Runoff recession features in an analytical probabilistic streamflow model

Arai, Ryosuke; Toyoda, Yasushi; Kazama, So

doi:10.1016/j.jhydrol.2020.125745

Cited by 14 publications

(9 citation statements)

References 36 publications

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“…PHEV 12,17 is a mechanistic-stochastic characterization of the magnitude and probability of streamflow maxima occurring in a given reference period (for example, a season). It results from a well-established mathematical description of catchment-scale daily precipitation, soil moisture and runoff dynamics [41][42][43][44][45] , which has been proved suitable for a wide array of physioclimatic conditions [46][47][48][49][50][51][52][53][54] . This framework describes precipitation as a marked Poisson process with frequency λ P (1/T) and exponentially distributed depth with average α (L), where T stands for time and L for length.…”

Section: Phev Distribution Of River Flowsmentioning

confidence: 99%

Extreme flooding controlled by stream network organization and flow regime

et al. 2023

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River floods are among the most common natural disasters worldwide, with substantial economic and humanitarian costs. Despite enormous efforts, gauging the risk of extreme floods with unprecedented magnitude is an outstanding challenge. Limited observational data from very high-magnitude flood events hinders prediction efforts and the identification of discharge thresholds marking the rise of progressively larger floods, termed flood divides. Combining long hydroclimatic records and a process-based model for flood hazard assessment, here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. In contrast with their ubiquitous attribution to extreme rainfall and anomalous antecedent conditions, we show that the propensity to generate extreme floods is well predicted by intrinsic properties of river basins. Most importantly, it can be assessed prior to the occurrence of catastrophes through measurable metrics of these properties derived from commonly available discharge data, namely the hydrograph recession exponent and the coefficient of variation of daily flows. These results highlight the propensity of certain rivers for generating extreme floods and demonstrate the importance of using hazard mapping tools that, rather than solely relying on past flood records, identify regions susceptible to the occurrence of extreme floods from ordinary discharge dynamics.

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Section: Phev Distribution Of River Flowsmentioning

confidence: 99%

Extreme flooding controlled by stream network organization and flow regime

et al. 2023

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“…PHEV 17,19 is a mechanistic-stochastic characterization of the magnitude and probability of streamflow maxima occurring in a given reference period (e.g., a season). It results from a well-established mathematical description of catchment-scale daily precipitation, soil moisture and runoff dynamics [45][46][47][48][49] , which has been proved suitable for a wide array of physioclimatic conditions [50][51][52][53][54][55][56][57][58] . This framework describes precipitation as a marked Poisson process with frequency λP [1/T] and exponentially distributed depth with average α [L].…”

Section: Physically-based Extreme Value (Phev) Distribution Of River ...mentioning

confidence: 99%

Extreme flooding foretold by stream network organization and flow regime

Basso

Merz

Tarasova

et al. 2022

Preprint

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River floods are the commonest natural disaster worldwide causing substantial monetary losses and death tolls yearly. Despite enormous efforts, gauging the peril of extreme floods is an outstanding challenge for varied economic sectors and the society at large. Appraisal of the flood hazard is especially difficult when the magnitude of the rarer floods strongly increases. Limited data records in fact hinder prediction of these instances and the identification of flood divides marking the rise of progressively larger floods. Here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. Unlike their ubiquitous attribution to anomalous hydrometeorological conditions, we show for the first time that the propensity to generate extreme floods is well predicted by intrinsic properties of river basins. Most importantly it can be assessed prior to the occurrence of catastrophes through measurable metrics of these properties derived from commonly available discharge data, with far-reaching implications for flood hazard assessment and preparedness.

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“…The related hydrograph recession is described via a power law function with exponent 𝑎 [−] and coefficient 𝐾 [L 1−a /T 2−a ] , which allows for mimicking the joint effect of different flow components . Such a description of runoff generation and streamflow dynamics was successfully tested in a variety of hydro-climatic and physiographic conditions (Arai et al, 2020;Botter et al, 2007a;Ceola et manuscript submitted to Geophysical Research Letters al., 2010;.…”

Section: Identifying Tail Behavior From Hydrological Dynamicsmentioning

confidence: 99%

A hydrogeomorphological index of heavy-tailed flood behavior

Wang

Merz

Yang

et al. 2022

Preprint

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Floods are often disastrous due to underestimation of the magnitude of rare events. When the occurrence of floods follows a heavy-tailed distribution the chance of extreme events is sizable. However, identifying heavy-tailed flood behavior is challenging because of limited data records and the lack of physical support for currently used indices. We address these issues by deriving a new index of heavy-tailed flood behavior from a physically-based description of streamflow dynamics. The proposed index, which is embodied by the hydrograph recession exponent, enables inferring heavy-tailed flood behavior from daily flow records. We test the index in a large set of case studies across Germany. Results show its ability to identify cases with either heavy- or nonheavy-tailed flood behavior, and to evaluate the tail heaviness. Remarkably, the results are robust also for decreasing the lengths of data records. The new index thus allows for assessing flood hazards from commonly available data.

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Runoff recession features in an analytical probabilistic streamflow model

Cited by 14 publications

References 36 publications

Extreme flooding controlled by stream network organization and flow regime

Extreme flooding controlled by stream network organization and flow regime

Extreme flooding foretold by stream network organization and flow regime

A hydrogeomorphological index of heavy-tailed flood behavior

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