Modeling, Parameter Optimization, And Ecohydrologic Assessment Of Watershed Systems
Author: Xuan Yu
Publisher:
Published: 2014
Total Pages:
ISBN-13:
DOWNLOAD EBOOKIntegrated watershed models describe the land-phase of hydrologic cycles by coupling processes such as canopy interception, infiltration, recharge, evapotranspiration, overland flow, vadose zone flow, groundwater flow, and channel routing. This modeling scheme serves as an important strategy for understanding the moisture redistribution processes across the watershed and river-basin landscape. For example, the Penn State Integrated Hydrologic Model (PIHM) has successfully been applied to explain the impacts of antecedent soil moisture on peak streamflow and timing. However, due to the heavy computational cost of solving integrated models with complex model structure, efficient parameter estimation for PIHM is a major computational and algorithmic challenge. The focus of this dissertation has four main themes: (1) develop an efficient calibration strategy for PIHM; (2) develop a weighted-objective calibration scheme for multi-variable distributed parameters (e.g., streamflow, water table depth, and eddy flux data); (3) test the parameter-estimation process for spatial shallow groundwater calibration of PIHM using national wetland geospatial data (National Wetland Inventory: NWI); (4) extend the capabilities of PIHM for linking vegetation dynamics from an ecosystem model and evaluating the importance of vegetation growth in water balance studies.The temporal and geospatial complexity of the data requirements for integrated and fully coupled catchment models increases the difficulty of applying parameter optimization in real watershed applications. In this research, a new strategy known as partition calibration was proposed to enable the automatic calibration of PIHM. The concept can be thought of as a "divide-and-conquer algorithm," where the parameter space is divided into two or more sub-problems that can be solved sequentially. The first partition of the parameter vector is divided according to the two dominant time-scales of catchment hydrological processes: 1) event-scale hydrologic response parameters; and 2) seasonal-scale response parameters. Once divided, the event-scale group parameters and seasonal-scale group parameters are then calibrated sequentially. The second partition focused on the separation of the total calibration objective onto multiple targets to predict each observed hydrological variable. The "informativeness" of each calibration target was defined in terms of a weighted objective function. Application of the scheme suggested the use of an informativeness-based partitioning of streamflow, groundwater, and ET parameters and demonstrated that partition calibration was superior to both single-objective calibration and un-weighted average multi-objective calibration.Applications of the PIHM were found to be efficient with the partition calibration strategy. The first PIHM application involves characterization of the freshwater wetland response to climate change at seven catchments within the Susquehanna River Basin. In this case, streamflow time series and geospatial mapping of wetlands in the National Wetland Inventory (NWI) were used to calibrate the model to capture the distributed groundwater and streamflow dynamics. After calibration, the model was applied to an IPCC climate change scenario (2046-2065), and the modeling results suggested that upland groundwater levels were more sensitive to climate change than water levels of wetlands in lower parts of the catchment, as expected. In the final part of this research, long-term modeling of PIHM compared the role of fixed seasonal variation in LAI (Leaf Area Index) and a fully dynamic vegetation growth model. The community ecosystem model BIOME-BGC was linked to PIHM to test the hypothesis that default monthly LAI values are sufficient to represent long-term water balances in a catchment. By comparing model results for fixed LAI and dynamic LAI, it was demonstrated that fixed LAI is not sufficient for capturing interannual variability of forest vegetation and water flow dynamics, especially as it relates to the onset and growth of forest.
