Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide.Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences,especially for the second mode.The first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central Pacific.The second mode,initialized from boreal summer and autumn,leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring.Our findings hint that ENSO,as an early signal,is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes.Still,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features.The multimodel ensemble(MME)mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features.The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes.The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

Dam deformation analysis based on BPNN merging models

Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging models.The monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the experiments.The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies.Hence,their short-term forecasts can provide valuable references for the dam safety.

Application of Time-Series Model to Predict Groundwater Dynamic in Sanjiang Plain, Northeast China

To study the groundwater dynamic in the typical region of Sanjiang Plain, long-term groundwater level observation data in the Honghe State Farm were collected and analyzed in this paper. The seasonal and long-term groundwater dynamic was explored. From 1996 to 2008, groundwater level kept declining due to intensive exploitation of groundwater resources for rice irrigation. A decline of nearly 5 m was found for almost all the monitoring wells. A time-series method was established to model the groundwater dynamic. Modeled results by time-series model showed that the groundwater level in this region would keep declining according to the current exploitation intensity. A total dropdown of 1.07 m would occur from 2009 to 2012. Time-series model can be used to model and forecast the groundwater dynamic with high accuracy. Measures including control on groundwater exploitation amount and application of water saving irrigation technique should be taken to prevent the continuing declining of groundwater in the Sanjiang Plain.