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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]

With the advancement of computer science, Artificial Intelligence (AI) is being incorporated into many fields to increase prediction performance. Disaster management is one of the main fields embracing the techniques of AI. It is essential to forecast the occurrence of disasters in advance to take the necessary mitigation steps and reduce damage to life and property. Therefore, many types of research are conducted to predict such events due to climate change in advance using hydrological, mathematical, and AI-based approaches. This paper presents a comparison of three major accepted AI-based approaches in flood and drought forecasting. In this study, fluvial floods are measured by the runoff change in rivers whereas meteorological droughts are measured using the Standard Precipitation Index (SPI). The performance of the Convolutional Neural Network (CNN), Long-Short Term Memory network (LSTM), and Wavelet decomposition functions combined with the Adaptive Neuro-Fuzzy Inference System (WANFIS) are compared in flood and drought forecasting, with five statistical performance criteria and accepted flood and drought indicators used for comparison, extending to two climatic regions: arid and tropical. The results suggest that the CNN performs best in flood forecasting with WANFIS for meteorological drought forecasting, regardless of the climate of the region under study. Besides, the results demonstrate the increased accuracy of the CNN in applications with multiple features in the input. [Display omitted] • Performances of three AI models were compared for flood and drought forecasting. • Comparisons were done for an arid and a tropical basins separately. • Statistical measures and event indicators were used to determine the best model. • WANFIS model showed the highest accuracy for drought forecasts in both regions. • CNN model was the best forecast regardless of the climate of the region. [ABSTRACT FROM AUTHOR]

• Drought loss models of wheat are built using two different statistical models. • The important SPEI variables recognized by two methods are different. • The random forest-derived model outperforms the stepwise regression model. Agricultural drought loss models provide services for the rapid risk assessment of agricultural disasters, and regional disaster prevention and mitigation efforts. This paper takes wheat as an example, and chooses counties dominated by rain-fed farmland in Henan Province as the study area. Counties dominated by rain-fed farmland are determined by setting a rain-fed threshold that is related to the proportion of the effective irrigation area to the cultivated land area. Modeling samples are screened by considering both drought occurrence time and wheat yield reductions. Under different thresholds (30%, 40%, 50% and 60%), we use the yield loss ratio as the dependent variable and 24 standardized precipitation evapotranspiration index parameters as independent variables to build drought loss models using both a multivariate stepwise regression model and a random forest model. Yield loss ratio from 1990 to 2015 is calculated by decomposing historical wheat yield time series. 24 standardized precipitation evapotranspiration index variables are 1–3 months' time scale standardized precipitation evapotranspiration index during the growth period (from October to May of the following year) of winter wheat in Henan Province. The results show that the random forest-derived model outperforms the stepwise regression model in all tests. The accuracy of all the models increases with an increase of the proportion of the rain-fed threshold. When the rain-fed threshold is 60%, the R2 values of the random forest model and the multivariate stepwise regression equation are 0.720 and 0.523, respectively. The validation results show that the mean absolute error and the root mean square error of the multivariate stepwise regression are 1.38 times and 1.31 times larger than the mean absolute error and the root mean square error from the random forests model. Moreover, both models identify that standardized precipitation evapotranspiration indices in October (sowing/planting stage) and February (overwintering stage) are important variables. However, the multivariate stepwise regression model fails to recognize the importance of standardized precipitation evapotranspiration indices during April–May (filling stage). [ABSTRACT FROM AUTHOR]