This paper examined the impact of rainfall regimes shifts on some hydrologic factors and forest growth in North Patagonian temperate rainforests using a dynamic forest model and a rainfall time series generator. My feeling is that the paper is potentially interesting and the authors made a reasonable effort but insufficient descriptions of the model and its validation caused something like unre...

Abstract—In this paper, we study the rainfall using a time series for weather stations in Nakhon Ratchasima province in Thailand by various statistical methods to enable us to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. The ARIMA and Holt-Winter models were built on the basis of exponential smoothing. All...

Within this study we present a robust method for generating precipitation time series for the Anas catchment in North Western India. The method employs a multivariate stochastic simulation model that is driven by a time series of objectively classified circulation patterns (CPs). In a companion study (Zehe et al., 2006) it was already shown that CPs classified from the 500 or 700 Hpa levels are...

To evaluate the state of ecosystems in Mauritania, rainfall time series and a GIMMSNDVI (global inventory modeling and mapping study-normalized difference vegetation index) data set were used for analysis of rainfall and NDVI trends and their relationships in different ecological zones. Linear regression analysis and the non-parametric Mann-Kendall test were applied to detect NDVI and rainfall ...

BACKGROUND Patterns of concerted fluctuation in populations-synchrony-can reveal impacts of climatic variability on disease dynamics. We examined whether malaria transmission has been synchronous in an area with a common rainfall regime and sensitive to the Indian Ocean Dipole (IOD), a global climatic phenomenon affecting weather patterns in East Africa. METHODS We studied malaria synchrony i...

Abstract In this article, the performance evaluation of four univariate time-series forecasting techniques, namely Hyndman Khandakar-Seasonal Autoregressive Integrated Moving Average (HK-SARIMA), Non-Stationary Thomas-Fiering (NSTF), Yeo-Johnson Transformed (YJNSTF) and Seasonal Naïve (SN) method, is carried out. The techniques are applied to forecast rainfall time series stations located in Ke...

Accurate rainfall prediction remains a challenging problem because of the high volatility and complicated essence atmospheric data. This study proposed hybrid model (DSP) that combines advantages discrete wavelet transform (DWT), support vector regression (SVR), Prophet to forecast First, time series is decomposed into high-frequency low-frequency subseries using (DWT). The SVR models are then ...