Abstract

Flooding is a worldwide destructive disaster that severely restricts agricultural production. Meteorological indices are common tools for regionally assessing the impact of flooding on crop yields, but their performances are rarely compared. In this work, six convenient meteorological indices, namely, precipitation (P), standardized precipitation anomaly (PA), China Z index (CZI), standardized precipitation index (SPI), standardized precipitation and evapotranspiration index (SPEI), and standardized antecedent precipitation and evapotranspiration index (SAPEI), were employed to establish correlations between flooding intensity and crop meteorological yield (FL-Ym correlation). Four major crops in the middle-lower reach of the Yangtze River (cotton, oilseed rape, wheat, and maize) were selected as the study crops. The results indicated that the flooding intensities quantified by the SPI, SPEI, and CZI had strong interconnections, whereas those quantified by the P and SAPEI were less related to the others. In the cases with stronger negative FL-Ym correlations, different indices were more likely to yield consistent identifications. In terms of the districts witnessing significant FL-Ym correlations, in only 26%, 41%, 44%, and 75% of them (respectively corresponding to cotton, wheat, maize, and oilseed rape), the correlations were consistently identified as significant by the majority of the indices, demonstrating the nonnegligible influence of index selection. The relative performances of the examined indices varied with the employed calculation periods (whole growth period and single critical stage), whereas the SPEI was generally the best performer. The SAPEI performed best in assessing the flooding impact during crop critical growth stages, in sharp contrast with its mediocre performance over the whole crop growth period. According to the results of multiple indices, flooding during the middle stages of the study crops exerted the greatest negative impact. Additionally, oilseed rape and Anhui Province were identified as the crop and the region most affected by flooding. This work can provide support for flooding disaster assessment and agricultural water management.

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