Emergence of heavy tails in streamflow distributions: the role of spatial rainfall variability

Wang, Hsing-Jui; Merz, Ralf; Yang, Se Hwan; Tarasova, Larisa; Basso, Stefano

doi:10.1016/j.advwatres.2022.104359

Cited by 16 publications

(33 citation statements)

References 93 publications

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“…A continuous probabilistic model simulated catchment-scale water transport, investigating the impact of spatial rainfall variability on streamflow distribution tail heaviness in five catchments of varying sizes and shapes. The study validated results using recorded data from 175 river catchments by Wang et al [23]. The notable likelihood of extreme streamflow occurrences emphasizes an elevated flood risk concerning both the frequency and magnitude of the flow.…”

Section: Introductionsupporting

confidence: 80%

“…The notable likelihood of extreme streamflow occurrences emphasizes an elevated flood risk concerning both the frequency and magnitude of the flow. Across different catchments worldwide, the presence of streamflow patterns with heavy tails has been observed by Wang et al [24]. Rainfall has been largely overlooked in the majority of prior investigations, with only a small subset of recent studies concentrating on forecasting the yearly likelihood due to rainfall-induced slope failure on particular slopes [25], [26].…”

Section: Introductionmentioning

confidence: 99%

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Probability distributions in Kerala’s rainfall: implications for hydro energy planning

Baranitharan,

Chandran,

Subramaniyan Mathan

et al. 2024

IJECE

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Heavy rainfall has consistently acted as the primary catalyst for floods, resulting in numerous casualties and significant economic losses globally. Rainfall forecasting is accomplished by analysing existing rainfall data, which is then used to analyse the hydraulic system’s features. Gaining an understanding of rainfall requirements is a crucial challenge for every location, particularly in the case of India, given its diverse geographical area, population, and other influencing factors that impact various demands. This study evaluated the rainfall data for a span of 1990-2021 in six districts of Kerala State, India. To match the rainfall data from all districts, we utilized both Kaumarasamy-distribution and Dagum-distributions. Various Probabilistic tests, were employed to comparing these distributions. The results revealed that, in Kasargod, the Kumarasamy distribution demonstrates superior goodness-of-fit with the lowest Kolmogorov-Smirnov statistic (0.0597) and Anderson-darling statistic (2.271). However, in Wayanad, Malappuram, Palakkad, Idukki, and Trivandrum, the Dagum distribution consistently exhibits the most accurate fit, evident from its lowest Kolmogorov-Smirnov statistics (0.07447, 0.05435, 0.0556, 0.03636, 0.04291) and favourable Chi-Squared statistics (19.471, 8.4907, 19.239, 5.7318, 7.5297). These results emphasize the regional variation in precipitation data and the suitability of specific distribution models for accurate representation across differentlocations.

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Section: Introductionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Probability distributions in Kerala’s rainfall: implications for hydro energy planning

Baranitharan,

Chandran,

Subramaniyan Mathan

et al. 2024

IJECE

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“…The total duration method more closely represents the rainfall and runoff of a specific basin, while the fitting curve calculated by the multi‐year average method can fully capture the changes of FDC between years (Cheng et al, 2012; Costa & Fernandes, 2021; Liang, 2019). FDC can be used to diagnose the rainfall‐runoff response of a watershed to help develop or validate rainfall‐runoff models through the transition from precipitation change to runoff (Wang et al, 2023). It can be also used to analyse the similarity and difference between watersheds, establish a model using precipitation and watershed characteristics as input, and predict the hydrological elements of ungauged sub‐watersheds from a limited number of stations observed in the past (Burgan & Aksoy, 2022; Wolff & Duarte, 2021).…”

Section: Introductionmentioning

confidence: 99%

Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution

Zhou,

Zhang

2024

Hydrological Processes

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The flow duration curve (FDC) is the cumulative distribution function, which represents the relationship between the frequency and magnitude of streamflow, and the precipitation duration curves (PDC) follows the same principle. Nowadays, the correlation between the shape of PDC and FDCs, their respective physical control factors including human activities, and their fitting conditions in unmeasured catchments across China have not been fully understood. In this paper, daily precipitation and streamflow data with 30 years records from 224 hydrological stations in the middle and lower Yangtze River basin were chosen to fit PDC and FDCs through gamma distribution. Framework was proposed for modelling FDCs to analyse the relationship, similarity, regional patterns and response mechanism of fitting parameters between PDC and FDCs, dividing the streamflow time series into fast and base flow and stations into three categories in consideration of human activities to attribute the shapes of PDC and FDCs to catchment meteorological and geographical characteristics and physical processes under natural conditions. Results indicate that the parameters of PDC and certain FDCs (TFDC, FFDC, SFDC) share similar spatial patterns but vary for the different duration and interactions of the processes. The climate and catchment characteristics such as extreme properties of precipitation, base flow index (BFI), Pmax*αp and concentration ratio index (CIM) will influence the shape of PDC and FDCs. The relationship between BFI and SFDC/TFDC can be better reflected in “Regulated watersheds”, while CIM/Pmax*αp and PDC/FFDC in “watersheds in the mainstream” are more related. This paper provides a deeper understanding and more accurate way of the application of these parameters in describing and predicting hydrological processes and estimation of PDC and FDCs in unmeasured catchments, and can be applied to more future research about processes based on catchment rainfall‐runoff responses and physical controls.

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“…The unprecedented magnitude of extreme floods often characterizes these hazards, which is better depicted by the heavy-tailed behavior exhibited in flood frequency distributions (Smith et al, 2018;Merz et al, 2021;. The concept of heavy-tailed behavior finds broad application in various fields to describe the likelihood of extreme event occurrences (Katz, 2002;Kondor et al, 2014;Malamud, 2004;Sartori and Schiavo, 2015;Wang et al, 2022). In particular, it is widely recognized as a prevalent feature in hydrologic extremes (Papalexiou and Koutsoyiannis, 2013;Smith et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Constructing a geography of heavy-tailed flood distributions: insights from common streamflow dynamics

Wang,

Merz

et al. 2024

Preprint

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Key Points 1. Regional propensity of flood tail behavior is shown across a diverse geographical range based on the analysis of common streamflow dynamics. 2. Temporal variability in catchment storage, driven by high evapotranspiration and dry soils, is a key mechanism for heavy-tailed floods. 3. Examining flood generation processes aids in unraveling the connections between flood tail behavior and seasons or catchment sizes.

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Emergence of heavy tails in streamflow distributions: the role of spatial rainfall variability

Cited by 16 publications

References 93 publications

Probability distributions in Kerala’s rainfall: implications for hydro energy planning

Probability distributions in Kerala’s rainfall: implications for hydro energy planning

Physical controls and regional pattern similarities of precipitation and flow duration curves using the three‐parameter gamma distribution

Constructing a geography of heavy-tailed flood distributions: insights from common streamflow dynamics

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