Design Considerations For Lysimeters Used To Evaluate Alternative Earthen Final Covers

Alternative earthen final covers are being considered throughout North America as cost-effective alternatives to prescriptive covers. Regulatory agencies typically require field testing to demonstrate the equivalency of percolation rates from prescriptive and alternative covers. Lysimetry, which consists of collecting percolating water from the base of a test section, provides a direct measurement of the percolation rate, and can be used in equivalency demonstrations. This paper describes a modeling study that investigated how lysimeter geometry and boundary conditions affect lateral diversion and percolation rates measured using lysimeters. Lysimeters with various geometries were simulated with HYDRUS-2D using constant meteorological and vegetation data. Simulations showed that sidewalls, which are 0.35m high, can minimize the lateral diversion of flow around the sides of the lysimeter. Up-slope and down-slope endwalls of lysimeters need to extend to the surface of the lysimeter, especially when the lysimeter is inclined (4:1 or 3:1 slope). Modeling has shown that lysimeters underestimate percolation by 8 to 14%. Based on these simulations and the writers\u27 experience in the design and construction of lysimeters, a recommended design of lysimeters is suggested. © 2006 ASCE

Post-hurricane vegetative debris assessment using spectral indices derived from satellite imagery

Transportation systems are vulnerable to hurricanes and yet their recovery plays a critical role in returning a community to its pre-hurricane state. Vegetative debris is among the most significant causes of disruptions on transportation infrastructure. Therefore, identifying the driving factors of hurricane-caused debris generation can help clear roadways faster and improve the recovery time of infrastructure systems. Previous studies on hurricane debris assessment are generally based on field data collection, which is expensive, time consuming, and dangerous. With the availability and convenience of remote sensing powered by the simple yet accurate estimations on the vigor of vegetation or density of manufactured features, spectral indices can change the way that emergency planners prepare for and perform vegetative debris removal operations. Thus, this study proposes a data fusion framework combining multispectral satellite imagery and various vector data to evaluate post-hurricane vegetative debris with an exploratory analysis in small geographical units. Actual debris removal data were obtained from the City of Tallahassee, Florida after Hurricane Michael (2018) and aggregated into U.S. Census Block Groups along with four groups of datasets representing vegetation, storm surge, land use, and socioeconomics. Findings suggest that vegetation and other land characteristics are more determinant factors on debris generation, and Modified Soil-Adjusted Vegetation Index (MSAVI2) outperforms other vegetation indices for hurricane debris assessment. The proposed framework can help better identify equipment stack locations and temporary debris collection centers while providing resilience enhancements with a focus on the transportation infrastructure

The distant chant. Climate reconstruction and landscape history. The last two millennia in Southeast Tunisia

A record of laminated sediments of Sebkha Mehabeul in Southeast Tunisia provided a high-resolution environmental archive for the last two millennia. It allowed a detailed reconstruction of the sedimentological evolution, of the mineral environment and hydrological history as well as the vegetation and landscape evolution and of climatic development. The impluvium character of the sebkha allowed it to establish a palaeopluviometric record, as well as the reconstruction of drought and flood periods. A pollen diagram revealed the persistent semi desert environment in the context of a cultural landscape. Despite the resilience of the semi desert ecosystems and periods of favourable climatic episodes the human impact was always strong enough to impede a change to more demanding ecosystems like steppe or woodlands

Challenges and Adaptive Measures for U.S. Municipal Solid Waste Management Systems during the COVID-19 Pandemic

The coronavirus pandemic has resulted in major disruptions in the way municipal solid waste management systems (MSWMSs) operate due to substantial distortions in waste generation trends, along with a variety of significant operational and managerial challenges. As critical infrastructure, MSWMSs have endeavored to adapt in response to such unprecedented stresses in order to maintain their operations during the pandemic. The challenges and their relevant adaptive measures, however, have varied with the progression of the pandemic across different MSWMSs. Currently, there is a limited understanding of such time-bound and system-specific phenomena, which impedes timely and effective adaptation. This study aims to fill this knowledge gap by performing a detailed and documented investigation of the longitudinal impact of the coronavirus pandemic on different MSWMSs across the United States, along with its evolution over time, using collected qualitative and quantitative data (i.e., monthly interviews with waste management personnel, online news media, and waste tonnages). This study also develops a relational database system to facilitate the systematic recording and monitoring of the pandemic’s impact on MSWMSs, as well as guide the implementation of different adaptation strategies based on distinct systems’ characteristics. Findings of this study will help solid waste decision-makers better understand the current pandemic, along with serving as a knowledge base for future pandemic scenarios towards more resilient MSWMSs

Automated Satellite-based Assessment of Hurricane Impacts on Roadways

During extreme weather events like hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This paper proposes an approach to automatically identify fallen trees on roadways using high-resolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a co-voting strategy of three different algorithms and tailored dissimilarity scores. The proposed method does not rely on the large manually labeled satellite image data, making it more practical than existing approaches. Our solution has been implemented and validated on an actual roadway closure dataset from Hurricane Michael in Tallahassee, Florida, in October 201