scholarly journals Capability of Remotely Sensed Drought Indices for Representing the Spatio–Temporal Variations of the Meteorological Droughts in the Yellow River Basin

2018 ◽  
Vol 10 (11) ◽  
pp. 1834 ◽  
Author(s):  
Fei Wang ◽  
Zongmin Wang ◽  
Haibo Yang ◽  
Yong Zhao ◽  
Zhenhong Li ◽  
...  
Keyword(s):  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
Condition Index ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Drought Indices ◽  
Spatio Temporal ◽  
The Yellow River Basin ◽  
Meteorological Droughts

Due to the advantages of wide coverage and continuity, remotely sensed data are widely used for large-scale drought monitoring to compensate for the deficiency and discontinuity of meteorological data. However, few studies have focused on the capability of various remotely sensed drought indices (RSDIs) to represent the spatio–temporal variations of meteorological droughts. In this study, five RSDIs, namely the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Modified Temperature Vegetation Dryness Index (MTVDI), and Normalized Vegetation Supply Water Index (NVSWI), were calculated using monthly Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The monthly NDVI and LST data were filtered by the Savitzky–Golay (S-G) filtering method. A meteorological station-based drought index represented by the Standardized Precipitation Evapotranspiration Index (SPEI) was compared with the RSDIs. Additionally, the dimensionless Skill Score (SS) method was adopted to identify the spatiotemporally optimal RSDIs for presenting meteorological droughts in the Yellow River basin (YRB) from 2000 to 2015. The results indicated that: (1) RSDIs revealed a decreasing drought trend in the overall YRB consistent with the SPEI except for in winter, and different variations of seasonal trends spatially; (2) the optimal RSDIs in spring, summer, autumn, and winter were VHI, TCI, MTVDI, and VCI, respectively, and the average correlation coefficient between the RSDIs and the SPEI was 0.577 (α = 0.05); and (3) different RSDIs have time lags of zero–three months compared with the meteorological drought index.

scholarly journals Capability of Remotely Sensed Drought Indices for Representing the Spatio–Temporal Variations of the Meteorological Droughts in the Yellow River Basin

2018 ◽  
Author(s):  
Fei Wang ◽  
Zongmin Wang ◽  
Haibo Yang ◽  
Yong Zhao ◽  
Zhenhong Li ◽  
...  
Keyword(s):  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
Condition Index ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Drought Indices ◽  
The Yellow River ◽  
The Yellow River Basin ◽  
Meteorological Droughts

Due to the advantages of wide coverage and continuity, remotely sensed data are widely used for large-scale drought monitoring to compensate the deficiency and discontinuity of meteorological data. However, few researches have focused on the capability of various remotely sensed drought indices (RSDIs) for representing the spatio-temporal variations of the meteorological droughts. In this study, five RSDIs, namely Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Modified Temperature Vegetation Dryness Index (MTVDI) and Normalized Vegetation Supply Water Index (NVSWI) were calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) monthly NDVI and LST. The monthly NDVI and LST data were filtered by Savitzky-Golay (S-G) filtering method. Meteorological station-based drought index represented by Standardized Precipitation Evapotranspiration Index (SPEI) was compared with RSDIs. And the dimensionless Skill Score (SS) method was adopted to identify the spatiotemporally optimal RSDIs for presenting the meteorological droughts in the Yellow River basin (YRB) from 2000 to 2015. The results indicated that (1) RSDIs revealed a decreasing trend to the overall YRB consistent with SPEI except for in winter, and different variations of seasonal trends spatially; (2) the optimal RSDIs in spring, summer, autumn and winter were VHI, TCI, MTVDI and VCI, respectively, and the average correlation coefficient between the RSDIs and SPEI was 0.577 (=0.05); (3) different RSDIs have a 0–3 months’ time-lags compared with meteorological drought index.

scholarly journals Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

2021 ◽  
Vol 13 (18) ◽  
pp. 3748
Author(s):  
Xiaoyang Zhao ◽  
Haoming Xia ◽  
Li Pan ◽  
Hongquan Song ◽  
Wenhui Niu ◽  
...  
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
Condition Index ◽  
Drought Indices ◽  
The Yellow River ◽  
Drought Conditions ◽  
The Yellow River Basin

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

The spatio-temporal variations of vegetation cover in the Yellow River Basin from 2000 to 2010

2013 ◽  
Vol 33 (24) ◽  
Author(s):  
袁丽华 YUAN Lihua ◽  
蒋卫国 JIANG Weiguo ◽  
申文明 SHEN Wenming ◽  
刘颖慧 LIU Yinghui ◽  
王文杰 WANG Wenjie ◽  
...  
Keyword(s):  
River Basin ◽  
Vegetation Cover ◽  
Yellow River ◽  
Yellow River Basin ◽  
Temporal Variations ◽  
The Yellow River ◽  
Spatio Temporal ◽  
The Yellow River Basin

Characteristics of integrated droughts based on a nonparametric standardized drought index in the Yellow River Basin, China

2015 ◽  
Vol 47 (2) ◽  
pp. 454-467 ◽  
Author(s):  
Yuelu Zhu ◽  
Jianxia Chang ◽  
Shengzhi Huang ◽  
Qiang Huang
Keyword(s):  
River Basin ◽  
Drought Index ◽  
Yellow River ◽  
Standardized Precipitation Index ◽  
Yellow River Basin ◽  
Drought Indices ◽  
The Yellow River ◽  
The Yellow River Basin ◽  
Multivariate Drought Index ◽  
Annual Time

Most current drought indices rely on a representative parametric distribution function to fit data, which results in different tail behaviors. Additionally, a drought index based on a single variable may not be sufficient for monitoring drought conditions timely and reliably. Therefore, a nonparametric multivariate drought index (NMSDI) combined with the information of precipitation and streamflow was introduced in this study, without assuming representative parametric distributions. It was applied to characterize drought in the Yellow River Basin (YRB) on seasonal and annual scales. Results indicate that: (1) the variations of developed NMSDI is well consistent with those of 1-month SPI (standardized precipitation index) and SSI (standardized streamflow index), indicating the reliability and effectiveness of the newly proposed nonparametric based drought index; (2) a decreasing NMSDI trend was found over the period of 1952–2012 at seasonal and annual time scales, which would reverse in the future as suggested by the Hurst index; (3) no significant change points were detected for the annual NMSDI series over the YRB except one (i.e. the year 1991) in the middle streamflow sub-basin Wei River Basin (WRB) which was primarily caused by the combined effects of climate change and human activities.

scholarly journals Drought Evaluation with CMORPH Satellite Precipitation Data in the Yellow River Basin by Using Gridded Standardized Precipitation Evapotranspiration Index

2019 ◽  
Vol 11 (5) ◽  
pp. 485 ◽  
Author(s):  
Fei Wang ◽  
Haibo Yang ◽  
Zongmin Wang ◽  
Zezhong Zhang ◽  
Zhenhong Li
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Large Scale ◽  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Precipitation Data ◽  
Standardized Precipitation Evapotranspiration Index ◽  
The Yellow River Basin

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.

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