Aggregation simulation model of flow and rainfall series

Synthetic hydrology series is useful for evaluating the consequences of water supply management decisions and reservoir design. The main objective of this study is to identify and confirm the best model in flow and rainfall simulation. The study covers the application of aggregation and disaggregation methods for flow and rainfall stochastic simulation. In general, the application of various periodic models for the flow simulation was mostly successful. The application of disaggregation models was found to yield sufficient performance and competitive to the periodic models. It has been proven that the transformation does not always guarantee improvement in the candidate models performance. The Periodic Autoregressive of Order One (PAR (1)) model is the best performer for the monthly and annual flow simulation using periodic models for both untransformed and transformed series. The Valencia and Schaake (VLSH) model is the robust model from disaggregation group for the monthly and annual flow simulation. Simulation for monthly and annual rainfall series shows that the VLSH model is the best performer to produce sufficient results for both untransformed and transformed series. The results from this study are based on investigation from graphs and frequency analysis. The outcome of study has potential to assist the water engineers and consultant in making decisions for the operation of the water resources systems. It is suggested that the rainfall simulation should be applied in water resources planning because observed flow series are subjected to disturbance due to development

Synthetic simulation of streamflow and rainfall data using disaggregation models

Synthetic hydrological series is useful for evaluating water supply management decision and reservoir design. This paper examines stochastic disaggregation models that are capable of reproducing statistical characteristics especially mean and standard deviation of historical data series. Simulation was carried out on both transformed and untransformed streamflow and rainfall series of Sungai Muar. The Synthetic Streamflow Generation Software Package (SPIGOT) model was found to be the most robust for streamflow simulation. On the other hand, the Valencia-Schaake (VLSH) model is more superior for generating rainfall series

Regional climate scenarios using a statistical downscalling approach

The climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, General Circulation Models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. The results presented in this report have indicated that it is feasible to link large-scale atmospheric variables by GCM simulations from Hadley Centre 3rd generation (HadCM3) outputs with daily precipitation at a local site. Statistical Downscaling Model (SDSM) was applied using three set of data; daily precipitation data for the period 1961-1990 corresponding to Endau rainfall (Station no. 2536168) and Muar (Station no. 2228016) located in Johor at the Southern region of Peninsular Malaysia; The observed daily data of large-scale predictor variables derived from the National Centre for Environmental Prediction (NCEP) and GCM simulations from Hadley Centre 3rd generation (HadCM3). The HadCM3 data from 1961 to 2099 were extracted for 30-year time slices. The result clearly shows increasing increment of daily mean precipitation of most of the months within a year in comparison to current 1961-1990 to future projections 2020’s, 2050’s and 2080’s considering SRES A2 and B2 scenarios developed by the Intergovernmental Panel on Climate Change (IPCC). Frequency analysis techniques were carried out using the observed annual daily maximum precipitation for period 1961-1990 and downscaled future periods 2020’s, 2050’s and 2080’s. Therefore, it does appear that SDSM can be considered as a bench mark model to interpret the impact of climate change