Hydroclimatic time series features at multiple time scales

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Hydroclimatic time series features at multiple time scales

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Hydroclimatic time series features at multiple time scales

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10510 - Climatic research

Projekt

<a href="/cs/project/GM22-33266M" target="_blank" >GM22-33266M: Vyhodnocení intenzifikace suchozemského hydrologického cyklu</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Journal of Hydrology

ISSN

0022-1694

e-ISSN

0022-1694

Svazek periodika

618

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

21

Strana od-do

1-21

Kód UT WoS článku

000946383600001

EID výsledku v databázi Scopus

2-s2.0-85147843784