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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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The long term series of characteristics of floods that happened to 12 rivers in Finland and northern Russia.

Authors: Elena Shevnina;

The long term series of characteristics of floods that happened to 12 rivers in Finland and northern Russia.

Abstract

The dataset of flood’s characteristics (annual and spring): the volume of spring flood (in mm of the depth of runoff), the dates of spring flood begin and end, the length of spring flooding period, the yearly maximum daily discharge and its date were estimated for each year from the daily series of water discharges observed at the hydrometric sites. To define the dates of spring flood begin and end we applied the semi-empirical method given in Shevnina (2013). The yearly maximum water discharges have been obtained in Gudmundsson et al. (2018) for the period until 2017; this dataset gives a good agreement in the estimations for the overlapping periods. The series of volume of spring flood (in mm of the depth of runoff), the dates of spring flood begin and end, the length of spring flooding period, the yearly maximum daily discharge and its date are given in the dataset supplementing the study submitted to the Water resource research journal (https://agupubs.onlinelibrary.wiley.com/journal/19447973 ). The daily series of water river discharges at the sites located in Finland were extracted from (a) the Global runoff database https://portal.grdc.bafg.de/ (for the period from beginning of the observations to 2017); (b) the archive of the Finnish Environmental Institute https://www.syke.fi (for the period 2018–2020) and these series can be obtained after its representatives’ permission from the author. The daily series of water discharges at the sites located in the Russian Federation were extracted from (a) the yearly hydrological books published by the State Hydrological Institute http://www.hydrology.ru/en (for the period from the beginning of observation to 2007); (b) the automated information system for state monitoring of water bodies https://gmvo.skniivh.ru/ (for the period 2008–2020) and these series are available from its web-site after a registration. The dataset consists of the CSV/TXT files, each file contains the long term series of the characteristics listed in the header: "year", "DFB" (date when a spring flooding period begins, day of year, DOY),"DFE" (date when the spring flooding period ends, DOY),"Length" (length of the spring flooding period, days), "DFMax" (date when the yearly maximum water discharge is recorded, DOY), "Qmax" (the yearly maximum water discharge, cubic m per second), "FRD" (the volume of spring flood expressed in mm per flooding period), "YRD" (volume of annual flow, expressed in mm per year),"Ftype" (the source of annual flood equaling to 1 of the yearly maximum water discharge is recorded in the spring flooding period or 0 if it is not). The dataset was obtained in the study funded by the Academy of Finland under the contract number 317999. It will become freely available once the manuscript is published. References Gudmundsson, L., Do, H. X., Leonard, M., & Westra, S. (2018), The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018. Shevnina E. (2013), Method to calculate characteristics of spring flood from daily water discharges, Problems of the Arctic and Antarctic, 1(95), pp. 12-21. In Russian

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Keywords

River floods, timing of annual floods, spring flooding period

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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