Meteorological drought analysis with different indices for the Betwa River basin, India

Abstract

Climate change is adversely affecting the development, management, and planning of surface and groundwater resources. The meteorological drought becomes a severe natural problem, and it can occur in any climatic region of the world. So, monitoring and minimizing drought is a crucial stage for analyzing and predicting drought impacts. A single drought index can't assess each aspect of the meteorological drought. In this study, we considered seven drought indices such as the Standardized Precipitation Index (SPI), China Z Index (CZI), Modified China Z Index (MCZI), Percent Normal drought index (PNI), Deciles Index (DI), Rainfall Anomaly Index (RAI), and Z-score index (ZSI). The drought was analyzed for 3, 6, 9, and 12 months' time-step, and drought classification and threshold values were estimated. SPI showed maximum correlation values (0.389, 0.412, 0.560, and 0.996) for 3, 6, 9, and 12-month time steps compared to the other drought indices. The value of correlation is increased with the increase in time step for all drought indices; therefore, the accuracy of drought assessment also increases with an increase in time step. The Mann-Kendall's trend test was analyzed at a 5% level of significance for drought assessments. The drought magnitude and severity of the Betwa river basin were estimated based on the meteorological data (Rainfall) for the year between 1970 to 2014.

Comparison of drought indices in the analysis of temporal and spatial changes of climatic drought events: a case study in the Egirdir Lake basin (Isparta/Turkey)

“…10) revealed a cumulative variance of 90.14%. These indices exhibited a strong correlation with each other, attributable to the fact that the similarity between indices based on a temporal scale increases when calculated over longer timeframes (Jain et al 2015;Mahmoudi et al 2019;Vergni et al 2021;Singh et al 2022). Furthermore, the results indicated the presence of three distinct groups (Egirdir, Senirkent, and Yalvaç) in the indices correlations for each meteorological measurement station, this differentiation arises from the utilization of precipitation data that varies annually for each measurement station used (Bettahar and Şener 2022).…”

“…The similarity in index classifications at certain times and their differences at other times arise from the fact that, despite the strong correlation demonstrated by the various indicators, they often exhibit differences in classification categories. These discrepancies can be attributed to variations in calculation methods, severity classification criteria, and the time period considered (Singh et al 2022), all of which are designed to enhance the accuracy of drought estimation, as drought estimation tends to be more precise for longer time intervals compared to shorter ones (Abu Hajar et al 2019;Das et al 2020;Khalil 2020;Vergni et al 2021;Sa'adi et al 2022;Bettahar & Şener 2022).…”

A CMIP6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises

“…Several drought indicators have been developed to describe and identify various forms of drought. Standardized drought indicators are frequently applied to assess the diverse types of drought, enabling comparisons across various timeframes and geographic extents [37]. Drought indices used in this study, accompanied by their respective classifications are listed in Table 2.…”

Regional Frequency Analysis of Drought Indexes by L-Moments Method

“…The MCZI is calculated in the same way as the China Z-Index. Only the precipitation median is used instead of the average of precipitation data (Singh et al 2022). The values found after applying the MCZI method are classified according to the value ranges given in Table 2 for the determination of drought.…”