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Droughts are increasing in frequency and intensity as the global climate changes. Risk assessment for agricultural drought disasters is an important approach for disaster prevention and mitigation. Based on the formation mechanism of natural disaster risk, the index system model of agricultural drought risk assessment in North China was constructed, and the robustness of the established risk assessment model was tested by using the internal test method. Then based on the characteristics of agricultural drought risk formation in North China, the paper proposes corresponding risk prevention recommendations. The conclusions were as follows: when adjusting the indices and methods in the risk assessment model, the range of changes in the agricultural drought risk ranking for each city was mostly 1-2.5 places, and the model was relatively robust. With the exception of Beijing and Tianjin, which had the highest disaster prevention and mitigation capacity, central and northern China had the highest agricultural drought risk, which was previously linked to the high risk and vulnerability of these regions. The agricultural drought risk in northern Henan and southern Shandong is low, mainly due to the low risk. The 34 cities in North China can be divided into 6 agricultural drought risk clusters, which are not dispersed in space and cross-city distribution, and have good aggregation. Cluster 4, located in the northernmost mountainous areas, has the highest risk of drought, which is associated with high environmental vulnerability and low capacity for disaster prevention and mitigation, so agricultural areas should be reduced and agricultural input should be increased. Clusters 2, 5 and 6, located in the central and southern regions, have higher grain acreage and yields due to drought, so agricultural drought risk can be further reduced by increasing agricultural input and improving irrigation guarantee rate. [ABSTRACT FROM AUTHOR]

The cultivation of apples plays an important role in China's fruit production. Climate change has brought about more frequent and severe drought and waterlogging disasters, which have negatively impacted apple yield quantity and quality. Through the summary of previous research studies done on the impact of drought and waterlogging disasters and its effect on apple production, methods for disaster prevention and mitigation can be provided to reduce the impact of these disasters to the industry. In this paper, the methods of classification and systematic analysis combined with studying actual disaster cases were used to review the latest research into the effects of droughts and waterlogging disasters on apple production in China and identify the next steps for future research and development into this subject. The intensity and impacts of droughts and waterlogging disasters on apple production are determined by weather, regional climate, topography, soil composition, and several other factors. Adopting the morphological indicators to study these disasters, most existing research into this area only focuses on meteorological factors and rarely consider agrometeorological factors such as soil composition, current crop conditions, and more. The distribution of apple drought disasters in China decreases from north to south. The prevention measures of apple drought and waterlogging included irrigation, strengthening orchard soil management, implementing the integration of soil and fertilizer, etc. More of the current research done into this subject takes place in the Northern parts of China and often lack the necessary information to be put into use in disaster monitoring and early warning applications. In the future, research into apple droughts and waterlogging disasters should focus on increasing in orchard observation data collection, constructing a disaster risk monitoring and early warning model, and implementing the model into apple growing. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) with high precision and high temporal resolution can be obtained based on the global navigation and satellite positioning system (GNSS) technique, which is important for GNSS in disaster prevention and mitigation. However, related studies on drought monitoring using PWV have rarely been performed before, which becomes the focus of this paper. This paper proposes a novel drought monitoring method using GNSS-derived PWV and precipitation, and a multi-time-scale standardized precipitation conversion index (SPCI) is established. This index is different from the traditional index in terms of expression, standardization, and time scale. The proposed SPCI is then compared with the standardized precipitation index/standardized precipitation evapotranspiration index/self-calibrating Palmer drought severity index (SPI/SPEI/scPDSI) and applied to local and global drought monitoring. Validated results show that multi-time-scale SPCI has good consistency with the corresponding SPI/SPEI/scPDSI. The correlation between SPCI and SPEI is the strongest (more than 0.96) on a 12-month scale, which indicates the application potential of SPCI in drought monitoring. In addition, applications for regional (Queensland, Australia) and global drought/wet monitoring further verify the capability of the proposed SPCI. The average percentage deviations of drought/wet monitoring between SPCI and SPEI are 2.77% and 3.75%, respectively on a global scale. The above results show that the SPCI developed in this study is efficiently applied to global flood/wet studies. [ABSTRACT FROM AUTHOR]

Frequent drought disasters have significant impacts on socio-economic and ecosystems, and it is crucial to explore the propagation characteristics of drought for disaster prevention and mitigation. In this paper, based on 0.25° × 0.25° grid-point Meteorological hydrological and large-scale climate factor data, the start-to-start, peak-to-peak, and end-to-end drought propagation times, and the periods and rates of drought development and recovery were estimated for 11 regions in the Luanhe River Basin under stationary and non-stationary conditions. The Generalized Additive Model for Location, Scale and Shape (GAMLSS) framework was used to construct stationary and non-stationary models to capture the stationary and non-stationary drought characteristics respectively, and then the drought development and recovery patterns, and drought propagation times were assessed. The results showed that under the influence of climate factors, severe drought events occurred in most study areas, the duration of meteorological drought was prolonged by 0.1–0.6 months, and the peak intensity was increased by about 0.05. The severity of hydrological drought increased by 0.2–0.8, and the peak intensity decreased by 0.1–0.2. The rate of development and recovery of hydrological drought was accelerated by 0.1–0.2 per month. The propagation time of start-to-start was shortened by 0.2 months, and the propagation time of end-to-end was extended by 1–2 months. These mean that water resource control authorities need to propose responses to droughts more quickly. • The non-stationary drought indices considering climatic factors were constructed. • The drought propagation time was calculated from start to start, peak to peak, and end to end. • Quantification of the effects of climate change on drought propagation [ABSTRACT FROM AUTHOR]

Regional agricultural drought vulnerability (RADV) is a complex nonlinear problem caused by the interaction of multiple factors, and an objective and systematic method is proposed by this paper to identify its influencing factors, which plays an important role in preventing and regulating the risks of regional agricultural drought. Firstly, to provide a reference for the evaluation problem in selecting the number of factors, the influencing factors affecting RADV are revealed by using the method of phase space reconstruction (PSR). Secondly, to rank the importance of influencing factors, a grey trend relational analysis (TGRA) method is proposed, considering the dynamic development relationship between the RADV index and the influencing factors and integrating the absolute and relative variation of sequences in each corresponding period. Finally, to reduce the collinearity between the influencing factors, a grey trend relational clustering (TGRC) analysis method is proposed. According to the above steps, the process of identifying factors based on PSR-TGRC method is formed. Taking Henan Province as an example, 14 main influencing factors and their effects on RADV are identified from all 42 factors, and the identification results which are consistent with the actual drought relief work show the rationality and practicality of PSR-TGRC method and provide theoretical support for formulating strategies of regional agricultural disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) changes the air flow in the tropics, causing global droughts, floods, typhoons, and other natural disasters. Therefore, monitoring and forecasting of ENSO events effectively play an important role in disaster prevention and mitigation. This paper proposes a novel ENSO monitoring index using GNSS-derived zenith tropospheric delay (ZTD) and temperature (T), which is called the standardized ENSO monitoring index (SEMI), and validated its performance by introducing the standardized precipitation evapotranspiration index (SPEI). Multichannel singular spectral analysis (MSSA) and Lomb-Scargle periodogram are first applied to determine the periodic signals of ZTD and T. Empirical Orthogonal Function (EOF) is then used to analyze the spatio-temporal characteristics of ZTD and T. Finally, the response thresholds of ZTD and T to ENSO events are determined using the moving window correlation analysis (MWCA) method, and the SEMI is established based on the relationship between abnormal changes of sea surface temperature anomaly (SSTa) and ZTD/T. ZTD and T of 97 GNSS stations and the corresponding SPEI over the period of 2006–2016 in Taiwan province has been obtained to validate the capacity of the proposed SEMI for ENSO monitoring. Experimental results reveal that (1) annual and semi-annual periods existed in ZTD and T with the values of 365.25 and 182.625 days, respectively. (2) ZTD and T have good correlations with SSTa, and the coefficients are 0.64 and 0.70, respectively. (3) The response thresholds of ZTD and T to ENSO events in Taiwan province are [−1.13 °C, 0.84 °C] and [−1.09 °C, 0.69 °C], respectively. (4) The proposed SEMI has a good relationship with SSTa with a correlation coefficient of 0.77 and has been proven to have good ENSO monitoring capacity by comparing with SPEI with a correlation coefficient of 0.83. This result is of great significance for drought and flood disaster monitoring by the GNSS technique. • A standardized ENSO monitoring index (SEMI) is proposed. • SEMI only considers the parameters of GNSS-derived ZTD and temperature. • Response thresholds of ZTD and T to ENSO events are determined. • The performance of SEMI is validated by drought monitoring index (SPEI). [ABSTRACT FROM AUTHOR]