Streamflow Simulations Under Future Climate Scenarios In The Boulder Creek Watershed, 欧美口爆视频
Zhang, Qinghuan 1 ; Williams, Mark 2 ; Livneh, Ben 3
1 University of 欧美口爆视频 Boulder
2 University of 欧美口爆视频 Boulder
3 University of 欧美口爆视频 Boulder
Mountainous areas have complex geological features and climatic variability, which limit our ability to simulate and predict hydrologic processes in these areas, especially under the context of a changing climate. Hydrologic models can improve our understanding of land surface water and energy budgets in these regions. In this study, a distributed physically-based hydrologic model is applied to the Boulder Creek Watershed, USA to study streamflow conditions under climatic scenario RCP 8.5. Most studies focus on a broad scale, whereas we apply the hydrologic model at two spatial resolutions to the regional watershed by using multiple objective functions to adjust the model. Model parameters were first identified at 1/8th and 1/16th degree spatial resolutions using historical streamflow data. Climatic forcing data using two statistical downscaling methods from eighteen CMIP5 GCMs are applied to the study area. It shows that the multivariate adaptive constructed analogues (MACA) is more proper than the bias-corrected and statistical disaggregation (BCSD) method in the study area. The two methods show that daily minimum and maximum temperature will increase from all the models, while daily precipitation may increase or decrease depending on different models. Using the MACA dataset, average annual precipitation will change by -1.2%~4.5%. Daily maximum temperature will increase by 1~5.7 掳C. Daily minimum temperature will increase by 1.7~6.1 潞C, compared to baseline values. Using the BCSD dataset, average annual precipitation will change by -3.7%~5.3%. Daily maximum temperature will increase 1.4~6.5 潞C, while daily minimum temperature will increase 1.5~6 潞C. Average annual streamflow will decrease by 16%~33% using the MACA dataset, and decrease by 14.6% using the BCSD dataset. Understanding streamflow conditions under future climate scenarios is helpful for water resources management.