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Hydroclimatic time series features at multiple time scales

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F23%3A97215" target="_blank" >RIV/60460709:41330/23:97215 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1016/j.jhydrol.2023.129160" target="_blank" >http://dx.doi.org/10.1016/j.jhydrol.2023.129160</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.jhydrol.2023.129160" target="_blank" >10.1016/j.jhydrol.2023.129160</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Hydroclimatic time series features at multiple time scales

  • Original language description

    A comprehensive understanding of the behaviours of the various geophysical processes and an effective evalu-ation of time series (else referred to as stochastic) simulation models require, among others, detailed in-vestigations across temporal scales. In this work, we propose a novel and detailed methodological framework for advancing and enriching such investigations in a hydroclimatic context. This specific framework is primarily based on a new feature compilation for multi-scale hydroclimatic analyses, and can facilitate largely interpret-able feature investigations and comparisons in terms of temporal dependence, temporal variation, forecast-ability, lumpiness, stability, nonlinearity (and linearity), trends, spikiness, curvature and seasonality. Multifaceted characterizations are herein obtained by computing the values of the proposed feature compilation across nine temporal resolutions (i.e., the 1-day, 2-day, 3-day, 7-day, 0.5-month, 1-month, 2-month, 3-month and 6-month ones) and three hydroclimatic time series types (i.e., temperature, precipitation and streamflow) for 34-year-long time series records originating from 511 geographical locations across the contiguous United States. Based on the acquired information and knowledge, similarities and differences between the examined time series types with respect to the evolution patterns characterizing their feature values with increasing (or decreasing) temporal resolution are identified. Moreover, the computed features are used as inputs to unsupervised random forests for detecting any meaningful clusters between the examined hydroclimatic time series. This clustering plays an illustrative role within this research, as it facilitates the identification of spatial patterns (with them consisting an important scientific target in hydroclimatic research) and their cross-scale comparison. We find that these specific patterns are largely analogous across temporal resolutions for the examined continental-scale re-gion. We also apply explainable machine learning to compare the features with respect to their usefulness in clustering the time series at the various temporal resolutions. These latter investigations play a vital role within the proposed methodological framework, as they allow interpretation of hydroclimatic similarity at the various temporal resolutions. For most of the features, this usefulness can vary to a notable degree across temporal resolutions and time series types, thereby implying the need for conducting multifaceted time series charac-terizations for the study of hydroclimatic similarity.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10510 - Climatic research

Result continuities

  • Project

    <a href="/en/project/GM22-33266M" target="_blank" >GM22-33266M: Investigation of the Terrestrial HydrologicAl Cycle Acceleration (ITHACA)</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Journal of Hydrology

  • ISSN

    0022-1694

  • e-ISSN

    0022-1694

  • Volume of the periodical

    618

  • Issue of the periodical within the volume

    2023

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    21

  • Pages from-to

    1-21

  • UT code for WoS article

    000946383600001

  • EID of the result in the Scopus database

    2-s2.0-85147843784