摘要:农业干旱是导致作物减产的主要灾害之一，及时、准确地监测农业干旱状况有助于制定区域减灾策略，降低灾害损失。标准化土壤湿度指数（SSMI）是基于历史土壤湿度时间序列构建的一种农业干旱指数，目前分析该指数监测农业干旱的适宜性研究十分缺乏。本文以黄淮海平原为研究区，利用数据同化的根区土壤湿度数据构建SSMI，并通过与标准化降水蒸散指数（SPEI）、农业干旱灾害记录数据的对比以及与冬小麦产量的关系分析，综合评价SSMI监测农业干旱的适宜性。结果表明，SSMI与SPEI具有良好的一致性，二者之间具有极显著相关关系（P < 0.001）；利用SSMI识别的农业干旱与农气站点干旱灾害记录是基本一致的，SSMI能够有效反映干旱发生、发展直至减轻的演变过程；冬小麦生长季SSMI与减产率显著相关，利用SSMI识别的农业干旱发生区域与基于统计数据计算的减产区域基本相符，SSMI能够对农业干旱引起的冬小麦减产起到一定的指示作用。综上所述，基于同化数据构建的SSMI能够反映黄淮海平原的农业干旱状况，利用SSMI监测区域农业干旱状况是适宜的。研究可为基于土壤湿度的农业干旱监测业务化运行提供依据，为黄淮海平原的抗旱减灾提供科学参考。



Abstract:Drought is a recurring extreme climate event. Frequent droughts have serious impacts on agriculture and threaten food security. The Huang-Huai-Hai (HHH) Plain is one of the most important food-producing areas in China, and agricultural drought is one of the main factors leading to the decline of grain production in this region. Therefore, accurate and effective agricultural drought monitoring is of great significance to develop disaster mitigation strategies and reduce disaster losses. The standardized soil moisture index (SSMI) is an agricultural drought index based on historical soil moisture time series. Currently, the suitability of SSMI for monitoring agricultural drought is scarce. In previous studies, an agricultural drought index was mainly evaluated by comparisons with other commonly used drought indices or meteorological elements. Only a few studies considered the drought disaster records and the impact of drought on crop yield. In this study, the SSMI was established by using the assimilated root zone soil moisture to monitor agricultural drought in the HHH Plain. The SSMI-based results showed that several drought events occurred between 2002 and 2010. Of these droughts, the moderate or extreme droughts occurred in 2002, 2004, and 2006, matching well with the reality. Subsequently, the suitability of the SSMI was evaluated by comparing with the standardized precipitation-evapotranspiration index (SPEI), agricultural drought records, and winter wheat yield. The results showed that the average correlation coefficient between the SSMI and SPEI was 0.52, indicating a significant correlation (P < 0.001). As a whole, the SSMI and SPEI showed good agreement, which could accurately identify large-scale agricultural droughts. From a regional perspective, the SSMI could effectively track drought occurrence, evolution, and mitigation. The comparisons at site scales showed that the SSMI-based drought results were consistent with the drought records of agro-meteorological sites, and the SSMI could accurately monitor the intensity of agricultural drought. Crop yield is the ultimate performance of the crops affected by drought, and the relationship between the drought index and crop yield is an important aspect of testing the effectiveness of a drought index. We observed that the SSMI was closely related to the winter wheat yield loss ratio, and the SSMI-based drought areas generally existed in accordance with the statistical data-based yield reduction areas. To some extent, the SSMI provided an indication of the drought-induced yield reduction. In summary, the assimilated data-based SSMI could effectively reflect the drought conditions in the HHH Plain, and it was highly appropriate to use the SSMI to monitor agricultural drought. This study will facilitate the operational soil moisture-based agricultural drought monitoring and provide a scientific reference for drought prevention and mitigation in the HHH Plain.





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