Study On Non-stationary Analysis And Calculation Of Runoff Under Environmental Change In Panjiakou Reservoir Basin

Under the changing environment,influenced by climate change and human activities,hydrological features such as precipitation and runoff have changed to a certain extent in the process of water cycle,which leads to the questioning of the traditional hydrological analysis method based on the stationarity assumption.Therefore,it is of great scientific significance and practical application value to carry out the analysis and calculation of runoff non-stationarity in the changing environment.Taking Panjiakou Reservoir Basin as an example,which is significantly affected by climate change and human activities,this paper discussed the issues of non-stationarity of runoff under changing environment.The main research contents and achievements are as follows:(1)Bayesian analysis of time series model parameters under changing environment.The time series model was used to simulate the runoff series of Panjiakou Reservoir from 1961 to 2010.Considering the impact of environmental changes on the runoff series,the maximum likelihood method,Bayesian method and Bayesian method considering the change point in runoff series were used to estimate the model parameters,and the performance of the three models was compared.The results showed that the runoff series of Panjiakou reservoir from 1961 to 2010 is non-stationary and 1979 is the most probable change point.Among the three models,the time series model considering the change point of runoff sequence and based on bayesian method for parameter estimation has highest sample fitting accuracy and prediction accuracy,which further confirms that the runoff sequence of Panjiakou Reservoir has non-stationary characteristics under the changing environment.(2)Non-stationary analysis and uncertainty of runoff frequency under the changing environment.The main purpose is to analyze the non-stationary impact of future climate change on the runoff of Panjiakou Reservoir Basin,and quantify the sources of uncertainty in the frequency analysis of non-stationary runoff series.Based on Generalized Additive Models for Location,Scale and Shape(GAMLSS),time and precipitation were taken as covariates of runoff series of Panjiakou Reservoir,and the optimal non-stationarity model was established and optimized.The future precipitation series simulated through statistical downscaling method were substituted into the parameters of the optimal model to obtain the future runoff frequency curve.In order to analyze the effects of the uncertainties of GCMs(MIROC-ESM-CHEM,Can ESM2,BCC-CSM1.1),RCPs(RCP2.6,RCP4.5,RCP8.5)and GAMLSS model parameters on the simulated runoff,the variance analysis method was used to quantify the three uncertainties and their mutual effects.The results showed that the performance of non-stationary GAMLSS model is better than that of stationary GAMLSS model,and the non-stationary GAMLSS model with precipitation as covariate is better than that of time as covariate.The parameters of GCMs and GAMLSS models have the greatest impact on runoff uncertainty,accounting for 14%and 83% of the total sources of uncertainty,while the RCPs and the interaction among the uncertainty accounts for 2% and 1% respectively.Further analysis showed that the fluctuation of precipitation is the main factor leading to the uncertainty of non-stationarity GAMLSS model parameters,which proves again the rationality of using precipitation series as a covariable in runoff frequency analysis.(3)Effects of climate change on runoff related structures in different seasons.The main purpose is to study the impact of future(2031?2080)climate change on the spring,summer and autumn runoff series and their correlation in Panjiakou Reservoir Basin.The correlation between rainfall and runoff and the correlation between runoff in different seasons in the study area were tested.The uniform marginal distribution fitting different seasonal runoff series and the nonstationary marginal distribution taking the precipitation series under the three revised emission scenarios RCPs as covariates were established and optimized.Based on the correlation between runoff in different seasons,the Copula model with constant(stationary structure)and time-varying correlation parameters(non-stationary structure)were established respectively based on the optimize the marginal distribution of runoff in different seasons.The two types of models were compared under different conditions.The results showed that the runoff series in spring,summer and autumn fitted by the non-stationary marginal distributions are better than that fitted by the stationary marginal distribution,which proves that the runoff series in spring,summer and autumn are non-stationary.Moreover,under the same conditions,the Copula models with non-stationary structure were superior to the stationary Copula model with stationary structure,which indicated that the correlation between spring-summer and summer-autumn runoff series are non-stationary.Under different emission scenarios,different periods and different marginal probabilities,the Copula models with on stationary and nonstationary structures were also performed differently,which indicated that the correlation between spring-summer and summer-autumn runoff series are affected by the above conditions.(4)Effects of climate change and human activities on hydrological drought.The main purpose is to consider the impact of large-scale climate models and human activities on hydrological drought in order to improve the accuracy of hydrological drought prediction models under changing environments.The climatic factors related to runoff at hydrological stations in the basin are screened by remote correlation test,and the human activity factors which influence runoff during human activity period are constructed.Based on the multivariate normal distribution,the selected climatic and human activity factors were substituted into the hydrological drought prediction model and calculated.The Monte Carlo simulation was applied to verify the influence of climate factors and human activity factors on the transformation probabilities.The prediction accuracy of the models considering climate prediction factors and human activity factors and not considering the two factors were evaluated and compared.The results showed that the hydrological drought prediction model considering climate factors and human activity factors can significantly improve the prediction accuracy.