Impact of lateral flow and spatial scaling on the simulation of semi‐arid urban land surfaces in an integrated hydrologic and land surface model

Understanding and representing hydrologic fluxes in the urban environment is challenging because of fine scale land cover heterogeneity and lack of coherent scaling relationships. Here, the impact of urban land cover heterogeneity, scale, and configuration on the hydrologic and surface energy budget (SEB) is assessed using an integrated, coupled land surface/hydrologic model at high spatial resolutions. Archetypes of urban land cover are simulated at varying resolutions using both the National Land Cover Database (NLCD; 30 m) and an ultra high‐resolution land cover dataset (0.6 m). The analysis shows that the impact of highly organized, yet heterogeneous, land cover typical of the urban domain can cause large variations in hydrologic and energy fluxes within areas of similar land cover. The lateral flow processes that occur within each simulation create variations in overland flow of up to ±200% and ±4% in evapotranspiration. The impact on the SEB is smaller and largely restricted to the wet season for our semi‐arid forcing scenarios. Finally, we find that this seasonal bias, predominantly caused by lateral flow, is displaced by a systematic diurnal bias at coarser resolutions caused by deficiencies in the method used for scaling of land surface and hydrologic parameters. As a result of this research, we have produced land surface parameters for the widely used NLCD urban land cover types. This work illustrates the impact of processes that remain unrepresented in traditional high‐resolutions land surface models and how they may affect results and uncertainty in modeling of local water resources and climate. Copyright © 2015 John Wiley & Sons, Ltd.     

Geological and petrologic evolution of seamounts near the EPR based on submersible and camera study

Observations from 17 ALVIN dives and 14 ANGUS runs plus laboratory study of basalt samples collected with ALVIN help to constrain the morphologic, volcanic and petrologic evolution of four seamounts near the East Pacific Rise (EPR). Comparison among the four volcanoes provides evidence for a general pattern of near-EPR seamount evolution and shows the importance of sedimentation, mass wasting, hydrothermal activity and other geologic processes that occur on submerged oceanic volcanoes. Seamount 5, closest to the EPR (1.0 Ma) is the youngest seamount and may still be active. Its summit is covered by fresh lavas, recent faults and hydrothermal deposits. Seamount D is on crust 1.55 Ma and is inactive; like seamount 5, it has a breached caldera and is composed exclusively of N-MORB. Seamounts 5 and D represent the last stages of growth of typical N-MORB-only seamounts near the EPR axis. Seamounts 6 and 7 have bumpy, flattish summits composed of transitional and alkalic lavas. These lavas probably represent caldera fillings and caps overlying an edifice composed of N-MORB. Evolution from N-MORB-only cratered edifices to the alkalic stage does not occur on all near-EPR seamounts and may be favored by location on structures with relative-motion-parallel orientation.     

Chemical flushing from an urban-fringe watershed: hydrologic and riparian soil dynamics

The goal of the current study is to better understand the role of storm dynamics on stream water chemical variability in a highly polluted urban-fringe watershed. The study was conducted in the upper reach of the Arroyo Seco watershed located on the eastern edge of the densely urbanized Los Angeles basin in California. During the 2008–2009 study period, high-frequency stream water observations of chloride, fluoride, sulfate, and nitrate were monitored through a series of storm events and were compared to pre- and post-winter storm season geochemical soil profiles. Of the four solutes measured, nitrate demonstrated hydrologically enhanced behavior. Chloride, fluoride, and sulfate exhibited enhanced behavior initially (first flush), but transitioned to dilution behavior as the season progressed. Soil chemistry analyses in the riparian zone confirmed the abundance of nitrate on the soil surface, serving as a source for stream water nitrate. Observations and analyses collectively suggest that the chemical variability observed during the storms is dependent not only on discharge, but also on the magnitude and intensity of rainfall, the length of the antecedent dry period, and riparian soil composition. A further understanding of these factors will ultimately improve geochemical models for prediction of downstream chemical loads from regional urban-fringe watersheds.     

Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes

The National Weather Service (NWS) uses the SNOW17 model to forecast snow accumulation and ablation processes in snow-dominated watersheds nationwide. Successful application of the SNOW17 relies heavily on site-specific estimation of model parameters. The current study undertakes a comprehensive sensitivity and uncertainty analysis of SNOW17 model parameters using forcing and snow water equivalent (SWE) data from 12 sites with differing meteorological and geographic characteristics. The Generalized Sensitivity Analysis and the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm are utilized to explore the parameter space and assess model parametric and predictive uncertainty. Results indicate that SNOW17 parameter sensitivity and uncertainty generally varies between sites. Of the six hydroclimatic characteristics studied, only air temperature shows strong correlation with the sensitivity and uncertainty ranges of two parameters, while precipitation is highly correlated with the uncertainty of one parameter. Posterior marginal distributions of two parameters are also shown to be site-dependent in terms of distribution type. The SNOW17 prediction ensembles generated by the DREAM-derived posterior parameter sets contain most of the observed SWE. The proposed uncertainty analysis provides posterior parameter information on parameter uncertainty and distribution types that can serve as a foundation for a data assimilation framework for hydrologic models.