Abstract:
Sudan is one of the countries which economy depends on rain- fed
agriculture with recurring cycles of natural drought. The drought
phenomenon has significant widespread impacts on the community,
environment and economy.
The main objectives of this research are to study the characteristics of
rainfall in Sudan, find suitable tools for drought characterization to be used
during drought periods and propose monthly rainfall forecasting methods
accuracy with inspection of the model forecasting ability.
As time series analysis and forecasting have become a major tool in different
applications in hydrology and environmental management fields, linear
stochastic models known as ARIMA and multiplicative Seasonal
Autoregressive Integrated Moving Average (SARIMA) models were used to
simulate droughts based on the procedures of the models developments. The
models were applied to simulate droughts using standardized precipitation
index (SPI) series in many rainfall stations in the Sudan. The SPI index was
used as a drought indicator for drought forecasting due to its advantages
over other drought indices. These models were also used for simulating and
forecasting the monthly rainfall in many rainfall stations across Sudan.
The results of this research proved that the linear stochastic models
(ARIMA) can be used for the rainfall stations for predicting SPI time series
of multiple time scales to detect the drought severity in future. A time series
model for monthly rainfall stations across Sudan, taking Gadaref station as a
typical station was adjusted, processed, diagnostically checked and a typical
SARIMA (0, 0, 0) (0, 1, 1)12 model was established. The model was used to
forecast three years monthly rainfall values.
The stochastic models developed for the stations can be employed for the
development of a drought emergency management plan so as to ensure
sustainable water resources management in these stations. The model was
found appropriate to forecast the monthly rainfall in Gadaref station and
assist decision makers to establish priorities for water demand, storage,
distribution, and disaster management
