Inclusion of Narrow Flow Paths between Buildings in Coarser Grids for Urban Flood Modeling: Virtual Surface Links

Urban flood modeling benefits from new remote sensing technologies, which provide high-resolution data and allow the consideration of small-scale urban key features. Since high-resolution data often result in large simulation runtimes, coarsening of the 2D grid via resampling techniques can be used to achieve a good balance between accuracy and computation time. However, the representation of urban features and topographical properties degrades, since small-scale features are blurred. Therefore, narrow flow paths between buildings are often not considered, building’s sizes are overestimated, and their arrangement in the grid changes. Thus, flow paths change and waterways are blocked, leading to incorrect inundations around buildings. This paper develops a method to improve the simulation results of coarser grids by adding virtual surface links (VSL) between buildings. The VSL mimic the flow paths of a high-resolution model in the areas of interest. The approach is developed for dual-drainage 1D/2D models. The approach shows a visible improvement at the localized level where the VSL are applied, in terms of under/overestimating flooding and a moderate overall improvement of the simulation results. Relatively to the model resolution of 2 m, the computational time, by applying this method, is reduced by 93.6% when using a 5 m grid and by 99% when using a 10 m grid. For a small test case, where the local effects are investigated, the error in the maximum water volume, relative to a grid size of 2 m, is reduced from 69.63% to 5.03% by using a 5 m grid and from 152.75% to 22.92% for a 10 m grid.

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Objective Determination of Feature Resolution in Two Sea Surface Temperature Analyses

Journal of Climate ◽

10.1175/jcli-d-12-00787.1 ◽

2013 ◽

Vol 26 (8) ◽

pp. 2514-2533 ◽

Cited By ~ 18

Author(s):

Richard W. Reynolds ◽

Dudley B. Chelton ◽

Jonah Roberts-Jones ◽

Matthew J. Martin ◽

Dimitris Menemenlis ◽

...

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Satellite Data ◽

Sea Surface ◽

Small Scale ◽

High Resolution Data ◽

Data Coverage ◽

Feature Resolution ◽

Resolution Data

Abstract Considerable effort is presently being devoted to producing high-resolution sea surface temperature (SST) analyses with a goal of spatial grid resolutions as low as 1 km. Because grid resolution is not the same as feature resolution, a method is needed to objectively determine the resolution capability and accuracy of SST analysis products. Ocean model SST fields are used in this study as simulated “true” SST data and subsampled based on actual infrared and microwave satellite data coverage. The subsampled data are used to simulate sampling errors due to missing data. Two different SST analyses are considered and run using both the full and the subsampled model SST fields, with and without additional noise. The results are compared as a function of spatial scales of variability using wavenumber auto- and cross-spectral analysis. The spectral variance at high wavenumbers (smallest wavelengths) is shown to be attenuated relative to the true SST because of smoothing that is inherent to both analysis procedures. Comparisons of the two analyses (both having grid sizes of roughly ) show important differences. One analysis tends to reproduce small-scale features more accurately when the high-resolution data coverage is good but produces more spurious small-scale noise when the high-resolution data coverage is poor. Analysis procedures can thus generate small-scale features with and without data, but the small-scale features in an SST analysis may be just noise when high-resolution data are sparse. Users must therefore be skeptical of high-resolution SST products, especially in regions where high-resolution (~5 km) infrared satellite data are limited because of cloud cover.

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Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/365 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xiaowei Jia ◽

Mengdie Wang ◽

Ankush Khandelwal ◽

Anuj Karpatne ◽

Vipin Kumar

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Remote Sensing Data ◽

Temporal Patterns ◽

Small Scale ◽

High Resolution Data ◽

Sensing Data ◽

Coarse Resolution ◽

Sequential Models ◽

Resolution Data

Effective and timely monitoring of croplands is critical for managing food supply. While remote sensing data from earth-observing satellites can be used to monitor croplands over large regions, this task is challenging for small-scale croplands as they cannot be captured precisely using coarse-resolution data. On the other hand, the remote sensing data in higher resolution are collected less frequently and contain missing or disturbed data. Hence, traditional sequential models cannot be directly applied on high-resolution data to extract temporal patterns, which are essential to identify crops. In this work, we propose a generative model to combine multi-scale remote sensing data to detect croplands at high resolution. During the learning process, we leverage the temporal patterns learned from coarse-resolution data to generate missing high-resolution data. Additionally, the proposed model can track classification confidence in real time and potentially lead to an early detection. The evaluation in an intensively cultivated region demonstrates the effectiveness of the proposed method in cropland detection.

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