Abstract
Stochastic precipitation simulation is of great importance for the design and operation of water infrastructures. The objective of this research is to develop a stochastic simulation method for daily precipitation. Daily precipitation generation needs special treatment because of many zero values appearing due to dry days. It is implemented for 26 rain gauge stations located in Singapore. This research follows three steps. First, a hidden autoregressive (AR) model is fitted to time series data at each gauging station using a power transformation. Zero precipitation amounts are treated as censored values of the power-transformed Gaussian process. The hidden AR has four parameters: mean, autocorrelation, power transformation, and variance of error. Second, a conditional multivariate Gaussian distribution is fitted to residuals of the AR models and used to fill in censored values corresponding to errors of the AR at dry events. Third, stochastic simulations from the created spatial-temporal model are carried out. Single and multi-site statistical characteristics such as empirical distribution function, cross-correlation coefficient and entropy are used for evaluation of the model. The results of this research show that the developed model produces synthetic precipitation amounts having statistical characteristics very similar to the observed ones.
Highlights
Stochastic precipitation simulation is of great importance for the design and operation of water infrastructure projects because precipitation is one of the key inputs of the hydrologic systems analysis which is required in every step of projects such as planning, design, operation, and monitoring
Unlike annual and monthly precipitation amounts which can be modelled with simple autoregressive moving average (ARMA) processes, daily precipitation amounts cannot be directly modelled by the ARMA model [2,3]
The main difficulty with modelling daily precipitation amounts arises from the intermittent property of precipitation values both in space and time
Summary
Stochastic precipitation simulation is of great importance for the design and operation of water infrastructure projects because precipitation is one of the key inputs of the hydrologic systems analysis which is required in every step of projects such as planning, design, operation, and monitoring. The entropy-based measure is used to evaluate the performance of precipitation simulations [2,6]. It provides a measure of dispersion, uncertainty, disorder and diversification of precipitation [5]. Precipitation model development is more demanding and needs special treatment because daily precipitation has unique characteristics including zero-inflated data due to dry days. Dry days with high probability due to zero-inflated data can be modelled by a discrete distribution and the rainfall amounts on rainy days at a selected location can be described by a continuous distribution [1], but the continuous distribution is usually skewed
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