Spatiotemporal Characteristics of Drought in the Heihe River Basin Based on the Extreme-Point Symmetric Mode Decomposition Method

Abstract

Assessment of spatiotemporal characteristics of drought under climate change is significant for drought mitigation. In this study, the standardized precipitation evapotranspiration index (SPEI) calculated at different timescales was adopted to describe the drought conditions in the Heihe River Basin (HRB) from 1961 to 2014. The period characteristics and spatiotemporal distribution of drought were analyzed by using the extreme-point symmetric mode decomposition (ESMD) and inverse distance weight interpolation methods. Four main results were obtained. (1) The SPEI series of the upper reaches of the HRB at different timescales showed an upward trend (not significant) during 1961-2014. In the middle and lower reaches, the SPEI series exhibited significant downward trends. (2) The annual SPEI series of the lower reaches was decomposed through the ESMD method and exhibited a fluctuating downward trend as a whole. The oscillation showed quasi-3.4-year and quasi-4.5-year periods in the interannual variation, while a quasi-13.5-year period occurred in the interdecadal variation. The interannual period plays a leading role in drought variation across the HRB. (3) The entire research period was divided into three subperiods by the Bernaola-Galvan segmentation algo

A multi-scale daily SPEI dataset for drought characterization at observation stations over mainland China from 1961 to 2018

“…PDSI and SPI are the most popular drought studies worldwide (Dai et al, 2004;McKee et al, 1993); however, they have some limitations. PDSI is only suitable to the agricultural drought through characterization of the soil water deficit, and it cannot identify the meteorological, hydrological, and socioeconomic droughts (Feng and Su, 2019). In addition, PDSI limits the spatial comparability of drought due to the fact that it is heavily dependent on data calibration (Sheffield et al, 2009;Yu et al, 2014).…”

“…To solve the above problems, the standardized precipitation evapotranspiration index (SPEI), which considers the advantages of both PDSI and SPI, was developed to monitor and assess droughts (Vicente-Serrano et al, 2010). It not only accounts for the effect of evaporation on drought, but also has the capability of spatial comparability and characterization of different drought types with multiple timescales (Feng and Su, 2019;Wang et al, 2015). SPEI can be used to delineate spatialtemporal evolution of drought, drought characteristics, and impacts of drought at the regional and global scales (Mallya et al, 2016;Wang et al, 2014).…”

Novel Fault Location for High Permeability Active Distribution Networks Based on Improved VMD and S-transform

“…Empirical mode decomposition (EMD) is an adaptive iterative filtering algorithm, it can deal with the fault signals, but will cause the phenomenon of the end effect and mode aliasing [18]. Based on EMD, the ensemble empirical mode decomposition (EEMD) is brought out [19], which can only inhibit the phenomenon of mode alising, but cannot completely eliminate it. Compared with EMD and EEMD, variational mode decomposition (VMD) is an algorithm for non-stationary signal decomposition [20].…”

A Multi-Scale Daily SPEI Dataset for Drought Monitoring at Observation Stations over the Mainland China from 1961 to 2018

“…PDSI and SPI are the most popular drought studies worldwide (Dai et al, 2004;McKee et al, 1993), however, they have some limitation. PDSI is only suitable to the agricultural drought through characterizing the soil water deficit, and it cannot identify the meteorological, hydrological, and socioeconomic droughts (Feng and Su, 2019). In addition, PDSI limits the spatial comparability of drought due to the fact that it is heavily depending on data calibration (Sheffield et al, 2009;Yu et al, 2014).…”

“…To solve the above problems, the Standardized Precipitation Evapotranspiration Index (SPEI), which considers the advantage of both PDSI and SPI, was developed to monitor and assess droughts https://doi.org/10.5194/essd-2020-172 (Vicente-Serrano et al, 2010). It not only accounts for the effect of evaporation on drought, but also have the capability of spatial comparability and characterizing different drought types with multiple time scales (Feng and Su, 2019;Wang et al, 2015). SPEI has been widely used to delineate drought spatial-temporal evolution, drought characteristics, and impacts of drought at the regional and global scales (Mallya et al, 2016;Wang et al, 2014).…”