Jeffrey D. Colby

To mitigate climate change, it will be necessary to decouple greenhouse gas emissions and economic development. We explore trends in CO2/GDP as an indicator of decoupling. CO2/GDP is most often studied at the national level, but data are available for the 2010-2015 time period to explore the indicator at both the state and county scales in the U.S. County-level data reveal large heterogeneity with adjacent counties often exhibiting very different trends and many states showing diverse patterns of change. Although CO2 emissions have been decreasing as GDP increases in the U.S. as a whole, the same is true in only four-fifths of its states and in only around one-third of its counties. There are many counties in which CO2 is increasing as GDP declines, or in other combinations. Decoupling of CO2 emissions and economic growth are most apparent in counties with a large fraction of their emissions coming from electricity generation while decou-pling is less common in counties with large emissions from the industrial sector. Counties from large urban concentrations are also more likely to be decoupling. The spatial heterogeneity at the county level suggests the variety and challenges in motivating the decoupling of emissions from economic growth.

Anisotropic reflectance correction (ARC) of satellite imagery is required to remove multi-scale topographic effects in imagery. Commonly utilized ARC approaches have not effectively accounted for atmosphere-topographic coupling. Furthermore, it is not clear which topographic effects need to be formally accounted for. Consequently, we simulate the direct and diffuse-skylight irradiance components and formally account for multi-scale topographic effects. A sensitivity analysis was used to determine if characterization schemes can account for a collective treatment of effects, using our parameterization scheme as a basis for comparison. We found that commonly used assumptions could not account for topographic modulation in our simulations. We also found that the use of isotropic diffuse irradiance and a topographic shielding parameter also failed to characterize topographic modulation. Our results reveal that topographic effects govern irradiance variations in a synergistic way, and that issues of ARC need to be formally addressed given atmosphere-topography coupling. Collectively, our results suggest that empirical ARC methods cannot be used to effectively address topographic effects, given inadequate parameterization schemes. Characterizing and removing spectral variation from multispectral imagery will most likely require numerical modeling efforts. More research is warranted to develop/evaluate parameterization schemes that better characterize the anisotropic nature of atmosphere-topography coupling.

The protection and effective management of watersheds is critical in order to preserve water quality and healthy ecosystems and to provide ecosystems services. The importance of incorporating the spatial dimension of watershed features and processes in watershed models has long been recognized. Geographic information systems (GIS) provide capabilities to characterize, visualize, and analyze the spatial nature of watersheds. The integration of GIS and watershed models provide a powerful combination for analyzing spatiotemporal watershed processes and supporting watershed management. The purpose of this article is to provide a foundation for understanding GIS as applied to watershed characterization and modeling, in particular, for hydrologic modeling. This article discusses the co‐evolution of GIS and hydrologic modeling and their eventual integration. GIS topics include data models, data acquisition, metadata, georeferencing, fundamental thematic data layers, and issues of scale and data uncertainty. Forms of integration for GIS and hydrologic models are outlined and the range of applications of GIS and watershed models is presented. In addition, areas in which potential advancements may emerge are considered including the fields of geographic information science (GIScience) and geocomputation, new data sources, Web‐GIS, and integrated watershed management.

Research involving anisotropic-reflectance correction (ARC) of multispectral imagery to account for topographic effects has been ongoing for approximately 40 years. A large body of research has focused on evaluating empirical ARC methods, resulting in inconsistent results. Consequently, our research objective was to evaluate commonly used ARC methods using first-order radiation-transfer modeling to simulate ASTER multispectral imagery over Nanga Parbat, Himalaya. Specifically, we accounted for orbital dynamics, atmospheric absorption and scattering, direct- and diffuse-skylight irradiance, land cover structure, and surface biophysical variations to evaluate their effectiveness in reducing multi-scale topographic effects. Our results clearly reveal that the empirical methods we evaluated could not reasonably account for multi-scale topographic effects at Nanga Parbat. The magnitude of reflectance and the correlation structure of biophysical properties were not preserved in the topographically-corrected multispectral imagery. The CCOR and SCS+C methods were able to remove topographic effects, given the Lambertian assumption, although atmospheric correction was required, and we did not account for other primary and secondary topographic effects that are thought to significantly influence spectral variation in imagery acquired over mountains. Evaluation of structural-similarity index images revealed spatially variable results that are wavelength dependent. Collectively, our simulation and evaluation procedures strongly suggest that empirical ARC methods have significant limitations for addressing anisotropic reflectance caused by multi-scale topographic effects. Results indicate that atmospheric correction is essential, and most methods failed to adequately produce the appropriate magnitude and spatial variation of surface reflectance in corrected imagery. Results were also wavelength dependent, as topographic effects influence radiation-transfer components differently in different regions of the electromagnetic spectrum. Our results explain inconsistencies described in the literature, and indicate that numerical modeling efforts are required to better account for multi-scale topographic effects in various radiation-transfer components.

This study examined the utility of a high resolution ground-based (mobile and terrestrial) Light Detection and Ranging (LiDAR) dataset (0.2 m point-spacing) supplemented with a coarser resolution airborne LiDAR dataset (5 m point-spacing) for use in a flood inundation analysis. The techniques for combining multi-platform LiDAR data into a composite dataset in the form of a triangulated irregular network (TIN) are described, and quantitative comparisons were made to a TIN generated solely from the airborne LiDAR dataset. For example, a maximum land surface elevation difference of 1.677 m and a mean difference of 0.178 m were calculated between the datasets based on sample points. Utilizing the composite and airborne LiDAR-derived TINs, a flood inundation comparison was completed using a one-dimensional steady flow hydraulic modeling analysis. Quantitative comparisons of the water surface profiles and depth grids indicated an underestimation of flooding extent, volume, and maximum flood height using the airborne LiDAR data alone. A 35% increase in maximum flood height was observed using the composite LiDAR dataset. In addition, the extents of the water surface profiles generated from the two datasets were found to be statistically significantly different. The urban and mountainous characteristics of the study area as well as the density (file size) of the high resolution ground based LiDAR data presented both opportunities and challenges for flood modeling analyses.

The number of flood disasters has increased worldwide in recent decades. Identifying the optimal resolution or scale at which to represent digital terrain models (DTMs) is critical in order to improve our ability to accurately and efficiently model floods. Few studies have attempted to compare flood modeling results using different resolutions of DTMs in divergent environments. In this study flooding on the Tar River in the coastal plains and the Watauga River in the mountains of North Carolina were modeled using hydrologic information obtained following Hurricanes Floyd and Ivan. The effectiveness of DTMs derived from light detection and ranging and United States Geological Survey elevation data at commonly available resolutions in North Carolina were assessed. A quantitative diagnostic method based on measuring the distance flooded along transects was applied for evaluating the horizontal extent and internal pattern of flooding. The use of additional diagnostic metrics (area, volume, and shape) along wi...