Reservoir storage simulation and forecasting models for muda irrigation scheme, Malaysia

Abstract

Reservoir operation policies aim at deriving maximum benefits from water that can be stored in it and allocated to crops. Water shortage is the main constraint in establishing stable irrigation water management in Muda Irrigation Scheme, Kedah, Malaysia. Thus, the objective of this study is to a develop reservoir simulation model and to consider stochastic models and Log-Pearson Type III distribution to generate storage to compare with the observed storage, and to forecast future storage to examine the performance of the reservoir with reliability under changing conditions. The reservoir simulation model storage amounts were calculated for 1998-2008 using measured values of rainfall and evaporation (reservoir station no. 61), reservoir inflow, release, seepage, spill, and Muda reservoir inflow. The developed reservoir simulation model results simulated well with the mean monthly observed long-term storage amounts (1998-2008), except for a few months where the model storages are found relatively higher than the observed storage amounts. A stage-storage curve is plotted using the monthly observed values of storage and water level from 1998- 2008 to covert water level into storage and vice versa. The first order Markov model with periodicity and Log-Pearson Type III distribution are considered to generate storage amounts to compare with the mean monthly observed storages, and hence to forecast future storage with reliability. The first order Markov model generated and observed mean storage amounts were compared for each month. The comparison results imply that the monthly statistical parameters of the historic record, except the lag1 serial correlation between December and January months (i.e., over-year monthly correlations), are preserved satisfactorily. The storage amounts are forecasted for year 2009-2015 to be used in future reservoir operation, using first order Markov model. The expected mean and minimum storage amounts for different return periods are estimated, using Log-Pearson Type III distribution and trendlines with equations and R2 values are shown, to help decision makers to estimate future storage with corresponding return period under any changing weather conditions and or demand.