Browsing by Author "Chaney, Nathaniel"

This paper investigates climate change impact on the water resources in the Greenbrier basin using a distributed hydrological model VIC and future climate series. The GCM outputs under the SRES A2 greenhouse gas emission scenario is downscaled and bias-corrected by the BCCAQ method to obtain the future climate series. The VIC model performance is satisfactory with the Nash–Sutcliffe efficiency coefficient (NSE) of 0.62 and 0.58 in calibration and validation periods. The bias-corrected precipitation and temperature indicate a warmer and more humid climate with precipitation and temperature increase by 14% and 1.8°C in the future. Under climate change background, the mean annual cycles of water balance components keep similar seasonal fluctuation but have larger magnitudes in the future. The discharge in the future also has close monthly distribution with that in the historical observations. The results show that the future discharge is larger than historical observation, implying water resources would be more abundant in summer from 2046 to 2065. The hydrological simulations in the Greenbrier basin have a system error of underestimating the peak flows, and the extreme discharge would be larger and more frequent in the mid of 21st century.

To better simulate terrestrial energy and water processes, great efforts have been made to improve the representation of spatial heterogeneity in land surface models. HydroBlocks, a field-scale resolving land surface model, was developed to address this challenge while minimizing increases in computational demands by employs a new tiling framework – the hierarchical multivariate clustering (HMC) approach. The HMC is used to cluster grid cells with similar surface characteristics into hydrologic response units (HRUs, i.e., sub-grid tiles) using high-resolution data. HRUs model land surface processes separately and HRUs are connected through subsurface flow. Hence, the HRU configuration plays a critical role in the model performance. Furthermore, since the outputs for each HRU can be mapped in space, it is possible to explicitly calibrate the model at the tile level based on satellite-derived data. To investigate the advantages that satellite observations can have to tune HydroBlocks, the model was set up to run from 2013 to 2019 at the 30-meter spatial resolution and hourly temporal resolution over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), U.S. This study demonstrated that HydroBlocks benefits from considering the temporal mean and standard deviation map of Landsat LST when generating HRUs. The effects of inputting the albedo, emissivity, and leaf area index derived directly from MODIS into the model were also explored. Finally, six soil and vegetation parameters in HydroBlocks were calibrated by maximizing the linear correlation coefficient of the time-series of simulated LST and observed MODIS LST at each HRU. The regional-level and site-level evaluation indicate the effectiveness of the calibration approach.