Comparative study of different meteorological yield separation methods in waterlogging disaster assessment

Various meteorological yield separation methods have been applied in research on agricultural drought and flood disaster assessment. However, a specific comparison of the performance of these methods is rarely performed. The middle-and-lower reach of Yangtze River is an important cotton-production belt in China, but the region is frequently flooded, resulting in severe cotton yield losses. Hence, the objective of the present work was to assess the impacts of waterlogging disasters on cotton yield fluctuation in this cotton-production belt and to compare the accuracy of different meteorological yield separation methods for characterizing the correlations between waterlogging intensity and cotton climatic yields. Six provinces located in this belt were selected as the study areas, and the cotton climatic yields were calculated using various meteorological yield separation methods: linear fitness (LF), quadratic polynomial fitness (QP), cubic polynomial fitness (CP), HP filtering (HP), 3-year moving average (TMA), 5-year moving average (FMA), and five-point quadratic smoothing (FPQS). The performances of the employed methods were compared and well-performing methods were recommended. Specifically, the waterlogging intensity over cotton growth periods was quantified using the widely used standardized precipitation evapotranspiration index (SPEI). Next, according to the correlation between waterlogging intensity and cotton climatic yield, the performances of the seven methods were compared; in addition, this comparison was further performed on historical waterlogging area data. The results indicated that the long-term trends of cotton climatic yield derived from different methods were similar, whereas the short-term trends could be different or even opposite. The absolute values of the cotton climatic yield in Zhejiang Province were obviously lower than those in other provinces, indicating that the cotton plants in Zhejiang suffered much lower yield losses from climatic disasters. Regarding the correlation between waterlogging intensity and cotton climatic yield, LF, QP, and HP were preferable at the provincial scale, and HP, TMA, FMA, and FPQS performed satisfactorily at the county scale. Considering the ability of the seven methods to make predictions in historical waterlogging areas, HP, QP, and CP were the most satisfactory. In general, HP performed the best in various aspects. In a few cases (e.g., counties in Anhui Province), the relationships identified by various methods between waterlogging intensity and cotton climatic yields were different, which implies that the selection of meteorological yield separation methods may alter conclusions in some areas. All methods concluded that Hubei and Anhui Provinces suffered the most severe yield-reducing effect of cotton waterlogging, whereas results in other provinces were inconsistent. Although Hubei and Anhui Provinces were identified as the most waterlogging-affected provinces, the waterlogging intensity over cotton growth periods in these two provinces was lower than that in other provinces. In conclusion, HP filtering was demonstrated to be a preferable method for agricultural waterlogging assessment, and to prevent and control cotton waterlogging disasters in the middle-and-lower reach of the Yangtze River Plain, special attention should be given to Hubei and Anhui Provinces.