In the paper, we tested and compared the potential of some standardized meteorological indices to identify agricultural drought impacts in central Italy. The indices considered are the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Reconnaissance Drought Index (RDI), and the Standardized Deficit Distance Index (SDDI), a new index that is defined and evaluated in this paper. SDDI is a function of the Euclidean distance between the actual (P-ET0) and a reference deficit series (P-ET0 = max). SDDI, unlike other indices, also considers how the deficit is distributed in a certain span and assigns greater severities when the distribution is uneven (e.g., presence of peaks).

The comparative analysis refers to 24 provinces of Central Italy and sunflower, a typical non-irrigated crop in the area considered. The sunflower yield time series (1980–2019) were de-trended and standardized for each province, finally obtaining Standardized Yield Residuals (SYRs). The climatic data required for calculating the drought indices (precipitation and temperatures) for each province derive from the E-OBS gridded dataset with 0.25° resolution in the period 1980–2019. From the minimum and maximum daily temperatures, the ET0 was estimated by the FAO Penman-Monteith equation. The drought indices were calculated for different time scales (from 1 to 5 months) and the months corresponding to the sunflower growing season (April–August). The performance of the various indices in the prediction of SYRs was assessed using the Pearson correlation coefficient.

For all the indices, the best correlations are found for the 2-month time scale and for July. SPI's performance is only slightly lower than that of the indices that integrate both precipitation and evapotranspiration (SPEI, RDI, SDDI). Among these, SDDI and SPEI provide somewhat better results considering both the percentage of significant correlation (63% and 67%, respectively) and the corresponding mean correlation (0.49 and 0.48, respectively). SDDI demonstrates good potential in assessing agricultural drought impacts while maintaining the advantage of limited data input.

在论文中，我们测试并比较了一些标准化气象指数的潜力，以确定意大利中部的农业干旱影响。考虑的指数是标准化降水指数 (SPI)、标准化降水蒸散指数 (SPEI)、侦察干旱指数 (RDI) 和标准化赤字距离指数 (SDDI)，这是本文定义和评估的新指数. SDDI 是实际 (P-ET0) 和参考赤字系列 (P-ET0 = 最大值) 之间欧几里得距离的函数。与其他指数不同，SDDI 还考虑了赤字在特定跨度内的分布情况，并在分布不均匀（例如，存在峰值）时分配更大的严重性。

比较分析涉及意大利中部的 24 个省和向日葵，这是该地区典型的非灌溉作物。向日葵产量时间序列（1980 - 2019）针对每个省进行去趋势和标准化，最终获得标准化产量残差（SYR）。计算各省干旱指数（降水和温度）所需的气候数据来自 1980年期间分辨率为 0.25°的 E-OBS 网格数据集–2019. 根据每日最低和最高温度，通过粮农组织 Penman-Monteith 方程估计 ET0。干旱指数是针对不同的时间尺度（从 1 到 5 个月）和与向日葵生长季节（4 月至 8 月）相对应的月份计算的。使用 Pearson 相关系数评估各种指标在 SYR 预测中的性能。

对于所有指数，最佳相关性出现在 2 个月时间尺度和 7 月。SPI 的性能仅略低于综合降水和蒸发量的指数（SPEI、RDI、SDDI）。其中，考虑到显着相关性的百分比（分别为 63% 和 67%）和相应的平均相关性（分别为 0.49 和 0.48），SDDI 和 SPEI 提供了更好的结果。SDDI 在评估农业干旱影响方面表现出良好的潜力，同时保持了有限数据输入的优势。