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Drought often occurs and is the costliest one of all natural disasters over the world, leading to significant societal, economic, and ecologic impacts [1-2]. Drought usually affects human lives more than any other form of natural hazards, and is widely considered to be the most complex and least understood of all the natural hazards [3-4]. Drought not only affects agricultural systems but also has a serious impact on the environment. Therefore, drought monitoring and assessment and so on, are hot topics among hydrologists and meteorologists, and attract worldwide attention [5-9]. In order to prevent and mitigate the effects of future occurrences of drought, a number of drought indices exist that have been used to represent different types of drought, including meteorological or climatological,

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In order to achieve effective agricultural production, the impact of drought must be mitigated. An important requirement for mitigating the impact of drought is an effective method of forecasting future drought events. This paper presents the correlations between sea surface temperature anomalies (SSTA) and both the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at four areas monitoring El Nino-Southern Oscillation (ENSO) activities at the Cai River basin in Vietnam. The correlation analyses for selecting potential variables serves as a forecasting mechanism, and SSTAs events in NinoW and Nino4 zones are used to construct Adaptive Neuro-Fuzzy Inference System (ANFIS) forecasting models. Different ANFIS forecasting models for SPI and SPEI (1-, 3-, 6-, and 12-month) are trained and tested. The results of our research show that the best performing models are M5, M11, and M13. For drought forecasting in the short-term (1-or 3-month models), the SPI should be used, because it has a better performance than the SPEI . Drought forecasting with seasonal or long-term indexes (6-or 12-month models) should use the SPEI, because the SPEI performs better than SPI in these cases. We find that the ANFIS forecasting model (M11) for SPEI-12 is the best forecasting model. Furthermore, the ANFIS method with input variables constituting SSTA events can be successfully applied in order to establish accurate and reliable drought forecasting models.

Yellow Stone National Park (YNP), USA, is well-known as its glorious travertine geomorphology as well as Huanglong National Scenic District in Sichuan, China. But there were some difference between them. The travertine deposition of YNP were formed from geothermal activity, meanwhile Huanglong Scenic District is located in high attitude and cold area and the travertine deposition appeared in the cold water. With the increasing attention to global carbon cycle, origin of life and the life action in the extreme environment, the geothermal travertine formation origin and mechanism were focused by the scientists from different field. The research plan was also a part of “the Mars project”. The reason was that the Mars’s surface environment is similar to that of the ancient Earth. This article reviewed the research results of the microbiology diversity, community structure and distribution, function gene and special microbial carbon metabolism passway in the hot spring of YNP. We also summarized the biological factor in the process of the travertine deposition of YNP. After comparing the research progress on the travertine deposition between YNP and Huanglong Scenic District, we put foreword the further strategy to understand the biological effect on the travertine deposition of Huanglong Scenic District.