Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

Zhang, Xiang; Wei, Chehan; Obringer, Renee; Li, Deren; Chen, Nengcheng; Niyogi, Dev

doi:10.3390/rs9080767

Cited by 10 publications

(5 citation statements)

References 45 publications

(68 reference statements)

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“…This result is consistent with those obtained in previous studies [14]. For example, Zhang [18,52] indicated that the agricultural drought index increases with meteorological drought.…”

Section: Discussionsupporting

confidence: 93%

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

et al. 2019

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Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.

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“…This result is consistent with those obtained in previous studies [14]. For example, Zhang [18,52] indicated that the agricultural drought index increases with meteorological drought.…”

Section: Discussionsupporting

confidence: 93%

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

et al. 2019

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“…RSDIs are used to quantitatively evaluate the effects of drought and directly determine the accuracy of drought monitoring; therefore, it is particularly important to study their capability under different spatio-temporal patterns. However, few studies have combined RSDIs with the meteorological drought index to assess whether the remotely sensed index is suitable for drought monitoring, and the research on the capability of RSDIs under different spatio-temporal patterns is insufficient [28,29]. Moreover, most of the methods to evaluate capability are based on a single statistical indicator (e.g., average deviation and root-mean-square error).…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Yang

et al. 2018

Remote Sensing

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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.

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“…First, other drought indices such as the Palmer drought severity index (Palmer, 1965) and the standardized precipitation index (McKee et al., 1993) can also be used. Second, the risk indicators can be more diverse and comprehensive, and different indicators may result in inconsistent results (Yao et al., 2018; Zhang et al., 2017). For instance, other socioeconomic factors that influence exposure and vulnerability, such as the age/sex structure, the industrial structure, and the distribution of reservoirs should also be considered when the data are available.…”

Section: Discussionmentioning

confidence: 99%

Projecting Global Drought Risk Under Various SSP‐RCP Scenarios

et al. 2023

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Drought risk assessment can identify high‐risk areas and bridge the gap between impacts and adaptation. It is thus vital to investigate changes in drought risk and exposed social economy under climate change. Here, the future global meteorological drought risk is projected for the 2021–2100 period under four combined scenarios of Representative Concentration Pathways and Shared Socioeconomic Pathways (SSPs), i.e., SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. And then the exposed population and Gross Domestic Product (GDP) under high risk are analyzed. Drought risk will further strengthen in the future under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, with large increases distributed in South Asia, the Mediterranean, East Asia, Southeast Asia, and Central America. The strongest increasing trends in drought risk are concentrated in equatorial regions including South Asia, West Africa, and Central America. A large percentage of the population and economy are exposed to high drought risk. The exposed population under the highest risk level is the largest under SSP5‐8.5 and the smallest under SSP3‐7.0 for regions in North America, while being the largest under SSP3‐7.0 for other regions. The exposed GDP under the highest risk level is the highest under SSP5‐8.5 and the lowest under SSP3‐7.0, while the disparity among diverse scenarios is larger for the rich regions than the developing ones.

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Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

Cited by 10 publications

References 45 publications

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

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

Projecting Global Drought Risk Under Various SSP‐RCP Scenarios

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