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DEVELOPMENT OF DAILY, MONTHLY, INTER-ANNUAL, AND MEAN ANNUAL HYDROLOGICAL MODELS BASED ON A UNIFIED RUNOFF GENERATION FRAMEWORK
Title
DEVELOPMENT OF DAILY, MONTHLY, INTER-ANNUAL, AND MEAN ANNUAL HYDROLOGICAL MODELS BASED ON A UNIFIED RUNOFF GENERATION FRAMEWORK.
Creator
Kheimi, Marwan, Wang, Dingbao, Wahl, Thomas, Singh, Arvind, Zheng, Qipeng, University of Central Florida
Abstract / Description
The main goal of this dissertation develops a unified model structure for runoff generation based on observations from a large number of catchments. Furthermore, obtaining a comprehensive understanding of the physical controlling factors that control daily, monthly, and annual water balance models. Meanwhile, applying the developed Unified model on different climate conditions, and comparing it with different well-known models.The proposed model was compared with a similar timescale model ...
Show moreThe main goal of this dissertation develops a unified model structure for runoff generation based on observations from a large number of catchments. Furthermore, obtaining a comprehensive understanding of the physical controlling factors that control daily, monthly, and annual water balance models. Meanwhile, applying the developed Unified model on different climate conditions, and comparing it with different well-known models.The proposed model was compared with a similar timescale model (HyMOD, and abcd) and applied on 92 catchments from MOPEX dataset across the United States. The HyMOD and abcd are a well-known daily and monthly hydrological model used on a variety of researchers. The differences between the new model and HyMOD, and abcd include 1) the distribution function for soil water storage capacity is different and the new distribution function leads to the SCS curve number method; and 2) the computation of evaporation is also based on the distribution function considering the spatial variability of available water evaporation. The performance of all models along with parameters used is examined to understand the controlling factors. The generated results were calibrated and validated using the Nash-Sutcliffe efficiency coefficient (NSE), indicating that the Unified model has a moderate better performance against the HyMOD at a daily time scale, and abcd model at a monthly timescale. The proposed model using the SCS-CN method shows the effect of improving the performance.
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Date Issued
2019
Identifier
CFE0007478, ucf:52684
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007478
Biomass density based adjustment of LiDAR-derived digital elevation models: a machine learning approach
Title
Biomass density based adjustment of LiDAR-derived digital elevation models: a machine learning approach.
Creator
Abdelwahab, Khalid, Medeiros, Stephen, Mayo, Talea, Wahl, Thomas, University of Central Florida
Abstract / Description
Salt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in...
Show moreSalt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in the salt marshes near Saint Marks Lighthouse in Crawfordville, Florida. Between summer of 2017 and spring of 2018, two field trips were conducted to acquire true elevation and biomass density measures. Lidar point cloud data were combined with vegetation monitoring imagery acquired from Sentinel-2 and Landsat Thematic Mapper (LTM) satellites, and 64 field biomass density samples were used as target variables for developing the models. Biomass density classes were assigned to each biomass sample using a quartile approach. Moreover, 346 in-situ elevation measures were used to calculate the lidar DEM errors. The best model was then used to estimate biomass densities at all 346 locations. Finally, an adjusted DEM was produced by deducting the quartile-based adjustment values from the original lidar DEM. A random forest regression model achieved the highest pseudo R2 value of 0.94 for predicting biomass density in g/m2. The adjusted DEM based on the estimated biomass densities reduced the root mean squared error of the original DEM from 0.38 m to 0.18 m while decreasing the raw mean error from 0.33 m to 0.14 m, improving both measures by 54% and 58%, respectively.
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Date Issued
2019
Identifier
CFE0007594, ucf:52535
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007594