Improvement of drought assessment capability based on optimal weighting methods and a new threshold classification scheme

• Published in: Journal of hydrology (Amsterdam) Vol. 631; p. 130758
• Main Authors: Cai, Siyang, Zuo, Depeng, Wang, Huixiao, Han, Yuna, Xu, Zongxue, Wang, Guoqing, Yang, Hong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2024
• Subjects: Agricultural drought; Drought index; Optimal weights; Threshold classification scheme; Wei River; Wei River; Optimal weights; Agricultural drought; Drought index; Threshold classification scheme
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2024.130758

•The drought indices SDCI, VHI and IDCI were constructed by four weighting methods.•Determination of weights was validated by comparing against scPDSI, SMCI and CY.•New classification schemes were executed by cumulative frequency curve of scPDSI.•Reliability of schemes was verified by drought affected area and frequencies. With the continuous development of remote sensing technology, an increasing number of drought indices are proposed to assess the drought process in different regions. However, most of the studies used the empirical coefficient method and standard drought classification scheme, without considering regional applicability, which may overestimate or underestimate drought intensity. Therefore, the GLDAS soil moisture dataset was firstly verified in the study area, Wei River basin, China, and then the response of precipitation accumulation to NDVI and the lagged effect were analyzed. Secondly, the Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI), and Integrated Drought Condition Index (IDCI) were constructed and the optimal weights of the drought indicators were determined against the scPDSI, SMCI and CY based on four weighting methods. Finally, the new classification schemes of the three indicators were executed based on the cumulative frequency curve of the self-calibrating Palmer Drought Severity Index (scPDSI) from June to September during the period 2000–2019, and the reliability of the new schemes was verified by comparing with drought frequency and drought affected area. Furthermore, the variabilities of drought frequency of the new classification scheme based on the three indicators were analyzed. The results showed that the correlation coefficients of 2-month accumulation of precipitation and NDVI produced the best value and no lagged effect was detected. The SDCI and VHI determined by the AHP weighting method and the IDCI determined by the entropy weighting method showed better performance. The new classified schemes of the three drought indicators demonstrated better performance than the original classified scheme with more similar frequency distributions to scPDSI and higher correlation coefficients against the drought affected area. Additionally, the three indicators based on the revised classification scheme all indicated a decreasing trend in the frequency of drought occurrence for all the different drought levels; the SDCI and VHI were better at capturing drought events than IDCI. Overall, the results of this study proved that the optimal weighting methods and new classification schemes could effectively improve the precision of drought assessment and may provide helpful information for agricultural drought disaster prevention.