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Monitoring winter wheat drought threat in Northern China using multiple climate-based drought indices and soil moisture during 2000–2013

Wang, Hongshuo, Vicente-serrano, Sergio M., Tao, Fulu, Zhang, Xiaodong, Wang, Pengxin, Zhang, Chao, Chen, Yingyi, Zhu, Dehai, Kenawy, Ahmed El
Agricultural and forest meteorology 2016 v.228-229 pp. 1-12
atmospheric precipitation, crop models, crop yield, drought, evapotranspiration, food security, monitoring, soil water, uncertainty, winter wheat, China
Increasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson’s r<0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1–5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield (r>0.40, p<0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts.