Drought is a ubiquitous and recurring hazard that has wide-ranging impacts on society, agriculture and the environment. Drought indices are vital for characterising the nature and severity of drought hazards, and there have been extensive efforts to identify the most suitable drought indices for drought monitoring and risk assessment. However, to date, little effort has been made to explore which index (or indices) best represents drought impacts for various sectors in China. This is a critical knowledge gap, as impacts provide important ground truth information for indices used in monitoring activities. The aim of this study is to explore the link between drought indices and drought impacts, using Liaoning province (northeast China) as a case study due to its history of drought occurrence. To achieve this we use independent, but complementary, methods (correlation and random forest analysis) to identify which indices link best to drought impacts for prefectural-level cities in Liaoning province, using a comprehensive database of reported drought impacts in which impacts are classified into a range of categories. The results show that the standardised precipitation evapotranspiration index with a 6-month accumulation (SPEI6) had a strong correlation with all categories of drought impacts, while the standardised precipitation index with a 12-month accumulation (SPI12) had a weak correlation with drought impacts. Of the impact datasets, “drought-suffering area” and “drought impact area” had a strong relationship with all drought indices in Liaoning province, while “population and number of livestock with difficulty in accessing drinking water” had weak correlations with the indices. The results of this study can support drought planning efforts in the region and provide context for the indices used in drought-monitoring applications, so enabling improved preparedness for drought impacts. The study also demonstrates the potential benefits of routine collection of drought impact information on a local scale.

Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

Less water at critical period has significant effect on crop yield. The deficit irrigation scheduling is important especially for the drought-prone area. The crop water production function frequently used for deficit irrigation analysis neither has the disadvantage in mechanism explanation. In this study, we use crop model instead of the crop water production function to study deficit irrigation scheduling issue for winter wheat in Jinan City, in North Plain of China. The crop model we employed is APSIM model. The model was calibrated and validated specifically for winter wheat in Jinan using data in 2000-2013 years. Three typical years were selected to represent the normal, light drought and drought conditions. There are 24 irrigation scheduling schemes obtained by combination of three deficit irrigation quotas, the 150 mm, 100 mm and 50 mm, and different irrigation dates from early April to mid-May. By simulating and comparing the ultimate yield of each irrigation scheduling for winter wheat in Jinan, we strongly suggest that: 1) if the antecedent soil moisture is low, perform irrigation at the beginning of critical period, 2) if it under drought stress, perform irrigation once in early May, and 3) if severe drought hits, perform irrigation twice, and allocating more quota for the second time.