Recently, widespread valley-bottom damming for water power was identified as a primary control on valley sedimentation in the mid-Atlantic US during the late seventeenth to early twentieth century. The timing of damming coincided with that of accelerated upland erosion during post-European settlement land-use change. In this paper, we examine the impact of local drops in base level on incision into historic reservoir sediment as thousands of ageing dams breach. Analysis of lidar and field data indicates that historic milldam building led to local base-level rises of 2–5 m (typical milldam height) and reduced valley slopes by half. Subsequent base-level fall with dam breaching led to an approximate doubling in slope, a significant base-level forcing. Case studies in forested, rural as well as agricultural and urban areas demonstrate that a breached dam can lead to stream incision, bank erosion and increased loads of suspended sediment, even with no change in land use. After dam breaching, key predictors of stream bank erosion include number of years since dam breach, proximity to a dam and dam height. One implication of this work is that conceptual models linking channel condition and sediment yield exclusively with modern upland land use are incomplete for valleys impacted by milldams. With no equivalent in the Holocene or late Pleistocene sedimentary record, modern incised stream-channel forms in the mid-Atlantic region represent a transient response to both base-level forcing and major changes in land use beginning centuries ago. Similar channel forms might also exist in other locales where historic milling was prevalent.

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Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open-source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat-8 Operational Land Imager sensor, elevation data from the Shuttle Radar Topography Mission, and precipitation data from the Global Precipitation Measurement mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change-detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-Time Increased Precipitation (DRIP) model that helps to identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state of the art for landslide detection. A case study and validation exercise in Nepal were performed for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium-resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool.

No abstract

Flood events pose a severe threat to communities in the Lower Mekong River Basin. The combination of population growth, urbanization, and economic development exacerbate the impacts of these events. Flood damage assessments, critical for understanding the effects of flooding on the local population and informing decision-makers about future risks, are frequently used to quantify the economic losses due to storms. Remote sensing systems provide a valuable tool for monitoring flood conditions and assessing their severity more rapidly than traditional post-event evaluations. The frequency and severity of extreme flood events are projected to increase, further highlighting the need for improved flood monitoring and impact analysis. In this study we integrate a socioeconomic damage assessment model with a near real-time flood remote sensing and decision support tool (NASA's Project Mekong). Direct damages to populations, infrastructure, and land cover are assessed using the 2011 Southeast Asian flood as a case study. Improved land use/land cover and flood depth assessments result in rapid loss estimates throughout the Mekong River Basin. Results suggest that rapid initial estimates of flood impacts can provide valuable information to governments, international agencies, and disaster responders in the wake of extreme flood events.