Stochastic Modeling of Shiroro River Stream flow Process.

Abstract

Economists, social scientists and engineers provide insights into the drivers of anthropogenic climate change and the options for adaptation and mitigation, and yet other scientists, including geographers and biologists, study the impacts of climate change. This project concentrates mainly on the discharge from the Shiroro River. A stochastic approach is presented for modeling a time series by an Autoregressive Moving Average model (ARMA). The development and use of a stochastic stream flow model involves some basic steps such as obtain stream flow record and other information, Selecting models that best describes the marginal probability distribution of flows. The flow discharge of about 22 years (1990-2011) was gotten from the Meteorological Station at Shiroro and analyzed with three different models namely; Autoregressive (AR) model, Autoregressive Moving Average (ARMA) model and Autoregressive Integrated Moving Average (ARIMA) model. The initial model identification is done by using the autocorrelation function (ACF) and partial autocorrelation function (PACF). Based on the model analysis and evaluations, proper predictions for the effective usage of the flow from the river for farming activities and generation of power for both industrial and domestic us were made. It also highlights some recommendations to be made to utilize the possible potentials of the river effectively.