A Stochastic Spatio-Temporal Simulation Approach for Multi-site Streamflow Generation
Date Issued
2014
Date
2014
Author(s)
Hsieh, Hsin-I
Abstract
Characterizing and simulating streamflow series is an essential task for regional water resources planning and management. It generally involves temporal variation and spatial correlation of streamflow data at different sites. Like many other environmental variables, streamflow data have been found to be asymmetric and non-Gaussian. Such properties exacerbate the difficulties in spatio-temporal modeling of environmental variables. In this study, we developed a stochastic spatio-temporal simulation model which is capable of generating non-Gaussian multi-site ten-day-period (TDP) streamflow data series. Historical streamflow data during 1975 to 2000 from twelve flow stations of an irrigation district in southern Taiwan were used to exemplify application of the proposed model.
TDP streamflow data at different sites in the study area were firstly standardized using site-specific long-term averages and standard deviations. Spatial and temporal variations/ correlations of the standardized streamflow data were analyzed through anisotropic semivariogram modeling, and then the multi-site standardized stremflow data were modeled by a spatio-temporal anisotropic multivariate PT3 distribution. In order to simplify the multivariate non-Gaussian simulation, a frequency-factor-based algorithm was adopted to convert the multivariate PT3 distribution to a corresponding multivariate standard normal distribution with a unique correlation matrix which was derived from the correlation matrix of the multivariate PT3 distribution. Then stochastic simulation of the anisotropic multivariate standard normal distribution was conducted, yielding a large set of multi-site standard normal realizations. Finally, these realizations were converted to realizations of the multi-site PT3 distribution using the general equation of hydrological frequency analysis. Simulated realizations of the spatio-temporal anisotropic multivariate PT3 distribution were validated by comparing different moments of the simulated data and the observed streamflow data. For average, the statistics of simulation results show less than 5% difference with the parameter of raw streamflow data. Short of streamflow stations and streamflow data may cause error of fitting spatial semi-variogram. The simulated data can be separated to 288 distributions, however, the sample size of each distribution is 26. Insufficient sample size and streamflow stations would reduce accuracy and precision for fitting spatio-temporal semivariogram model.
Simulated streamflow data was applied to analyze improvement of water shortage rate after developing 17 ground water well in the case study. The mitigation measure caused 5 to 10% decreasing of water shortage rate of each irrigation command area during culture period. The simulation results is suitable to be applied to regional water resource management which involving multiple streamflows. This simulation model could maintain statistic characteristic of each distribution and correlation between distributions as well. The proposed approach can also be applied for spatio-temporal modeling of other non-Gaussian distributions or even higher dimensions simulation structure.
Subjects
多元流量
非常態變數
時空架構
頻率因子
聯合分布模擬
Type
thesis
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