Abstract: Bedrock outcrops are common on central Appalachian Mountain ridgelines. Because these ridgelines define watersheds, the rate at which they erode influences the pace of landscape evolution. To estimate ridgeline erosion rates, we sampled 72 quartz-bearing outcrops from the Potomac and Susquehanna River Basins and measured in situ produced Be-10. Ridgeline erosion rates average 9 +/- 1 m m.y.(-1) (median = 6 m m.y.(-1)), similar to Be-10-derived rates previously reported for the region. The range of erosion rates we calculated reflects the wide distribution of samples we collected and the likely inclusion of outcrops affected by episodic loss of thick slabs and periglacial activity. Outcrops on main ridgelines erode slower than those on mountainside spur ridges because ridgelines are less likely to be covered by soil, which reduces the production rate of Be-10 and increases the erosion rate of rock. Ridgeline outcrops erode slower than drainage basins in the Susquehanna and Potomac River watersheds, suggesting a landscape in disequilibrium. Erosion rates are more similar for outcrops meters to tens of meters apart than those at greater distances, yet semivariogram analysis suggests that outcrop erosion rates in the same physiographic province are similar even though they are hundreds of kilometers apart. This similarity may reflect underlying lithological and/or structural properties common to each physiographic province. Average Be-10-derived outcrop erosion rates are similar to denudation rates determined by other means (sediment flux, fission-track thermochronology, [U-Th]/He dating), indicating that the pace of landscape evolution in the central Appalachian Mountains is slow, and has been since post-Triassic rifting events.
Abstract: In this work, we develop and test two artificial neural networks (ANNs) to forecast streamflow in ungauged basins. The model inputs include time-lagged records of precipitation and temperature. In addition, recurrent feedback loops allow the ANN streamflow estimates to be used as model inputs. Publically available climate and US Geological Survey streamflow records from sub-basins in Northern Vermont are used to train and test the methods. Time-series analysis of the climate-flow data provides a transferable and systematic methodology to determine the appropriate number of time-lagged input data. To predict streamflow in an ungauged basin, the recurrent ANNs are trained on climate-flow data from one basin and used to forecast streamflow in a nearby basin with different (more representative) climate inputs. One of the key results of this work, and the reason why time-lagged predictions of steamflow improve forecasts, is these recurrent flow predictions are being driven by time-lagged locally-measured climate data. The successful demonstration of these flow prediction methods with publicly available USGS flow and NCDC climate datasets shows that the ANNs, trained on a climate-discharge record from one basin, prove capable of predicting streamflow in a nearby basin as accurately as in the basin on which they were trained. This suggests that the proposed methods are widely applicable, at least in the humid, temperate climate zones shown in this work. A scaling ratio, based on a relationship between bankfull discharge and basin drainage area, accounts for the change in drainage area from one basin to another. Hourly streamflow predictions were superior to those using daily data for the small streams tested due the loss of critical lag times through upscaling. The ANNs selected in this work always converge, avoid stochastic training algorithms, and are applicable in small ungauged basins.