted river levels and flood water depths. This marks a new step towards real-world DG2 flood inundation modelling.
Oliver E. J. Wing, Andrew M. Smith, Michael L. Marston, Jeremy R. Porter, Mike F. Amodeo, Christopher C. Sampson, and Paul D. Bates
Nat. Hazards Earth Syst. Sci., 21, 559–575, https://doi.org/10.5194/nhess-21-559-2021, https://doi.org/10.5194/nhess-21-559-2021, 2021
Short summary
Short summary
Global flood models are difficult to validate. They generally output theoretical flood events of a given probability rather than an observed event that they can be tested against. Here, we adapt a US-wide flood model to enable the rapid simulation of historical flood events in order to more robustly understand model biases. For 35 flood events, we highlight the challenges of model validation amidst observational data errors yet evidence the increasing skill of large-scale models.
Thomas O'Shea, Paul Bates, and Jeffrey Neal
Nat. Hazards Earth Syst. Sci., 20, 2281–2305, https://doi.org/10.5194/nhess-20-2281-2020, https://doi.org/10.5194/nhess-20-2281-2020, 2020
Short summary
Short summary
Outlined here is a multi-disciplinary framework for analysing and evaluating the nature of vulnerability to, and capacity for, flood hazard within a complex urban society. It provides scope beyond the current, reified, descriptors of
flood riskand models the role of affected individuals within flooded areas. Using agent-based modelling coupled with the LISFLOOD-FP hydrodynamic model, potentially influential behaviours that give rise to the flood hazard system are identified and discussed.
Giuliano Di Baldassarre, Heidi Kreibich, Sergiy Vorogushyn, Jeroen Aerts, Karsten Arnbjerg-Nielsen, Marlies Barendrecht, Paul Bates, Marco Borga, Wouter Botzen, Philip Bubeck, Bruna De Marchi, Carmen Llasat, Maurizio Mazzoleni, Daniela Molinari, Elena Mondino, Johanna Mård, Olga Petrucci, Anna Scolobig, Alberto Viglione, and Philip J. Ward
Hydrol. Earth Syst. Sci., 22, 5629–5637, https://doi.org/10.5194/hess-22-5629-2018, https://doi.org/10.5194/hess-22-5629-2018, 2018
Short summary
Short summary
One common approach to cope with floods is the implementation of structural flood protection measures, such as levees. Numerous scholars have problematized this approach and shown that increasing levels of flood protection can generate a false sense of security and attract more people to the risky areas. We briefly review the literature on this topic and then propose a research agenda to explore the unintended consequences of structural flood protection.
Keith J. Beven, Susana Almeida, Willy P. Aspinall, Paul D. Bates, Sarka Blazkova, Edoardo Borgomeo, Jim Freer, Katsuichiro Goda, Jim W. Hall, Jeremy C. Phillips, Michael Simpson, Paul J. Smith, David B. Stephenson, Thorsten Wagener, Matt Watson, and Kate L. Wilkins
Nat. Hazards Earth Syst. Sci., 18, 2741–2768, https://doi.org/10.5194/nhess-18-2741-2018, https://doi.org/10.5194/nhess-18-2741-2018, 2018
Short summary
Short summary
This paper discusses how uncertainties resulting from lack of knowledge are considered in a number of different natural hazard areas including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. As every analysis is necessarily conditional on the assumptions made about the nature of sources of such uncertainties it is also important to follow the guidelines for good practice suggested in Part 2.
Keith J. Beven, Willy P. Aspinall, Paul D. Bates, Edoardo Borgomeo, Katsuichiro Goda, Jim W. Hall, Trevor Page, Jeremy C. Phillips, Michael Simpson, Paul J. Smith, Thorsten Wagener, and Matt Watson
Nat. Hazards Earth Syst. Sci., 18, 2769–2783, https://doi.org/10.5194/nhess-18-2769-2018, https://doi.org/10.5194/nhess-18-2769-2018, 2018
Short summary
Short summary
Part 1 of this paper discussed the uncertainties arising from gaps in knowledge or limited understanding of the processes involved in different natural hazard areas. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities. A conceptual framework for good practice in dealing with epistemic uncertainties is outlined and implications of applying the principles to natural hazard science are discussed.
Zhanjie Li, Jingshan Yu, Xinyi Xu, Wenchao Sun, Bo Pang, and Jiajia Yue
Proc. IAHS, 379, 335–341, https://doi.org/10.5194/piahs-379-335-2018, https://doi.org/10.5194/piahs-379-335-2018, 2018
Short summary
Short summary
Multi-model ensemble hydrological simulation has been an effective method for improving simulation accuracy. This study explored the feasibility of applying a multi-model ensemble simulation to the upper Yalongjiang River Basin. The results of the BPNN multi-model ensemble simulation are better than that of a single model. Multi-model ensemble simulation should become an important direction in hydrological simulation research.
Chonghao Li, Kaige Chi, Bo Pang, and Hongbin Tang
Proc. IAHS, 379, 125–129, https://doi.org/10.5194/piahs-379-125-2018, https://doi.org/10.5194/piahs-379-125-2018, 2018
Short summary
Short summary
The reservoirs of cascade hydropower stations in Hongshuihe basin are facing more and more integrated water resources utilization problem. This paper introduces the integrated water demand of cascade reservoirs in Hongshuihe and analyses the impact of various types of integrated water demand on power generation. It provides a technical and management guide and demonstration for cascade reservoirs operation and basin integrated water management.
Zongxue Xu, Dingzhi Peng, Wenchao Sun, Bo Pang, Depeng Zuo, Andreas Schumann, and Yangbo Chen
Proc. IAHS, 379, 463–464, https://doi.org/10.5194/piahs-379-463-2018, https://doi.org/10.5194/piahs-379-463-2018, 2018
Andreas Paul Zischg, Guido Felder, Rolf Weingartner, Niall Quinn, Gemma Coxon, Jeffrey Neal, Jim Freer, and Paul Bates
Hydrol. Earth Syst. Sci., 22, 2759–2773, https://doi.org/10.5194/hess-22-2759-2018, https://doi.org/10.5194/hess-22-2759-2018, 2018
Short summary
Short summary
We developed a model experiment and distributed different rainfall patterns over a mountain river basin. For each rainfall scenario, we computed the flood losses with a model chain. The experiment shows that flood losses vary considerably within the river basin and depend on the timing of the flood peaks from the basin's sub-catchments. Basin-specific characteristics such as the location of the main settlements within the floodplains play an additional important role in determining flood losses.
Jannis M. Hoch, Jeffrey C. Neal, Fedor Baart, Rens van Beek, Hessel C. Winsemius, Paul D. Bates, and Marc F. P. Bierkens
Geosci. Model Dev., 10, 3913–3929, https://doi.org/10.5194/gmd-10-3913-2017, https://doi.org/10.5194/gmd-10-3913-2017, 2017
Short summary
Short summary
To improve flood hazard assessments, it is vital to model all relevant processes. We here present GLOFRIM, a framework for coupling hydrologic and hydrodynamic models to increase the number of physical processes represented in hazard computations. GLOFRIM is openly available, versatile, and extensible with more models. Results also underpin its added value for model benchmarking, showing that not only model forcing but also grid properties and the numerical scheme influence output accuracy.
Laurent Guillaume Courty, Adrián Pedrozo-Acuña, and Paul David Bates
Geosci. Model Dev., 10, 1835–1847, https://doi.org/10.5194/gmd-10-1835-2017, https://doi.org/10.5194/gmd-10-1835-2017, 2017
Short summary
Short summary
This paper presents Itzï, a new free software for the simulation of floods. It is integrated with a geographic information system (GIS), which reduces the human time necessary for preparing the entry data and analysing the results of the simulation.
Itzï uses a simplified numerical scheme that permits to obtain results faster than with other types of models using more complex equations.
In this article, Itzï is tested with three cases that show its suitability to simulate urban floods.
Melissa Wood, Renaud Hostache, Jeffrey Neal, Thorsten Wagener, Laura Giustarini, Marco Chini, Giovani Corato, Patrick Matgen, and Paul Bates
Hydrol. Earth Syst. Sci., 20, 4983–4997, https://doi.org/10.5194/hess-20-4983-2016, https://doi.org/10.5194/hess-20-4983-2016, 2016
Short summary
Short summary
We propose a methodology to calibrate the bankfull channel depth and roughness parameters in a 2-D hydraulic model using an archive of medium-resolution SAR satellite-derived flood extent maps. We used an identifiability methodology to locate the parameters and suggest the SAR images which could be optimally used for model calibration. We found that SAR images acquired around the flood peak provide best calibration potential for the depth parameter, improving when SAR images are combined.
K. J. Beven, S. Almeida, W. P. Aspinall, P. D. Bates, S. Blazkova, E. Borgomeo, K. Goda, J. C. Phillips, M. Simpson, P. J. Smith, D. B. Stephenson, T. Wagener, M. Watson, and K. L. Wilkins
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2015-295, https://doi.org/10.5194/nhess-2015-295, 2016
Preprint withdrawn
Short summary
Short summary
Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. This is Part 2 of 2 papers reviewing these epistemic uncertainties and covers different areas of natural hazards including landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. It is based on the work of the UK CREDIBLE research consortium.
K. J. Beven, W. P. Aspinall, P. D. Bates, E. Borgomeo, K. Goda, J. W. Hall, T. Page, J. C. Phillips, J. T. Rougier, M. Simpson, D. B. Stephenson, P. J. Smith, T. Wagener, and M. Watson
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-7333-2015, https://doi.org/10.5194/nhessd-3-7333-2015, 2015
Preprint withdrawn
Short summary
Short summary
Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. This is Part 1 of 2 papers reviewing these epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities. It is based on the work of the CREDIBLE research consortium on Risk and Uncertainty in Natural Hazards.
R. Hostache, C. Hissler, P. Matgen, C. Guignard, and P. Bates
Hydrol. Earth Syst. Sci., 18, 3539–3551, https://doi.org/10.5194/hess-18-3539-2014, https://doi.org/10.5194/hess-18-3539-2014, 2014
C. C. Sampson, T. J. Fewtrell, F. O'Loughlin, F. Pappenberger, P. B. Bates, J. E. Freer, and H. L. Cloke
Hydrol. Earth Syst. Sci., 18, 2305–2324, https://doi.org/10.5194/hess-18-2305-2014, https://doi.org/10.5194/hess-18-2305-2014, 2014
B. Jongman, H. Kreibich, H. Apel, J. I. Barredo, P. D. Bates, L. Feyen, A. Gericke, J. Neal, J. C. J. H. Aerts, and P. J. Ward
Nat. Hazards Earth Syst. Sci., 12, 3733–3752, https://doi.org/10.5194/nhess-12-3733-2012, https://doi.org/10.5194/nhess-12-3733-2012, 2012
Related subject area
Subject: Global hydrology | Techniques and Approaches: Mathematical applications
Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed
Integrating process-related information into an artificial neural network for root-zone soil moisture prediction
Coherence of global hydroclimate classification systems
Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach
The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window
Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model
Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016
A framework for deriving drought indicators from the Gravity Recovery and Climate Experiment (GRACE)
Hydrological effects of climate variability and vegetation dynamics on annual fluvial water balance in global large river basins
Spatial patterns and characteristics of flood seasonality in Europe
Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate
Effects of different reference periods on drought index (SPEI) estimations from 1901 to 2014
The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis
Global trends in extreme precipitation: climate models versus observations
A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble
A generic method for hydrological drought identification across different climate regions
Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria
Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space
Fadji Z. Maina, Alan Rhoades, Erica R. Siirila-Woodburn, and Peter-James Dennedy-Frank
Hydrol. Earth Syst. Sci., 26, 3589–3609, https://doi.org/10.5194/hess-26-3589-2022, https://doi.org/10.5194/hess-26-3589-2022, 2022
Short summary
Short summary
In this work, we assess the effects of end-of-century extreme dry and wet conditions on the hydrology of California. Our results, derived from cutting-edge and high-resolution climate and hydrologic models, highlight that (1) water storage will be larger and increase earlier in the year, yet the summer streamflow will decrease as a result of high evapotranspiration rates, and that (2) groundwater and lower-order streams are very sensitive to decreases in snowmelt and higher evapotranspiration.
Roiya Souissi, Mehrez Zribi, Chiara Corbari, Marco Mancini, Sekhar Muddu, Sat Kumar Tomer, Deepti B. Upadhyaya, and Ahmad Al Bitar
Hydrol. Earth Syst. Sci., 26, 3263–3297, https://doi.org/10.5194/hess-26-3263-2022, https://doi.org/10.5194/hess-26-3263-2022, 2022
Short summary
Short summary
In this study, we investigate the combination of surface soil moisture information with process-related features, namely, evaporation efficiency, soil water index and normalized difference vegetation index, using artificial neural networks to predict root-zone soil moisture. The joint use of process-related features yielded more accurate predictions in the case of arid and semiarid conditions. However, they have no to little added value in temperate to tropical conditions.
Kathryn L. McCurley Pisarello and James W. Jawitz
Hydrol. Earth Syst. Sci., 25, 6173–6183, https://doi.org/10.5194/hess-25-6173-2021, https://doi.org/10.5194/hess-25-6173-2021, 2021
Short summary
Short summary
Climate classification systems divide the Earth into zones of similar climates. We compared the within-zone hydroclimate similarity and zone shape complexity of a suite of climate classification systems, including new ones formed in this study. The most frequently used system had high similarity but high complexity. We propose the Water-Energy Clustering framework, which also had high similarity but lower complexity. This new system is therefore proposed for future hydroclimate assessments.
Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021, https://doi.org/10.5194/hess-25-5717-2021, 2021
Short summary
Short summary
This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection using the coefficient of determination. Three cases of attribution are effectively facilitated, which are significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection.
Paul H. Whitfield, Philip D. A. Kraaijenbrink, Kevin R. Shook, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 25, 2513–2541, https://doi.org/10.5194/hess-25-2513-2021, https://doi.org/10.5194/hess-25-2513-2021, 2021
Short summary
Short summary
Using only warm season streamflow records, regime and change classifications were produced for ~ 400 watersheds in the Nelson and Mackenzie River basins, and trends in water storage and vegetation were detected from satellite imagery. Three areas show consistent changes: north of 60° (increased streamflow and basin greenness), in the western Boreal Plains (decreased streamflow and basin greenness), and across the Prairies (three different patterns of increased streamflow and basin wetness).
Elham Rouholahnejad Freund, Massimiliano Zappa, and James W. Kirchner
Hydrol. Earth Syst. Sci., 24, 5015–5025, https://doi.org/10.5194/hess-24-5015-2020, https://doi.org/10.5194/hess-24-5015-2020, 2020
Short summary
Short summary
Evapotranspiration (ET) is the largest flux from the land to the atmosphere and thus contributes to Earth's energy and water balance. Due to its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. In this paper, we demonstrate how averaging over land surface heterogeneity contributes to substantial overestimates of ET fluxes. We also demonstrate how one can correct for the effects of small-scale heterogeneity without explicitly representing it in land surface models.
Steefan Contractor, Markus G. Donat, Lisa V. Alexander, Markus Ziese, Anja Meyer-Christoffer, Udo Schneider, Elke Rustemeier, Andreas Becker, Imke Durre, and Russell S. Vose
Hydrol. Earth Syst. Sci., 24, 919–943, https://doi.org/10.5194/hess-24-919-2020, https://doi.org/10.5194/hess-24-919-2020, 2020
Short summary
Short summary
This paper provides the documentation of the REGEN dataset, a global land-based daily observational precipitation dataset from 1950 to 2016 at a gridded resolution of 1° × 1°. REGEN is currently the longest-running global dataset of daily precipitation and is expected to facilitate studies looking at changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity.
Helena Gerdener, Olga Engels, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 24, 227–248, https://doi.org/10.5194/hess-24-227-2020, https://doi.org/10.5194/hess-24-227-2020, 2020
Short summary
Short summary
GRACE-derived drought indicators enable us to detect hydrological droughts based on changes observed in all storages. By performing synthetic experiments, we find that droughts identified by existing and modified indicators are biased by trends and GRACE-based spatial noise. A modified version of the Zhao et al. (2017) indicator is found to be particularly robust against spatial noise and is therefore applied to real GRACE data over South Africa.
Jianyu Liu, Qiang Zhang, Vijay P. Singh, Changqing Song, Yongqiang Zhang, Peng Sun, and Xihui Gu
Hydrol. Earth Syst. Sci., 22, 4047–4060, https://doi.org/10.5194/hess-22-4047-2018, https://doi.org/10.5194/hess-22-4047-2018, 2018
Short summary
Short summary
Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration,
E0), a climate seasonality and asynchrony index (SAI) were proposed in terms of both phase and amplitude mismatch between P and E0.
Julia Hall and Günter Blöschl
Hydrol. Earth Syst. Sci., 22, 3883–3901, https://doi.org/10.5194/hess-22-3883-2018, https://doi.org/10.5194/hess-22-3883-2018, 2018
Sanaa Hobeichi, Gab Abramowitz, Jason Evans, and Anna Ukkola
Hydrol. Earth Syst. Sci., 22, 1317–1336, https://doi.org/10.5194/hess-22-1317-2018, https://doi.org/10.5194/hess-22-1317-2018, 2018
Short summary
Short summary
We present a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009 and 0.5 grid cell size. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. We confirm that point-based estimates of flux towers provide information at the grid scale of these products. We also show that the weighted product performs better than 10 different existing global ET datasets in a range of metrics.
Myoung-Jin Um, Yeonjoo Kim, Daeryong Park, and Jeongbin Kim
Hydrol. Earth Syst. Sci., 21, 4989–5007, https://doi.org/10.5194/hess-21-4989-2017, https://doi.org/10.5194/hess-21-4989-2017, 2017
Short summary
Short summary
This study aims to understand how different reference periods (i.e., calibration periods) of climate data for estimating the drought index influence regional drought assessments. Specifically, we investigate the influence of different reference periods on historical drought characteristics such as trends, frequency, intensity and spatial extents using the Standard Precipitation Evapotranspiration Index (SPEI) estimated from the two widely used global datasets.
Lorenzo Mentaschi, Michalis Vousdoukas, Evangelos Voukouvalas, Ludovica Sartini, Luc Feyen, Giovanni Besio, and Lorenzo Alfieri
Hydrol. Earth Syst. Sci., 20, 3527–3547, https://doi.org/10.5194/hess-20-3527-2016, https://doi.org/10.5194/hess-20-3527-2016, 2016
Short summary
Short summary
The climate is subject to variations which must be considered
studying the intensity and frequency of extreme events.
We introduce in this paper a new methodology
for the study of variable extremes, which consists in detecting
the pattern of variability of a time series, and applying these patterns
to the analysis of the extreme events.
This technique comes with advantages with respect to the previous ones
in terms of accuracy, simplicity, and robustness.
B. Asadieh and N. Y. Krakauer
Hydrol. Earth Syst. Sci., 19, 877–891, https://doi.org/10.5194/hess-19-877-2015, https://doi.org/10.5194/hess-19-877-2015, 2015
Short summary
Short summary
We present a systematic comparison of changes in historical extreme precipitation in station observations (HadEX2) and 15 climate models from the CMIP5 (as the largest and most recent sets of available observational and modeled data sets), on global and continental scales for 1901-2010, using both parametric (linear regression) and non-parametric (the Mann-Kendall as well as Sen’s slope estimator) methods, taking care to sample observations and models spatially and temporally in comparable ways.
A. I. J. M. van Dijk, L. J. Renzullo, Y. Wada, and P. Tregoning
Hydrol. Earth Syst. Sci., 18, 2955–2973, https://doi.org/10.5194/hess-18-2955-2014, https://doi.org/10.5194/hess-18-2955-2014, 2014
M. H. J. van Huijgevoort, P. Hazenberg, H. A. J. van Lanen, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 16, 2437–2451, https://doi.org/10.5194/hess-16-2437-2012, https://doi.org/10.5194/hess-16-2437-2012, 2012
D. Brochero, F. Anctil, and C. Gagné
Hydrol. Earth Syst. Sci., 15, 3307–3325, https://doi.org/10.5194/hess-15-3307-2011, https://doi.org/10.5194/hess-15-3307-2011, 2011
D. Brochero, F. Anctil, and C. Gagné
Hydrol. Earth Syst. Sci., 15, 3327–3341, https://doi.org/10.5194/hess-15-3327-2011, https://doi.org/10.5194/hess-15-3327-2011, 2011
Cited articles
Ahmad, M. I., Sinclair, C., and Spurr, B.: Assessment of flood frequency
models using empirical distribution function statistics, Water Resour. Res.,
24, 1323–1328, 1988.
Alexandersson, H.: A Homogeneity Test Applied to Precipitation Data, J
Climatol., 6, 661–675, https://doi.org/10.1002/joc.3370060607, 1986.
Bárdossy, A., Pegram, G. G., and Samaniego, L.: Modeling data
relationships with a local variance reducing technique: Applications in
hydrology, Water Resour. Res., 41, W08404, https://doi.org/10.1029/2004WR003851, 2005.
Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage,
J., Olcese, G., Neal, J., and Schumann, G.: Combined modelling of US
fluvial, pluvial and coastal flood hazard under current and future climates,
Water Resour. Res., e2020WR028673, https://doi.org/10.1029/2020WR028673, 2020.
Beames, P., Lehner, B., and Anand, M.:
Global Reservoir and Dam Database (GRanD), available at:
http://globaldamwatch.org/grand/, last access:
October 2021.
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.
Breiman, L. and Cutler, A.: Random Forests, available at:
https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm, last
access: October 2021.
Center for International Earth Science Information Network (CIESIN):
Gridded Population of the World (GPW), v4, available at:
https://sedac.ciesin.columbia.edu/data/collection/gpw-v4, last
access: October 2021.
Chib, S. and Greenberg, E.: Understanding the metropolis-hastings
algorithm, Am. Stat., 49, 327–335, 1995.
CRED and UNISDR: The Human Cost of Weather Related Disasters – 1995–2015,
United Nations Office for Disaster Risk Reduction (UNISDR) and Centre for
Research on the Epidemiology of Disasters (CRED), 2015.
Cunnane, C.: Methods and Merits of Regional Flood Frequency-Analysis, J.
Hydrol., 100, 269–290, https://doi.org/10.1016/0022-1694(88)90188-6, 1988.
Dalrymple, T.: Flood-frequency analyses, manual of hydrology: Part 3, USGPO,
https://doi.org/10.3133/wsp1543A, 1960.
Davies, D. L. and Bouldin, D. W.: A Cluster Separation Measure, IEEE T. Pattern Anal., PAMI-1, 224–227, https://doi.org/10.1109/TPAMI.1979.4766909, 1979.
Desai, S. and Ouarda, T. B.: Regional hydrological frequency analysis at
ungauged sites with random forest regression, J. Hydrol., 594, 125861, https://doi.org/10.1016/j.jhydrol.2020.125861, 2021.
Di Baldassarre, G. and Montanari, A.: Uncertainty in river discharge observations: a quantitative analysis, Hydrol. Earth Syst. Sci., 13, 913–921, https://doi.org/10.5194/hess-13-913-2009, 2009.
Di Baldassarre, G., Laio, F., and Montanari, A.: Effect of observation
errors on the uncertainty of design floods, Phys. Chem.
Earth, Parts A/B/C, 42, 85–90, 2012.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018a.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.:
Global Streamflow Indices and Metadata Archive – Part 1: Station catalog and Catchment boundary, PANGAEA, https://doi.org/10.1594/PANGAEA.887477,
2018b.
Doxsey-Whitfield, E., MacManus, K., Adamo, S. B., Pistolesi, L., Squires,
J., Borkovska, O., and Baptista, S. R.: Taking advantage of
the improved availability of census data: a first look at the gridded
population of the world, version 4, Papers in Applied Geography, 1, 226–234, 2015.
Drucker, H., Burges, C. J., Kaufman, L., Smola, A., and Vapnik, V.: Support
vector regression machines, Adv. Neural Inf. Process. Syst 9, 155–161, 1997.
Fick, S. E., and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315,
https://doi.org/10.1002/joc.5086, 2017.
Fick, S. E. and Hijmans, R. J.:
Global climate and weather data, available at:
https://worldclim.org/, last access: October 2021.
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K., Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F., Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts of 1.5 ∘C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, 2017.
Gao, S., Liu, P., Pan, Z., Ming, B., Guo, S., Cheng, L., and Wang, J.:
Incorporating reservoir impacts into flood frequency distribution functions,
J. Hydrol., 568, 234–246, 2019.
Garcia, F. A. A.: Tests to identify outliers in data series, Pontifical
Catholic University of Rio de Janeiro, Industrial Engineering Department,
Rio de Janeiro, Brazil, 2012.
Garmdareh, E. S., Vafakhah, M., and Eslamian, S. S.: Regional flood
frequency analysis using support vector regression in arid and semi-arid
regions of Iran, Hydrolog. Sci. J., 63, 426–440, https://doi.org/10.1080/02626667.2018.1432056,
2018.
Gaume, E.: Flood frequency analysis: The Bayesian choice, Wiley
Interdisciplinary Reviews: Water, 5, e1290, https://doi.org/10.1002/wat2.1290, 2018.
Gizaw, M. S. and Gan, T. Y.: Regional Flood Frequency Analysis using
Support Vector Regression under historical and future climate, J. Hydrol.,
538, 387–398, https://doi.org/10.1016/j.jhydrol.2016.04.041, 2016.
Griffis, V. W. and Stedinger, J. R.: Log-Pearson Type 3 distribution and
its application in flood frequency analysis. I: Distribution
characteristics, J. Hydrol. Eng., 12, 482–491,
https://doi.org/10.1061/(Asce)1084-0699(2007)12:5(482), 2007.
Gudmundsson, L., Do, H. X., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018, 2018.
Haddad, K. and Rahman, A.: Selection of the best fit flood frequency
distribution and parameter estimation procedure: a case study for Tasmania
in Australia, Stoch. Env. Res. Risk A., 25,
415–428, 2011.
Halbert, K., Nguyen, C. C., Payrastre, O., and Gaume, E.: Reducing
uncertainty in flood frequency analyses: A comparison of local and regional
approaches involving information on extreme historical floods, J. Hydrol.,
541, 90–98, 2016.
Hamed, K. H.: Trend detection in hydrologic data: the
Mann–Kendall trend test under the scaling hypothesis, 349, 350–363, 2008.
Hammond, M. J., Chen, A. S., Djordjevic, S., Butler, D., and Mark, O.: Urban
flood impact assessment: A state-of-the-art review, Urban Water J., 12,
14–29, https://doi.org/10.1080/1573062x.2013.857421, 2015.
Hosking, J. R. M. and Wallis, J. R.: The Effect of Intersite Dependence on
Regional Flood Frequency-Analysis, Water Resour. Res., 24, 588–600, https://doi.org/10.1029/WR024i004p00588, 1988.
Hosking, J. R. M. and Wallis, J. R.: Regional frequency analysis: an
approach based on L-moments, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9780511529443, 2005.
Hutengs, C. and Vohland, M.: Downscaling land surface temperatures at
regional scales with random forest regression, Remote Sens. Environ., 178,
127–141, https://doi.org/10.1016/j.rse.2016.03.006, 2016.
Kalai, C., Mondal, A., Griffin, A., and Stewart, E.: Comparison of
nonstationary regional flood frequency analysis techniques based on the
index-flood approach, J. Hydrol. Eng., 25, 06020003, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001939, 2020.
Laio, F.: Cramer–von Mises and Anderson-Darling goodness of fit tests for
extreme value distributions with unknown parameters, Water Resour. Res., 40, W09308, https://doi.org/10.1029/2004WR003204,
2004.
Lee, J.-Y., Choi, C., Kang, D., Kim, B. S., and Kim, T.-W.: Estimating
Design Floods at Ungauged Watersheds in South Korea Using Machine Learning
Models, Water, 12, 3022, https://doi.org/10.3390/w12113022, 2020.
Lehner, B. and Döll, P.: Development and validation of a global database of
lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22,
https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004.
Lehner, B. and Döll, P.:
Global Lakes and Wetlands Database: Lakes and Wetlands Grid, available at:
https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database, last access: October 2021.
Lehner, B., Liermann, C. R., Revenga, C., Vorosmarty, C., Fekete, B.,
Crouzet, P., Doll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C.,
Robertson, J. C., Rodel, R., Sindorf, N., and Wisser, D.: High-resolution
mapping of the world's reservoirs and dams for sustainable river-flow
management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Li, B., Yang, G. S., Wan, R. R., Dai, X., and Zhang, Y. H.: Comparison of
random forests and other statistical methods for the prediction of lake
water level: a case study of the Poyang Lake in China, Hydrol. Res., 47,
69–83, https://doi.org/10.2166/nh.2016.264, 2016.
Lin, G. F. and Chen, L. H.: Identification of homogeneous regions for
regional frequency analysis using the self-organizing map, J. Hydrol., 324,
1–9, https://doi.org/10.1016/j.jhydrol.2005.09.009, 2006.
Liu, X., Liu, W., Yang, H., Tang, Q., Flörke, M., Masaki, Y., Müller Schmied, H., Ostberg, S., Pokhrel, Y., Satoh, Y., and Wada, Y.: Multimodel assessments of human and climate impacts on mean annual streamflow in China, Hydrol. Earth Syst. Sci., 23, 1245–1261, https://doi.org/10.5194/hess-23-1245-2019, 2019.
MathWorks: kmeans, available at: https://www.mathworks.com/help/stats/kmeans.html, last access: October 2021a.
MathWorks: Support Vector Machine Regression, available at: https://uk.mathworks.com/help/stats/support-vector-machine-regression.html, last access: October 2021b.
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest, N. E. C., Franz, T. E., Shi, J., Gao, H., and Wood, E. F.: The future of Earth observation in hydrology, Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, 2017.
Merz, R. and Blöschl, G.: Flood frequency hydrology: 2. Combining data
evidence, Water Resour. Res., 44, W08433,
https://doi.org/10.1029/2007wr006745, 2008a.
Merz, R. and Blöschl, G.: Flood frequency hydrology: 1. Temporal, spatial,
and causal expansion of information, Water Resour. Res., 44, W08432,
https://doi.org/10.1029/2007wr006744, 2008b.
Merz, B. and Thieken, A. H.: Separating natural and epistemic uncertainty
in flood frequency analysis, J. Hydrol., 309, 114–132, 2005.
Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P.: Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use, Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, 2016.
O'Brien, N. L., and Burn, D. H.: A nonstationary index-flood technique for
estimating extreme quantiles for annual maximum streamflow, J. Hydrol., 519,
2040–2048, 2014.
Pohlert, T.: Non-Parametric Trend Tests and Change-Point Detection,
available at: https://cran.r-project.org/web/packages/trend/index.html, last access: 31 October 2021.
Prihodko, L., Denning, A. S., Hanan, N. P., Baker, I., and Davis, K.:
Sensitivity, uncertainty and time dependence of parameters in a complex land
surface model, Agr. Forest Meteorol., 148, 268–287,
https://doi.org/10.1016/j.agrformet.2007.08.006, 2008.
Raykov, Y. P., Boukouvalas, A., Baig, F., and Little, M. A.: What to do when
K-means clustering fails: a simple yet principled alternative algorithm,
PloS one, 11, e0162259, https://doi.org/10.1371/journal.pone.0162259, 2016.
Reis, D. S. and Stedinger, J. R.: Bayesian MCMC flood frequency analysis
with historical information, J. Hydrol., 313, 97–116, 2005.
Richter, B. D., Baumgartner, J. V., Wigington, R., and Braun, D. P.: How
much water does a river need?, Freshwater Biol, 37, 231–249, https://doi.org/10.1046/j.1365-2427.1997.00153.x, 1997.
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.
Samaniego, L., Bárdossy, A., and Schulz, K.: Supervised classification
of remotely sensed imagery using a modified $ k $-NN technique, IEEE
T. Geosci. Remote, 46, 2112–2125, 2008.
Sampson, C. C., Smith, A. M., Bates, P. B., Neal, J. C., Alfieri, L., and
Freer, J. E.: A high-resolution global flood hazard model, Water Resour. Res.,
51, 7358–7381, https://doi.org/10.1002/2015wr016954, 2015.
Schumann, G., Bates, P. D., Apel, H., and Aronica, G. T.: Global flood
hazard mapping, modeling, and forecasting: challenges and perspectives,
Global Flood Hazard: Applications in Modeling, Mapping, and Forecasting,
239–244, https://doi.org/10.1002/9781119217886.ch14, 2018.
Schumann, G. J. P., Andreadis, K. M., and Bates, P. D.:
Downscaling coarse grid hydrodynamic model simulations over large domains, J. Hydrol.,
508, 289–298, 2014a.
Schumann, G. J.-P., Andreadis, K. M., and Bates, P. D.: Downscaling coarse
grid hydrodynamic model simulations over large domains, J. Hydrol., 508,
289–298, https://doi.org/10.1016/j.jhydrol.2013.08.051, 2014b.
Sharifi Garmdareh, E., Vafakhah, M., and Eslamian, S. S.: Regional flood
frequency analysis using support vector regression in arid and semi-arid
regions of Iran, Hydrolog. Sci. J., 63, 426–440, 2018.
Sharma, A., Wasko, C., and Lettenmaier, D. P.: If precipitation extremes are
increasing, why aren't floods?, Water Resour. Res., 54, 8545–8551, 2018.
Shiffler, R. E. J. T. A. S.: Maximum Z scores and outliers, Am. Stat., 42, 79–80, 1988.
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, https://doi.org/10.1016/j.jhydrol.2007.10.050, 2008.
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.
Stedinger, J. R.: Estimating a regional flood frequency distribution, Water Resour. Res., 19, 503–510, 1983.
Stein, L., Pianosi, F., and Woods, R.: Event-based classification for global
study of river flood generating processes, Hydrol. Process., 34, 1514–1529, https://doi.org/10.1002/hyp.13678, 2019.
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., and Kim, S.:
Flood inundation modelling: A review of methods, recent advances and
uncertainty analysis, Environ. Modell. Softw., 90, 201–216,
https://doi.org/10.1016/j.envsoft.2017.01.006, 2017.
Trigg, M. A., Birch, C. E., Neal, J. C., Bates, P. D., Smith, A., Sampson,
C. C., Yamazaki, D., Hirabayashi, Y., Pappenberger, F., Dutra, E., Ward, P.
J., Winsemius, H. C., Salamon, P., Dottori, F., Rudari, R., Kappes, M. S.,
Simpson, A. L., Hadzilacos, G., and Fewtrell, T. J.: The credibility
challenge for global fluvial flood risk analysis, Environ. Res. Lett., 11,
094014, https://doi.org/10.1088/1748-9326/11/9/094014, 2016.
United States Soil Conservation Service: National Engineering Handbook, Section 19, Construction Inspection, Washington, D.C., U.S. Dept. of Agriculture, Soil Conservation Service, 1985.
Viglione, A.: Non-Supervised Regional Frequency Analysis, available at:
https://CRAN.R-project.org/package=nsRFA,
last access: October 2021.
Vogel, R. M., McMahon, T. A., and Chiew, F. H.: Floodflow frequency model
selection in Australia, J. Hydrol., 146, 421–449, 1993.
Wang, J., Liang, Z., Hu, Y., and Wang, D.: Modified weighted function method
with the incorporation of historical floods into systematic sample for
parameter estimation of Pearson type three distribution, J. Hydrol., 527,
958–966, 2015.
Water Resources Council (US): Hydrology Committee, Guidelines for determining flood flow frequency, US Water Resources Council, Hydrology Committee, 1975.
Wing, O. E., Bates, P. D., Sampson, C. C., Smith, A. M., Johnson, K. A., and
Erickson, T. A. J. W. R. R.: Validation of a 30 m resolution flood hazard
model of the conterminous United States, Water Resour. Res., 53, 7968–7986, 2017.
Wing, O. E., Bates, P. D., Smith, A. M., Sampson, C. C., Johnson, K. A.,
Fargione, J., and Morefield, P.: Estimates of present and future flood risk
in the conterminous United States, Environ. Res. Lett., 13, 034023, https://doi.org/10.1088/1748-9326/aaac65, 2018.
Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A.: A framework for global river flood risk assessments, Hydrol. Earth Syst. Sci., 17, 1871–1892, https://doi.org/10.5194/hess-17-1871-2013, 2013.
Winsemius, H. C., Aerts, J. C., Van Beek, L. P., Bierkens, M. F., Bouwman,
A., Jongman, B., Kwadijk, J. C., Ligtvoet, W., Lucas, P. L., and Van Vuuren,
D. P.: Global drivers of future river flood risk, Nat. Clim. Change, 6,
381–385, 2016.
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based
description of floodplain inundation dynamics in a global river routing
model, Water Resour. Res., 47, W04501,
https://doi.org/10.1029/2010wr009726, 2011.
Yamazaki, D.:
MERIT DEM: Multi-Error-Removed Improved-Terrain DEM, available at:
http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/, last
access: October 2021.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F.,
Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy
map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017gl072874, 2017.
Yang, T., Sun, F., Gentine, P., Liu, W., Wang, H., Yin, J., Du, M., and
Changming, L.: Evaluation and machine learning improvement of global flood
simulations, AGUFM, 2019, H33L-2122, 2019a.
Yang, T., Sun, F., Gentine, P., Liu, W., Wang, H., Yin, J., Du, M., and Liu,
C.: Evaluation and machine learning improvement of global hydrological
model-based flood simulations, Environ. Res. Lett., 14, 114027, https://doi.org/10.1088/1748-9326/ab4d5e, 2019b.
Zeng, Z. Y., Tang, G. Q., Hong, Y., Zeng, C., and Yang, Y.: Development of
an NRCS curve number global dataset using the latest geospatial remote
sensing data for worldwide hydrologic applications, Remote Sens. Lett., 8,
528–536, https://doi.org/10.1080/2150704x.2017.1297544, 2017.
Zhang, Y., Chiew, F. H., Li, M., and Post, D.: Predicting runoff signatures
using regression and hydrological modeling approaches, Water Resour. Res., 54,
7859–7878, 2018.
Zhao, G., Pang, B., Xu, Z. X., Yue, J. J., and Tu, T. B.: Mapping flood
susceptibility in mountainous areas on a national scale in China, Sci. Total
Environ., 615, 1133–1142, https://doi.org/10.1016/j.scitotenv.2017.10.037, 2018.
Zhao, G., Bates, P., and Neal, J.: The impact of dams on design floods in
the Conterminous US, Water Resour. Res., 56, e2019WR025380, https://doi.org/10.1029/2019WR025380, 2020.
Short summary
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based...
