Risk Evaluation Of MWRA Water Resources System Water Supply Under Climate Change

Since the Industrial Revolution, increasing CO2concentration in the atmospheremay change the energy balance of the climate system, and consequently global climate.Hydrological cycle, as an important component of the climate system, will be affectedeasily by climate change which can alter the original hydrological to change globalspatial and temporal distribution of water resources. The impact of climate change on waterresources has been received considerable attention worldwide and there are extensiveresearches on it.Most of current studies assessing the impact of climate change on water resourcesare conducted traditionally using an approach which always begins with obtainingfuture meteorological data from general circulation model (GCM) and then input thesedata to appropriate hydrological models or water resources model directly to assess thedirect consequences in water resources systems. However, such methods involve lotshortcomings such as reducing uncertainties and limitation of GCM data quantity. Thisstudy attempts to improve understanding of the impacts of climate change on the waterresources by introducing an inverse approach assessing the water resource risk underclimate change. This approach doesn’t only combine large volumes data can begenerated by stochastic model and GCM data accuracy advantage, but alsosimultaneously quantifies the statistics of climate conditions or hydrological factors onwater resources system. The approach has been applied to Massachusetts WaterResources Association (MWRA) water resources system, Massachusetts, United Statesand Qimaling reservoir located in Yunan province, China. The main results are:（1）Through the analysis of the MWRA water resources system, the MWRAwater resources model was built and then the data of base period (1950~1999) wasemployed to test the simulation ability of the model.（2）Based on the statistical characteristics of hydrologic series, one standardizedARMA (autoregressive moving average) model was built in this study. To evaluate theperformance of the model, the model is compared with PARMA (periodicautoregressive moving average) model through parameters estimating based on1950~1999net basin inflow (NBS) of Quabbin reservoir. The result shows that theperformance of the standardized ARMA model is better than that of PARMA model.（3）Under scenario A2, A1B and B2which represents respectively high, mediumand low level of greenhouse gases in the atmosphere,112GCMs project the rainwaterand the air temperature in the region of MWRA water resources system. Most of GCMsindicate the rainwater and the air temperature may increase in2050s (2036~2065) and2080s (2066~2095) compared with that in the base period (1950~1999). （4）The parameters of ABCD model which is a hydrological model are estimatedthrough the historical data of the region of MWRA water resources system. Then theABCD model is employed as a tool to transform meteorological elements from GCMinto future corresponding hydrological elements. The result indicates that futuremonthly NBS has significant temporal difference and annual NBS doesn’t change a lotcompared with that of the base period.（5）The reliability indicator is used as a performance indicator for evaluatingMWRA water resources system with reference climate change. Then the inverse methodis used to evaluate the possible risk of MWRA water resources under future climatechange. The results show that when the interannual variance of NBS is100%,110%,120%,130%and140%of historical data, MWRA water resources system has a highrisk under A2, A1B and B1scenario. MWRA must make necessary polices to cope withpossible negative effects under future climate change.（6）To investigate the adaptive of the inverse method, the inverse method is alsoapplied to the climate informed risk evaluation in Qimaling reservoir located in Yunnanprovince, China. Through the climate change response function and the threshold of thereliability indicator, the thresholds of annual inflow are identified, which are92.8%94.3%,95.9%,97.4%and99.0%of historical inflows with the interannual variance is100%,110%,120%,130%and140%of historical data respectively. And the result canhelp decision-makers to make further polices to cope with future climate change.