Urban pluvial flash floods have become a matter of widespread concern, as they severely impact people’s lives in urban areas. Hydrological and hydraulic models have been widely used for urban flood management and urban planning. Traditionally, to reduce the complexity of urban flood modelling and simulations, simplification or generalization methods have been used; for example, some models focus on the simulation of overland water flow, and some models focus on the simulation of the water flow in sewer systems. However, the water flow of urban floods includes both overland flow and sewer system flow. The overland flow processes are impacted by many different geographical features in what is an extremely spatially heterogeneous environment. Therefore, this article is based on two widely used models (SWMM and ANUGA) that are coupled to develop a bi-directional method of simulating water flow processes in urban areas. The open source overland flow model uses the unstructured triangular as the spatial discretization scheme. The unstructured triangular-based hydraulic model can be better used to capture the spatial heterogeneity of the urban surfaces. So, the unstructured triangular-based model is an essential condition for heterogeneous feature-based urban flood simulation. The experiments indicate that the proposed coupled model in this article can accurately depict surface waterlogged areas and that the heterogeneous feature-based urban flood model can be used to determine different types of urban flow processes.
The altitude effect of isotopes in precipitation is not as significant on the leeward side of a mountain as it is on the windward side, which makes it difficult to use isotopes at leeward sites, especially if estimating elevation of groundwater recharge or reconstructing paleoelevations. Samples of precipitation were taken at three stations with different elevations—2,306–3,243 m above mean sea level (asl)—on the leeward side of the Meili Snow Mountains on the southeastern Tibetan Plateau from August 2017 to July 2018. The isotope vs. altitude gradients were calculated based on two adjacent stations at the daily, monthly, and annual scales. Most of the gradients are beyond the global ranges of –0.5 to –0.1‰ per 100 m for δ18O and –5 to –1‰ per 100 m for δ2H, and some of the gradients are even positive. Local processes of sub-cloud evaporation and mixing with recycled moisture are identified for the ambiguous altitude effect, while regional atmospheric circulation processes dominate the major patterns of stable isotope variation at the three stations. The groundwater recharge elevation is estimated to be in a very large range, 2,562–6,321 m asl, which could be caused by the differences in isotope vs. altitude gradient in the studied catchments. Considering the complex atmospheric processes affecting precipitation isotopes, sampling of event-based/monthly precipitation at more than two altitudes for at least one complete hydrological year is a minimum requirement to establish a reasonable isotope vs. altitude gradient.
Acta Geotechnica - Due to sedimentation, irregular particles of sand arrange anisotropically in nature. Anisotropy diversifies the internal friction angles between relative research planes at... 相似文献
Many past space‐time GIS data models viewed the world mainly from a spatial perspective. They attached a time stamp to each state of an entity or the entire area of study. This approach is less efficient for certain spatio‐temporal analyses that focus on how locations change over time, which require researchers to view each location from a temporal perspective. In this article, we present a data model to organize multi‐temporal remote sensing datasets and track their changes at the individual pixel level. This data model can also integrate raster datasets from heterogeneous sources under a unified framework. The proposed data model consists of several object classes under a hierarchical structure. Each object class is associated with specific properties and behaviors to facilitate efficient spatio‐temporal analyses. We apply this data model to a case study of analyzing the impact of the 2007 freeze in Knoxville, Tennessee. The characteristics of different vegetation clusters before, during, and after the 2007 freeze event are compared. Our findings indicate that the majority of the study area is impacted by this freeze event, and different vegetation types show different response patterns to this freeze. 相似文献