Abstract

The traditional station-based drought index is vulnerable because of the inadequate spatial distribution of the station, and also, it does not fully reflect large-scale, dynamic drought information. Thus, large-scale drought monitoring has been widely implemented by using remote sensing precipitation products. Compared with station data, remote sensing precipitation products have the advantages of wide coverage and dynamic, continuous data, which can effectively compensate for the deficiency in the spatial distribution of the ground stations and provide a new data source for the calculation of a drought index. In this study, the Gridded Standardized Precipitation Evapotranspiration Index (GSPEI) was proposed based on a remote sensing dataset produced by the Climate Prediction Center morphing technique (CMORPH), in order to evaluate the gridded drought characteristics in the Yellow River basin (YRB) from 1998 to 2016. The optimal Ordinary Kriging interpolation method was selected to interpolate meteorological station data to the same spatial resolution as CMORPH data (8 km), in order to compare the ground-based meteorological parameters to remote sensing-based data. Additionally, the gridded drought trends were identified based on the Modified Mann–Kendall (MMK) trend test method. The results indicated that: (1) the GSPEI was suitable for drought evaluation in the YRB using CMORPH precipitation data, which were consistent with ground-based meteorological data; (2) the positive correlation between GSPEI and SPEI was high, and all the correlation coefficients (CCs) passed the significance test of α = 0.05, which indicated that the GSPEI could better reflect the gridded drought characteristics of the YRB; (3) the drought severity in each season of the YRB was highest in summer, followed by spring, autumn, and winter, with an average GSPEI of −1.51, −0.09, 0.30, and 1.33, respectively; and (4) the drought showed an increasing trend on the monthly scale in March, May, August, and October, and a decreasing trend on the seasonal and annual scale.

Highlights

  • Drought disaster is the natural disaster with the highest frequency, the most serious socio–economic and ecological losses and the most extensive impact [1,2,3]

  • Most of the maximum CCs between Gridded Standardized Precipitation Evapotranspiration Index (GSPEI) and Standardized Precipitation Evapotranspiration Index (SPEI) appeared in Sanmenxia to Huayuankou (SH) and below Huayuankou (BH) in each month and season

  • This paper proposed a gridded drought index (GSPEI) to study the temporal evolution, spatial distribution and trend information of drought, in order to evaluate the drought monitoring effectiveness of Center morphing technique (CMORPH) remote sensing precipitation products and reveal the gridded drought characteristics in the Yellow River basin (YRB) during 1998–2016

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Summary

Introduction

Drought disaster is the natural disaster with the highest frequency, the most serious socio–economic and ecological losses and the most extensive impact [1,2,3]. Drought indices are calculated by using the observational data from surface meteorological stations. Due to the influence of geographical and economic factors, meteorological stations often have problems such as sparse and uneven distribution and a lack of appropriate spatial representation. As a direct or indirect parameter for various drought indices, precipitation is more uncertain and discontinuous in spatial and temporal distribution compared to other meteorological data such as temperature and wind speed [4,5]. It is generally difficult to obtain accurate precipitation information in areas of missing data by spatial interpolation. When the available precipitation data are relatively sparse, the drought indices calculated from the station-observed data are usually unable to reflect the drought information of the entire area [6,7]

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