<p>Statistically downscaled CMIP5 precipitation data are available at higher spatial resolution compared to global climate models. The downscaled climate models have been used in many hydrological applications. However, limited numbers of studies focused on downscaled CMIP5 precipitation data for Louisiana. Statistically downscaled precipitation data for Louisiana is critically needed for various water resources engineering, planning and design purposes. This study has focused on assessing the skill of CMIP5 climate models in reproducing observed precipitation of Louisiana and application of CMIP5 precipitation data to analyze the impact of precipitation on hydrology (salinity and water level). Assessment of CMIP5 precipitation showed that statistically downscaled and bias corrected precipitation data reproduce observed average annual precipitation. But for other statistics (standard deviation), model data are not the same as observation data. The bias correction procedure ensured that models would reproduce the observed average precipitation. The maps of correlation distance for the models do not match with that of observation. This may be an indication that bias correction does not force the model to perform better in all statistics except annual average. Based on the analysis over climate divisions, it can be stated that spatial and temporal aggregation enables the models to perform better than gridded dataset. Application of CMIP5 precipitation data indicates that precipitation has a significant effect on salinity and almost zero effect on water level. Different salinity variables control the hydrologic and habitat suitability indices in coastal Louisiana. The cell-based analysis shows that different variables have different degrees of effect on vegetation and species (brown shrimp and oyster). Some species thrive in a high salinity environment while some others in low salinity. The uncertainty in the salinity and water level may occur due to insufficient data and boundary conditions provided in the Eco-hydrology model environment.
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:1594512 |
| Date | 27 August 2015 |
| Creators | Sumi, Selina Jahan |
| Publisher | University of Louisiana at Lafayette |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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