A substantial decrease in snow cover extent (SCE) and snow depth over North America has been observed over the 1960–2000 period. One explanation for the changes in North American snow cover is a change in the frequency and/or intensity of snow ablation. This study uses a gridded dataset of United States and Canadian surface observations from 1960 to 2000 to examine patterns of snow ablation over North America. An ablation event is defined as an interdiurnal snow depth change exceeding a critical value. Results show a significant positive trend in the frequency of ablation events during March (p < 0.05) and a significant negative trend in May (p < 0.05), indicating an earlier onset of ablation. This pattern is consistent for ablation of varying intensity. Surface energy budget components and air mass frequencies are examined in relation to the observed trends in snow ablation. Changes in March ablation frequency were shown to be dominated by increases in the sensible heat flux. A higher frequency of dry moderate instead of moist polar air masses during high ablation years may explain the increase in sensible heat flux and ablation over the study period. Copyright © 2006 Royal Meteorological Society

Changes in land cover throughout the Chesapeake Bay watershed, accompanied by variability in climate patterns, can impact runoff and water quality. A study was conducted using the Soil and Water Assessment Tool (SWAT) for the James River watershed in Virginia, the southernmost tributary of the Chesapeake Bay, from 1986 to 2018, in order to evaluate factors that affect water quality in the river. This research focuses on statistical analysis of land use, precipitation, and water quality indicators. Land cover changes derived from satellite imagery and geographic information system (GIS) tools were compared with water quality parameters throughout that timeframe. Marked decreases in forest land cover were observed throughout the watershed, as well as increased residential development. Our findings suggest strong links between land cover modification, such as residential development, and degraded water quality indicators such as nitrogen, phosphorus, and sediment. In addition, we note direct improvements in water quality when forest land areas are preserved throughout the watershed.

Given the sensitivity of natural environments to freshwater availability in the Southeast US, as well as the reliance of many municipal and commercial water consumers on surface water supplies, specific issues related to low river streamflow are apparent. As a result, the need for quantifying the spatial distribution, frequency, and intensity of low flow events (a.k.a., hydrologic drought) is critical to define areas most susceptible to water shortages and subsequent environmental and societal risk. To that end, daily mean discharge values from the National Water Model (NWM) retrospective data (v. 2.0) are used to assess low flow frequency, intensity, and spatial distribution within the Southeast US. Low flow events are defined using the US EPA 7Q10 approach, based on the flow duration curve (FDC) developed using a 1993–2018 period of record. Results reflect the general climatological patterns of the region, with a higher probability of low flow events occurring during the warm season (June–August) while low flow events in the cool season (January–March) are generally less common and have a higher average discharge. Spatial analysis shows substantial regional variability, with an area from southeastern Mississippi through central South Carolina showing higher low flow event frequency during the cool season. This same area is also highlighted in the warm season, albeit along a more expansive area from central Alabama into the piedmont region of North Carolina. Results indicate that the NWM retrospective data are able to show general patterns of hydrologic drought across the Southeast US, although local-scale assessment is limited due to potential issues associated with infiltration and runoff during periods of warm-season convective rainfall.

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for the reduction of atmospheric contributions in unmanned aerial systems (UAS) imagery and for the production of scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions were used to develop and test an ELM radiometric calibration framework designed for the reduction of atmospheric contributions from UAS imagery in studies with limited site accessibility or data acquisition time constraints. The ELM radiometric calibration framework was developed specifically for water-based operations and the efficacy of using generalized study area calibration equations averaged across variable illumination and atmospheric conditions was assessed. The framework was effective in reducing atmospheric and other external contributions in UAS imagery. Unique to the proposed radiometric calibration framework is the radiance-to-reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equation development. While image-by-image calibrations are still preferred for high accuracy applications, this paper provides an ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in UAS imagery in situations with limited site accessibility or data acquisition constraints.