Abstract
Global climate change, temperature rise and some kinds of extreme meteorological disaster, such as the drought, threaten the development of the natural ecosystem and human society. Forecasting in drought is an important step toward developing a disaster mitigation system. In this study, we utilized the statistical, autoregressive integrated moving average (ARIMA) model to predict drought conditions based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in a major tributary in the lower reaches of Nu River. We employed data from 2001 to 2010 to fit the model and data from 2011 to 2013 for model validation. The results showed that the coefficients of determination (R2) was over 0.85 in each index series, and the root-mean-square error and mean absolute error were low, implying that the ARIMA model is effective and adequate for this region.
Highlights
Global climate change, rising temperatures, and various extreme meteorological conditions have seriously threatened the natural ecosystem and the development of human society and economy (Vicente-Serrano et al, 2012)
We utilized the statistical, autoregressive integrated moving average (ARIMA) model to predict drought conditions based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in a major tributary in the lower reaches of Nu River
We take the year 2000 as the breakpoint, and counted the frequency of occurrence of moderate or higher droughts in each month, which are represented by SPI and SPEI indicators, in the study area from 1966 to 2000 and from 2001 to 2013
Summary
Global climate change, rising temperatures, and various extreme meteorological conditions have seriously threatened the natural ecosystem and the development of human society and economy (Vicente-Serrano et al, 2012). Determining when droughts occur and end is often difficult because the effects of droughts can sometimes last for months (Li et al, 2015), and sometimes for years. Another challenge and the fundamental aspect of drought research are determining its occurrence and severity. The indexes that use river runoff as indicators to describe drought include runoff drought index (SDI), standard runoff index (SRI), standard hydrological index (SHI), etc. (Sharma & Panu, 2014). Tsakiris et al (2013) proposed a method that can systematically assess the interactions between meteorology, runoff drought, and economic and environmental impacts on a river basin scale
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