During recent decades, more frequent flood-drought alternations have been seen in China as a result of global climate change and intensive human activities, which have significant implications on water and food security. To better identify the characteristics of flood-drought alternations, we proposed a modified dry-wet abrupt alternation index (DWAAI) and applied the new method in the middle and lower reaches of the Yangtze River Basin (YRB-ML) to analyze the long-term spatio-temporal characteristics of dry-wet abrupt alternation (DWAA) events based on the daily precipitation observations at 75 rainfall stations in summer from 1960 to 2015. We found that the DWAA events have been spreading in the study area with higher frequency and intensity since 1960. In particular, the DWAA events mainly occurred in May and June in the northwest of the YRB-ML, including Hanjiang River Basin, the middle reaches of the YRB, north of Dongting Lake and northwest of Poyang Lake. In addition, we also analyzed the impact of El Niño Southern Oscillation (ENSO) on DWAA events in the YRB-ML. The results showed that around 41.04% of DWAA events occurred during the declining stages of La Niña or within the subsequent 8 months after La Niña, which implies that La Niña events could be predictive signals of DWAA events. Besides, significant negative correlations have been found between the modified DWAAI values of all the rainfall stations and the sea surface temperature anomalies in the Nino3.4 region within the 6 months prior to the DWAA events, particularly for the Poyang Lake watershed and the middle reaches Keywords: dry-wet abrupt alternation; the middle and lower reaches of the Yangtze River Basin; spatio-temporal characteristics; La Niña

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Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classifiedZ-Rrelationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.

Our research analyzes the regional changes of extreme dry spell, represented by the annual maximum dry spell length (noted as AMDSL) during the rainy season in the Wei River Basin (WRB) of China for 1960-2014 using the L-moments method. The mean AMDSL values increase from the west to the east of the WRB, suggesting a high dry risk in the east compared to the west in the WRB. To investigate the regional frequency more reasonably, the WRB is clustered into four homogenous subregions via the Kmeans method and some subjective adjustments. The goodness-of-fit test shows that the GEV, PE3, and GLO distribution can be accepted as the "best-fit" model for subregions 1 and 4, subregion 2, and subregion 3, respectively. The quantiles of AMDSL under various return levels figure out a similar spatial distribution with mean AMDSL. We also find that the dry risk in subregion 2 and subregion 4 might be higher than that in subregion 1. The relationship between ENSO events and extreme dry spell events in the rainy season with cross wavelet analysis method proves that ENSO events play a critical role in triggering extreme dry events during rainy season in the WRB.