Uncertainty Analysis Of The Extreme Flows Under The Impact Of Climate Change

The recent century has witnessed the enormous increase of population, greenhouse gas emission and global mean temperature. The global climate change and its impact on human beings have drawn wide attention from all walks of life. Water cycle as a part of the climate system has been affected by the climate change as well. In recent years, the extreme climate events like rainstorm and drought have taken place with a higher intensity and frequency in some regions. Some extreme precipitation and discharges could be scarcely found even in the historical record. The research on the impact of the climate change on extreme events is crucial for the damage prevention and water resources management.Up to now, the commonly used tools to study the impact of the climate changes on the hydrological processes are GCMs, RCMs, hydrobgical models and so on. There are several steps in assessing the impact. Firstly the climate changes under different scenarios are obtained by the GCMs. Then, the output from the GCMs are downscaled through downscaling methods, including the statistical downscaling and dynamic downscaling, to satisfy the requirement of the resolution for the river basin studies. After that, the outputs from the GCMs are used as the input for the hydrological models to make the estimation of the river discharges. However, the uncertainties are involved in every step above and they could not be eliminated thoroughly. Therefore how to quantify the uncertainties in every step of climate change impact analysis and deal with the uncertainties in the river discharges are the basis for policy makers to make the robust decisions.In the thesis, the trend of the historical precipitation is calculated and the rainfall period is divided into two seasons, the plum season and the typhoon season based on the cause of rainfall. In order to study the impact of climate change on the discharges, the Jinhua river basin was chosen as the study area and the uncertainty resources like emission scenarios, GCMs, parameters and structure of hydrological models are considered. The major work and conclusion of the thesis are like follows:（1） a selection of seven extreme indices is used to analyze the trend of precipitation extremes of18meteorological stations located in Zhejiang Province, east China using the Mann-Kendall test. Then the precipitation trends in the plum season （from May to July） and typhoon season （from August to October） are studied separately. The results show that the precipitation trend varies from east to west. There is a positive trend in the east and a negative one in the west. The largest part of Zhejiang Province shows a positive trend in heavy precipitation. Although the upward trend of extreme precipitation is not prevailing, the range of increase in specific areas is apparent. Precipitation intensity exhibits an upward trend in most areas. Precipitation intensity in both plum and typhoon seasons has increased too, especially for the coastal stations.（2） three different rainfall-runoff models, namely GR4J, HBV and Xinanjiang, are applied to Jinhua River basin, eastern China. The Generalised Likelihood Uncertainty Estimation （GLUE） approach is used for estimating the uncertainty of the three models due to parameter values. Uncertainty in simulated extreme flows is evaluated by means of the annual maximum discharge （MHQ） and mean annual7-day minimum discharge （MAM7）. The results show that the uncertainty in high flows increases with the discharge magnitude. The parameter uncertainty in high flows is the largest in the HBV model and smallest in the Xinanjiang model. Low flows are mostly underestimated by all models with optimum parameter sets. The uncertainty originating from parameters is larger than uncertainty due to model structure.（3） uncertainties in extreme high flows originating from greenhouse gas emission scenarios, hydrological model structures and their parameters for the Jinhua River basin, China are assessed. The baseline （1961-1990） climate and future （2011-2040） climate for A1B, A2and B2scenarios were downscaled from the General Circulation Model （GCM） using the PRECIS Regional Climate Model with a spatial resolution of50km×50km. Bias correction methods are applied to the temperature and precipitation of PRECIS. The bias corrected precipitation and temperature are used as input for three hydrological models （GR4J, HBV and Xinanjiang） to simulate extreme high flows. It is found that bias correction before the use of the RCM Data for assessment study could improve the results, which are of a higher degree of consistency with the observation. Under scenario A1B, A2and B2the extreme high flows decreased in the future. The order of the uncertainty range from high to low are hydro togical model parameters, hydro logical model structure and the emission scenarios.（4） the representative concentration projection （RCP）2.6, RCP4.5, RCP6.0and RCP8.5in the5th IPCC report are used as the future emission scenarios. Meanwhile the impacts of the GCMs on the extreme flows are considered. The weather generator LARS-WG is used for the output of three GCMs namely BCC_CSM1₁, HadGEM2-ES and GISS_E2_R to generate a longer series. Hydro logical models GR4J, HBV and Xinanjiang are applied to simulate the river discharges in the past and the future. The results show that there are increase in the temperature, the range of the increase is larger for the daily lowest temperature than that for the daily highest temperature. Among the three GCMs, the uncertainty of the extreme flows from the emission scenarios is largest for the HADGEM2-ES, followed by the GISS_E2_Rand the smallest is for the BCC_CSM1₁. The extreme high flows would increase under the RCP4.5and RCP6.0by HADGEM2-ES. However, it would decrease under the RCP6.0and RCP8.5. The extreme high flows would decrease as well under RCP4.5by GISS_E2_R. the uncertainty of the extreme flows from the GCMs is the largest for the RCP4.5. the extent of the impact of uncertainty resources on the extreme flows from high to low are:hydrological parameters, GCMs, RCPs and hydrological model structures.