scholarly journals On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution

2015 ◽  
Vol 19 (2) ◽  
pp. 691-709 ◽  
Author(s):  
G. Bruni ◽  
R. Reinoso ◽  
N. C. van de Giesen ◽  
F. H. L. R. Clemens ◽  
J. A. E. ten Veldhuis
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Time Shift ◽  
Hydrodynamic Modelling ◽  
Radar Rainfall ◽  
Catchment Size ◽  
Urban Catchments ◽  
Storm Characteristics ◽  
Water Depths ◽  
Sewer Water

Abstract. Cities are increasingly vulnerable to floods generated by intense rainfall, because of urbanisation of flood-prone areas and ongoing urban densification. Accurate information of convective storm characteristics at high spatial and temporal resolution is a crucial input for urban hydrological models to be able to simulate fast runoff processes and enhance flood prediction in cities. In this paper, a detailed study of the sensitivity of urban hydrodynamic response to high resolution radar rainfall was conducted. Rainfall rates derived from X-band dual polarimetric weather radar were used as input into a detailed hydrodynamic sewer model for an urban catchment in the city of Rotterdam, the Netherlands. The aim was to characterise how the effect of space and time aggregation on rainfall structure affects hydrodynamic modelling of urban catchments, for resolutions ranging from 100 to 2000 m and from 1 to 10 min. Dimensionless parameters were derived to compare results between different storm conditions and to describe the effect of rainfall spatial resolution in relation to storm characteristics and hydrodynamic model properties: rainfall sampling number (rainfall resolution vs. storm size), catchment sampling number (rainfall resolution vs. catchment size), runoff and sewer sampling number (rainfall resolution vs. runoff and sewer model resolution respectively). Results show that for rainfall resolution lower than half the catchment size, rainfall volumes mean and standard deviations decrease as a result of smoothing of rainfall gradients. Moreover, deviations in maximum water depths, from 10 to 30% depending on the storm, occurred for rainfall resolution close to storm size, as a result of rainfall aggregation. Model results also showed that modelled runoff peaks are more sensitive to rainfall resolution than maximum in-sewer water depths as flow routing has a damping effect on in-sewer water level variations. Temporal resolution aggregation of rainfall inputs led to increase in de-correlation lengths and resulted in time shift in modelled flow peaks by several minutes. Sensitivity to temporal resolution of rainfall inputs was low compared to spatial resolution, for the storms analysed in this study.

scholarly journals On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution

2014 ◽  
Vol 11 (6) ◽  
pp. 5991-6033 ◽  
Author(s):  
G. Bruni ◽  
R. Reinoso ◽  
N. C. van de Giesen ◽  
F. H. L. R. Clemens ◽  
J. A. E. ten Veldhuis
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Rainfall Variability ◽  
Accurate Information ◽  
Hydrological Response ◽  
Radar Rainfall ◽  
Catchment Size ◽  
Maximum Water ◽  
Urban Catchments ◽  
Water Depths

Abstract. Cities are increasingly vulnerable to floods generated by intense rainfall, because of their high degree of imperviousness, implementation of infrastructures, and changes in precipitation patterns due to climate change. Accurate information of convective storm characteristics at high spatial and temporal resolution is a crucial input for urban hydrological models to be able to simulate fast runoff processes and enhance flood prediction. In this paper, a detailed study of the sensitivity of urban hydrological response to high resolution radar rainfall was conducted. Rainfall rates derived from X-band dual polarimetric weather radar for four rainstorms were used as input into a detailed hydrodynamic sewer model for an urban catchment in Rotterdam, the Netherlands. Dimensionless parameters were derived to compare results between different storm conditions and to describe the effect of rainfall spatial resolution in relation to storm and hydrodynamic model properties: rainfall sampling number (rainfall resolution vs. storm size), catchment sampling number (rainfall resolution vs. catchment size), runoff and sewer sampling number (rainfall resolution vs. runoff and sewer model resolution respectively). Results show catchment smearing effect for rainfall resolution approaching half the catchment size, i.e. for catchments sampling numbers greater than 0.5 averaged rainfall volumes decrease about 20%. Moreover, deviations in maximum water depths, form 10 to 30% depending on the storm, occur for rainfall resolution close to storm size, describing storm smearing effect due to rainfall coarsening. Model results also show the sensitivity of modelled runoff peaks and maximum water depths to the resolution of the runoff areas and sewer density respectively. Sensitivity to temporal resolution of rainfall input seems low compared to spatial resolution, for the storms analysed in this study. Findings are in agreement with previous studies on natural catchments, thus the sampling numbers seem to be promising as an approach to describe sensitivity of hydrological response to rainfall variability for intra-urban catchments and local convective storms. More storms and different urban catchments of varying characteristics need to be analysed in order to validate these findings.

A four-channel ICCD framing camera with nanosecond temporal resolution and high spatial resolution

2021 ◽  
pp. 1-9
Author(s):  
Yuman Fang ◽  
Minrui Zhang ◽  
Junfeng Wang ◽  
Lehui Guo ◽  
Xueling Liu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Framing Camera

scholarly journals Super-resolution time-resolved imaging using computational sensor fusion

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. Callenberg ◽  
A. Lyons ◽  
D. den Brok ◽  
A. Fatima ◽  
A. Turpin ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Single Photon ◽  
Super Resolution ◽  
Numerical Data ◽  
Data Cube ◽  
Fluorescence Lifetime Imaging ◽  
Time Resolved ◽  
Temporal Precision

AbstractImaging across both the full transverse spatial and temporal dimensions of a scene with high precision in all three coordinates is key to applications ranging from LIDAR to fluorescence lifetime imaging. However, compromises that sacrifice, for example, spatial resolution at the expense of temporal resolution are often required, in particular when the full 3-dimensional data cube is required in short acquisition times. We introduce a sensor fusion approach that combines data having low-spatial resolution but high temporal precision gathered with a single-photon-avalanche-diode (SPAD) array with data that has high spatial but no temporal resolution, such as that acquired with a standard CMOS camera. Our method, based on blurring the image on the SPAD array and computational sensor fusion, reconstructs time-resolved images at significantly higher spatial resolution than the SPAD input, upsampling numerical data by a factor $$12 \times 12$$ 12 × 12 , and demonstrating up to $$4 \times 4$$ 4 × 4 upsampling of experimental data. We demonstrate the technique for both LIDAR applications and FLIM of fluorescent cancer cells. This technique paves the way to high spatial resolution SPAD imaging or, equivalently, FLIM imaging with conventional microscopes at frame rates accelerated by more than an order of magnitude.

Investigation of Local Dynamics on the Sub-micron Scale in Organic Blends Using an Ultrafast Confocal Microscope

2010 ◽  
Vol 1270 ◽  
Author(s):  
Giulia Grancini ◽  
Dario Polli ◽  
Jenny Clark ◽  
Tersilla Virgili ◽  
Giulio Cerullo ◽  
...  
Keyword(s):  
Excited State ◽  
Confocal Microscopy ◽  
Polymer Blends ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Electronic States ◽  
Pump Probe ◽  
Peculiar Behaviour ◽  
Spatio Temporal ◽  
Probe Spectroscopy

AbstractWe introduce a novel instrument combining femtosecond pump-probe spectroscopy and confocal microscopy for spatio-temporal imaging of excited-state dynamics of phase-separated polymer blends. Phenomena occurring at interfaces between different materials are crucial for optimizing the device performances, but are poorly understood due to the variety of possible electronic states and processes involved and to their complicated dynamics. Our instrument (with 200-fs temporal resolution and 300-nm spatial resolution) provides new insights into the properties of polymer blends, revealing spatially variable photo-relaxation paths and dynamics and highlighting a peculiar behaviour at the interface between the phase-separated domains.

scholarly journals Development of Low-Cost Fast Photoacoustic Computed Tomography: System Characterization and Phantom Study

2019 ◽  
Vol 9 (3) ◽  
pp. 374 ◽  
Author(s):  
Mohsin Zafar ◽  
Karl Kratkiewicz ◽  
Rayyan Manwar ◽  
Mohammad Avanaki
Keyword(s):  
Computed Tomography ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Performance ◽  
Low Cost ◽  
Single Element ◽  
Tomography System ◽  
System Characterization ◽  
2D Images ◽  
Fast Rotating

A low-cost Photoacoustic Computed Tomography (PACT) system consisting of 16 single-element transducers has been developed. Our design proposes a fast rotating mechanism of 360o rotation around the imaging target, generating comparable images to those produced by large-number-element (e.g., 512, 1024, etc.) ring-array PACT systems. The 2D images with a temporal resolution of 1.5 s and a spatial resolution of 240 µm were achieved. The performance of the proposed system was evaluated by imaging complex phantom. The purpose of the proposed development is to provide researchers a low-cost alternative 2D photoacoustic computed tomography system with comparable resolution to the current high performance expensive ring-array PACT systems.

Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

2021 ◽  
Author(s):  
Stephen Howell ◽  
Mike Brady ◽  
Alexander Komarov
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Sar Images ◽  
Sea Ice Extent ◽  
Arctic Sea

<p>As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.</p>

Sewer Flow Prediction at a Large Urban Scale: Influence of Radar Rainfall Spatial Resolution

2018 ◽  
pp. 794-798
Author(s):  
Sara Simona Cipolla ◽  
Giulia Paola Di Ventura ◽  
Marco Maglionico ◽  
Pier Paolo Alberoni ◽  
Attilio Castellarin
Keyword(s):  
Spatial Resolution ◽  
Radar Rainfall ◽  
Flow Prediction

scholarly journals A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation

2020 ◽  
Vol 12 (23) ◽  
pp. 3900
Author(s):  
Bingxin Bai ◽  
Yumin Tan ◽  
Gennadii Donchyts ◽  
Arjen Haag ◽  
Albrecht Weerts
Keyword(s):  
Linear Regression ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Fusion Method ◽  
Temporal Data ◽  
Resolution Image ◽  
Coarse Resolution ◽  
Fine Spatial Resolution ◽  
Spatio Temporal

High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

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