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The Midwest of the USA is a highly productive agricultural region, in part due to the installation of perforated subsurface pipes, known as tile drains that remove excess water from wet soils. Tile drains rapidly move water to nearby streams, influencing how quickly streamflow rises and falls (i.e., streamflow “flashiness”). Currently, there are no comprehensive studies that compare the extent to which tile drainage influences flashiness across large and diverse agricultural regions. We address this knowledge gap by examining growing‐season (April–October) flashiness using the Richards‐Baker Index (RBI) in 139 watersheds located throughout the Midwest. Using a spatial tile‐drainage dataset, watersheds were split into low, medium, and high tile‐drainage classes. We found no significant differences between the flashiness of these three classes using a one‐way Kruskal–Wallis test. When watersheds were separated into infiltration groups to help control for different soil types, the high tile‐drainage class RBI was significantly higher than the low tile‐drainage class RBI in the high infiltration group. To further understand the causes of flashiness, additional environmental variables and their relationship to flashiness were examined using multivariate regression. In the low infiltration group, tile drainage significantly reduced flashiness, with watershed area and average depth to water table being the largest influences on flashiness. Tile drainage produced a larger reduction in flashiness in the high infiltration watersheds, with the largest influences being percent clay in the watershed and watershed area. These results indicate that the influence of tile drainage on flashiness emerges only after other watershed variables are accounted for. Given that tile drainage may increase in the future as precipitation patterns and extremes change, flashiness will likely continue to be modified. These results lead to an improved understanding of flood‐generating and nutrient transport mechanisms that are relevant to stakeholders across a wide range of sectors.

 

Understanding the response of tropical cyclone precipitation to ongoing climate change is essential to determine associated flood risk. However, instrumental records are short-term and fail to capture the full range of variability in seasonal totals of precipitation from tropical cyclones. Here we present a 473-year-long tree-ring proxy record comprised of longleaf pine from excavated coffins, a historical house, remnant stumps, and living trees in southern Mississippi, USA. We use cross-dating dendrochronological analyses calibrated with instrumental records to reconstruct tropical cyclone precipitation stretching back to 1540 CE. We compare this record to potential climatic controls of interannual and multidecadal tropical cyclone precipitation variability along the Gulf Coast. We find that tropical cyclone precipitation declined significantly in the two years following large Northern Hemisphere volcanic eruptions and is influenced by the behavior of the North Atlantic subtropical high-pressure system. Additionally, we suggest that tropical cyclone precipitation variability is significantly, albeit weakly, related to Atlantic multidecadal variability. Finally, we suggest that we need to establish a network for reconstructing precipitation from tropical cyclones in the Southeast USA if we want to capture regional tropical cyclone behavior and associated flood risks.

 

The impacts of inland flooding caused by tropical cyclones (TCs), including loss of life, infrastructure disruption, and alteration of natural landscapes, have increased over recent decades. While these impacts are well documented, changes in TC precipitation extremes—the proximate cause of such inland flooding—have been more difficult to detect. Here, we present a latewood tree-ring–based record of seasonal (June 1 through October 15) TC precipitation sums (ΣTCP) from the region in North America that receives the most ΣTCP: coastal North and South Carolina. Our 319-y-long ΣTCP reconstruction reveals that ΣTCP extremes (≥0.95 quantile) have increased by 2 to 4 mm/decade since 1700 CE, with most of the increase occurring in the last 60 y. Consistent with the hypothesis that TCs are moving slower under anthropogenic climate change, we show that seasonal ΣTCP along the US East Coast are positively related to seasonal average TC duration and TC translation speed.

 

The southern Appalachian forests have been threatened by several large-scale disturbances, such as wildfire and infestation, which alter the forest ecosystem structures and functions. Hemlock Woolly Adelgid (Adelges tsugae Annand, HWA) is a non-native pest that causes widespread foliar damage and eventual mortality, resulting in irreversible tree decline in eastern (Tsuga canadensis) and Carolina (T. caroliniana) hemlocks throughout the eastern United States. It is important to monitor the extent and severity of these disturbances over space and time to better understand their implications in the biogeochemical cycles of forest landscapes. Using all available Landsat images, we investigate and compare the performance of Tasseled Cap Transformation (TCT)-based indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Disturbance Index (DI) in capturing the spectral-temporal trajectory of both abrupt and gradual forest disturbances (e.g., fire and hemlock decline). For each Landsat pixel, the temporal trajectories of these indices were fitted into a time series model, separating the inter-annual disturbance patterns (low frequency) and seasonal phenology (high frequency) signals. We estimated the temporal dynamics of disturbances based on the residuals between the observed and predicted values of the model, investigated the performance of all the indices in capturing the hemlock decline intensity, and further validated the results with the number of individual dead hemlocks identified from high-resolution aerial images. Our results suggested that the overall performance of NDVI, followed by TCT wetness, was most accurate in detecting both the disturbance timing and hemlock decline intensity, explaining over 90% of the variability in the number of dead hemlocks. Despite the overall good performance of TCT wetness in characterizing the disturbance regime, our analysis showed that this index has some limitations in characterizing disturbances due to its recovery patterns following infestation. 

The selection of calibration and validation time periods in hydrologic modelling is often done arbitrarily. Nonstationarity can lead to an optimal parameter set for one period which may not accurately simulate another. However, there is still much to be learned about the responses of hydrologic models to nonstationary conditions. We investigated how the selection of calibration and validation periods can influence water balance simulations. We calibrated Soil and Water Assessment Tool hydrologic models with observed streamflow for three United States watersheds (St. Joseph River of Indiana/Michigan, Escambia River of Florida/Alabama, and Cottonwood Creek of California), using time period splits for calibration/validation. We found that the choice of calibration period (with different patterns of observed streamflow, precipitation, and air temperature) influenced the parameter sets, leading to dissimilar simulations of water balance components. In the Cottonwood Creek watershed, simulations of 50‐year mean January streamflow varied by 32%, because of lower winter precipitation and air temperature in earlier calibration periods on calibrated parameters, which impaired the ability for models calibrated to earlier periods to simulate later periods. Peaks of actual evapotranspiration for this watershed also shifted from April to May due to different parameter values depending on the calibration period's winter air temperatures. In the St. Joseph and Escambia River watersheds, adjustments of the runoff curve number parameter could vary by 10.7% and 20.8%, respectively, while 50‐year mean monthly surface runoff simulations could vary by 23%–37% and 169%–209%, depending on the observed streamflow and precipitation of the chosen calibration period. It is imperative that calibration and validation time periods are chosen selectively instead of arbitrarily, for instance using change point detection methods, and that the calibration periods are appropriate for the goals of the study, considering possible broad effects of nonstationary time series on water balance simulations. It is also crucial that the hydrologic modelling community improves existing calibration and validation practices to better include nonstationary processes.

 

Tropical cyclones (TCs) are an important source of precipitation for much of the eastern United States. However, our understanding of the spatiotemporal variability of tropical cyclone precipitation (TCP) and the connections to large-scale atmospheric circulation is limited by irregularly distributed rain gauges and short records of satellite measurements. To address this, we developed a new gridded (0.25° × 0.25°) publicly available dataset of TCP (1948–2015; Tropical Cyclone Precipitation Dataset, or TCPDat) using TC tracks to identify TCP within an existing gridded precipitation dataset. TCPDat was used to characterize total June–November TCP and percentage contribution to total June–November precipitation. TCP totals and contributions had maxima on the Louisiana, North Carolina, and Texas coasts, substantially decreasing farther inland at rates of approximately 6.2–6.7 mm km⁻¹. Few statistically significant trends were discovered in either TCP totals or percentage contribution. TCP is positively related to an index of the position and strength of the western flank of the North Atlantic subtropical high (NASH), with the strongest correlations concentrated in the southeastern United States. Weaker inverse correlations between TCP and El Niño–Southern Oscillation are seen throughout the study site. Ultimately, spatial variations of TCP are more closely linked to variations in the NASH flank position or strength than to the ENSO index. The TCP dataset developed in this study is an important step in understanding hurricane–climate interactions and the impacts of TCs on communities, water resources, and ecosystems in the eastern United States.