Articles | Volume 27, issue 24
https://doi.org/10.5194/hess-27-4369-2023
© 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-27-4369-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Dec 2023
Research article |
| 14 Dec 2023
| 14 Dec 2023
Inferring heavy tails of flood distributions through hydrograph recession analysis
Department of Catchment Hydrology, Helmholtz Centre for Environmental Research – UFZ, 06120 Halle (Saale), Germany
Ralf Merz
Department of Catchment Hydrology, Helmholtz Centre for Environmental Research – UFZ, 06120 Halle (Saale), Germany
Institute of Geosciences and Geography, Martin-Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany
Soohyun Yang
Department of Aquatic Ecosystem Analysis, Helmholtz Centre for Environmental Research – UFZ, 39114 Magdeburg, Germany
Department of Civil and Environmental Engineering, Seoul National University, Seoul, 08826, Republic of Korea
Stefano Basso
Department of Catchment Hydrology, Helmholtz Centre for Environmental Research – UFZ, 06120 Halle (Saale), Germany
Norwegian Institute for Water Research (NIVA), Oslo, 0579, Norway
Related authors
Hsing-Jui Wang, Ralf Merz, and Stefano Basso
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-159, https://doi.org/10.5194/hess-2024-159, 2024
Revised manuscript under review for HESS
Short summary
Short summary
Extreme floods are more common than expected. Knowing where these floods are likely to occur is key for risk management. Traditional methods struggle with limited data, causing uncertainty. We use common streamflow dynamics to indicate extreme flood propensity. Analyzing data from Atlantic Europe, Northern Europe, and the U.S., we validate this novel approach and unravel intrinsic linkages between regional geographic patterns and extreme flood drivers.
Soohyun Yang, Kwanghun Choi, and Kyungrock Paik
Hydrol. Earth Syst. Sci., 28, 3119–3132, https://doi.org/10.5194/hess-28-3119-2024, https://doi.org/10.5194/hess-28-3119-2024, 2024
Short summary
Short summary
In extracting a river network from a digital elevation model, an arbitrary pruning area should be specified. As this value grows, the apparent drainage density is reduced following a power function. This reflects the fractal topographic nature. We prove this relationship related to the known power law in the exceedance probability distribution of drainage area. The power-law exponent is expressed with fractal dimensions. Our findings are supported by analysis of 14 real river networks.
Christina Franziska Radtke, Xiaoqiang Yang, Christin Müller, Jarno Rouhiainen, Ralf Merz, Stefanie R. Lutz, Paolo Benettin, Hong Wei, and Kay Knöller
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-109, https://doi.org/10.5194/hess-2024-109, 2024
Revised manuscript under review for HESS
Short summary
Short summary
Most studies assume no difference between transit times of water and nitrate, because nitrate is transported by water. With an 8-year high-frequency dataset of isotopic signatures of both, water and nitrate, and a transit time model, we show the temporal varying difference of nitrate and water transit times. This finding is highly relevant for applied future research related to nutrient dynamics in landscapes under anthropogenic forcing and for managing impacts of nitrate on aquatic ecosystems.
Hsing-Jui Wang, Ralf Merz, and Stefano Basso
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-159, https://doi.org/10.5194/hess-2024-159, 2024
Revised manuscript under review for HESS
Short summary
Short summary
Extreme floods are more common than expected. Knowing where these floods are likely to occur is key for risk management. Traditional methods struggle with limited data, causing uncertainty. We use common streamflow dynamics to indicate extreme flood propensity. Analyzing data from Atlantic Europe, Northern Europe, and the U.S., we validate this novel approach and unravel intrinsic linkages between regional geographic patterns and extreme flood drivers.
Matteo Pesce, Alberto Viglione, Jost von Hardenberg, Larisa Tarasova, Stefano Basso, Ralf Merz, Juraj Parajka, and Rui Tong
Proc. IAHS, 385, 65–69, https://doi.org/10.5194/piahs-385-65-2024, https://doi.org/10.5194/piahs-385-65-2024, 2024
Short summary
Short summary
The manuscript describes an application of PArameter Set Shuffling (PASS) approach in the Alpine region. A machine learning decision-tree algorithm is applied for the regional calibration of a conceptual semi-distributed hydrological model. Regional model efficiencies don't decrease significantly when moving in space from catchments used for the regional calibration (training) to catchments used for the procedure validation (test) and, in time, from the calibration to the verification period.
Felipe A. Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova
Hydrol. Earth Syst. Sci., 26, 6227–6245, https://doi.org/10.5194/hess-26-6227-2022, https://doi.org/10.5194/hess-26-6227-2022, 2022
Short summary
Short summary
Nitrate contamination of rivers from agricultural sources is a challenge for water quality management. During runoff events, different transport paths within the catchment might be activated, generating a variety of responses in nitrate concentration in stream water. Using nitrate samples from 184 German catchments and a runoff event classification, we show that hydrologic connectivity during runoff events is a key control of nitrate transport from catchments to streams in our study domain.
Soohyun Yang, Kwanghun Choi, and Kyungrock Paik
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-237, https://doi.org/10.5194/hess-2022-237, 2022
Revised manuscript not accepted
Short summary
Short summary
In extracting a river network from a digital elevation model, one should specify the pruning area. As pruning area grows, the 'apparent' drainage density reduces, following a power function. We suggest that this power-law reflects the fractal topographic nature of a catchment, and indeed follows the exceedance probability distribution of drainage area, a power-law known in earlier studies. We evaluated this theory by analyzing topographies of four study catchments.
Xing Chen, Mukesh Kumar, Stefano Basso, and Marco Marani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-65, https://doi.org/10.5194/hess-2018-65, 2018
Preprint withdrawn
J. Hall, B. Arheimer, G. T. Aronica, A. Bilibashi, M. Boháč, O. Bonacci, M. Borga, P. Burlando, A. Castellarin, G. B. Chirico, P. Claps, K. Fiala, L. Gaál, L. Gorbachova, A. Gül, J. Hannaford, A. Kiss, T. Kjeldsen, S. Kohnová, J. J. Koskela, N. Macdonald, M. Mavrova-Guirguinova, O. Ledvinka, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, M. Osuch, J. Parajka, R. A. P. Perdigão, I. Radevski, B. Renard, M. Rogger, J. L. Salinas, E. Sauquet, M. Šraj, J. Szolgay, A. Viglione, E. Volpi, D. Wilson, K. Zaimi, and G. Blöschl
Proc. IAHS, 370, 89–95, https://doi.org/10.5194/piahs-370-89-2015, https://doi.org/10.5194/piahs-370-89-2015, 2015
U. Mallast, R. Gloaguen, J. Friesen, T. Rödiger, S. Geyer, R. Merz, and C. Siebert
Hydrol. Earth Syst. Sci., 18, 2773–2787, https://doi.org/10.5194/hess-18-2773-2014, https://doi.org/10.5194/hess-18-2773-2014, 2014
J. Hall, B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, J. Kriaučiūnienė, Z. W. Kundzewicz, M. Lang, M. C. Llasat, N. Macdonald, N. McIntyre, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, C. Neuhold, J. Parajka, R. A. P. Perdigão, L. Plavcová, M. Rogger, J. L. Salinas, E. Sauquet, C. Schär, J. Szolgay, A. Viglione, and G. Blöschl
Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, https://doi.org/10.5194/hess-18-2735-2014, 2014
B. Merz, J. Aerts, K. Arnbjerg-Nielsen, M. Baldi, A. Becker, A. Bichet, G. Blöschl, L. M. Bouwer, A. Brauer, F. Cioffi, J. M. Delgado, M. Gocht, F. Guzzetti, S. Harrigan, K. Hirschboeck, C. Kilsby, W. Kron, H.-H. Kwon, U. Lall, R. Merz, K. Nissen, P. Salvatti, T. Swierczynski, U. Ulbrich, A. Viglione, P. J. Ward, M. Weiler, B. Wilhelm, and M. Nied
Nat. Hazards Earth Syst. Sci., 14, 1921–1942, https://doi.org/10.5194/nhess-14-1921-2014, https://doi.org/10.5194/nhess-14-1921-2014, 2014
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Mathematical applications
A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks
Processes and controls of regional floods over eastern China
Landscape structures regulate the contrasting response of recession along rainfall amounts
Hydrological objective functions and ensemble averaging with the Wasserstein distance
Spatial variability in Alpine reservoir regulation: deriving reservoir operations from streamflow using generalized additive models
Regional significance of historical trends and step changes in Australian streamflow
River flooding mechanisms and their changes in Europe revealed by explainable machine learning
Changes in nonlinearity and stability of streamflow recession characteristics under climate warming in a large glaciated basin of the Tibetan Plateau
A data-driven method for estimating the composition of end-members from stream water chemistry time series
Evaporation loss estimation of the river-lake continuum of arid inland river: Evidence from stable isotopes
Technical note: PMR – a proxy metric to assess hydrological model robustness in a changing climate
Causal effects of dams and land cover changes on flood changes in mainland China
Can the two-parameter recursive digital filter baseflow separation method really be calibrated by the conductivity mass balance method?
Simultaneously determining global sensitivities of model parameters and model structure
Technical note: Calculation scripts for ensemble hydrograph separation
Specific climate classification for Mediterranean hydrology and future evolution under Med-CORDEX regional climate model scenarios
A line-integral-based method to partition climate and catchment effects on runoff
Technical note: A two-sided affine power scaling relationship to represent the concentration–discharge relationship
On the flood peak distributions over China
New water fractions and transit time distributions at Plynlimon, Wales, estimated from stable water isotopes in precipitation and streamflow
Does the weighting of climate simulations result in a better quantification of hydrological impacts?
A 50-year analysis of hydrological trends and processes in a Mediterranean catchment
Technical Note: On the puzzling similarity of two water balance formulas – Turc–Mezentsev vs. Tixeront–Fu
Climate or land cover variations: what is driving observed changes in river peak flows? A data-based attribution study
Quantifying new water fractions and transit time distributions using ensemble hydrograph separation: theory and benchmark tests
Land cover effects on hydrologic services under a precipitation gradient
Technical note: Long-term persistence loss of urban streams as a metric for catchment classification
Responses of runoff to historical and future climate variability over China
Characterization and evaluation of controls on post-fire streamflow response across western US watersheds
Analysis and modelling of a 9.3 kyr palaeoflood record: correlations, clustering, and cycles
Climate change impacts on Yangtze River discharge at the Three Gorges Dam
Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?
Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression
Multivariate hydrological data assimilation of soil moisture and groundwater head
On the propagation of diel signals in river networks using analytic solutions of flow equations
Dominant climatic factors driving annual runoff changes at the catchment scale across China
Data assimilation in integrated hydrological modelling in the presence of observation bias
Recent changes in climate, hydrology and sediment load in the Wadi Abd, Algeria (1970–2010)
Technical Note: Testing an improved index for analysing storm discharge–concentration hysteresis
Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis
Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization
Time series analysis of the long-term hydrologic impacts of afforestation in the Águeda watershed of north-central Portugal
Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance
Attribution of high resolution streamflow trends in Western Austria – an approach based on climate and discharge station data
A constraint-based search algorithm for parameter identification of environmental models
Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA
Teleconnection analysis of runoff and soil moisture over the Pearl River basin in southern China
Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling
Streamflow input to Lake Athabasca, Canada
Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, and Raphael J. M. Schneider
Hydrol. Earth Syst. Sci., 28, 2871–2893, https://doi.org/10.5194/hess-28-2871-2024, https://doi.org/10.5194/hess-28-2871-2024, 2024
Short summary
Short summary
We developed hybrid schemes to enhance national-scale streamflow predictions, combining long short-term memory (LSTM) with a physically based hydrological model (PBM). A comprehensive evaluation of hybrid setups across Denmark indicates that LSTM models forced by climate data and catchment attributes perform well in many regions but face challenges in groundwater-dependent basins. The hybrid schemes supported by PBMs perform better in reproducing long-term streamflow behavior and extreme events.
Yixin Yang, Long Yang, Jinghan Zhang, and Qiang Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-168, https://doi.org/10.5194/hess-2024-168, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We introduce a machine-learning framework to study flood spatial characteristics and drivers in eastern China, using 37 years of flood peak data from a vast gauging network. Our analyses provide better understanding of contrasting flood behaviors by explicitly characterizing their spatial extents. This knowledge can help improve flood risk management.
Jun-Yi Lee, Ci-Jian Yang, Tsung-Ren Peng, Tsung-Yu Lee, and Jr-Chuan Huang
Hydrol. Earth Syst. Sci., 27, 4279–4294, https://doi.org/10.5194/hess-27-4279-2023, https://doi.org/10.5194/hess-27-4279-2023, 2023
Short summary
Short summary
Streamflow recession, shaped by landscape and rainfall, is not well understood. This study examines their combined impact using data from 19 mountainous rivers. Longer, gentler hillslopes promote flow and reduce nonlinearity, while larger catchments with more rainfall show increased landscape heterogeneity. In small catchments, the exponent decreases with rainfall, indicating less landscape and runoff variation. Further research is needed to validate these findings across diverse regions.
Jared C. Magyar and Malcolm Sambridge
Hydrol. Earth Syst. Sci., 27, 991–1010, https://doi.org/10.5194/hess-27-991-2023, https://doi.org/10.5194/hess-27-991-2023, 2023
Short summary
Short summary
Measuring the similarity of distributions of water is a useful tool for model calibration and assessment. We provide a new way of measuring this similarity for streamflow time series. It is derived from the concept of the amount of
workrequired to rearrange one mass distribution into the other. We also use similar mathematical techniques for defining a type of
averagebetween water distributions.
Manuela Irene Brunner and Philippe Naveau
Hydrol. Earth Syst. Sci., 27, 673–687, https://doi.org/10.5194/hess-27-673-2023, https://doi.org/10.5194/hess-27-673-2023, 2023
Short summary
Short summary
Reservoir regulation affects various streamflow characteristics. Still, information on when water is stored in and released from reservoirs is hardly available. We develop a statistical model to reconstruct reservoir operation signals from observed streamflow time series. By applying this approach to 74 catchments in the Alps, we find that reservoir management varies by catchment elevation and that seasonal redistribution from summer to winter is strongest in high-elevation catchments.
Gnanathikkam Emmanuel Amirthanathan, Mohammed Abdul Bari, Fitsum Markos Woldemeskel, Narendra Kumar Tuteja, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 27, 229–254, https://doi.org/10.5194/hess-27-229-2023, https://doi.org/10.5194/hess-27-229-2023, 2023
Short summary
Short summary
We used statistical tests to detect annual and seasonal streamflow trends and step changes across Australia. The Murray–Darling Basin and other rivers in the southern and north-eastern areas showed decreasing trends. Only rivers in the Timor Sea region in northern Australia showed significant increasing trends. Our results assist with infrastructure planning and management of water resources. This study was undertaken by the Bureau of Meteorology with its responsibility under the Water Act 2007.
Shijie Jiang, Emanuele Bevacqua, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 26, 6339–6359, https://doi.org/10.5194/hess-26-6339-2022, https://doi.org/10.5194/hess-26-6339-2022, 2022
Short summary
Short summary
Using a novel explainable machine learning approach, we investigated the contributions of precipitation, temperature, and day length to different peak discharges, thereby uncovering three primary flooding mechanisms widespread in European catchments. The results indicate that flooding mechanisms have changed in numerous catchments over the past 70 years. The study highlights the potential of artificial intelligence in revealing complex changes in extreme events related to climate change.
Jiarong Wang, Xi Chen, Man Gao, Qi Hu, and Jintao Liu
Hydrol. Earth Syst. Sci., 26, 3901–3920, https://doi.org/10.5194/hess-26-3901-2022, https://doi.org/10.5194/hess-26-3901-2022, 2022
Short summary
Short summary
The accelerated climate warming in the Tibetan Plateau after 1997 has strong consequences for hydrology, geography, and social wellbeing. In hydrology, the change in streamflow as a result of changes in dynamic water storage originating from glacier melt and permafrost thawing in a warming climate directly affects the available water resources for societies of some of the most populated nations in the world.
Esther Xu Fei and Ciaran Joseph Harman
Hydrol. Earth Syst. Sci., 26, 1977–1991, https://doi.org/10.5194/hess-26-1977-2022, https://doi.org/10.5194/hess-26-1977-2022, 2022
Short summary
Short summary
Water in streams is a mixture of water from many sources. It is sometimes possible to identify the chemical fingerprint of each source and track the time-varying contribution of that source to the total flow rate. But what if you do not know the chemical fingerprint of each source? Can you simultaneously identify the sources (called end-members), and separate the water into contributions from each, using only samples of water from the stream? Here we suggest a method for doing just that.
Guofeng Zhu, Zhigang Sun, Yuanxiao Xu, Yuwei Liu, Zhuanxia Zhang, Liyuan Sang, and Lei Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-75, https://doi.org/10.5194/hess-2022-75, 2022
Revised manuscript not accepted
Short summary
Short summary
We analyzed the stable isotopic composition of surface water and estimated its evaporative loss in the Shiyang River Basin. The characteristics of stable isotopes in surface water show a gradual enrichment from mountainous areas to deserts, and the evaporation loss of surface water also shows a gradually increasing trend from upstream to downstream. The study of evaporative losses in the river-lake continuum contributes to the sustainable use of water resources.
Paul Royer-Gaspard, Vazken Andréassian, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 25, 5703–5716, https://doi.org/10.5194/hess-25-5703-2021, https://doi.org/10.5194/hess-25-5703-2021, 2021
Short summary
Short summary
Most evaluation studies based on the differential split-sample test (DSST) endorse the consensus that rainfall–runoff models lack climatic robustness. In this technical note, we propose a new performance metric to evaluate model robustness without applying the DSST and which can be used with a single hydrological model calibration. Our work makes it possible to evaluate the temporal transferability of any hydrological model, including uncalibrated models, at a very low computational cost.
Wencong Yang, Hanbo Yang, Dawen Yang, and Aizhong Hou
Hydrol. Earth Syst. Sci., 25, 2705–2720, https://doi.org/10.5194/hess-25-2705-2021, https://doi.org/10.5194/hess-25-2705-2021, 2021
Short summary
Short summary
This study quantified the causal effects of land cover changes and dams on the changes in annual maximum discharges (Q) in 757 catchments of China using panel regressions. We found that a 1 % point increase in urban areas causes a 3.9 % increase in Q, and a 1 unit increase in reservoir index causes a 21.4 % decrease in Q for catchments with no dam before. This study takes the first step to explain the human-caused flood changes on a national scale in China.
Weifei Yang, Changlai Xiao, Zhihao Zhang, and Xiujuan Liang
Hydrol. Earth Syst. Sci., 25, 1747–1760, https://doi.org/10.5194/hess-25-1747-2021, https://doi.org/10.5194/hess-25-1747-2021, 2021
Short summary
Short summary
This study analyzed the effectiveness of the conductivity mass balance (CMB) method for correcting the Eckhardt method. The results showed that the approach of calibrating the Eckhardt method against the CMB method provides a
falsecalibration of total baseflow by offsetting the inherent biases in the baseflow sequences generated by the two methods. The reason for this phenomenon is the baseflow series generated by the two methods containing different transient water sources.
Juliane Mai, James R. Craig, and Bryan A. Tolson
Hydrol. Earth Syst. Sci., 24, 5835–5858, https://doi.org/10.5194/hess-24-5835-2020, https://doi.org/10.5194/hess-24-5835-2020, 2020
James W. Kirchner and Julia L. A. Knapp
Hydrol. Earth Syst. Sci., 24, 5539–5558, https://doi.org/10.5194/hess-24-5539-2020, https://doi.org/10.5194/hess-24-5539-2020, 2020
Short summary
Short summary
Ensemble hydrograph separation is a powerful new tool for measuring the age distribution of streamwater. However, the calculations are complex and may be difficult for researchers to implement on their own. Here we present scripts that perform these calculations in either MATLAB or R so that researchers do not need to write their own codes. We explain how these scripts work and how to use them. We demonstrate several potential applications using a synthetic catchment data set.
Antoine Allam, Roger Moussa, Wajdi Najem, and Claude Bocquillon
Hydrol. Earth Syst. Sci., 24, 4503–4521, https://doi.org/10.5194/hess-24-4503-2020, https://doi.org/10.5194/hess-24-4503-2020, 2020
Short summary
Short summary
With serious concerns about global change rising in the Mediterranean, we established a new climatic classification to follow hydrological and ecohydrological activities. The classification coincided with a geographical distribution ranging from the most seasonal and driest class in the south to the least seasonal and most humid in the north. RCM scenarios showed that northern classes evolve to southern ones with shorter humid seasons and earlier snowmelt which might affect hydrologic regimes.
Mingguo Zheng
Hydrol. Earth Syst. Sci., 24, 2365–2378, https://doi.org/10.5194/hess-24-2365-2020, https://doi.org/10.5194/hess-24-2365-2020, 2020
Short summary
Short summary
This paper developed a mathematically precise method to partition climate and catchment effects on streamflow. The method reveals that both the change magnitude and pathway (timing of change), not the magnitude alone, dictate the partition unless for a linear system. The method has wide relevance. For example, it suggests that the global warming effect of carbon emission is path dependent, and an optimal pathway would facilitate a higher global budget of carbon emission.
José Manuel Tunqui Neira, Vazken Andréassian, Gaëlle Tallec, and Jean-Marie Mouchel
Hydrol. Earth Syst. Sci., 24, 1823–1830, https://doi.org/10.5194/hess-24-1823-2020, https://doi.org/10.5194/hess-24-1823-2020, 2020
Short summary
Short summary
This paper deals with the mathematical representation of concentration–discharge relationships. We propose a two-sided affine power scaling relationship (2S-APS) as an alternative to the classic one-sided power scaling relationship (commonly known as
power law). We also discuss the identification of the parameters of the proposed relationship, using an appropriate numerical criterion, based on high-frequency chemical time series of the Orgeval-ORACLE observatory.
Long Yang, Lachun Wang, Xiang Li, and Jie Gao
Hydrol. Earth Syst. Sci., 23, 5133–5149, https://doi.org/10.5194/hess-23-5133-2019, https://doi.org/10.5194/hess-23-5133-2019, 2019
Julia L. A. Knapp, Colin Neal, Alessandro Schlumpf, Margaret Neal, and James W. Kirchner
Hydrol. Earth Syst. Sci., 23, 4367–4388, https://doi.org/10.5194/hess-23-4367-2019, https://doi.org/10.5194/hess-23-4367-2019, 2019
Short summary
Short summary
We describe, present, and make publicly available two extensive data sets of stable water isotopes in streamwater and precipitation at Plynlimon, Wales, consisting of measurements at 7-hourly intervals for 17 months and at weekly intervals for 4.25 years. We use these data to calculate new water fractions and transit time distributions for different discharge rates and seasons, thus quantifying the contribution of recent precipitation to streamflow under different conditions.
Hui-Min Wang, Jie Chen, Chong-Yu Xu, Hua Chen, Shenglian Guo, Ping Xie, and Xiangquan Li
Hydrol. Earth Syst. Sci., 23, 4033–4050, https://doi.org/10.5194/hess-23-4033-2019, https://doi.org/10.5194/hess-23-4033-2019, 2019
Short summary
Short summary
When using large ensembles of global climate models in hydrological impact studies, there are pragmatic questions on whether it is necessary to weight climate models and how to weight them. We use eight methods to weight climate models straightforwardly, based on their performances in hydrological simulations, and investigate the influences of the assigned weights. This study concludes that using bias correction and equal weighting is likely viable and sufficient for hydrological impact studies.
Nathalie Folton, Eric Martin, Patrick Arnaud, Pierre L'Hermite, and Mathieu Tolsa
Hydrol. Earth Syst. Sci., 23, 2699–2714, https://doi.org/10.5194/hess-23-2699-2019, https://doi.org/10.5194/hess-23-2699-2019, 2019
Short summary
Short summary
The long-term study of precipitation, flows, flood or drought mechanisms, in the Réal Collobrier research Watershed, located in South-East France, in the Mediterranean forest, improves knowledge of the water cycle and is unique tool for understanding of how catchments function. This study shows a small decrease in rainfall and a marked tendency towards a decrease in the water resources of the catchment in response to climate trends, with a consistent increase in drought severity and duration.
Vazken Andréassian and Tewfik Sari
Hydrol. Earth Syst. Sci., 23, 2339–2350, https://doi.org/10.5194/hess-23-2339-2019, https://doi.org/10.5194/hess-23-2339-2019, 2019
Short summary
Short summary
In this Technical Note, we present two water balance formulas: the Turc–Mezentsev and Tixeront–Fu formulas. These formulas have a puzzling numerical similarity, which we discuss in detail and try to interpret mathematically and hydrologically.
Jan De Niel and Patrick Willems
Hydrol. Earth Syst. Sci., 23, 871–882, https://doi.org/10.5194/hess-23-871-2019, https://doi.org/10.5194/hess-23-871-2019, 2019
James W. Kirchner
Hydrol. Earth Syst. Sci., 23, 303–349, https://doi.org/10.5194/hess-23-303-2019, https://doi.org/10.5194/hess-23-303-2019, 2019
Short summary
Short summary
How long does it take for raindrops to become streamflow? Here I propose a new approach to this old problem. I show how we can use time series of isotope data to measure the average fraction of same-day rainfall appearing in streamflow, even if this fraction varies greatly from rainstorm to rainstorm. I show that we can quantify how this fraction changes from small rainstorms to big ones, and from high flows to low flows, and how it changes with the lag time between rainfall and streamflow.
Ane Zabaleta, Eneko Garmendia, Petr Mariel, Ibon Tamayo, and Iñaki Antigüedad
Hydrol. Earth Syst. Sci., 22, 5227–5241, https://doi.org/10.5194/hess-22-5227-2018, https://doi.org/10.5194/hess-22-5227-2018, 2018
Short summary
Short summary
This study establishes relationships between land cover and river discharge. Using discharge data from 20 catchments of the Bay of Biscay findings showed the influence of land cover on discharge changes with the amount of precipitation, with lower annual water resources associated with the greater presence of forests. Results obtained illustrate the relevance of land planning to the management of water resources and the opportunity to consider it in future climate-change adaptation strategies.
Dusan Jovanovic, Tijana Jovanovic, Alfonso Mejía, Jon Hathaway, and Edoardo Daly
Hydrol. Earth Syst. Sci., 22, 3551–3559, https://doi.org/10.5194/hess-22-3551-2018, https://doi.org/10.5194/hess-22-3551-2018, 2018
Short summary
Short summary
A relationship between the Hurst (H) exponent (a long-term correlation coefficient) within a flow time series and various catchment characteristics for a number of catchments in the USA and Australia was investigated. A negative relationship with imperviousness was identified, which allowed for an efficient catchment classification, thus making the H exponent a useful metric to quantitatively assess the impact of catchment imperviousness on streamflow regime.
Chuanhao Wu, Bill X. Hu, Guoru Huang, Peng Wang, and Kai Xu
Hydrol. Earth Syst. Sci., 22, 1971–1991, https://doi.org/10.5194/hess-22-1971-2018, https://doi.org/10.5194/hess-22-1971-2018, 2018
Short summary
Short summary
China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal–spatial patterns of water resources. In this paper, the Budyko-based elasticity method was used to investigate the responses of runoff to historical and future climate variability over China at both grid and catchment scales. The results help to better understand the hydrological effects of climate change and adapt to a changing environment.
Samuel Saxe, Terri S. Hogue, and Lauren Hay
Hydrol. Earth Syst. Sci., 22, 1221–1237, https://doi.org/10.5194/hess-22-1221-2018, https://doi.org/10.5194/hess-22-1221-2018, 2018
Short summary
Short summary
We investigate the impact of wildfire on watershed flow regimes, examining responses across the western United States. On a national scale, our results confirm the work of prior studies: that low, high, and peak flows typically increase following a wildfire. Regionally, results are more variable and sometimes contradictory. Our results may be significant in justifying the calibration of watershed models and in contributing to the overall observational analysis of post-fire streamflow response.
Annette Witt, Bruce D. Malamud, Clara Mangili, and Achim Brauer
Hydrol. Earth Syst. Sci., 21, 5547–5581, https://doi.org/10.5194/hess-21-5547-2017, https://doi.org/10.5194/hess-21-5547-2017, 2017
Short summary
Short summary
Here we present a unique 9.5 m palaeo-lacustrine record of 771 palaeofloods which occurred over a period of 10 000 years in the Piànico–Sèllere basin (southern Alps) during an interglacial period in the Pleistocene (sometime between 400 000 and 800 000 years ago). We analyse the palaeoflood series correlation, clustering, and cyclicity properties, finding a long-range cyclicity with a period of about 2030 years superimposed onto a fractional noise.
Steve J. Birkinshaw, Selma B. Guerreiro, Alex Nicholson, Qiuhua Liang, Paul Quinn, Lili Zhang, Bin He, Junxian Yin, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 21, 1911–1927, https://doi.org/10.5194/hess-21-1911-2017, https://doi.org/10.5194/hess-21-1911-2017, 2017
Short summary
Short summary
The Yangtze River basin in China is home to more than 400 million people and susceptible to major floods. We used projections of future precipitation and temperature from 35 of the most recent global climate models and applied this to a hydrological model of the Yangtze. Changes in the annual discharge varied between a 29.8 % decrease and a 16.0 % increase. The main reason for the difference between the models was the predicted expansion of the summer monsoon north and and west into the basin.
Maurizio Mazzoleni, Martin Verlaan, Leonardo Alfonso, Martina Monego, Daniele Norbiato, Miche Ferri, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 839–861, https://doi.org/10.5194/hess-21-839-2017, https://doi.org/10.5194/hess-21-839-2017, 2017
Short summary
Short summary
This study assesses the potential use of crowdsourced data in hydrological modeling, which are characterized by irregular availability and variable accuracy. We show that even data with these characteristics can improve flood prediction if properly integrated into hydrological models. This study provides technological support to citizen observatories of water, in which citizens can play an active role in capturing information, leading to improved model forecasts and better flood management.
Martin Durocher, Fateh Chebana, and Taha B. M. J. Ouarda
Hydrol. Earth Syst. Sci., 20, 4717–4729, https://doi.org/10.5194/hess-20-4717-2016, https://doi.org/10.5194/hess-20-4717-2016, 2016
Short summary
Short summary
For regional flood frequency, it is challenging to identify regions with similar hydrological properties. Therefore, previous works have mainly proposed to use regions with similar physiographical properties. This research proposes instead to nonlinearly predict the desired hydrological properties before using them for delineation. The presented method is applied to a case study in Québec, Canada, and leads to hydrologically relevant regions, while enhancing predictions made inside them.
Donghua Zhang, Henrik Madsen, Marc E. Ridler, Jacob Kidmose, Karsten H. Jensen, and Jens C. Refsgaard
Hydrol. Earth Syst. Sci., 20, 4341–4357, https://doi.org/10.5194/hess-20-4341-2016, https://doi.org/10.5194/hess-20-4341-2016, 2016
Short summary
Short summary
We present a method to assimilate observed groundwater head and soil moisture profiles into an integrated hydrological model. The study uses the ensemble transform Kalman filter method and the MIKE SHE hydrological model code. The proposed method is shown to be more robust and provide better results for two cases in Denmark, and is also validated using real data. The hydrological model with assimilation overall improved performance compared to the model without assimilation.
Morgan Fonley, Ricardo Mantilla, Scott J. Small, and Rodica Curtu
Hydrol. Earth Syst. Sci., 20, 2899–2912, https://doi.org/10.5194/hess-20-2899-2016, https://doi.org/10.5194/hess-20-2899-2016, 2016
Short summary
Short summary
We design and implement a theoretical experiment to show that, under low-flow conditions, observed streamflow discrepancies between early and late summer can be attributed to different flow velocities in the river network. By developing an analytic solution to represent flow along a given river network, we emphasize the dependence of streamflow amplitude and time delay on the geomorphology of the network. We also simulate using a realistic river network to highlight the effects of scale.
Zhongwei Huang, Hanbo Yang, and Dawen Yang
Hydrol. Earth Syst. Sci., 20, 2573–2587, https://doi.org/10.5194/hess-20-2573-2016, https://doi.org/10.5194/hess-20-2573-2016, 2016
Short summary
Short summary
The hydrologic processes have been influenced by different climatic factors. However, the dominant climatic factor driving annual runoff change is still unknown in many catchments in China. By using the climate elasticity method proposed by Yang and Yang (2011), the elasticity of runoff to climatic factors was estimated, and the dominant climatic factors driving annual runoff change were detected at catchment scale over China.
Jørn Rasmussen, Henrik Madsen, Karsten Høgh Jensen, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 20, 2103–2118, https://doi.org/10.5194/hess-20-2103-2016, https://doi.org/10.5194/hess-20-2103-2016, 2016
Short summary
Short summary
In the paper, observations are assimilated into a hydrological model in order to improve the model performance. Two methods for detecting and correcting systematic errors (bias) in groundwater head observations are used leading to improved results compared to standard assimilation methods which ignores any bias. This is demonstrated using both synthetic (user generated) observations and real-world observations.
Mohammed Achite and Sylvain Ouillon
Hydrol. Earth Syst. Sci., 20, 1355–1372, https://doi.org/10.5194/hess-20-1355-2016, https://doi.org/10.5194/hess-20-1355-2016, 2016
Short summary
Short summary
Changes of T, P, Q and sediment fluxes in a semi-arid basin little affected by human activities are analyzed from 40 years of measurements. T increased, P decreased, an earlier onset of first summer rains occurred. The flow regime shifted from perennial to intermittent. Sediment flux almost doubled every decade. The sediment regime shifted from two equivalent seasons of sediment delivery to a single major season regime. The C–Q rating curve ability declined due to enhanced hysteresis effects.
C. E. M. Lloyd, J. E. Freer, P. J. Johnes, and A. L. Collins
Hydrol. Earth Syst. Sci., 20, 625–632, https://doi.org/10.5194/hess-20-625-2016, https://doi.org/10.5194/hess-20-625-2016, 2016
Short summary
Short summary
This paper examines the current methodologies for quantifying storm behaviour through hysteresis analysis, and explores a new method. Each method is systematically tested and the impact on the results is examined. Recommendations are made regarding the most effective method of calculating a hysteresis index. This new method allows storm hysteresis behaviour to be directly compared between storms, parameters, and catchments, meaning it has wide application potential in water quality research.
W. Hu and B. C. Si
Hydrol. Earth Syst. Sci., 20, 571–587, https://doi.org/10.5194/hess-20-571-2016, https://doi.org/10.5194/hess-20-571-2016, 2016
Short summary
Short summary
Spatiotemporal SWC was decomposed into into three terms (spatial forcing, temporal forcing, and interactions between spatial and temporal forcing) for near surface and root zone; Empirical orthogonal function indicated that underlying patterns exist in the interaction term at small watershed scales; Estimation of spatially distributed SWC benefits from decomposition of the interaction term; The suggested decomposition of SWC with time stability analysis has potential in SWC downscaling.
Y. Chen, J. Li, and H. Xu
Hydrol. Earth Syst. Sci., 20, 375–392, https://doi.org/10.5194/hess-20-375-2016, https://doi.org/10.5194/hess-20-375-2016, 2016
Short summary
Short summary
Parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. A method for parameter optimization with particle swam optimization (PSO) algorithm has been proposed for physically based distributed hydrological model in catchment flood forecasting and validated in southern China. It has found that the appropriate particle number and maximum evolution number of PSO algorithm are 20 and 30 respectively.
D. Hawtree, J. P. Nunes, J. J. Keizer, R. Jacinto, J. Santos, M. E. Rial-Rivas, A.-K. Boulet, F. Tavares-Wahren, and K.-H. Feger
Hydrol. Earth Syst. Sci., 19, 3033–3045, https://doi.org/10.5194/hess-19-3033-2015, https://doi.org/10.5194/hess-19-3033-2015, 2015
J. Rasmussen, H. Madsen, K. H. Jensen, and J. C. Refsgaard
Hydrol. Earth Syst. Sci., 19, 2999–3013, https://doi.org/10.5194/hess-19-2999-2015, https://doi.org/10.5194/hess-19-2999-2015, 2015
C. Kormann, T. Francke, M. Renner, and A. Bronstert
Hydrol. Earth Syst. Sci., 19, 1225–1245, https://doi.org/10.5194/hess-19-1225-2015, https://doi.org/10.5194/hess-19-1225-2015, 2015
S. Gharari, M. Shafiei, M. Hrachowitz, R. Kumar, F. Fenicia, H. V. Gupta, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 18, 4861–4870, https://doi.org/10.5194/hess-18-4861-2014, https://doi.org/10.5194/hess-18-4861-2014, 2014
S. G. Leibowitz, R. L. Comeleo, P. J. Wigington Jr., C. P. Weaver, P. E. Morefield, E. A. Sproles, and J. L. Ebersole
Hydrol. Earth Syst. Sci., 18, 3367–3392, https://doi.org/10.5194/hess-18-3367-2014, https://doi.org/10.5194/hess-18-3367-2014, 2014
J. Niu, J. Chen, and B. Sivakumar
Hydrol. Earth Syst. Sci., 18, 1475–1492, https://doi.org/10.5194/hess-18-1475-2014, https://doi.org/10.5194/hess-18-1475-2014, 2014
S. Galelli and A. Castelletti
Hydrol. Earth Syst. Sci., 17, 2669–2684, https://doi.org/10.5194/hess-17-2669-2013, https://doi.org/10.5194/hess-17-2669-2013, 2013
K. Rasouli, M. A. Hernández-Henríquez, and S. J. Déry
Hydrol. Earth Syst. Sci., 17, 1681–1691, https://doi.org/10.5194/hess-17-1681-2013, https://doi.org/10.5194/hess-17-1681-2013, 2013
M. Nied, Y. Hundecha, and B. Merz
Hydrol. Earth Syst. Sci., 17, 1401–1414, https://doi.org/10.5194/hess-17-1401-2013, https://doi.org/10.5194/hess-17-1401-2013, 2013
Cited articles
Arai, R., Toyoda, Y., and Kazama, S.: Runoff recession features in an analytical probabilistic streamflow model, J. Hydrol., 597, 125745, https://doi.org/10.1016/j.jhydrol.2020.125745, 2020.
Archfield, S. A., Hirsch, R. M., Viglione, A., and Blöschl, G.: Fragmented patterns of flood change across the United States, Geophys. Res. Lett., 43, 10232–10239, https://doi.org/10.1002/2016GL070590, 2016.
Bart, R. and Hope, A.: Inter-seasonal variability in baseflow recession rates: The role of aquifer antecedent storage in central California watersheds, J. Hydrol., 519, 205–213, https://doi.org/10.1016/j.jhydrol.2014.07.020, 2014.
Basso, S., Schirmer, M., and Botter, G.: On the emergence of heavy-tailed streamflow distributions, Adv. Water Resour., 82, 98–105, https://doi.org/10.1016/j.advwatres.2015.04.013, 2015.
Basso, S., Schirmer, M., and Botter, G.: A physically based analytical model of flood frequency curves, Geophys. Res. Lett., 43, 9070–9076, https://doi.org/10.1002/2016GL069915, 2016.
Basso, S., Botter, G., Merz, R., and Miniussi, A.: PHEV! The PHysically-based Extreme Value distribution of river flows, Environ. Res. Lett., 16, 124065, https://doi.org/10.1088/1748-9326/ac3d59, 2021.
Basso, S., Merz, R., Tarasova, L., and Miniussi, A.: Extreme flooding controlled by stream network organization and flow regime, Nat. Geosci., 16, 339–343, https://doi.org/10.1038/s41561-023-01155-w, 2023.
Baudin, M., Dutfoy, A., Iooss, B., and Popelin, A.-L.: OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation BT – Handbook of Uncertainty Quantification, edited by: Ghanem, R., Higdon, D., and Owhadi, H., Springer International Publishing, Cham, 2001–2038, https://doi.org/10.1007/978-3-319-12385-1_64, 2017.
Bayerisches Landesamt für Umwelt: Abfluss Bayern, Bayerisches Landesamt für Umwelt [data set], https://www.gkd.bayern.de/de/fluesse/abfluss (last access: 26 August 2022), 2022.
Beirlant, J., Goegebeur, Y., Teugels, J., Segers, J., De Waal, D., and Ferro, C.: Statistics of extremes: Theory and applications, Wiley, https://doi.org/10.1002/0470012382, 2004.
Bevere, L. and Remondi, F.: Natural catastrophes in 2021: the floodgates are open, Swiss Re Institute sigma research, https://www.swissre.com/institute/research/sigma-research/sigma-2022-01.html (last access: 8 May 2023), 2022.
Biswal, B.: Decorrelation is not dissociation: There is no means to entirely decouple the Brutsaert-Nieber parameters in streamflow recession analysis, Adv. Water Resour., 147, 103822, https://doi.org/10.1016/j.advwatres.2020.103822, 2021.
Biswal, B. and Kumar, D. N.: Study of dynamic behaviour of recession curves, Hydrol. Process., 792, 784–792, https://doi.org/10.1002/hyp.9604, 2014.
Biswal, B. and Marani, M.: Geomorphological origin of recession curves, Geophys. Res. Lett., 37, 1–5, https://doi.org/10.1029/2010GL045415, 2010.
Botter, G., Peratoner, F., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Signatures of large-scale soil moisture dynamics on streamflow statistics across U.S. climate regimes, Water Resour. Res., 43, 1–10, https://doi.org/10.1029/2007WR006162, 2007a.
Botter, G., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Basin-scale soil moisture dynamics and the probabilistic characterization of carrier hydrologic flows: Slow, leaching-prone components of the hydrologic response, Water Resour. Res., 43, 1–14, https://doi.org/10.1029/2006WR005043, 2007b.
Botter, G., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Nonlinear storage-discharge relations and catchment streamflow regimes, Water Resour. Res., 45, 1–16, https://doi.org/10.1029/2008WR007658, 2009.
Botter, G., Basso, S., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Natural streamflow regime alterations: Damming of the Piave river basin (Italy), Water Resour. Res., 46, 1–14, https://doi.org/10.1029/2009WR008523, 2010.
Brutsaert, W. and Nieber, J. L.: Regionalized drought flow hydrographs from a mature glaciated plateau, Water Resour. Res., 13, 637–643, https://doi.org/10.1029/WR013i003p00637, 1977.
Bundesanstalt für Gewässerkunde: Global Runoff Database, Bundesanstalt für Gewässerkunde [data set], https://www.bafg.de/GRDC (last access: 29 August 2022), 2022.
Cai, Y. and Hames, D.: Minimum sample size determination for generalized extreme value distribution, Commun. Stat. Simul. Comput., 40, 87–98, https://doi.org/10.1080/03610918.2010.530368, 2010.
Ceola, S., Botter, G., Bertuzzo, E., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Comparative study of ecohydrological streamflow probability distributions, Water Resour. Res., 46, 1–12, https://doi.org/10.1029/2010WR009102, 2010.
Clauset, A., Shalizi, C. R., and Newman, M. E. J.: Power-law distributions in empirical data, SIAM Rev., 51, 661–703, https://doi.org/10.1137/070710111, 2009.
Cooke, R. M. and Nieboer, D.: Heavy-Tailed Distributions: Data, Diagnostics, and New Developments, Resour. Futur. Discuss. Pap. No. 11-19, SSRN, https://doi.org/10.2139/ssrn.1811043, 2011.
Cooke, R. M., Nieboer, D., and Misiewicz, J.: Fat-Tailed Distributions: Data, Diagnostics and Dependence, in: Vol. 1, John Wiley & Sons, ISBN 1848217927, 2014.
Cox, D. R. and Isham, V.: A simple spatial-temporal model of rainfall, P. Roy. Soc. Lond. A, 415, 317–328, https://doi.org/10.1098/rspa.1988.0016, 1988.
Cunderlik, J. M. and Burn, D. H.: The use of flood regime information in regional flood frequency analysis, Hydrolog. Sci. J., 47, 77–92, https://doi.org/10.1080/02626660209492909, 2002.
Deutscher Wetterdienst: Climate Data Center, https://cdc.dwd.de/portal/ (last access: 21 August 2022), 2022.
Dimitriadis, P., Koutsoyiannis, D., Iliopoulou, T., and Papanicolaou, P.: A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8, 59, https://doi.org/10.3390/hydrology8020059, 2021.
Doulatyari, B., Betterle, A., Basso, S., Biswal, B., Schirmer, M., and Botter, G.: Predicting streamflow distributions and flow duration curves from landscape and climate, Adv. Water Resour., 83, 285–298, https://doi.org/10.1016/j.advwatres.2015.06.013, 2015.
Dralle, D. N., Karst, N. J., Charalampous, K., Veenstra, A., and Thompson, S. E.: Event-scale power law recession analysis: Quantifying methodological uncertainty, Hydrol. Earth Syst. Sci., 21, 65–81, https://doi.org/10.5194/hess-21-65-2017, 2017.
Durrans, S. R., Eiffe, M. A., Thomas, W. O., and Goranflo, H. M.: Joint Seasonal/Annual Flood Frequency Analysis, J. Hydrol. Eng., 8, 181–189, https://doi.org/10.1061/(asce)1084-0699(2003)8:4(181), 2003.
El Adlouni, S., Bobée, B., and Ouarda, T. B. M. J.: On the tails of extreme event distributions in hydrology, J. Hydrol., 355, 16–33, https://doi.org/10.1016/j.jhydrol.2008.02.011, 2008.
Eliazar, I. and Sokolov, I.: Gini characterization of extreme-value statistics, Physica A, 389, 4462–4472, https://doi.org/10.1016/j.physa.2010.07.005, 2010.
Embrechts, P., Klüppelberg, C., and Mikosch, T.: Modelling extreme events for insurance and finance, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-33483-2, 1997.
Fiorentino, M., Manfreda, S., and Iacobellis, V.: Peak runoff contributing area as hydrological signature of the probability distribution of floods, Adv. Water Resour., 30, 2123–2134, https://doi.org/10.1016/j.advwatres.2006.11.017, 2007.
Fischer, S. and Schumann, A.: Robust flood statistics: comparison of peak over threshold approaches based on monthly maxima and TL-moments, Hydrolog. Sci. J., 61, 457–470, https://doi.org/10.1080/02626667.2015.1054391, 2016.
Gioia, A., Iacobellis, V., Manfreda, S., and Fiorentino, M.: Influence of infiltration and soil storage capacity on the skewness of the annual maximum flood peaks in a theoretically derived distribution, Hydrol. Earth Syst. Sci., 937–951, https://doi.org/10.5194/hess-16-937-2012, 2012.
Godrèche, C., Majumdar, S. N., and Schehr, G.: Statistics of the longest interval in renewal processes, J. Stat. Mech. Theory Exp., 2015 P03014, https://doi.org/10.1088/1742-5468/2015/03/P03014, 2015.
Hall, J., Arheimer, B., Borga, M., Brázdil, R., Claps, P., Kiss, A., Kjeldsen, T. R., Kriaučiūnienė, J., Kundzewicz, Z. W., Lang, M., Llasat, M. C., Macdonald, N., McIntyre, N., Mediero, L., Merz, B., Merz, R., Molnar, P., Montanari, A., Neuhold, C., Parajka, J., Perdigão, R. A. P., Plavcová, L., Rogger, M., Salinas, J. L., Sauquet, E., Schär, C., Szolgay, J., Viglione, A., and Blöschl, G.: Understanding flood regime changes in Europe: A state-of-the-art assessment, Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, 2014.
Hodgkins, G. A., Whitfield, P. H., Burn, D. H., Hannaford, J., Renard, B., Stahl, K., Fleig, A. K., Madsen, H., Mediero, L., Korhonen, J., Murphy, C., and Wilson, D.: Climate-driven variability in the occurrence of major floods across North America and Europe, J. Hydrol., 552, 704–717, https://doi.org/10.1016/j.jhydrol.2017.07.027, 2017.
Hu, L., Nikolopoulos, E. I., Marra, F., and Emmanouil, N. A.: Toward an improved estimation of flood frequency statistics from simulated flows, J. Flood Risk Manage., https://doi.org/10.1111/jfr3.12891, in press, 2023.
Jachens, E. R., Rupp, D. E., Roques, C., and Selker, J. S.: Recession analysis revisited: Impacts of climate on parameter estimation, Hydrol. Earth Syst. Sci., 24, 1159–1170, https://doi.org/10.5194/hess-24-1159-2020, 2020.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, CGIAR CSI [data set], https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1 (last access: 8 August 2022), 2022.
Jepsen, S. M., Harmon, T. C., and Shi, Y.: Watershed model calibration to the base flow recession curve with and without evapotranspiration effects, Water Resour. Res., 52, 2919–2933, https://doi.org/10.1002/2015WR017827, 2016.
Karlsen, R. H., Bishop, K., Grabs, T., Ottosson-Löfvenius, M., Laudon, H., and Seibert, J.: The role of landscape properties, storage and evapotranspiration on variability in streamflow recessions in a boreal catchment, J. Hydrol., 570, 315–328, https://doi.org/10.1016/j.jhydrol.2018.12.065, 2019.
Kousar, S., Khan, A. R., Hassan, M. U., Noreen, Z., and Bhatti, S. H.: Some best-fit probability distributions for at-site flood frequency analysis of the Ume River, J. Flood Risk Manage., 13, 1–11, https://doi.org/10.1111/jfr3.12640, 2020.
Koutsoyiannis, D.: Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation, Hydrolog. Sci. J., 49, 575–590, https://doi.org/10.1623/hysj.49.4.575.54430, 2004a.
Koutsoyiannis, D.: Statistics of extremes and estimation of extreme rainfall: II. Empirical investigation of long rainfall records, Hydrolog. Sci. J., 49, 591–610, https://doi.org/10.1623/hysj.49.4.591.54424, 2004b.
Koutsoyiannis, D.: Stochastics of Hydroclimatic Extremes – A Cool Look at Risk, in: 2nd Edn., Open Academic Editions, Athens, https://doi.org/10.57713/kallipos-1, 2022.
Krakauer, N. Y. and Temimi, M.: Stream recession curves and storage variability in small watersheds, Hydrol. Earth Syst. Sci., 15, 2377–2389, https://doi.org/10.5194/hess-15-2377-2011, 2011.
Kumar, M., Sharif, M., and Ahmed, S.: Flood estimation at Hathnikund Barrage, River Yamuna, India using the Peak-Over-Threshold method, ISH J. Hydraul. Eng., 26, 291–300, https://doi.org/10.1080/09715010.2018.1485119, 2020.
Laio, F., Porporato, A., Fernandez-Illescas, C. P., and Rodriguez-Iturbe, I.: Plants in water-controlled ecosystems: Active role in hydrologic processes and responce to water stress IV. Discussion of real cases, Adv. Water Resour., 24, 745–762, https://doi.org/10.1016/S0309-1708(01)00007-0, 2001.
Lang, M., Ouarda, T. B. M. J., and Bobée, B.: Towards operational guidelines for over-threshold modeling, J. Hydrol., 225, 103–117, https://doi.org/10.1016/S0022-1694(99)00167-5, 1999.
Lehner, B., Liermann, C. R., Revenga, C., Vörömsmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, 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.
Lins, H. F.: Challenges to hydrological observations, WMO Bull., 57, 55–58, 2008.
Lombardo, F., Napolitano, F., Russo, F., and Koutsoyiannis, D.: On the Exact Distribution of Correlated Extremes in Hydrology, Water Resour. Res., 55, 10405–10423, https://doi.org/10.1029/2019WR025547, 2019.
Lu, P., Smith, J. A., and Lin, N.: Spatial characterization of flood magnitudes over the drainage network of the Delaware river basin, J. Hydrometeorol., 18, 957–976, https://doi.org/10.1175/JHM-D-16-0071.1, 2017.
Malamud, B. D. and Turcotte, D. L.: The applicability of power-law frequency statistics to floods, J. Hydrol., 322, 168–180, https://doi.org/10.1016/j.jhydrol.2005.02.032, 2006.
Marani, M. and Ignaccolo, M.: A metastatistical approach to rainfall extremes, Adv. Water Resour., 79, 121–126, https://doi.org/10.1016/j.advwatres.2015.03.001, 2015.
Marra, F., Nikolopoulos, E. I., Anagnostou, E. N., and Morin, E.: Metastatistical Extreme Value analysis of hourly rainfall from short records: Estimation of high quantiles and impact of measurement errors, Adv. Water Resour., 117, 27–39, https://doi.org/10.1016/j.advwatres.2018.05.001, 2018.
Martinez-Villalobos, C. and Neelin, J. D.: Climate models capture key features of extreme precipitation probabilities across regions, Environ. Res. Lett., 16, 024017, https://doi.org/10.1088/1748-9326/abd351, 2021.
McCuen, R. H. and Smith, E.: Origin of Flood Skew, J. Hydrol. Eng., 13, 771–775, https://doi.org/10.1061/(asce)1084-0699(2008)13:9(771), 2008.
McDermott, T. K. J.: Global exposure to flood risk and poverty, Nat. Commun., 13, 6–8, https://doi.org/10.1038/s41467-022-30725-6, 2022.
Mejía, A., Daly, E., Rossel, F., Javanovic, T., and Gironás, J.: A stochastic model of streamflow for urbanized basins, Water Resour. Res., 50, 1984–2001, https://doi.org/10.1002/2013WR014834, 2014.
Merz, B. and Thieken, A. H.: Flood risk curves and uncertainty bounds, Nat. Hazards, 51, 437–458, https://doi.org/10.1007/s11069-009-9452-6, 2009.
Merz, B., Blöschl, G., Vorogushyn, S., Dottori, F., Aerts, J. C. J. H., Bates, P., Bertola, M., Kemter, M., Kreibich, H., Lall, U., and Macdonald, E.: Causes, impacts and patterns of disastrous river floods, Nat. Rev. Earth Environ., 2, 592–609, https://doi.org/10.1038/s43017-021-00195-3, 2021.
Merz, B., Basso, S., Fischer, S., Lun, D., Blöschl, G., Merz, R., Guse, B., Viglione, A., Vorogushyn, S., Macdonald, E., Wietzke, L., and Schumann, A.: Understanding heavy tails of flood peak distributions, Water Resour. Res., https://doi.org/10.1029/2021wr030506, in press, 2022.
Miniussi, A. and Marani, M.: Estimation of Daily Rainfall Extremes Through the Metastatistical Extreme Value Distribution: Uncertainty Minimization and Implications for Trend Detection, Water Resour. Res., 56, 1–18, https://doi.org/10.1029/2019WR026535, 2020.
Miniussi, A., Marani, M., and Villarini, G.: Metastatistical Extreme Value Distribution applied to floods across the continental United States, Adv. Water Resour., 136, 103498, https://doi.org/10.1016/j.advwatres.2019.103498, 2020.
Morrison, J. E. and Smith, J. A.: Stochastic modeling of flood peaks using the generalized extreme value distribution, Water Resour. Res., 38, 41-1–41-12, https://doi.org/10.1029/2001wr000502, 2002.
Müller, M. F., Dralle, D. N., and Thompson, S. E.: Analytical model for flow duration curves in seasonally dry climates, Water Resour. Res., 50, 5510–5531, https://doi.org/10.1002/2014WR015301, 2014.
Müller, M. F., Roche, K. R., and Dralle, D. N.: Catchment processes can amplify the effect of increasing rainfall variability, Environ. Res. Lett., 16, 084032, https://doi.org/10.1088/1748-9326/ac153e, 2021.
Mushtaq, S., Miniussi, A., Merz, R., and Basso, S.: Reliable estimation of high floods: A method to select the most suitable ordinary distribution in the Metastatistical extreme value framework, Adv. Water Resour., 161, 104127, https://doi.org/10.1016/j.advwatres.2022.104127, 2022.
Mushtaq, S., Miniussi, A., Merz, R., Tarasova, L., Marra, F., and Basso, S.: Prediction of Extraordinarily High Floods Emerging From Heterogeneous Flow Generation Processes, Geophys. Res. Lett., 50, 1–10, https://doi.org/10.1029/2023GL105429, 2023.
Mutzner, R., Weijs, S. V., Tarolli, P., Calaf, M., Oldroyd, H. J., and Parlange, M. B.: Controls on the diurnal streamflow cycles in two subbasins of an alpine headwater catchment Raphael, Water Resour. Res., 51, 3403–3418, https://doi.org/10.1002/2014WR016581, 2015.
Németh, L., Hübnerová, Z., and Zempléni, A.: Trend detection in GEV models, arXiv [preprint], arXiv:1907.09435 [stat.ME], 1–13, https://doi.org/10.48550/arXiv.1907.09435, 2019.
Nerantzaki, S. D. and Papalexiou, S. M.: Tails of extremes: Advancing a graphical method and harnessing big data to assess precipitation extremes, Adv. Water Resour., 134, 103448, https://doi.org/10.1016/j.advwatres.2019.103448, 2019.
Pall, P., Aina, T., Stone, D. A., Stott, P. A., Nozawa, T., Hilberts, A. G. J., Lohmann, D., and Allen, M. R.: Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000, Nature, 470, 382–385, https://doi.org/10.1038/nature09762, 2011.
Pan, X., Rahman, A., Haddad, K., and Ouarda, T. B. M. J.: Peaks-over-threshold model in flood frequency analysis: a scoping review, Stoch. Environ. Res. Risk A., 36, 2419–2435, https://doi.org/10.1007/s00477-022-02174-6, 2022.
Papalexiou, S. M. and Koutsoyiannis, D.: Battle of extreme value distributions: A global survey on extreme daily rainfall, Water Resour. Res., 49, 187–201, https://doi.org/10.1029/2012WR012557, 2013.
Papalexiou, S. M., Koutsoyiannis, D., and Makropoulos, C.: How extreme is extreme? An assessment of daily rainfall distribution tails, Hydrol. Earth Syst. Sci., 17, 851–862, https://doi.org/10.5194/hess-17-851-2013, 2013.
Pauritsch, M., Birk, S., Wagner, T., Hergarten, S., and Winkler, G.: Analytical approximations of discharge recessions for steeplysloping aquifers in alpine catchments, Water Resour. Res., 51, 8729–8740, https://doi.org/10.1002/2015WR017749, 2015.
Porporato, A., Daly, E., and Rodriguez-Iturbe, I.: Soil water balance and ecosystem response to climate change, Am. Nat., 164, 625–632, https://doi.org/10.1086/424970, 2004.
Pumo, D., Viola, F., La Loggia, G., and Noto, L. V.: Annual flow duration curves assessment in ephemeral small basins, J. Hydrol., 519, 258–270, https://doi.org/10.1016/j.jhydrol.2014.07.024, 2014.
Rahman, A. S., Rahman, A., Zaman, M. A., Haddad, K., Ahsan, A., and Imteaz, M.: A study on selection of probability distributions for at-site flood frequency analysis in Australia, Nat. Hazards, 69, 1803–1813, https://doi.org/10.1007/s11069-013-0775-y, 2013.
Rajah, K., O'Leary, T., Turner, A., Petrakis, G., Leonard, M., and Westra, S.: Changes to the temporal distribution of daily precipitation, Geophys. Res. Lett., 41, 8887–8894, https://doi.org/10.1002/2014GL062156, 2014.
Rentschler, J., Salhab, M., and Jafino, B. A.: Flood exposure and poverty in 188 countries, Nat. Commun., 13, 3527, https://doi.org/10.1038/s41467-022-30727-4, 2022.
Resnick, S. I.: Heavy-Tail Phenomena: Probabilistic and Statistical Modeling, Springer US, New York, https://doi.org/10.1007/978-0-387-45024-7, 2007.
Rodriguez-Iturbe, I., Porporato, A., Rldolfi, L., Isham, V., and Cox, D. R.: Probabilistic modelling of water balance at a point: The role of climate, soil and vegetation, P. Roy. Soc. A, 455, 3789–3805, https://doi.org/10.1098/rspa.1999.0477, 1999.
Rogger, M., Pirkl, H., Viglione, A., Komma, J., Kohl, B., Kirnbauer, R., and Merz, R.: Step changes in the flood frequency curve: Process controls, Water Resour. Res., 48, 1–15, https://doi.org/10.1029/2011WR011187, 2012.
Roques, C., Rupp, D. E., and Selker, J. S.: Improved streamflow recession parameter estimation with attention to calculation of
Rossi, M. W., Whipple, K. X., and Vivoni, E. R.: Precipitation and evapotranspiration controls on daily runoff variability in the contiguous United States and Puerto Rico, J. Geophys. Res.-Earth, 121, 128–145, https://doi.org/10.1002/2015JF003446, 2016.
Saf, B.: Regional flood frequency analysis using L-moments for the West Mediterranean region of Turkey, Water Resour. Manage., 23, 531–551, https://doi.org/10.1007/s11269-008-9287-z, 2009.
Santos, A. C., Portela, M. M., Rinaldo, A., and Schaefli, B.: Analytical flow duration curves for summer streamflow in Switzerland, Hydrol. Earth Syst. Sci., 22, 2377–2389, https://doi.org/10.5194/hess-22-2377-2018, 2018.
Sartori, M. and Schiavo, S.: Connected we stand: A network perspective on trade and global food security, Food Policy, 57, 114–127, https://doi.org/10.1016/j.foodpol.2015.10.004, 2015.
Schaefli, B., Rinaldo, A., and Botter, G.: Analytic probability distributions for snow-dominated streamflow, Water Resour. Res., 49, 2701–2713, https://doi.org/10.1002/wrcr.20234, 2013.
Seckin, N., Haktanir, T., and Yurtal, R.: Flood frequency analysis of Turkey using L-moments method, Hydrol. Process., 25, 3499–3505, https://doi.org/10.1002/hyp.8077, 2011.
Sharma, A., Wasko, C., and Lettenmaier, D. P.: If Precipitation Extremes Are Increasing, Why Aren't Floods?, Water Resour. Res., 54, 8545–8551, https://doi.org/10.1029/2018WR023749, 2018.
Sharma, D., Kadu, A., and Biswal, B.: Universal recession constants and their potential to predict recession flow, J. Hydrol., 626, 130244, https://doi.org/10.1016/j.jhydrol.2023.130244, 2023.
Smith, J. A., Cox, A. A., Baeck, M. L., Yang, L., and Bates, P.: Strange Floods: The Upper Tail of Flood Peaks in the United States, Water Resour. Res., 54, 6510–6542, https://doi.org/10.1029/2018WR022539, 2018.
Spearman, C.: The proof and measurement of association between two things, Am. J. Psychol., 15, 72–101, https://doi.org/10.2307/1412159, 1904.
Székely, G. J., Rizzo, M. L., and Bakirov, N. K.: Measuring and testing dependence by correlation of distances, Ann. Stat., 35, 2769–2794, https://doi.org/10.1214/009053607000000505, 2007.
Tarasova, L., Basso, S., Zink, M., and Merz, R.: Exploring Controls on Rainfall-Runoff Events: 1. Time Series-Based Event Separation and Temporal Dynamics of Event Runoff Response in Germany, Water Resour. Res., 54, 7711–7732, https://doi.org/10.1029/2018WR022587, 2018.
Tarasova, L., Basso, S., and Merz, R.: Transformation of Generation Processes From Small Runoff Events to Large Floods, Geophys. Res. Lett., 47, e2020GL090547, https://doi.org/10.1029/2020GL090547, 2020.
Tashie, A., Pavelsky, T., and Band, L. E.: An Empirical Reevaluation of Streamflow Recession Analysis at the Continental Scale, Water Resour. Res., 56, 1–18, https://doi.org/10.1029/2019WR025448, 2020a.
Tashie, A., Pavelsky, T., and Emanuel, R. E.: Spatial and Temporal Patterns in Baseflow Recession in the Continental United States, Water Resour. Res., 56, 1–18, https://doi.org/10.1029/2019WR026425, 2020b.
Troch, P. A., Berne, A., Bogaart, P., Harman, C., Hilberts, A. G. J., Lyon, S. W., Paniconi, C., Pauwels, V. R. N., Rupp, D. E., Selker, J. S., Teuling, A. J., Uijlenhoet, R., and Verhoest, N. E. C.: The importance of hydraulic groundwater theory in catchment hydrology: The legacy of Wilfried Brutsaert and Jean-Yves Parlange, Water Resour. Res., 49, 5099–5116, https://doi.org/10.1002/wrcr.20407, 2013.
Vázquez, A., Oliveira, J. G., Dezsö, Z., Goh, K. I., Kondor, I., and Barabási, A. L.: Modeling bursts and heavy tails in human dynamics, Phys. Rev. E, 73, 1–19, https://doi.org/10.1103/PhysRevE.73.036127, 2006.
Villarini, G. and Smith, J. A.: Flood peak distributions for the eastern United States, Water Resour. Res., 46, 1–17, https://doi.org/10.1029/2009WR008395, 2010.
Villarini, G., Smith, J. A., Baeck, M. L., Marchok, T., and Vecchi, G. A.: Characterization of rainfall distribution and flooding associated with U.S. landfalling tropical cyclones: Analyses of Hurricanes Frances, Ivan, and Jeanne (2004), J. Geophys. Res. Atmos., 116, D23116, https://doi.org/10.1029/2011JD016175, 2011.
Vogel, R. M., McMahon, T. A., and Chiew, F. H. S.: Floodflow frequency model selection in Australia, J. Hydrol., 146, 421–449, https://doi.org/10.1016/0022-1694(93)90288-K, 1993.
Volpi, E., Fiori, A., Grimaldi, S., Lombardo, F., and Koutsoyiannis, D.: Save hydrological observations! Return period estimation without data decimation, J. Hydrol., 571, 782–792, https://doi.org/10.1016/j.jhydrol.2019.02.017, 2019.
Wang, H., Merz, R., Yang, S., Tarasova, L., and Basso, S.: Emergence of heavy tails in streamflow distributions: the role of spatial rainfall variability, Adv. Water Resour., 171, 104359, https://doi.org/10.1016/j.advwatres.2022.104359, 2022.
Wietzke, L. M., Merz, B., Gerlitz, L., Kreibich, H., Guse, B., Castellarin, A., and Vorogushyn, S.: Comparative analysis of scalar upper tail indicators, Hydrolog. Sci. J., 65, 1625–1639, https://doi.org/10.1080/02626667.2020.1769104, 2020.
Wilson, P. S. and Toumi, R.: A fundamental probability distribution for heavy rainfall, Geophys. Res. Lett., 32, 1–4, https://doi.org/10.1029/2005GL022465, 2005.
Wittenberg, H.: Baseflow recession and recharge as nonlinear storage processes, Hydrol. Process., 13, 715–726, 1999.
Yunus, R. M., Hasan, M. M., Razak, N. A., Zubairid, Y. Z., and Dunne, P. K.: Modelling daily rainfall with climatological predictors: Poisson-gamma generalized linear modelling approach, Int. J. Climatol., 37, 1391–1399, https://doi.org/10.1002/joc.4784, 2017.
Zhang, X. S., Amirthanathan, G. E., Bari, M. A., Laugesen, R. M., Shin, D., Kent, D. M., MacDonald, A. M., Turner, M. E., and Tuteja, N. K.: How streamflow has changed across Australia since the 1950s: Evidence from the network of hydrologic reference stations, Hydrol. Earth Syst. Sci., 20, 3947–3965, https://doi.org/10.5194/hess-20-3947-2016, 2016.
Short summary
Accurately assessing heavy-tailed flood behavior with limited data records is challenging and can lead to inaccurate hazard estimates. Our research introduces a new index that uses hydrograph recession to identify heavy-tailed flood behavior, compare severity, and produce reliable results with short data records. This index overcomes the limitations of current metrics, which lack physical meaning and require long records. It thus provides valuable insight into the flood hazard of river basins.
Accurately assessing heavy-tailed flood behavior with limited data records is challenging and...
