Articles | Volume 25, issue 10
https://doi.org/10.5194/hess-25-5603-2021
© 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-25-5603-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Oct 2021
Research article |
| 25 Oct 2021
| 25 Oct 2021
Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
Research Applications Laboratory, National Center for Atmospheric
Research, Boulder, CO, USA
Amanda G. Stone
Technical Service Center, Bureau of Reclamation, Lakewood, CO,
USA
Manabendra Saharia
Research Applications Laboratory, National Center for Atmospheric
Research, Boulder, CO, USA
now at: Department of Civil Engineering, Indian Institute
of Technology, New Delhi, India
Kathleen D. Holman
Technical Service Center, Bureau of Reclamation, Lakewood, CO,
USA
Nans Addor
Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK
Martyn P. Clark
Research Applications Laboratory, National Center for Atmospheric
Research, Boulder, CO, USA
now at: Centre for Hydrology, University of Saskatchewan, Canmore,
Alberta, Canada
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Naoki Mizukami, Oldrich Rakovec, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, Hoshin V. Gupta, and Rohini Kumar
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This paper proposes a method for snow rate (SR) estimation using observations collected by NASA dual-frequency dual-polarized (D3R) radar during the GPM Cold-season Precipitation Experiment (GCPEx). The new method utilizes dual-wavelength radar reflectivity ratio (DWR) and 2-D-video disdrometer (2DVD) measurements to improve SR estimation accuracy. It is validated by comparing the D3R radar-retrieved SR with accumulated SR directly measured by a Pluvio gauge for an entire GCPEx synoptic event.
Camila Alvarez-Garreton, Pablo A. Mendoza, Juan Pablo Boisier, Nans Addor, Mauricio Galleguillos, Mauricio Zambrano-Bigiarini, Antonio Lara, Cristóbal Puelma, Gonzalo Cortes, Rene Garreaud, James McPhee, and Alvaro Ayala
Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, https://doi.org/10.5194/hess-22-5817-2018, 2018
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CAMELS-CL provides a catchment dataset in Chile, including 516 catchment boundaries, hydro-meteorological time series, and 70 catchment attributes quantifying catchments' climatic, hydrological, topographic, geological, land cover and anthropic intervention features. By using CAMELS-CL, we characterise hydro-climatic regional variations, assess precipitation and potential evapotranspiration uncertainties, and analyse human intervention impacts on catchment response.
Lieke A. Melsen, Nans Addor, Naoki Mizukami, Andrew J. Newman, Paul J. J. F. Torfs, Martyn P. Clark, Remko Uijlenhoet, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 22, 1775–1791, https://doi.org/10.5194/hess-22-1775-2018, https://doi.org/10.5194/hess-22-1775-2018, 2018
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Long-term hydrological predictions are important for water management planning, but are also prone to uncertainty. This study investigates three sources of uncertainty for long-term hydrological predictions in the US: climate models, hydrological models, and hydrological model parameters. Mapping the results revealed spatial patterns in the three sources of uncertainty: different sources of uncertainty dominate in different regions.
Nans Addor, Andrew J. Newman, Naoki Mizukami, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, https://doi.org/10.5194/hess-21-5293-2017, 2017
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We introduce a data set describing the landscape of 671 catchments in the contiguous USA: we synthesized various data sources to characterize the topography, climate, streamflow, land cover, soil, and geology of each catchment. This extends the daily time series of meteorological forcing and discharge provided by an earlier study. The diversity of these catchments will help to improve our understanding and modeling of how the interplay between catchment attributes shapes hydrological processes.
Chengcheng Huang, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, and Xiaogu Zheng
Hydrol. Earth Syst. Sci., 21, 635–650, https://doi.org/10.5194/hess-21-635-2017, https://doi.org/10.5194/hess-21-635-2017, 2017
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This study examined the potential of snow water equivalent data assimilation to improve seasonal streamflow predictions. We examined aspects of the data assimilation system over basins with varying climates across the western US. We found that varying how the data assimilation system is implemented impacts forecast performance, and basins with good initial calibrations see less benefit. This implies that basin-specific configurations and benefits should be expected given this modeling system.
A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan
Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, https://doi.org/10.5194/hess-19-209-2015, 2015
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The focus of this paper is to (1) present a community data set of daily forcing and hydrologic response data for 671 unimpaired basins across the contiguous United States that spans a very wide range of hydroclimatic conditions, and (2) provide a calibrated model performance benchmark using a common conceptual snow and hydrologic modeling system. This benchmark provides a reference level of model performance across a very large basin sample and highlights regional variations in performance.
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
Uncertainty estimation of regionalised depth–duration–frequency curves in Germany
FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Characteristics and process controls of statistical flood moments in Europe – a data-based analysis
Objective functions for information-theoretical monitoring network design: what is “optimal”?
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Numerical investigation on the power of parametric and nonparametric tests for trend detection in annual maximum series
Spatially dependent flood probabilities to support the design of civil infrastructure systems
Technical note: Stochastic simulation of streamflow time series using phase randomization
Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 1: theoretical development
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 2: numerical application
Characterizing the spatial variations and correlations of large rainstorms for landslide study
Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic
Dealing with uncertainty in the probability of overtopping of a flood mitigation dam
Flood frequency analysis of historical flood data under stationary and non-stationary modelling
Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland
Towards modelling flood protection investment as a coupled human and natural system
A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation
Examination of homogeneity of selected Irish pooling groups
Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models
Design flood hydrographs from the relationship between flood peak and volume
Introducing empirical and probabilistic regional envelope curves into a mixed bounded distribution function
HESS Opinions "A random walk on water"
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 2075–2097, https://doi.org/10.5194/hess-27-2075-2023, https://doi.org/10.5194/hess-27-2075-2023, 2023
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Design rainfall volumes at different duration and frequencies are necessary for the planning of water-related systems and facilities. As the procedure for deriving these values is subjected to different sources of uncertainty, here we explore different methods to estimate how precise these values are for different duration, locations and frequencies in Germany. Combining local and spatial simulations, we estimate tolerance ranges from approx. 10–60% for design rainfall volumes in Germany.
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
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A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
David Lun, Alberto Viglione, Miriam Bertola, Jürgen Komma, Juraj Parajka, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 5535–5560, https://doi.org/10.5194/hess-25-5535-2021, https://doi.org/10.5194/hess-25-5535-2021, 2021
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We investigate statistical properties of observed flood series on a European scale. There are pronounced regional patterns, for instance: regions with strong Atlantic influence show less year-to-year variability in the magnitude of observed floods when compared with more arid regions of Europe. The hydrological controls on the patterns are quantified and discussed. On the European scale, climate seems to be the dominant driver for the observed patterns.
Hossein Foroozand and Steven V. Weijs
Hydrol. Earth Syst. Sci., 25, 831–850, https://doi.org/10.5194/hess-25-831-2021, https://doi.org/10.5194/hess-25-831-2021, 2021
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In monitoring network design, we have to decide what to measure, where to measure, and when to measure. In this paper, we focus on the question of where to measure. Past literature has used the concept of information to choose a selection of locations that provide maximally informative data. In this paper, we look in detail at the proper mathematical formulation of the information concept as an objective. We argue that previous proposals for this formulation have been needlessly complicated.
Manuela I. Brunner and Eric Gilleland
Hydrol. Earth Syst. Sci., 24, 3967–3982, https://doi.org/10.5194/hess-24-3967-2020, https://doi.org/10.5194/hess-24-3967-2020, 2020
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Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency domain instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.
Vincenzo Totaro, Andrea Gioia, and Vito Iacobellis
Hydrol. Earth Syst. Sci., 24, 473–488, https://doi.org/10.5194/hess-24-473-2020, https://doi.org/10.5194/hess-24-473-2020, 2020
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We highlight the need for power evaluation in the application of null hypothesis significance tests for trend detection in extreme event analysis. In a wide range of conditions, depending on the underlying distribution of data, the test power may reach unacceptably low values. We propose the use of a parametric approach, based on model selection criteria, that allows one to choose the null hypothesis, to select the level of significance, and to check the test power using Monte Carlo experiments.
Phuong Dong Le, Michael Leonard, and Seth Westra
Hydrol. Earth Syst. Sci., 23, 4851–4867, https://doi.org/10.5194/hess-23-4851-2019, https://doi.org/10.5194/hess-23-4851-2019, 2019
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While conventional approaches focus on flood designs at individual locations, there are many situations requiring an understanding of spatial dependence of floods at multiple locations. This research describes a new framework for analyzing flood characteristics across civil infrastructure systems, including conditional and joint probabilities of floods. This work leads to a new flood estimation paradigm, which focuses on the risk of the entire system rather than each system element in isolation.
Manuela I. Brunner, András Bárdossy, and Reinhard Furrer
Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019, https://doi.org/10.5194/hess-23-3175-2019, 2019
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This study proposes a procedure for the generation of daily discharge data which considers temporal dependence both within short timescales and across different years. The simulation procedure can be applied to individual and multiple sites. It can be used for various applications such as the design of hydropower reservoirs, the assessment of flood risk or the assessment of drought persistence, and the estimation of the risk of multi-year droughts.
Cong Jiang, Lihua Xiong, Lei Yan, Jianfan Dong, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 23, 1683–1704, https://doi.org/10.5194/hess-23-1683-2019, https://doi.org/10.5194/hess-23-1683-2019, 2019
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We present the methods addressing the multivariate hydrologic design applied to the engineering practice under nonstationary conditions. A dynamic C-vine copula allowing for both time-varying marginal distributions and a time-varying dependence structure is developed to capture the nonstationarities of multivariate flood distribution. Then, the multivariate hydrologic design under nonstationary conditions is estimated through specifying the design criterion by average annual reliability.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 1993–2005, https://doi.org/10.5194/hess-22-1993-2018, https://doi.org/10.5194/hess-22-1993-2018, 2018
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A new method is proposed to solve the stochastic unsteady open-channel flow system in only one single simulation, as opposed to the many simulations usually done in the popular Monte Carlo approach. The derivation of this new method gave a deterministic and linear Fokker–Planck equation whose solution provided a powerful and effective approach for quantifying the ensemble behavior and variability of such a stochastic system, regardless of the number of parameters causing its uncertainty.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 2007–2021, https://doi.org/10.5194/hess-22-2007-2018, https://doi.org/10.5194/hess-22-2007-2018, 2018
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A newly proposed method is applied to solve a stochastic unsteady open-channel flow system (with an uncertain roughness coefficient) in only one simulation. After comparing its results to those of the Monte Carlo simulations, the new method was found to adequately predict the temporal and spatial evolution of the probability density of the flow variables of the system. This revealed the effectiveness, strength, and time efficiency of this new method as compared to other popular approaches.
Liang Gao, Limin Zhang, and Mengqian Lu
Hydrol. Earth Syst. Sci., 21, 4573–4589, https://doi.org/10.5194/hess-21-4573-2017, https://doi.org/10.5194/hess-21-4573-2017, 2017
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Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in space, which causes more landslides in the area of intense rainfall. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a trend surface and a random field of residuals. The scales of fluctuation of the residuals are found between 5 km and 30 km.
Elena Shevnina, Ekaterina Kourzeneva, Viktor Kovalenko, and Timo Vihma
Hydrol. Earth Syst. Sci., 21, 2559–2578, https://doi.org/10.5194/hess-21-2559-2017, https://doi.org/10.5194/hess-21-2559-2017, 2017
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This paper presents the probabilistic approach to evaluate design floods in a changing climate, adapted in this case to the northern territories. For the Russian Arctic, the regions are delineated, where it is suggested to correct engineering hydrological calculations to account for climate change. An example of the calculation of a maximal discharge of 1 % exceedance probability for the Nadym River at Nadym is provided.
Eleni Maria Michailidi and Baldassare Bacchi
Hydrol. Earth Syst. Sci., 21, 2497–2507, https://doi.org/10.5194/hess-21-2497-2017, https://doi.org/10.5194/hess-21-2497-2017, 2017
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In this research, we explored how the sampling uncertainty of flood variables (flood peak, volume, etc.) can reflect on a structural variable, which in our case was the maximum water level (MWL) of a reservoir controlled by a dam. Next, we incorporated additional information from different sources for a better estimation of the uncertainty in the probability of exceedance of the MWL. Results showed the importance of providing confidence intervals in the risk assessment of a structure.
M. J. Machado, B. A. Botero, J. López, F. Francés, A. Díez-Herrero, and G. Benito
Hydrol. Earth Syst. Sci., 19, 2561–2576, https://doi.org/10.5194/hess-19-2561-2015, https://doi.org/10.5194/hess-19-2561-2015, 2015
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A flood frequency analysis using a 400-year historical flood record was carried out using a stationary model (based on maximum likelihood estimators) and a non-stationary model that incorporates external covariates (climatic and environmental). The stationary model was successful in providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.
L. Gaál, P. Molnar, and J. Szolgay
Hydrol. Earth Syst. Sci., 18, 1561–1573, https://doi.org/10.5194/hess-18-1561-2014, https://doi.org/10.5194/hess-18-1561-2014, 2014
P. E. O'Connell and G. O'Donnell
Hydrol. Earth Syst. Sci., 18, 155–171, https://doi.org/10.5194/hess-18-155-2014, https://doi.org/10.5194/hess-18-155-2014, 2014
A. I. Requena, L. Mediero, and L. Garrote
Hydrol. Earth Syst. Sci., 17, 3023–3038, https://doi.org/10.5194/hess-17-3023-2013, https://doi.org/10.5194/hess-17-3023-2013, 2013
S. Das and C. Cunnane
Hydrol. Earth Syst. Sci., 15, 819–830, https://doi.org/10.5194/hess-15-819-2011, https://doi.org/10.5194/hess-15-819-2011, 2011
B. A. Botero and F. Francés
Hydrol. Earth Syst. Sci., 14, 2617–2628, https://doi.org/10.5194/hess-14-2617-2010, https://doi.org/10.5194/hess-14-2617-2010, 2010
L. Mediero, A. Jiménez-Álvarez, and L. Garrote
Hydrol. Earth Syst. Sci., 14, 2495–2505, https://doi.org/10.5194/hess-14-2495-2010, https://doi.org/10.5194/hess-14-2495-2010, 2010
B. Guse, Th. Hofherr, and B. Merz
Hydrol. Earth Syst. Sci., 14, 2465–2478, https://doi.org/10.5194/hess-14-2465-2010, https://doi.org/10.5194/hess-14-2465-2010, 2010
D. Koutsoyiannis
Hydrol. Earth Syst. Sci., 14, 585–601, https://doi.org/10.5194/hess-14-585-2010, https://doi.org/10.5194/hess-14-585-2010, 2010
Cited articles
Addor, N., Rossler, O., Koplin, N., Huss, M., Weingartner, R., and Seibert, J.:
Robust changes and sources of uncertainty in the projected hydrological
regimes of Swiss catchments, Water Resour. Res., 50, 7541–7562,
https://doi.org/10.1002/2014WR015549, 2014.
Anderson, E. A.: Calibration of conceptual hydrologic models for use in
river forecasting, Office of Hydrologic Development, US National Weather
Service, Silver Spring, MD, 2002.
Arnaud, P., Cantet P., and Odry, J.: Uncertainties of flood frequency estimation
approaches based on continuous simulation using data resampling, J. Hydrol., 554, 360–369, 2017.
Bell, F. C.: The areal reduction factors in rainfall-frequency estimation,
Natural Environmental Research Council, Report 35, Institute of Hydrology,
Wallingford, United Kingdom, 1976.
Bennett, T. H.: Development and application of a continuous soil moisture
accounting algorithm for the Hydrologic Engineering Center Hydrologic
Modeling System (HEC-HMS), University of California, Davis, 1998.
Blazkova, S. and Beven, K.: A limits of acceptability approach to model
evaluation and uncertainty estimation in flood frequency estimation by
continuous simulation: Skalka catchment, Czech Republic, Water Resour.
Res., 45, W00B16,
https://doi.org/10.1029/2007WR006726, 2009.
Bosshard, T., Carambia, M., Goergen, K., Kotlarski, S., Krahe, P., Zappa,
M., and Schär, C.: Quantifying uncertainty sources in an ensemble of
hydrological climate-impact projections, Water. Resour. Res., 49, 1523–1536,
https://doi.org/10.1029/2011WR011533, 2013.
Boughton, W. and Droop, O.: Continuous simulation for design flood estimation
– a review, Environ. Modell. Softw., 18, 309–318, 2003.
Breuer, L., Gosling, S. N., Yang, T., Hoffmann, P., Hattermann, F. F.,
Krysnaova, V., Wada, Y., Su, B., Masaki, Y., Müller, C., Daggupati, P.,
Stacke, T., Fekete, B., Motovilov, Y., Vetter, T., Flörke, F., Liersch,
S., Donnelly, C., and Samaniego, L.: Sources of uncertainty in hydrological
climate impact assessment: a cross-scale study, Environ. Res. Lett., 13, 015006, https://doi.org/10.1088/1748-9326/aa9938, 2017.
Calver, A., Lamb, R., and Morris, S. E.: River flood frequency estimation
using continuous runoff modelling, Proc. Inst. Civ. Eng. Water Marit.
Energy, 136, 225–234, 1999.
Chegwidden, O. S., Nijssen, B., Rupp, D. E., Arnold, J. R., Clark, M. P.,
Hamman, J. J., Kao, S. C., Mao, Y., Mizukami, N., Mote, P. W., Pan, M.,
Pytlak, E., and Xiao, M.: How Do Modeling Decisions Affect the Spread Among
Hydrologic Climate Change Projections? Exploring a Large Ensemble of
Simulations Across a Diversity of Hydroclimates, Earth's Futur., 7,
623–637, https://doi.org/10.1029/2018EF001047, 2019.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta,
H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural
Errors (FUSE): A modular framework to diagnose differences between
hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008.
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J.,
Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., Shook, K., and
Papalexiou, S.: The abuse of popular performance metrics in hydrologic
modeling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021.
Duan, Q. Y., Gupta, V. K., and Sorooshian, S.: Shuffled complex evolution
approach for effective and efficient global minimization,
J. Optimiz. Theory App., 76, 501–521, 1993.
England Jr., J. E., Godaire, J. E., Klinger, R. E., Bauer, T. R., and Julien, P.
Y.: Paleohydrologic bounds and extreme flood frequency of the Upper Arkansas
River, Colorado, USA, Geomorphology, 124, 1–16, 2010.
England Jr., J. E., Julien, P. Y., and Velleux, M. L.: Physically-based extreme
flood frequency with stochastic storm
transposition and paleoflood data on large watersheds, J. Hydrol., 510,
228–245, 2014.
Franchini, M., Hashemi, A. M., and O’Connell, P. E.: Climatic and basin factors affecting the flood frequency curve: PART II – A full sensitivity analysis based on the continuous simulation approach combined with a factorial experimental design, Hydrol. Earth Syst. Sci., 4, 483–498, https://doi.org/10.5194/hess-4-483-2000, 2000.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol, 377, 80–91,
2009.
Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., and Ye, M.: Towards a
comprehensive assessment of model structural adequacy, Water Resour. Res., 48,
W08301, https://doi.org/10.1029/2011WR011044, 2012.
Hansen, E. M., Schreiner, L. C., and Miller, J. F.: Application of Probable
Maximum Precipitation Estimates, United States East of the 105th Meridian,
Hydrometeorological Report No. 52, National Weather Service, National
Oceanic and Atmospheric Administration, U.S. Department of Commerce, Silver
Spring, MD, 168, 1982.
Hansen, E. M., Fenn, D. D., Schreiner, L. C., Stodt, R. W., and Miller, J. F.:
Probable Maximum Precipitation Estimates United States between the
Continental Divide and the 103rd Meridian, Hydrometeorological Report No.
55A, National Weather Service, National Oceanic and Atmospheric
Administration, U.S. Department of Commerce, Silver Spring, MD, 242,
1988.
Hansen, E. M., Fenn, D. D., Corrigan, P., Vogel, J. L., Schreiner, L. C., and
Stodt, R. W.: Probable Maximum Precipitation-Pacific Northwest States,
Columbia River (including portions of Canada), Snake River and Pacific
Coastal Drainages, Hydrometeorological Report No. 57, National Weather
Service, National Oceanic and Atmospheric Administration, U.S. Department of
Commerce, Silver Spring, MD, 338, 1994.
Hashemi, A. M., Franchini, M., and O’Connell, P. E.: Climatic and basin factors affecting the flood frequency curve: PART I – A simple sensitivity analysis based on the continuous simulation approach, Hydrol. Earth Syst. Sci., 4, 463–482, https://doi.org/10.5194/hess-4-463-2000, 2000.
Hawkins, E. and Sutton, R.: The Potential to Narrow Uncertainty in Regional
Climate Predictions, Bull. Am. Meteorol. Soc., 90, 1095–1107,
https://doi.org/10.1175/2009BAMS2607.1, 2009.
Henn, B., Clark, M. P., Kavetski, D., and Lundquist, J. D.: Estimating mountain
basin-mean precipitation from streamflow using Bayesian inference, Water Resour. Res., 51, 8012–8033, 2015.
Hosking, J. R. M. and Wallis, J. R.: Paleoflood hydrology and flood frequency
analysis, Water Resour. Res., 22, 543–550, 1986.
Hosking, J. R. M. and Wallis, J. R.: Regional Frequency Analysis, Cambridge
University Press, Cambridge, UK, 244 pp., https://doi.org/10.1017/CBO9780511529443, ISBN 9780511529443, 1997.
Hu, L., Nikolopoulos, E. I., Marra, F., and Anagnostou, E. N.: Sensitivity of
flood frequency analysis to data record, statistical model, and parameter
estimation methods: An evaluation over the contiguous United States, J. Flood Risk Manage., 13, e12580, https://doi.org/10.1111/jfr3.12580, 2020.
Ivancic, T. J. and Shaw, S. B.: Examining why trends in very heavy precipitation
should not be mistaken for trends in very high river discharge,
Climatic Change,
133, 681–693, https://doi.org/10.1007/s10584-015-1476-1, 2015.
Jakeman, A. J. and Hornberger, G. M.: How much complexity is warranted in a
rainfall-runoff model?, Water. Resour. Res., 29, 2637–2649, 1993.
Klemes, V.: Tall tales about tails of hydrological distributions. I,
J. Hydrol. Eng., 5, 227–231, 2000.
Knoben, W. J. M., Freer, J. E., Fowler, K. J. A., Peel, M. C., and Woods, R. A.: Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations, Geosci. Model Dev., 12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019, 2019.
Kuczera, G., Lambert, M. F., Heneker, T. M., Jennings, S., Frost, A., and
Coombes, P.: Joint probability and design storms at the Crossroads,
Australian Journal of Water Resources, 10, 63–79, 2006.
Kidson, R. and Richards, K. S.: Flood frequency analysis: assumptions and
alternatives, Prog. Phys. Geog., 29, 392–410, 2005.
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologicallybased model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99, 14415–14428, 1994.
Markstrom, S. L., Hay, L. E., and Clark, M. P.: Towards simplification of hydrologic modeling: identification of dominant processes, Hydrol. Earth Syst. Sci., 20, 4655–4671, https://doi.org/10.5194/hess-20-4655-2016, 2016.
Mendoza, P. A., Clark, M. P., Mizukami, N., Newman, A. J., Barlage, M.,
Gutmann, E. D., Rasmussen, R. M., Rajagopalan, B., Brekke, L. D., and Arnold, J.
R.: Effects of hydrologic model choice and calibration on the portrayal of
climate change impacts, J. Hydrometeorol, 16, 762–780, 2015.
Merz, B. and Thieken, A. H.: Separating natural and epistemic uncertainty in
flood frequency analysis, J. Hydrol., 309, 114–132, 2005.
Merz, B. and Thieken, A. H.: Flood risk curves and uncertainty bounds, Nat. Hazards,
51, 437–458, 2009.
Mizukami, N., Rakovec, O., Newman, A. J., Clark, M. P., Wood, A. W., Gupta, H. V., and Kumar, R.: On the choice of calibration metrics for “high-flow” estimation using hydrologic models, Hydrol. Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019, 2019.
Murphy, A. H.: Skill scores based on the mean square error and their
relationships to the correlation coefficient, Mon. Weather Rev., 116, 2417–2424,
1988.
National Research Council: Estimating Probabilities of Extreme Floods:
Methods and Recommended Research, National Academy Press, Washington, D.C., 160 pp., https://doi.org/10.17226/18935, 1988.
Nathan, R., Weinmann, E., and Hill, P.: Use of Monte Carlo simulation to
estimate the expected probability of large to extreme floods, The Institute
of Engineers Australia, 28th International Hydrology and Water
Resources Symposium, Wollongong, NSW, 10–14 November, 2003.
Newman, A. J., Clark, M. P., Craig, J., Nijssen, B., Wood, A., Gutmann, E.,
Mizukami, N., Brekke, L. D., and Arnold, J. R.: Gridded ensemble precipitation
and temperature estimates for the contiguous United States, J. Hydrometeorol., 16, 2481–2500, 2015.
Newman, A. J., Mizukami, N., Clark, M. P., Wood, A. W., Nijssen, B., and
Nearing, G.: Benchmarking of a physically based hydrologic model, J. Hydrometeorol.,
18, 2215–2225, 2017.
Newman, A. J., Stone, A. G., Saharia, M., and Holman, K. D.: Data and Report from S&T Project 1794: Identifying Sources of Uncertainty in Flood Frequency Analyses, U.S. Dept. of the Interior, Bureau of Reclamation, Reclamation Information Sharing Environment (RISE) [data set], available at: https://data.usbr.gov/catalog/4421 (last access: 13 October 2021), 2020.
Newman, A. J., Clark, M. P., Addor, N., Kavetski, D., and Henn, B.: Framework for Understanding Structural Errors (FUSE) with user specified initial states, Zenodo [code], https://doi.org/10.5281/zenodo.5567163, 2021.
Packman, J. and Kidd, C.: A logical approach to the design storm concept,
Water Resour. Res., 16, 994–1000, 1980.
Paquet, E., Garavaglia, F., Garçon, R., and Gailhard, J.: The SCHADEX
method: A semi-continuous rainfall–runoff simulation for extreme flood
estimation, J. Hydrol., 495, 23–37, 2013.
Pathiraja, S., Westra, S., and Sharma, A.: Why continuous simulation? The role
of antecedent moisture in design flood
estimation, Water Resour. Res., 48, W06534,
https://doi.org/10.1029/2011WR010997, 2012.
Peleg, N., Blumensaat, F., Molnar, P., Fatichi, S., and Burlando, P.: Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling, Hydrol. Earth Syst. Sci., 21, 1559–1572, https://doi.org/10.5194/hess-21-1559-2017, 2017.
Rahman, A., Weinmann, P. E., Hoang, T. M. T., and Laurenson, E. M.: Monte Carlo
simulation of flood frequency curves from rainfall, J. Hydrol., 256,
196–210, https://doi.org/10.1016/S0022-1694(01)00533-9, 2002.
Reclamation (Bureau of Reclamation): Altus Dam Hydrologic Hazard and
Reservoir Routing for Corrective Action Study. W.C. Austin Project, OK,
Billings, MT. U.S. Dept. of the Interior, Bureau of Reclamation Great Plains
Region, 230 pp., 2012.
Reclamation (Bureau of Reclamation): Island Park Dam Meteorology for Application in Hydrologic Hazard Analysis, Minidoka Project, ID, Boise, ID. U.S. Dept. of the Interior, Bureau of Reclamation Pacific Northwest Region, 88 pp., 2016a.
Reclamation (Bureau of Reclamation): Island Park Dam Hydrologic Hazard for Issue Evaluation, Minidoka Project, ID, Boise, ID, U.S. Dept. of the Interior, Bureau of Reclamation Pacific Northwest Region, 74 pp., 2016b.
Reclamation (Bureau of Reclamation): Unity Dam Hydrologic Hazard for Issue
Evaluation. Burnt River Project, Oregon, Boise, ID, U.S. Dept. of the
Interior, Bureau of Reclamation Pacific Northwest Region, Technical
Memorandum 8250-2018-002, 284 pp., 2018.
Schaefer, M. G. and Barker, B. L.: Stochastic Event Flood Model (SEFM), in: Mathematical Models of Small Watershed Hydrology
and Applications, edited by: Singh, V. J., 950 pp., Highlands Ranch, Colorado, USA, ISBN 9781887201353, 2002.
Schreiner, L. C. and Riedel, J. T.: Probable Maximum Precipitation Estimates,
United States East of the 105th Meridian, Hydrometeorological Report No. 51,
National Weather Service, National Oceanic and Atmospheric Administration,
U.S. Department of Commerce, Silver Spring, MD, 87 pp., 1978.
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.
Small, D., Islam, S., and Vogel, R. M.: Trends in precipitation and streamflow
in the eastern US: Paradox or perception?, Geophys. Res. Lett., 33, L03403, https://doi.org/10.1029/2005GL024995, 2006.
Stedinger, J. R., Vogel, R. M., and Foufoula-Georgiou, E.: Frequency analysis
of extreme events, in: Handbook of Hydrology, edited by: Maidment, D., 1st edition, 1424 pp.,
McGraw-Hill, New York, ISBN 13 978 0070397323, 1993.
13 978 0070397323
Swain, R. E., England, J. F., Bullard, K. L., Raff, D. A., and United
States.: Guidelines for evaluating hydrologic hazards, Denver, CO, U.S. Dept. of the Interior, Bureau of Reclamation, 91
pp., 2006.
Tijms, H. C.: A first course in stochastic models, John Wiley and Sons, 448 pp., West Sussex, England, ISBN 13 978 0471498803, 2003.
13 978 0471498803
Wright, D. B., Smith, J. A., and Baeck, M. L.: Flood frequency analysis using
radar rainfall fields and stochastic storm transposition, Water Resour. Res.,
50, 1592–1615, https://doi.org/10.1002/2013WR014224, 2014.
Wright, D. B., Yu, G., and England, J. F.: Six decades of rainfall and flood
frequency analysis using stochastic storm transposition: Review, progress,
and prospects, J. Hydrol., 585, 124816, https://doi.org/10.1016/j.jhydrol.2020.124816,
2020.
Yu, G., Wright, D. B., Zhu, Z., Smith, C., and Holman, K. D.: Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality, Hydrol. Earth Syst. Sci., 23, 2225–2243, https://doi.org/10.5194/hess-23-2225-2019, 2019.
Zhu, Z., Wright, D. B., and Yu, G.: The impact of rainfall space-time structure
in flood frequency analysis, Water Resour. Res., 54, 8983–8998,
https://doi.org/10.1029/2018WR023550, 2018.
Short summary
This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
This study assesses methods that estimate flood return periods to identify when we would obtain...
