Articles | Volume 26, issue 20
https://doi.org/10.5194/hess-26-5241-2022
© Author(s) 2022. 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-26-5241-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Oct 2022
Research article |
| 20 Oct 2022
| 20 Oct 2022
A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance
Department of Civil and Environmental Engineering, University of
Wisconsin-Madison, Madison, 53706, USA
Daniel B. Wright
Department of Civil and Environmental Engineering, University of
Wisconsin-Madison, Madison, 53706, USA
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Zhengzheng Zhou, James A. Smith, Mary Lynn Baeck, Daniel B. Wright, Brianne K. Smith, and Shuguang Liu
Hydrol. Earth Syst. Sci., 25, 4701–4717, https://doi.org/10.5194/hess-25-4701-2021, https://doi.org/10.5194/hess-25-4701-2021, 2021
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The role of rainfall space–time structure in flood response is an important research issue in urban hydrology. This study contributes to this understanding in small urban watersheds. Combining stochastically based rainfall scenarios with a hydrological model, the results show the complexities of flood response for various return periods, implying the common assumptions of spatially uniform rainfall in urban flood frequency are problematic, even for relatively small basin scales.
Guo Yu, Daniel B. Wright, Zhihua Zhu, Cassia Smith, and Kathleen D. Holman
Hydrol. Earth Syst. Sci., 23, 2225–2243, https://doi.org/10.5194/hess-23-2225-2019, https://doi.org/10.5194/hess-23-2225-2019, 2019
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The relationship between flood severity and probability is a key component of flood risk management, and depends on factors including rainfall, soil wetness, and watershed properties. In this study, we combine radar rainfall data and flood simulations to better understand how these factors
shapeflood frequency. We apply our method to an agricultural watershed in the Midwestern US where the flood properties are changing. Conventional methods will fail to account for these changes.
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Stochastic approaches
Scientific logic and spatio-temporal dependence in analyzing extreme-precipitation frequency: negligible or neglected?
Assessing downscaling techniques for frequency analysis, total precipitation and rainy day estimation in CMIP6 simulations over hydrological years
Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models: case studies from Germany and South Korea
Synoptic weather patterns conducive to compound extreme rainfall–wave events in the NW Mediterranean
Exploring the joint probability of precipitation and soil moisture over Europe using copulas
Water cycle changes in Czechia: a multi-source water budget perspective
A statistical–dynamical approach for probabilistic prediction of sub-seasonal precipitation anomalies over 17 hydroclimatic regions in China
A gridded multi-site precipitation generator for complex terrain: an evaluation in the Austrian Alps
Technical note: A stochastic framework for identification and evaluation of flash drought
Stochastic simulation of reference rainfall scenarios for hydrological applications using a universal multi-fractal approach
Atmospheric conditions favouring extreme precipitation and flash floods in temperate regions of Europe
Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective
Stochastic daily rainfall generation on tropical islands with complex topography
Modeling seasonal variations of extreme rainfall on different timescales in Germany
Compound flood potential from storm surge and heavy precipitation in coastal China: dependence, drivers, and impacts
Influence of ENSO and tropical Atlantic climate variability on flood characteristics in the Amazon basin
Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process
Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought
Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks
A standardized index for assessing sub-monthly compound dry and hot conditions with application in China
Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues
A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields
Modelling rainfall with a Bartlett–Lewis process: new developments
Nonstationary stochastic rain type generation: accounting for climate drivers
Conditional simulation of surface rainfall fields using modified phase annealing
Climate influences on flood probabilities across Europe
Flood-related extreme precipitation in southwestern Germany: development of a two-dimensional stochastic precipitation model
A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales
Mapping rainfall hazard based on rain gauge data: an objective cross-validation framework for model selection
On the skill of raw and post-processed ensemble seasonal meteorological forecasts in Denmark
Estimating radar precipitation in cold climates: the role of air temperature within a non-parametric framework
Dealing with non-stationarity in sub-daily stochastic rainfall models
Rainfall disaggregation for hydrological modeling: is there a need for spatial consistence?
Design water demand of irrigation for a large region using a high-dimensional Gaussian copula
Modeling the changes in water balance components of the highly irrigated western part of Bangladesh
A classification algorithm for selective dynamical downscaling of precipitation extremes
Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency
Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment
A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula
Censored rainfall modelling for estimation of fine-scale extremes
An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France
Precipitation extremes on multiple timescales – Bartlett–Lewis rectangular pulse model and intensity–duration–frequency curves
Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?
A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform
Regionalizing nonparametric models of precipitation amounts on different temporal scales
A combined statistical bias correction and stochastic downscaling method for precipitation
Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin
Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme
Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies
Nonstationarity of low flows and their timing in the eastern United States
Francesco Serinaldi
Hydrol. Earth Syst. Sci., 28, 3191–3218, https://doi.org/10.5194/hess-28-3191-2024, https://doi.org/10.5194/hess-28-3191-2024, 2024
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Neglecting the scientific rationale behind statistical inference leads to logical fallacies and misinterpretations. This study contrasts a model-based approach, rooted in statistical logic, with a test-based approach, widely used in hydro-climatology but problematic. It reveals the impact of dependence in extreme-precipitation analysis and shows that trends in the frequency of extreme events over the past century in various geographic regions can be consistent with the stationary assumption.
David A. Jimenez, Andrea Menapace, Ariele Zanfei, Eber José de Andrade Pinto, and Bruno Brentan
Hydrol. Earth Syst. Sci., 28, 1981–1997, https://doi.org/10.5194/hess-28-1981-2024, https://doi.org/10.5194/hess-28-1981-2024, 2024
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Most studies that aim to identify the impacts of climate change employ general circulation models. However, due to their low spatial resolution, it is necessary to apply downscaling techniques. This work assesses the performance of three methodologies in developing frequency analyses and estimating the number of rainy days and total precipitation per year. Quantile mapping and regression trees excelled in frequency analysis, and the delta method best estimated multiyear total precipitation.
Ivan Vorobevskii, Jeongha Park, Dongkyun Kim, Klemens Barfus, and Rico Kronenberg
Hydrol. Earth Syst. Sci., 28, 391–416, https://doi.org/10.5194/hess-28-391-2024, https://doi.org/10.5194/hess-28-391-2024, 2024
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High-resolution precipitation data are often a “must” as input for hydrological and hydraulic models (i.e. urban drainage modelling). However, station or climate projection data usually do not provide the required (e.g. sub-hourly) resolution. In the work, we present two new statistical models of different types to disaggregate precipitation from a daily to a 10 min scale. Both models were validated using radar data and then applied to climate models for 10 stations in Germany and South Korea.
Marc Sanuy, Juan C. Peña, Sotiris Assimenidis, and José A. Jiménez
Hydrol. Earth Syst. Sci., 28, 283–302, https://doi.org/10.5194/hess-28-283-2024, https://doi.org/10.5194/hess-28-283-2024, 2024
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The work presents the first classification of weather types associated to compound events of extreme rainfall and coastal storms. These are found to be characterized by upper-level lows and troughs in conjunction with Mediterranean cyclones, resulting in severe to extreme coastal storms combined with convective systems. We used objective classification methods coupled with a Bayesian Network, testing different variables, domains and number of weather types.
Carmelo Cammalleri, Carlo De Michele, and Andrea Toreti
Hydrol. Earth Syst. Sci., 28, 103–115, https://doi.org/10.5194/hess-28-103-2024, https://doi.org/10.5194/hess-28-103-2024, 2024
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Precipitation and soil moisture have the potential to be jointly used for the modeling of drought conditions. In this research, we analysed how their statistical inter-relationship varies across Europe. We found some clear spatial patterns, especially in the so-called tail dependence (which measures the strength of the relationship for the extreme values). The results suggest that the tail dependence needs to be accounted for to correctly assess the value of joint modeling for drought.
Mijael Rodrigo Vargas Godoy, Yannis Markonis, Oldrich Rakovec, Michal Jenicek, Riya Dutta, Rajani Kumar Pradhan, Zuzana Bešťáková, Jan Kyselý, Roman Juras, Simon Michael Papalexiou, and Martin Hanel
Hydrol. Earth Syst. Sci., 28, 1–19, https://doi.org/10.5194/hess-28-1-2024, https://doi.org/10.5194/hess-28-1-2024, 2024
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The study introduces a novel benchmarking method based on the water cycle budget for hydroclimate data fusion. Using this method and multiple state-of-the-art datasets to assess the spatiotemporal patterns of water cycle changes in Czechia, we found that differences in water availability distribution are dominated by evapotranspiration. Furthermore, while the most significant temporal changes in Czechia occur during spring, the median spatial patterns stem from summer changes in the water cycle.
Yuan Li, Kangning Xü, Zhiyong Wu, Zhiwei Zhu, and Quan J. Wang
Hydrol. Earth Syst. Sci., 27, 4187–4203, https://doi.org/10.5194/hess-27-4187-2023, https://doi.org/10.5194/hess-27-4187-2023, 2023
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A spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method is proposed. The calibration model is built by post-processing ECMWF raw forecasts, while the bridging models are built using atmospheric intraseasonal signals as predictors. The calibration model and bridging models are merged through a Bayesian modelling averaging (BMA) method. The results indicate that the newly developed method can generate skilful and reliable sub-seasonal precipitation forecasts.
Hetal P. Dabhi, Mathias W. Rotach, and Michael Oberguggenberger
Hydrol. Earth Syst. Sci., 27, 2123–2147, https://doi.org/10.5194/hess-27-2123-2023, https://doi.org/10.5194/hess-27-2123-2023, 2023
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Spatiotemporally consistent high-resolution precipitation data on climate are needed for climate change impact assessments, but obtaining these data is challenging for areas with complex topography. We present a model that generates synthetic gridded daily precipitation data at a 1 km spatial resolution using observed meteorological station data as input, thereby providing data where historical observations are unavailable. We evaluate this model for a mountainous region in the European Alps.
Yuxin Li, Sisi Chen, Jun Yin, and Xing Yuan
Hydrol. Earth Syst. Sci., 27, 1077–1087, https://doi.org/10.5194/hess-27-1077-2023, https://doi.org/10.5194/hess-27-1077-2023, 2023
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Flash drought is referred to the rapid development of drought events with a fast decline of soil moisture, which has serious impacts on agriculture, the ecosystem, human health, and society. While flash droughts have received much research attention, there is no consensus on its definition. Here we used a stochastic water balance framework to quantify the timing of soil moisture crossing different thresholds, providing an efficient tool for diagnosing and monitoring flash droughts.
Arun Ramanathan, Pierre-Antoine Versini, Daniel Schertzer, Remi Perrin, Lionel Sindt, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 26, 6477–6491, https://doi.org/10.5194/hess-26-6477-2022, https://doi.org/10.5194/hess-26-6477-2022, 2022
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Reference rainfall scenarios are indispensable for hydrological applications such as designing storm-water management infrastructure, including green roofs. Therefore, a new method is suggested for simulating rainfall scenarios of specified intensity, duration, and frequency, with realistic intermittency. Furthermore, novel comparison metrics are proposed to quantify the effectiveness of the presented simulation procedure.
Judith Meyer, Malte Neuper, Luca Mathias, Erwin Zehe, and Laurent Pfister
Hydrol. Earth Syst. Sci., 26, 6163–6183, https://doi.org/10.5194/hess-26-6163-2022, https://doi.org/10.5194/hess-26-6163-2022, 2022
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We identified and analysed the major atmospheric components of rain-intense thunderstorms that can eventually lead to flash floods: high atmospheric moisture, sufficient latent instability, and weak thunderstorm cell motion. Between 1981 and 2020, atmospheric conditions became likelier to support strong thunderstorms. However, the occurrence of extreme rainfall events as well as their rainfall intensity remained mostly unchanged.
Yuan Li, Zhiyong Wu, Hai He, and Hao Yin
Hydrol. Earth Syst. Sci., 26, 4975–4994, https://doi.org/10.5194/hess-26-4975-2022, https://doi.org/10.5194/hess-26-4975-2022, 2022
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The relationship between atmospheric intraseasonal signals and precipitation is highly uncertain and depends on the region and lead time. In this study, we develop a spatiotemporal projection, based on a Bayesian hierarchical model (STP-BHM), to address the above challenge. The results suggest that the STP-BHM model is skillful and reliable for probabilistic subseasonal precipitation forecasts over China during the boreal summer monsoon season.
Lionel Benoit, Lydie Sichoix, Alison D. Nugent, Matthew P. Lucas, and Thomas W. Giambelluca
Hydrol. Earth Syst. Sci., 26, 2113–2129, https://doi.org/10.5194/hess-26-2113-2022, https://doi.org/10.5194/hess-26-2113-2022, 2022
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This study presents a probabilistic model able to reproduce the spatial patterns of rainfall on tropical islands with complex topography. It sheds new light on rainfall variability at the island scale, and explores the links between rainfall patterns and atmospheric circulation. The proposed model has been tested on two islands of the tropical Pacific, and demonstrates good skills in simulating both site-specific and island-scale rain behavior.
Jana Ulrich, Felix S. Fauer, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, https://doi.org/10.5194/hess-25-6133-2021, 2021
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The characteristics of extreme precipitation on different timescales as well as in different seasons are relevant information, e.g., for designing hydrological structures or managing water supplies. Therefore, our aim is to describe these characteristics simultaneously within one model. We find similar characteristics for short extreme precipitation at all considered stations in Germany but pronounced regional differences with respect to the seasonality of long-lasting extreme events.
Jiayi Fang, Thomas Wahl, Jian Fang, Xun Sun, Feng Kong, and Min Liu
Hydrol. Earth Syst. Sci., 25, 4403–4416, https://doi.org/10.5194/hess-25-4403-2021, https://doi.org/10.5194/hess-25-4403-2021, 2021
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A comprehensive assessment of compound flooding potential is missing for China. We investigate dependence, drivers, and impacts of storm surge and precipitation for coastal China. Strong dependence exists between driver combinations, with variations of seasons and thresholds. Sea level rise escalates compound flood potential. Meteorology patterns are pronounced for low and high compound flood potential. Joint impacts from surge and precipitation were much higher than from each individually.
Jamie Towner, Andrea Ficchí, Hannah L. Cloke, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 25, 3875–3895, https://doi.org/10.5194/hess-25-3875-2021, https://doi.org/10.5194/hess-25-3875-2021, 2021
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We examine whether several climate indices alter the magnitude, timing and duration of floods in the Amazon. We find significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative SST years in the central Pacific Ocean. This response is not repeated when the negative anomaly is positioned further east. These results have important implications for both social and physical sectors working towards the improvement of flood early warning systems.
Jieru Yan, Fei Li, András Bárdossy, and Tao Tao
Hydrol. Earth Syst. Sci., 25, 3819–3835, https://doi.org/10.5194/hess-25-3819-2021, https://doi.org/10.5194/hess-25-3819-2021, 2021
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Accurate spatial precipitation estimates are important in various fields. An approach to simulate spatial rainfall fields conditioned on radar and rain gauge data is proposed. Unlike the commonly used Kriging methods, which provide a Kriged mean field, the output of the proposed approach is an ensemble of estimates that represents the estimation uncertainty. The approach is robust to nonlinear error in radar estimates and is shown to have some advantages, especially when estimating the extremes.
Hossein Tabari, Santiago Mendoza Paz, Daan Buekenhout, and Patrick Willems
Hydrol. Earth Syst. Sci., 25, 3493–3517, https://doi.org/10.5194/hess-25-3493-2021, https://doi.org/10.5194/hess-25-3493-2021, 2021
Sebastian Scher and Stefanie Peßenteiner
Hydrol. Earth Syst. Sci., 25, 3207–3225, https://doi.org/10.5194/hess-25-3207-2021, https://doi.org/10.5194/hess-25-3207-2021, 2021
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In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called generative adversarial networks (GANs) can
learnwhat a typical distribution over the day looks like.
Jun Li, Zhaoli Wang, Xushu Wu, Jakob Zscheischler, Shenglian Guo, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 1587–1601, https://doi.org/10.5194/hess-25-1587-2021, https://doi.org/10.5194/hess-25-1587-2021, 2021
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We introduce a daily-scale index, termed the standardized compound drought and heat index (SCDHI), to measure the key features of compound dry-hot conditions. SCDHI can not only monitor the long-term compound dry-hot events, but can also capture such events at sub-monthly scale and reflect the related vegetation activity impacts. The index can provide a new tool to quantify sub-monthly characteristics of compound dry-hot events, which are vital for releasing early and timely warning.
Damien Raynaud, Benoit Hingray, Guillaume Evin, Anne-Catherine Favre, and Jérémy Chardon
Hydrol. Earth Syst. Sci., 24, 4339–4352, https://doi.org/10.5194/hess-24-4339-2020, https://doi.org/10.5194/hess-24-4339-2020, 2020
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This research paper proposes a weather generator combining two sampling approaches. A first generator recombines large-scale atmospheric situations. A second generator is applied to these atmospheric trajectories in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series in Switzerland. It reproduces adequately the observed climatology and improves the reproduction of extreme precipitation values.
Marc Schleiss
Hydrol. Earth Syst. Sci., 24, 3699–3723, https://doi.org/10.5194/hess-24-3699-2020, https://doi.org/10.5194/hess-24-3699-2020, 2020
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A new way to downscale rainfall fields based on the notion of equal-volume areas (EVAs) is proposed. Experiments conducted on 100 rainfall events in the Netherlands show that the EVA method outperforms classical methods based on fixed grid cell sizes, producing fields with more realistic spatial structures. The main novelty of the method lies in its adaptive sampling strategy, which avoids many of the mathematical challenges associated with the presence of zero rainfall values.
Christian Onof and Li-Pen Wang
Hydrol. Earth Syst. Sci., 24, 2791–2815, https://doi.org/10.5194/hess-24-2791-2020, https://doi.org/10.5194/hess-24-2791-2020, 2020
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The randomised Bartlett–Lewis (RBL) model is widely used to synthesise rainfall time series with realistic statistical features. However, it tended to underestimate rainfall extremes at sub-hourly and hourly timescales. In this paper, we revisit the derivation of equations that represent rainfall properties and compare statistical estimation methods that impact model calibration. These changes effectively improved the RBL model's capacity to reproduce sub-hourly and hourly rainfall extremes.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, https://doi.org/10.5194/hess-24-2841-2020, 2020
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At subdaily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.
Jieru Yan, András Bárdossy, Sebastian Hörning, and Tao Tao
Hydrol. Earth Syst. Sci., 24, 2287–2301, https://doi.org/10.5194/hess-24-2287-2020, https://doi.org/10.5194/hess-24-2287-2020, 2020
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For applications such as flood forecasting of urban- or town-scale distributed hydrological modeling, high-resolution quantitative precipitation estimation (QPE) with enough accuracy is the most important driving factor and thus the focus of this paper. Considering the fact that rain gauges are sparse but accurate and radar-based precipitation estimates are inaccurate but densely distributed, we are merging the two types of data intellectually to obtain accurate QPEs with high resolution.
Eva Steirou, Lars Gerlitz, Heiko Apel, Xun Sun, and Bruno Merz
Hydrol. Earth Syst. Sci., 23, 1305–1322, https://doi.org/10.5194/hess-23-1305-2019, https://doi.org/10.5194/hess-23-1305-2019, 2019
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We investigate whether flood probabilities in Europe vary for different large-scale atmospheric circulation conditions. Maximum seasonal river flows from 600 gauges in Europe and five synchronous atmospheric circulation indices are analyzed. We find that a high percentage of stations is influenced by at least one of the climate indices, especially during winter. These results can be useful for preparedness and damage planning by (re-)insurance companies.
Florian Ehmele and Michael Kunz
Hydrol. Earth Syst. Sci., 23, 1083–1102, https://doi.org/10.5194/hess-23-1083-2019, https://doi.org/10.5194/hess-23-1083-2019, 2019
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The risk estimation of precipitation events with high recurrence periods is difficult due to the limited timescale with meteorological observations and an inhomogeneous distribution of rain gauges, especially in mountainous terrains. In this study a spatially high resolved analytical model, designed for stochastic simulations of flood-related precipitation, is developed and applied to an investigation area in Germany but is transferable to other areas. High conformity with observations is found.
Jeongha Park, Christian Onof, and Dongkyun Kim
Hydrol. Earth Syst. Sci., 23, 989–1014, https://doi.org/10.5194/hess-23-989-2019, https://doi.org/10.5194/hess-23-989-2019, 2019
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Rainfall data are often unavailable for the analysis of water-related problems such as floods and droughts. In such cases, researchers use rainfall generators to produce synthetic rainfall data. However, data from most rainfall generators can serve only one specific purpose; i.e. one rainfall generator cannot be applied to analyse both floods and droughts. To overcome this issue, we invented a multipurpose rainfall generator that can be applied to analyse most water-related problems.
Juliette Blanchet, Emmanuel Paquet, Pradeebane Vaittinada Ayar, and David Penot
Hydrol. Earth Syst. Sci., 23, 829–849, https://doi.org/10.5194/hess-23-829-2019, https://doi.org/10.5194/hess-23-829-2019, 2019
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We propose an objective framework for estimating rainfall cumulative distribution functions in a region when data are only available at rain gauges. Our methodology allows us to assess goodness-of-fit of the full distribution, but with a particular focus on its tail. It is applied to daily rainfall in the Ardèche catchment in the south of France. Results show a preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with a thin plate spline.
Diana Lucatero, Henrik Madsen, Jens C. Refsgaard, Jacob Kidmose, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 22, 6591–6609, https://doi.org/10.5194/hess-22-6591-2018, https://doi.org/10.5194/hess-22-6591-2018, 2018
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The present study evaluates the skill of a seasonal forecasting system for hydrological relevant variables in Denmark. Linear scaling and quantile mapping were used to correct the forecasts. Uncorrected forecasts tend to be more skillful than climatology, in general, for the first month lead time only. Corrected forecasts show a reduced bias in the mean; are more consistent; and show a level of accuracy that is closer to, although no higher than, that of ensemble climatology, in general.
Kuganesan Sivasubramaniam, Ashish Sharma, and Knut Alfredsen
Hydrol. Earth Syst. Sci., 22, 6533–6546, https://doi.org/10.5194/hess-22-6533-2018, https://doi.org/10.5194/hess-22-6533-2018, 2018
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This study investigates the use of gauge precipitation and air temperature observations to ascertain radar precipitation in cold climates. The use of air temperature as an additional variable in a non-parametric model improved the estimation of radar precipitation significantly. Further, it was found that the temperature effects became insignificant when air temperature was above 10 °C. The findings from this study could be important for using radar precipitation for hydrological applications.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, https://doi.org/10.5194/hess-22-5919-2018, 2018
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We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.
Hannes Müller-Thomy, Markus Wallner, and Kristian Förster
Hydrol. Earth Syst. Sci., 22, 5259–5280, https://doi.org/10.5194/hess-22-5259-2018, https://doi.org/10.5194/hess-22-5259-2018, 2018
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Rainfall time series are disaggregated from daily to hourly values to be used for rainfall–runoff modeling of mesoscale catchments. Spatial rainfall consistency is implemented afterwards using simulated annealing. With the calibration process applied, observed runoff statistics (e.g., summer and winter peak flows) are represented well. However, rainfall datasets with under- or over-estimation of spatial consistency lead to similar results, so the need for a good representation can be questioned.
Xinjun Tu, Yiliang Du, Vijay P. Singh, Xiaohong Chen, Kairong Lin, and Haiou Wu
Hydrol. Earth Syst. Sci., 22, 5175–5189, https://doi.org/10.5194/hess-22-5175-2018, https://doi.org/10.5194/hess-22-5175-2018, 2018
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For given frequencies of precipitation of a large region, design water demands of irrigation of the entire region among three methods, i.e., equalized frequency, typical year and most-likely weight function, slightly differed, but their alterations in sub-regions were complicated. A design procedure using the most-likely weight function in association with a high-dimensional copula, which built a linkage between regional frequency and sub-regional frequency of precipitation, is recommended.
A. T. M. Sakiur Rahman, M. Shakil Ahmed, Hasnat Mohammad Adnan, Mohammad Kamruzzaman, M. Abdul Khalek, Quamrul Hasan Mazumder, and Chowdhury Sarwar Jahan
Hydrol. Earth Syst. Sci., 22, 4213–4228, https://doi.org/10.5194/hess-22-4213-2018, https://doi.org/10.5194/hess-22-4213-2018, 2018
Edmund P. Meredith, Henning W. Rust, and Uwe Ulbrich
Hydrol. Earth Syst. Sci., 22, 4183–4200, https://doi.org/10.5194/hess-22-4183-2018, https://doi.org/10.5194/hess-22-4183-2018, 2018
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Kilometre-scale climate-model data are of great benefit to both hydrologists and end users studying extreme precipitation, though often unavailable due to the computational expense associated with such high-resolution simulations. We develop a method which identifies days with enhanced risk of extreme rainfall over a catchment, so that high-resolution simulations can be performed only when such a risk exists, reducing computational expense by over 90 % while still well capturing the extremes.
Diana Lucatero, Henrik Madsen, Jens C. Refsgaard, Jacob Kidmose, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 22, 3601–3617, https://doi.org/10.5194/hess-22-3601-2018, https://doi.org/10.5194/hess-22-3601-2018, 2018
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The skill of an experimental streamflow forecast system in the Ahlergaarde catchment, Denmark, is analyzed. Inputs to generate the forecasts are taken from the ECMWF System 4 seasonal forecasting system and an ensemble of observations (ESP). Reduction of biases is achieved by processing the meteorological and/or streamflow forecasts. In general, this is not sufficient to ensure a higher level of accuracy than the ESP, indicating a modest added value of a seasonal meteorological system.
Sanjeev K. Jha, Durga L. Shrestha, Tricia A. Stadnyk, and Paulin Coulibaly
Hydrol. Earth Syst. Sci., 22, 1957–1969, https://doi.org/10.5194/hess-22-1957-2018, https://doi.org/10.5194/hess-22-1957-2018, 2018
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The output from numerical weather prediction (NWP) models is known to have errors. River forecast centers in Canada mostly use precipitation forecasts directly obtained from American and Canadian NWP models. In this study, we evaluate the forecast performance of ensembles generated by a Bayesian post-processing approach in cold climates. We demonstrate that the post-processing approach generates bias-free forecasts and provides a better picture of uncertainty in the case of an extreme event.
Md Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Pere Quintana-Seguí, and Anaïs Barella-Ortiz
Hydrol. Earth Syst. Sci., 22, 1371–1389, https://doi.org/10.5194/hess-22-1371-2018, https://doi.org/10.5194/hess-22-1371-2018, 2018
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This study investigates the use of a nonparametric model for combining multiple global precipitation datasets and characterizing estimation uncertainty. Inputs to the model included three satellite precipitation products, an atmospheric reanalysis precipitation dataset, satellite-derived near-surface daily soil moisture data, and terrain elevation. We evaluated the technique based on high-resolution reference precipitation data and further used generated ensembles to force a hydrological model.
David Cross, Christian Onof, Hugo Winter, and Pietro Bernardara
Hydrol. Earth Syst. Sci., 22, 727–756, https://doi.org/10.5194/hess-22-727-2018, https://doi.org/10.5194/hess-22-727-2018, 2018
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Extreme rainfall is one of the most significant natural hazards. However, estimating very large events is highly uncertain. We present a new approach to construct intense rainfall using the structure of rainfall generation in clouds. The method is particularly effective at estimating short-duration extremes, which can be the most damaging. This is expected to have immediate impact for the estimation of very rare downpours, with the potential to improve climate resilience and hazard preparedness.
Jérémy Chardon, Benoit Hingray, and Anne-Catherine Favre
Hydrol. Earth Syst. Sci., 22, 265–286, https://doi.org/10.5194/hess-22-265-2018, https://doi.org/10.5194/hess-22-265-2018, 2018
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We present a two-stage statistical downscaling model for the probabilistic prediction of local precipitation, where the downscaling statistical link is estimated from atmospheric circulation analogs of the current prediction day.
The model allows for a day-to-day adaptive and tailored downscaling. It can reveal specific predictors for peculiar and non-frequent weather configurations. This approach noticeably improves the skill of the prediction for both precipitation occurrence and quantity.
Christoph Ritschel, Uwe Ulbrich, Peter Névir, and Henning W. Rust
Hydrol. Earth Syst. Sci., 21, 6501–6517, https://doi.org/10.5194/hess-21-6501-2017, https://doi.org/10.5194/hess-21-6501-2017, 2017
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A stochastic model for precipitation is used to simulate an observed precipitation series; it is compared to the original series in terms of intensity–duration frequency curves. Basis for the latter curves is a parametric model for the duration dependence of the underlying extreme value model allowing a consistent estimation of one single duration-dependent distribution using all duration series simultaneously. The stochastic model reproduces the curves except for very rare extreme events.
Poulomi Ganguli and Paulin Coulibaly
Hydrol. Earth Syst. Sci., 21, 6461–6483, https://doi.org/10.5194/hess-21-6461-2017, https://doi.org/10.5194/hess-21-6461-2017, 2017
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Using statistical models, we test whether nonstationary versus stationary models show any significant differences in terms of design storm intensity at different durations across Southern Ontario. We find that detectable nonstationarity in rainfall extremes does not necessarily lead to significant differences in design storm intensity, especially for shorter return periods. An update of 2–44 % is required in current design standards to mitigate the risk of storm-induced urban flooding.
Daniele Nerini, Nikola Besic, Ioannis Sideris, Urs Germann, and Loris Foresti
Hydrol. Earth Syst. Sci., 21, 2777–2797, https://doi.org/10.5194/hess-21-2777-2017, https://doi.org/10.5194/hess-21-2777-2017, 2017
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Stochastic generators are effective tools for the quantification of uncertainty in a number of applications with weather radar data, including quantitative precipitation estimation and very short-term forecasting. However, most of the current stochastic rainfall field generators cannot handle spatial non-stationarity. We propose an approach based on the short-space Fourier transform, which aims to reproduce the local spatial structure of the observed rainfall fields.
Tobias Mosthaf and András Bárdossy
Hydrol. Earth Syst. Sci., 21, 2463–2481, https://doi.org/10.5194/hess-21-2463-2017, https://doi.org/10.5194/hess-21-2463-2017, 2017
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Parametric distribution functions are commonly used to model precipitation amounts at gauged and ungauged locations. Nonparametric distributions offer a more flexible way to model precipitation amounts. However, the nonparametric models do not exhibit parameters that can be easily regionalized for application at ungauged locations. To overcome this deficiency, we present a new interpolation scheme for nonparametric models and evaluate the usage of daily gauges for sub-daily resolutions.
Claudia Volosciuk, Douglas Maraun, Mathieu Vrac, and Martin Widmann
Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, https://doi.org/10.5194/hess-21-1693-2017, 2017
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For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.
Aline Murawski, Gerd Bürger, Sergiy Vorogushyn, and Bruno Merz
Hydrol. Earth Syst. Sci., 20, 4283–4306, https://doi.org/10.5194/hess-20-4283-2016, https://doi.org/10.5194/hess-20-4283-2016, 2016
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To understand past flood changes in the Rhine catchment and the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. Here the link between patterns and local climate is tested, and the skill of GCMs in reproducing these patterns is evaluated.
Kue Bum Kim, Hyun-Han Kwon, and Dawei Han
Hydrol. Earth Syst. Sci., 20, 2019–2034, https://doi.org/10.5194/hess-20-2019-2016, https://doi.org/10.5194/hess-20-2019-2016, 2016
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A primary advantage of using model ensembles for climate change impact studies is to represent the uncertainties associated with models through the ensemble spread. Currently, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. However the proposed method is able to correct the bias and conform to the ensemble spread so that the ensemble information can be better used.
E. P. Maurer, D. L. Ficklin, and W. Wang
Hydrol. Earth Syst. Sci., 20, 685–696, https://doi.org/10.5194/hess-20-685-2016, https://doi.org/10.5194/hess-20-685-2016, 2016
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To translate climate model output from its native coarse scale to a finer scale more representative of that at which societal impacts are experienced, a common method applied is statistical downscaling. A component of many statistical downscaling techniques is quantile mapping (QM). QM can be applied at different spatial scales, and here we study how skill varies with spatial scale. We find the highest skill is generally obtained when applying QM at approximately a 50 km spatial scale.
S. Sadri, J. Kam, and J. Sheffield
Hydrol. Earth Syst. Sci., 20, 633–649, https://doi.org/10.5194/hess-20-633-2016, https://doi.org/10.5194/hess-20-633-2016, 2016
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Low flows are a critical part of the river flow regime but little is known about how they are changing in response to human influences and climate. We analyzed low flow records across the eastern US and identified sites that were minimally influenced by human activities. We found a general increasing trend in low flows across the northeast and decreasing trend across the southeast that are likely driven by changes in climate. The results have implications for how we manage our water resources.
Cited articles
Aas, K., Czado, C., Frigessi, A., and Bakken, H.: Pair-copula constructions
of multiple dependence, Insurance: Math. Econ., 44, 182–198,
https://doi.org/10.1016/j.insmatheco.2007.02.001, 2009.
Abdi, H.: The Kendall rank correlation coefficient, in: Encyclopedia of Measurement and Statistics, Sage Publications, Inc., 508–510, ISBN 9781412916110, 2007.
Alaya, M. A. B., Zwiers, F., and Zhang, X.: Probable Maximum Precipitation:
Its Estimation and Uncertainty Quantification Using Bivariate Extreme Value
Analysis, J. Hydrometeorol., 19, 679–694, 2018.
Alaya, M. A. B., Zwiers, F. W., and Zhang, X.: A bivariate approach to
estimating the probability of very extreme precipitation events, Weather Clim. Extrem., 30, 100290, https://doi.org/10.1016/j.wace.2020.100290, 2020.
Alexander, G. N.: Using the probability of storm transposition for estimating the frequency of rare floods, J. Hydrol., 1, 46–57, https://doi.org/10.1016/0022-1694(63)90032-5, 1963.
Alexander, G. N.: Application of probability to spillway design flood
estimation, in: Proceedings of the Leningrad Symposium on Floods and Their Computation, August 1967, Gentbrugge, Belgium, 536–543, 1969.
Alexander, L. V., Bador, M., Roca, R., Contractor, S., Donat, M. G., and Nguyen, P. L.: Intercomparison of annual precipitation indices and extremes
over global land areas from in situ, space-based and reanalysis products,
Environ. Res. Lett., 15, 055002, https://doi.org/10.1088/1748-9326/ab79e2, 2020.
Allison, M. A., Vosburg, B. M., Ramirez, M. T., and Meselhe, E. A.: Mississippi River channel response to the Bonnet Carré Spillway opening
in the 2011 flood and its implications for the design and operation of river
diversions, J. Hydrol., 477, 104–118, https://doi.org/10.1016/j.jhydrol.2012.11.011, 2013.
Asquith, W. H. and Famiglietti, J. S.: Precipitation areal-reduction factor
estimation using an annual-maxima centered approach, J. Hydrol., 230, 55–69, https://doi.org/10.1016/S0022-1694(00)00170-0, 2000.
Banacos, P. C. and Schultz, D. M.: The Use of Moisture Flux Convergence in
Forecasting Convective Initiation: Historical and Operational Perspectives,
Weather Forecast., 20, 351–366, https://doi.org/10.1175/waf858.1, 2005.
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A.
I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of
26 precipitation datasets using Stage-IV gauge-radar data for the CONUS,
Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019.
Bender, J., Wahl, T., and Jensen, J.: Multivariate design in the presence of
non-stationarity, J. Hydrol., 514, 123–130, https://doi.org/10.1016/j.jhydrol.2014.04.017, 2014.
Benedict, I., van Heerwaarden, C. C., van der Ent, R. J., Weerts, A. H., and
Hazeleger, W.: Decline in Terrestrial Moisture Sources of the Mississippi
River Basin in a Future Climate, J. Hydrometeorol., 21, 299–316,
https://doi.org/10.1175/JHM-D-19-0094.1, 2020.
Berrisford, P., Kållberg, P., Kobayashi, S., Dee, D., Uppala, S., Simmons, A. J., Poli, P., and Sato, H.: Atmospheric conservation properties
in ERA-Interim, Q. J. Roy. Meteorol. Soc., 137, 1381–1399, https://doi.org/10.1002/qj.864, 2011.
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M.:
Multivariate statistical modelling of compound events via pair-copula
constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, 2017.
Bosilovich, M. G., Chen, J., Robertson, F. R., and Adler, R. F.: Evaluation
of Global Precipitation in Reanalyses, J. Appl. Meteorol. Clim., 47, 2279–2299, https://doi.org/10.1175/2008JAMC1921.1, 2008.
Bosma, C. D., Wright, D. B., Nguyen, P., Kossin, J. P., Herndon, D. C., and
Shepherd, J. M.: An Intuitive Metric to Quantify and Communicate Tropical
Cyclone Rainfall Hazard, B. Am. Meteorol. Soc., 101, E206–E220, https://doi.org/10.1175/BAMS-D-19-0075.1, 2020.
Bradbury, D.: Moisture Analysis and Water Budget in Three Different Types of
Storms, J. Meteorol., 14, 559–565, https://doi.org/10.1175/1520-0469(1957)014<0559:maawbi>2.0.co;2, 1957.
Breinl, K., Müller-Thomy, H., and Blöschl, G.: Space–Time
Characteristics of Areal Reduction Factors and Rainfall Processes, J.
Hydrometeorol., 21, 671–689, https://doi.org/10.1175/JHM-D-19-0228.1, 2020.
Brown, P. J. and Kummerow, C. D.: An Assessment of Atmospheric Water Budget
Components over Tropical Oceans, J. Climate, 27, 2054–2071,
https://doi.org/10.1175/JCLI-D-13-00385.1, 2014.
Chang, W., Stein, M. L., Wang, J., Kotamarthi, V. R., and Moyer, E. J.:
Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions, J. Climate, 29, 8355–8376, https://doi.org/10.1175/JCLI-D-15-0844.1, 2016.
Chen, L.-C. and Bradley, A. A.: Adequacy of using surface humidity to
estimate atmospheric moisture availability for probable maximum precipitation, Water Resour. Res., 42, W09410, https://doi.org/10.1029/2005WR004469, 2006.
Coles, S.: An Introduction to Statistical Modeling of Extreme Values,
Springer, UK, 78–91, https://doi.org/10.1007/978-1-4471-3675-0, 2001.
Cunnane, C.: Unbiased plotting positions – A review, J. Hydrol., 37, 205–222, https://doi.org/10.1016/0022-1694(78)90017-3, 1978.
Davis, C., Brown, B., and Bullock, R.: Object-Based Verification of Precipitation Forecasts. Part I: Methodology and Application to Mesoscale
Rain Areas, Mon. Weather Rev., 134, 1772–1784, https://doi.org/10.1175/MWR3145.1, 2006.
Dawdy, D. R., Griffis, V. W., and Gupta, V. K.: Regional Flood-Frequency
Analysis: How We Got Here and Where We Are Going, J. Hydrol. Eng., 17, 953–959, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000584, 2012.
De Michele, C. and Salvadori, G.: A Generalized Pareto intensity-duration
model of storm rainfall exploiting 2-Copulas, J. Geophys. Res.-Atmos. 108, 4067, https://doi.org/10.1029/2002JD002534, 2003.
Dingman, S. L.: Physical hydrology, in: 3rd Edn., Waveland Press, Inc,
Long Grove, Illinois, 643 pp., ISBN 978-1-4786-1118-9, 2015.
Dixon, M. and Wiener, G.: TITAN: Thunder Identification, Tracking, Analysis,
and Nowcasting – A Radar-based Methodology, J. Atmos. Ocean. Tech., 10, 785–797, https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2, 1993.
Durrans, S. R., Julian, L. T., and Yekta, M.: Estimation of Depth-Area
Relationships using Radar-Rainfall Data, J. Hydrol. Eng., 7, 356–367,
https://doi.org/10.1061/(ASCE)1084-0699(2002)7:5(356), 2002.
Ebert, E. E., Janowiak, J. E., and Kidd, C.: Comparison of Near-Real-Time
Precipitation Estimates from Satellite Observations and Numerical Models, B. Am. Meteorol. Soc., 88, 47–64, https://doi.org/10.1175/BAMS-88-1-47, 2007.
Efstratiadis, A., Koussis, A. D., Koutsoyiannis, D., and Mamassis, N.: Flood
design recipes vs. reality: can predictions for ungauged basins be trusted?,
Nat. Hazards Earth Syst. Sci., 14, 1417–1428, https://doi.org/10.5194/nhess-14-1417-2014, 2014.
England, J. F., 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, https://doi.org/10.1016/j.jhydrol.2013.12.021, 2014.
Essou, G. R. C., Brissette, F., and Lucas-Picher, P.: The Use of Reanalyses
and Gridded Observations as Weather Input Data for a Hydrological Model:
Comparison of Performances of Simulated River Flows Based on the Density of
Weather Stations, J. Hydrometeorol., 18, 497–513, https://doi.org/10.1175/JHM-D-16-0088.1, 2017.
Foufoula-Georgiou, E.: A probabilistic storm transposition approach for
estimating exceedance probabilities of extreme precipitation depths, Water
Resour. Res., 25, 799–815, https://doi.org/10.1029/WR025i005p00799, 1989.
Gao, S., Cui, X., Zhou, Y., and Li, X.: Surface rainfall processes as simulated in a cloud-resolving model, J. Geophys. Res.-Atmos., 110, 202, https://doi.org/10.1029/2004JD005467, 2005.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gilleland, E. and Katz, R. W.: extRemes 2.0: An Extreme Value Analysis Package in R, J. Stat. Softw., 72, 1–39, https://doi.org/10.18637/jss.v072.i08, 2016.
Gori, A., Lin, N., Xi, D., and Emanuel, K.: Tropical cyclone climatology
change greatly exacerbates US extreme rainfall–surge hazard, Nat. Clim.
Change, 12, 171–178, https://doi.org/10.1038/s41558-021-01272-7, 2022.
Grebner, D. and Roesch, T.: Regional dependence and application of DAD relationships, in: FRIEND '97 Regional Hydrology – Concepts and Models for Sustainable Water Resource Management, IAHS Press, Wallingford, Oxfordshire, 223–230, ISBN 978-1-901502-35-0, 1997.
Groisman, P. Y., Knight, R. W., Karl, T. R., Easterling, D. R., Sun, B., and
Lawrimore, J. H.: Contemporary Changes of the Hydrological Cycle over the
Contiguous United States: Trends Derived from In Situ Observations, J. Hydrometeorol., 5, 64–85, https://doi.org/10.1175/1525-7541(2004)005<0064:CCOTHC>2.0.CO;2, 2004.
Gubareva, T. and Gartsman, B.: Estimating distribution parameters of extreme
hydrometeorological characteristics by L-moments method, Water Resour., 37, 437–445, https://doi.org/10.1134/S0097807810040020, 2010.
Gutenstein, M., Fennig, K., Schröder, M., Trent, T., Bakan, S., Roberts,
J. B., and Robertson, F. R.: Intercomparison of freshwater fluxes over ocean
and investigations into water budget closure, Hydrol. Earth Syst. Sci., 25, 121–146, https://doi.org/10.5194/hess-25-121-2021, 2021.
Gyasi-Agyei, Y. and Melching, C.: Modelling the dependence and internal
structure of storm events for continuous rainfall simulation, J. Hydrol., 464–465, 249–261, https://doi.org/10.1016/j.jhydrol.2012.07.014, 2012.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2022.
Holman, K. D. and Vavrus, S. J.: Understanding Simulated Extreme Precipitation Events in Madison, Wisconsin, and the Role of Moisture Flux
Convergence during the Late Twentieth and Twenty-First Centuries, J. Hydrometeorol., 13, 877–894, https://doi.org/10.1175/JHM-D-11-052.1, 2012.
Hoskins, B. J. and Hodges, K. I.: New Perspectives on the Northern
Hemisphere Winter Storm Tracks, J. Atmos. Sci., 59, 1041–1061,
https://doi.org/10.1175/1520-0469(2002)059<1041:NPOTNH>2.0.CO;2, 2002.
Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree × 0.1 degree V06, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/06, 2019.
Jiang, C., Xiong, L., Xu, C.-Y., and Guo, S.: Bivariate frequency analysis
of nonstationary low-flow series based on the time-varying copula, Hydrol. Process., 29, 1521–1534, https://doi.org/10.1002/hyp.10288, 2015.
Jiao, D., Xu, N., Yang, F., and Xu, K.: Evaluation of spatial-temporal
variation performance of ERA5 precipitation data in China, Sci. Rep., 11,
17956, https://doi.org/10.1038/s41598-021-97432-y, 2021.
Jun, C., Qin, X., Gan, T. Y., Tung, Y.-K., and De Michele, C.: Bivariate
frequency analysis of rainfall intensity and duration for urban stormwater
infrastructure design, J. Hydrol., 553, 374–383, https://doi.org/10.1016/j.jhydrol.2017.08.004, 2017.
Kao, S.-C. and Deneale, S. T.: Application of Point Precipitation Frequency
Estimates to Watersheds, ORNL – Oak Ridge National Lab., Oak Ridge, TN, USA,
https://doi.org/10.2172/1808414, 2021.
Kao, S.-C., DeNeale, S. T., Yegorova, E., Kanney, J., and Carr, M. L.:
Variability of precipitation areal reduction factors in the conterminous
United States, J. Hydrol. X, 9, 100064, https://doi.org/10.1016/j.hydroa.2020.100064, 2020.
Karl, T. R. and Knight, R. W.: Secular Trends of Precipitation Amount, Frequency, and Intensity in the United States, B. Am. Meteorol. Soc., 79, 231–242, https://doi.org/10.1175/1520-0477(1998)079<0231:STOPAF>2.0.CO;2, 1998.
Katz, R. W., Parlange, M. B., and Naveau, P.: Statistics of extremes in
hydrology, Adv. Water Resour., 25, 1287–1304, https://doi.org/10.1016/S0309-1708(02)00056-8, 2002.
Kim, E. and Kang, B.: Estimation of Storm-centered ARF Using Radar Rainfall
by Duration and Return Periods, in: Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017), Bankog, Thailand, https://doi.org/10.2991/msam-17.2017.59, 2017.
Kim, S., Sharma, A., Wasko, C., and Nathan, R.: Linking total precipitable
water to precipitation extremes globally, Earth's Future, 10, e2021EF002473, https://doi.org/10.1029/2021EF002473, 2022.
Klemeš, V.: Probability of extreme hydrometeorological events – a
different approach, in: Proceedings of the Yokohama Symposium, July 1993,
Yokohama, abstract no. 203, 1993.
Koutsoyiannis, D., Kozonis, D., and Manetas, A.: A mathematical framework
for studying rainfall intensity-duration-frequency relationships, J. Hydrol., 206, 118–135, https://doi.org/10.1016/S0022-1694(98)00097-3, 1998.
Krajewski, W. F.: Cokriging radar-rainfall and rain gage data, J. Geophys. Res.-Atmos., 92, 9571–9580, https://doi.org/10.1029/JD092iD08p09571, 1987.
Kunkel, K. E., Easterling, D. R., Karl, T. R., Biard, J. C., Champion, S.
M., Gleason, B. E., Johnson, K. M., Li, A., Stegall, S., Stevens, L. E.,
Stevens, S. E., Squires, M., Sun, L., and Yin, X.: Incorporation of the
Effects of Future Anthropogenically-Forced Climate Change in Intensity-Duration-Frequency Design Values, North Carolina Institute for
Climate Studies, North Carolina State University, https://precipitationfrequency.ncics.org/pdfs/RC_2517_Final_Report_version2_Sep_04_2020_clean.pdf
(last access: 1 September 2021), 2020a.
Kunkel, K. E., Stevens, S. E., Stevens, L. E., and Karl, T. R.: Observed
Climatological Relationships of Extreme Daily Precipitation Events With
Precipitable Water and Vertical Velocity in the Contiguous United States,
Geophys. Res. Lett., 47, e2019GL086721, https://doi.org/10.1029/2019GL086721, 2020b.
Langousis, A., Veneziano, D., Furcolo, P., and Lepore, C.: Multifractal
rainfall extremes: Theoretical analysis and practical estimation, Chaos Solit. Fract., 39, 1182–1194, https://doi.org/10.1016/j.chaos.2007.06.004, 2009.
Lee, O. and Kim, S.: Estimation of Future Probable Maximum Precipitation in
Korea Using Multiple Regional Climate Models, Water, 10, 637,
https://doi.org/10.3390/w10050637, 2018.
Lewis, J., McKinnie, C., Parrish, K., Crosby, W., Cruz, C., Dove, M., Gaines, R., Girdner, S., Gambill, R., Ramirez, D., Tayler, R., Agnew, M., Veatch, W., Dircksen, M., Brown, T., Griggs, F., Copeland, R., Rentfro, B., Ashley, J., Windham, J., and Howe, E.: Mississippi River and Tributaries flowline assessment main report, United States Army Corps of Engineers, Mississippi Valley Division, https://doi.org/10.21079/11681/32623, 2019.
Li, Z., Wright, D. B., Zhang, S. Q., Kirschbaum, D. B., and Hartke, S. H.:
Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall, J. Hydrometeorol., 21, 2759–2776, https://doi.org/10.1175/JHM-D-20-0041.1, 2020.
Liu, Y. and Wright, D.: STARCH (Storm Tracking And Regional Characterization), Zenodo [code], https://doi.org/10.5281/zenodo.7091017, 2022.
Madsen, H., Rasmussen, P. F., and Rosbjerg, D.: Comparison of annual maximum
series and partial duration series methods for modeling extreme hydrologic
events: 1. At-site modeling, Water Resour. Res., 33, 747–757,
https://doi.org/10.1029/96WR03848, 1997.
Mallakpour, I., Sadeghi, M., Mosaffa, H., Akbari Asanjan, A., Sadegh, M.,
Nguyen, P., Sorooshian, S., and AghaKouchak, A.: Discrepancies in changes in
precipitation characteristics over the contiguous United States based on six
daily gridded precipitation datasets, Weather Clim. Extrem., 36, 100433, https://doi.org/10.1016/j.wace.2022.100433, 2022.
Matsoukas, C., Islam, S., and Kothari, R.: Fusion of radar and rain gage
measurements for an accurate estimation of rainfall, J. Geophys. Res.-Atmos., 104, 31437–31450, https://doi.org/10.1029/1999JD900487, 1999.
Mayer, J., Mayer, M., and Haimberger, L.: Consistency and Homogeneity of Atmospheric Energy, Moisture, and Mass Budgets in ERA5, J. Climate, 34, 3955–3974, https://doi.org/10.1175/JCLI-D-20-0676.1, 2021.
Miller, J. F.: Two- to Ten-Day Precipitation for Return Periods of 2 to 100 Years in the Contiguous United States, US Weather Bureau, Washington, DC, https://www.weather.gov/media/owp/hdsc_documents/TechnicalPaper_No49.pdf
(last access: 15 January 2022), 1964.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz,
Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity Is Dead:
Whither Water Management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915, 2008.
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.
Moalafhi, D. B., Evans, J. P., and Sharma, A.: Influence of reanalysis datasets on dynamically downscaling the recent past, Clim. Dynam., 49, 1239–1255, https://doi.org/10.1007/s00382-016-3378-y, 2017.
Mutel, C. F. (Ed.): A watershed year: anatomy of the Iowa floods of 2008,
University of Iowa Press, Iowa City, 250 pp., ISBN 978-1-58729-854-7, 2010.
Myers, M. F. and White, G. F.: The Challenge of the Mississippi Flood,
Environment: Sci. Policy Sustain. Dev., 35, 6–35, https://doi.org/10.1080/00139157.1993.9929131, 1993.
Nagler, T., Schepsmeier, U., Stoeber, J., Brechmann, E. C., Graeler, B.,
Erhardt, T., Almeida, C., Min, A., Czado, C., Hofmann, M., Killiches, M.,
Joe, H., and Vatter, T.: VineCopula: Statistical Inference of Vine Copulas,
CRAN R project [code], https://cran.r-project.org/web/packages/VineCopula/VineCopula.pdf
(last access: 6 April 2022), 2021.
Nathan, R., Jordan, P., Scorah, M., Lang, S., Kuczera, G., Schaefer, M., and
Weinmann, E.: Estimating the exceedance probability of extreme rainfalls up
to the probable maximum precipitation, J. Hydrol., 543, 706–720,
https://doi.org/10.1016/j.jhydrol.2016.10.044, 2016.
National Climatic Data Center: Data documentation for dataset 3240 (DSI-3240) hourly precipitation, NCEI [data set], https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00313/html (last access: 15 August 2022), 2003.
National Research Council: Estimating probabilities of extreme floods:
methods and recommended research, National Academy Press, Washington,
ISBN 978-0-309-03791-4, 1988.
National Research Council: Estimating Bounds on Extreme Precipitation
Events: A Brief Assessment, https://doi.org/10.17226/9195, 1994.
NCAR: NCAR/Irose-titan, GitHub [code], https://github.com/NCAR/lrose-titan/ (last access: 19 May 2022), 2019.
Nerantzaki, S. D. and Papalexiou, S. M.: Assessing extremes in hydroclimatology: A review on probabilistic methods, J. Hydrol., 605, 127302, https://doi.org/10.1016/j.jhydrol.2021.127302, 2022.
Nogueira, M.: Inter-comparison of ERA-5, ERA-interim and GPCP rainfall over
the last 40 years: Process-based analysis of systematic and random
differences, J. Hydrol., 583, 124632, https://doi.org/10.1016/j.jhydrol.2020.124632, 2020.
Olivera, F., Asce, M., Choi, J., Kim, D., and Li, M.-H.: Estimation of
Average Rainfall Areal Reduction Factors in Texas Using NEXRAD Data, J. Hydrol. Eng., 13, 438–448, https://doi.org/10.1061/(ASCE)1084-0699(2008)13:6(438), 2008.
Osborn, H., Lane, L., and Myers, V. A.: Rainfall/Watershed Relationships for
Southwestern Thunderstorms, T. ASAE, 23, 82–87, https://doi.org/10.13031/2013.34529, 1980.
Pal, S., Lee, T. R., and Clark, N. E.: The 2019 Mississippi and Missouri
River Flooding and Its Impact on Atmospheric Boundary Layer Dynamics, Geophys. Res. Lett., 47, e2019GL086933, https://doi.org/10.1029/2019GL086933, 2020.
Pan, Z., Zhang, Y., Liu, X., and Gao, Z.: Current and future precipitation
extremes over Mississippi and Yangtze River basins as simulated in CMIP5
models, J. Earth Sci., 27, 22–36, https://doi.org/10.1007/s12583-016-0627-2, 2016.
Pavlovic, S., Perica, S., St Laurent, M., and Mejía, A.: Intercomparison of selected fixed-area areal reduction factor methods, J. Hydrol., 537, 419–430, https://doi.org/10.1016/j.jhydrol.2016.03.027, 2016.
Pereira, G. and Veiga, Á.: PAR(p)-vine copula based model for stochastic
streamflow scenario generation, Stoch. Environ. Res. Risk A., 32, 833–842,
https://doi.org/10.1007/s00477-017-1411-2, 2018.
Pérez-Alarcón, A., Coll-Hidalgo, P., Fernández-Alvarez, J. C.,
Sorí, R., Nieto, R., and Gimeno, L.: Moisture Sources for Precipitation
Associated With Major Hurricanes During 2017 in the North Atlantic Basin, J. Geophys. Res.-Atmos., 127, e2021JD035554, https://doi.org/10.1029/2021JD035554, 2022.
Rabalais, N. N., Turner, R. E., Wiseman Jr., W. J., and Dortch, Q.:
Consequences of the 1993 Mississippi River flood in the Gulf of Mexico, Regul. Rivers: Res. Manage., 14, 161–177,
https://doi.org/10.1002/(SICI)1099-1646(199803/04)14:2<161::AID-RRR495>3.0.CO;2-J, 1998.
Rakhecha, P. R. and Clark, C.: Revised estimates of one-day probable maximum
precipitation (PMP) for India, Meteorol. Appl., 6, 343–350,
https://doi.org/10.1017/s1350482799001280, 1999.
Restrepo-Posada, P. J. and Eagleson, P. S.: Identification of independent
rainstorms, J. Hydrol., 55, 303–319, https://doi.org/10.1016/0022-1694(82)90136-6, 1982.
Roderick, T. P., Wasko, C., and Sharma, A.: An Improved Covariate for
Projecting Future Rainfall Extremes?, Water Resour. Res., 56, e2019WR026924, https://doi.org/10.1029/2019WR026924, 2020.
Rousseau, A. N., Klein, I. M., Freudiger, D., Gagnon, P., Frigon, A., and
Ratté-Fortin, C.: Development of a methodology to evaluate probable
maximum precipitation (PMP) under changing climate conditions: Application
to southern Quebec, Canada, J. Hydrol., 519, 3094–3109,
https://doi.org/10.1016/j.jhydrol.2014.10.053, 2014.
Salvadori, G. and De Michele, C.: On the Use of Copulas in Hydrology: Theory
and Practice, J. Hydrol. Eng., 12, 369–380, https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(369), 2007.
Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D., and Diffenbaugh, N.
S.: Multidimensional risk in a nonstationary climate: Joint probability of
increasingly severe warm and dry conditions, Sci. Adv., 4, eaau3487, https://doi.org/10.1126/sciadv.aau3487, 2018.
Schaefer, M. G.: Regional analyses of precipitation annual maxima in
Washington State, Water Resour. Res., 26, 119–131,
https://doi.org/10.1029/WR026i001p00119, 1990.
Seager, R. and Henderson, N.: Diagnostic Computation of Moisture Budgets in the ERA-Interim Reanalysis with Reference to Analysis of CMIP-Archived Atmospheric Model Data, J. Climate, 26, 7876–7901, https://doi.org/10.1175/JCLI-D-13-00018.1, 2013.
Serinaldi, F.: Dismissing return periods!, Stoch. Environ. Res. Risk A., 29, 1179–1189, https://doi.org/10.1007/s00477-014-0916-1, 2015.
Serinaldi, F. and Kilsby, C. G.: Stationarity is undead: Uncertainty dominates the distribution of extremes, Adv. Water Resour., 77, 17–36, https://doi.org/10.1016/j.advwatres.2014.12.013, 2015.
Shaw, T. A., Baldwin, M., Barnes, E. A., Caballero, R., Garfinkel, C. I.,
Hwang, Y.-T., Li, C., O'Gorman, P. A., Rivière, G., Simpson, I. R., and
Voigt, A.: Storm track processes and the opposing influences of climate
change, Nat. Geosci., 9, 656–664, https://doi.org/10.1038/ngeo2783, 2016.
Smith, J. A. and Baeck, M. L.: “Prophetic vision, vivid imagination”: The
1927 Mississippi River flood, Water Resour. Res., 51, 9964–9994,
https://doi.org/10.1002/2015WR017927, 2015.
Steiner, M., Houze, R. A., and Yuter, S. E.: Climatological Characterization
of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge
Data, J. Appl. Meteorol. Clim., 34, 1978–2007,
https://doi.org/10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2, 1995.
Steiner, M., Smith, J. A., Burges, S. J., Alonso, C. V., and Darden, R. W.:
Effect of bias adjustment and rain gauge data quality control on radar
rainfall estimation, Water Resour. Res., 35, 2487–2503,
https://doi.org/10.1029/1999WR900142, 1999.
Su, Y. and Smith, J. A.: An Atmospheric Water Balance Perspective on Extreme
Rainfall Potential for the Contiguous US, Water Res., 57, e2020WR028387, https://doi.org/10.1029/2020WR028387, 2021.
Sudradjat, A., Brubaker, K. L., and Dirmeyer, P. A.: Interannual variability
of surface evaporative moisture sources of warm-season precipitation in the
Mississippi River basin, J. Geophys. Res.-Atmos., 108, 8612,
https://doi.org/10.1029/2002JD003061, 2003.
Svensson, C. and Jones, D.: Review of methods for deriving area reduction
factors, J. Flood Risk Manage., 3, 232–245, https://doi.org/10.1111/j.1753-318X.2010.01075.x, 2010.
Thorndahl, S., Nielsen, J. E., and Rasmussen, M. R.: Estimation of Storm-Centred Areal Reduction Factors From Radar Rainfall for Design in
Urban Hydrology, Water, 11, 263–266, https://doi.org/10.3390/w11061120, 2019.
Toride, K., Iseri, Y., Warner, M. D., Frans, C. D., Duren, A. M., England,
J. F., and Kavvas, M. L.: Model-Based Probable Maximum Precipitation
Estimation: How to Estimate the Worst-Case Scenario Induced by Atmospheric
Rivers?, J. Hydrometeorol., 20, 2383–2400, https://doi.org/10.1175/JHM-D-19-0039.1, 2019.
Troutman, B. M. and Karlinger, M. R.: Regional flood probabilities, Water
Resour. Res., 39, 1095, https://doi.org/10.1029/2001WR001140, 2003.
Urraca, R., Huld, T., Gracia-Amillo, A., Martinez-de-Pison, F. J., Kaspar,
F., and Sanz-Garcia, A.: Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data, Solar Energy, 164, 339–354, https://doi.org/10.1016/j.solener.2018.02.059, 2018.
USACE: Storm rainfall in the United States, US Govt. Print. Off., https://usace.contentdm.oclc.org/digital/collection/p266001coll1/id/7347/ (last access: 17 May 2022), 1945.
Vernieuwe, H., Vandenberghe, S., De Baets, B., and Verhoest, N.: A continuous rainfall model based on vine copulas, Hydrol. Earth Sys. Sci., 19, 2685–2699, https://doi.org/10.5194/hess-19-2685-2015, 2015.
Villarini, G., Mandapaka, P. V., Krajewski, W. F., and Moore, R. J.: Rainfall and sampling uncertainties: A rain gauge perspective, J. Geophys. Res.-Atmos., 113, D11102, https://doi.org/10.1029/2007JD009214, 2008.
Vose, R., Applequist, S., Squires, M., Durre, I., Menne, M., Williams Jr., C., Fenimore, C., Gleason, K., and Arndt, D.: Gridded 5 km GHCN-daily temperature and precipitation dataset (nCLIMGRID) version 1, NOAA [data set], http://datadiscoverystudio.org/geoportal/rest/metadata/item/c01f625b48b14c5d8031a76a9de2b45a/html (last access: 6 April 2022), 2014,
Walshaw, D.: Generalized Extreme Value Distribution, in: Encyclopedia of
Environmetrics, John Wiley & Sons, Ltd, https://doi.org/10.1002/9780470057339.vae062.pub2, 2013.
Weather Bureau: Manual for Depth-Area-Duration analysis of storm precipitation, Cooperative Studies Section, Division of Climatological and Hydrolic Services, https://books.google.com/books?id=7-Q3lWenPPoC&printsec=frontcover&hl=zh-CN&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false (last access: 17 May 2022), 1946.
Wilson, L. L. and Foufoula-Georgiou, E.: Regional Rainfall Frequency Analysis via Stochastic Storm Transposition, J. Hydraul. Eng., 116, 859–880,
https://doi.org/10.1061/(ASCE)0733-9429(1990)116:7(859), 1990.
World Meteorological Organization: Manual on estimation of probable maximum
precipitation (PMP), https://damfailures.org/wp-content/uploads/2020/10/WMO-1045-en.pdf
(last access: 2 July 2021), 2009.
Wright, D. B., Smith, J. A., Villarini, G., and Baeck, M. L.: Estimating the
frequency of extreme rainfall using weather radar and stochastic storm
transposition, J. Hydrol., 488, 150–165, https://doi.org/10.1016/j.jhydrol.2013.03.003, 2013.
Wright, D. B., Smith, J. A., and Baeck, M. L.: Critical examination of area
reduction factors, J. Hydrol. Eng., 19, 769–776, 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.
Xiong, L., Yu, K., and Gottschalk, L.: Estimation of the distribution of
annual runoff from climatic variables using copulas, Water Resour. Res., 50, 7134–7152, https://doi.org/10.1002/2013WR015159, 2014.
Xu, P., Wang, D., Singh, V. P., Lu, H., Wang, Y., Wu, J., Wang, L., Liu, J.,
and Zhang, J.: Multivariate Hazard Assessment for Nonstationary Seasonal
Flood Extremes Considering Climate Change, J. Geophys. Res.-Atmos., 125,
e2020JD032780, https://doi.org/10.1029/2020JD032780, 2020.
Yang, X., Xie, X., Liu, D. L., Ji, F., and Wang, L.: Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region, Adv. Meteorol., 2015, e563629, https://doi.org/10.1155/2015/563629, 2015.
Yu, G., Wright, D. B., and Holman, K. D.: Connecting Hydrometeorological
Processes to Low-Probability Floods in the Mountainous Colorado Front Range,
Water Resour. Res., 57, e2021WR029768, https://doi.org/10.1029/2021WR029768, 2021.
Zhang, Q., Ye, J., Zhang, S., and Han, F.: Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data, J. Sensors, 2018, e3428303, https://doi.org/10.1155/2018/3428303, 2018.
Zhou, Z., Smith, J. A., Wright, D. B., Baeck, M. L., Yang, L., and Liu, S.:
Storm Catalog-Based Analysis of Rainfall Heterogeneity and Frequency in a
Complex Terrain, Water Resour. Res., 55, 1871–1889, https://doi.org/10.1029/2018WR023567, 2019.
Zhu, L., Quiring, S. M., and Emanuel, K. A.: Estimating tropical cyclone
precipitation risk in Texas, Geophys. Res. Lett., 40, 6225–6230,
https://doi.org/10.1002/2013GL058284, 2013.
Zhuang, J., Dussin, R., Jüling, A., and Rasp, S.: xESMF: Universal Regridder for Geospatial Data, Zenodo [code], https://doi.org/10.5281/zenodo.1134365, 2020.
Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., Seneviratne, S. I.,
Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl,
T., and Zhang, X.: Future climate risk from compound events, Nat. Clim.
Change, 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018.
Short summary
We present a new approach to estimate extreme rainfall probability and severity using the atmospheric water balance, where precipitation is the sum of water vapor components moving in and out of a storm. We apply our method to the Mississippi Basin and its five major subbasins. Our approach achieves a good fit to reference precipitation, indicating that the rainfall probability estimation can benefit from additional information from physical processes that control rainfall.
We present a new approach to estimate extreme rainfall probability and severity using the...
