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Scientists and policymakers increasingly recognize that headwater regions contain numerous temporary streams that expand and contract in length, but accurately mapping and modeling dynamic stream networks remain a challenge. Flow intermittency sensors offer a relatively new approach to characterize wet stream length dynamics at high spatial and temporal resolutions. We installed 51 flow intermittency sensors at an average spacing of 40 m along the stream network of a high-relief, headwater catchment (33 ha) in the Valley and Ridge of southwest Virginia. The sensors recorded the presence or absence of water every 15 min for 10 months. Calculations of the wet network proportion from sensor data aligned with those from field measurements, confirming the efficacy of flow intermittency sensors. The fine temporal scale of the sensor data showed hysteresis in wet stream length: the wet network proportion was up to 50% greater on the rising limb of storm events than on the falling limb for dry antecedent conditions, at times with a delay of several hours between the maximum wet proportion and peak runoff at the catchment outlet. Less stream length hysteresis was evident for larger storms with higher event and antecedent precipitation that resulted in peak runoff > 15 mm/day. To assess spatial controls on stream wetting and drying, we performed a correlation analysis between flow duration at the sensor locations and common topographic metrics used in stream network modeling. Topography did not fully explain spatial variation in flow duration along the stream network. However, entrenched valleys had longer periods of flow on the rising limbs of events than unconfined reaches. In addition, large upslope contributing areas corresponded to higher flow duration on falling limbs. Future applications that explore the magnitude and drivers of stream length variability may provide further insights into solute and runoff generation processes in headwater regions.

Drainage is a globally common disturbance in forested peatlands that impacts peat soils, forest communities, and associated ecosystem functions, calling for informed hydrologic restoration strategies. The Great Dismal Swamp (GDS), located in Virginia and North Carolina, U.S.A., has been altered since colonial times, particularly by extensive ditch networks installed to lower water levels and facilitate timber harvests. Consequently, peat decomposition rates have accelerated, and red maple has become the dominant tree species, reducing the historical mosaic of bald cypress, Atlantic white-cedar, and pocosin stands. Recent repair and installation of water control structures aim to control drainage and, in doing so, enhance forest community composition and preserve peat depths. To help inform these actions, we established five transects of 15 plots each (75 plots total) along a hydrologic gradient where we measured continuous water levels and ecosystem attributes, including peat depths, microtopography, and forest composition and structure. We found significant differences among transects, with wetter sites having thicker peat, lower red maple importance, greater tree density, and higher overall stand richness. Plot-level analyses comported with these trends, clearly grouping plots by transects (via nonmetric multidimensional scaling) and resulting in significant correlations between specific hydrologic metrics and ecosystem attributes. Our findings highlight hydrologic controls on soil carbon storage, forest structure, and maple dominance, with implications for large-scale hydrologic restoration at GDS and in other degraded forested peatlands more broadly., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The emergence of infectious diseases over the past several decades has highlighted the need to better understand epidemics and prepare for the spread of diseases into new areas. As these diseases expand their geographic range, cases are recorded at different geographic locations over time, making the analysis and prediction of this expansion complicated. In this study, we analyze spatial patterns of the disease using a statistical smoothing analysis based on areal (census tract level) count data of Lyme disease cases in Virginia from 1998 to 2011. We also use space and space-time scan statistics to reveal the presence of clusters in the spatial and spatiotemporal distribution of Lyme disease. Our results confirm and quantify the continued emergence of Lyme disease to the south and west in states along the eastern coast of the United States. The results also highlight areas where education and surveillance needs are highest., (© The American Society of Tropical Medicine and Hygiene.)