Editing Urban flood inundation extent

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|+ SIGNAL Earth Structured Data
|-
! Object type
| Damage Signal
|-
! SIGNAL Earth ID
| DS-00731
|-
! Observable type
| Flooded area extent
|-
! Unit
| km2 (km2 (square kilometers of area))
|-
! Temporal structure
| Event-based
|-
! Monitoring backbone
| —
|}
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refers to the spatial area within urban environments that becomes covered by floodwaters during flooding events. This phenomenon is a critical aspect of urban hydrology and disaster management, as it directly affects infrastructure, human safety, and ecosystem services in cities. Flood inundation in urban areas can result from intense precipitation, river overflow, storm surges, or combined sewer overflows, often exacerbated by impervious surfaces and drainage limitations.

Understanding and quantifying urban flood inundation extent is essential for risk assessment, emergency response, and urban planning. It provides insight into the scale and distribution of flood impacts, enabling better preparedness and mitigation strategies. The complexity of urban landscapes, with their dense built environment and varied drainage systems, poses challenges to accurately monitoring and modeling flood extents.

Advances in remote sensing, hydrodynamic modeling, and machine learning have improved the ability to detect and map urban flood inundation in near real-time. These developments support a range of applications from emergency management to long-term urban resilience planning. Within the SIGNAL system, urban flood inundation extent is characterized as a structured environmental signal with defined measurement and aggregation conventions.

== Geographic / System Context ==
Urban flood inundation extent occurs within the built environment of cities and metropolitan areas worldwide. Urban regions are characterized by high densities of impervious surfaces such as roads, buildings, and pavements, which alter natural hydrological processes. These modifications can increase surface runoff and reduce infiltration, contributing to more frequent and severe flooding. The geographic scope of urban flood inundation is not limited to any specific region but varies according to local climate, topography, drainage infrastructure, and land use patterns. Flooding can affect diverse urban settings including coastal cities vulnerable to storm surges, riverine cities subject to fluvial flooding, and areas prone to intense convective precipitation.

== Monitoring and Measurement ==
Monitoring urban flood inundation extent relies on a combination of observational technologies and modeling approaches. Remote sensing platforms, particularly satellite-based Synthetic Aperture Radar (SAR), are widely used due to their ability to capture floodwater presence regardless of cloud cover and lighting conditions. SAR data enable mapping of flooded areas at high spatial resolution and frequent revisit intervals. Complementary data sources include optical satellite imagery, aerial photography, and ground-based sensors such as water level gauges and rain gauges.

Hydrodynamic flood models simulate water flow and accumulation in urban terrains, integrating topographic data, land cover, and drainage infrastructure information. Machine learning and deep learning methods have been increasingly applied to enhance flood extent mapping by assimilating multi-source data and improving classification accuracy. Institutions involved in urban flood monitoring include national meteorological and hydrological services, research organizations, and emergency management agencies.

Within the SIGNAL system, this phenomenon is treated as a defined environmental signal whose boundaries and measurement conventions are described below.

== Signal Definition ==
{{SignalTerm|type=DS|id=DS-00731|label=Urban flood inundation extent}} is defined as the spatial extent of surface area within urban environments that is covered by floodwaters during discrete flooding events. The observable type associated with this signal is {{SignalTerm|type=OT|id=OT-095|label=Flooded area extent}}, measured in square kilometers (km²). The temporal structure of this signal is event-based, capturing the dynamic progression and recession of floodwaters over the duration of an individual flood event.

== Boundary Conditions ==
Boundary inclusions for urban flood inundation extent encompass all surface areas within urban boundaries that are submerged by floodwaters, including streets, parks, open spaces, and built structures where water presence is detected. Floodwaters may originate from various sources such as pluvial runoff, fluvial overflow, or coastal surge.

Boundary exclusions include areas outside the defined urban extent, dry urban zones not affected by flooding during the event, and subsurface flooding such as sewer backups or basement inundations that do not manifest as surface water coverage. Temporary water bodies unrelated to flooding, such as swimming pools or retention ponds, are also excluded unless directly connected to floodwaters.

== Aggregation Semantics ==
Geographic aggregation of urban flood inundation extent typically involves summarizing flooded area measurements within defined urban spatial units such as neighborhoods, census tracts, or municipal boundaries. This facilitates comparison across locations and supports localized risk assessment. Temporal aggregation respects the event-based nature of the signal, aggregating data over the duration of individual flood events rather than fixed time intervals.

Cross-signal aggregation may integrate urban flood inundation extent with related environmental signals such as extreme precipitation intensity, combined sewer overflow discharge volume, and electricity service outage duration to provide a comprehensive understanding of flood impacts and cascading effects. Aggregation notes emphasize the importance of consistent spatial definitions and temporal alignment when combining data from multiple sources or signals.

== Observational Status ==
Monitoring of urban flood inundation extent is an active area of research and operational development. Current capabilities leverage satellite remote sensing, particularly SAR, alongside hydrodynamic modeling and machine learning techniques to produce timely and spatially detailed flood maps. However, challenges remain in achieving high accuracy in complex urban landscapes, integrating diverse data sources, and maintaining near real-time operational monitoring.

Future SIGNAL releases may incorporate enhanced datasets with improved spatial and temporal resolution, standardized monitoring backbones, and integration with complementary signals reflecting flood-related hazards and impacts. Continued advancements in sensor technology, data assimilation, and computational methods are expected to refine the observational status of this signal.

== Related Signals ==
* Backup generator combustion exposure index
* Combined sewer overflow discharge volume
* Drinking-water service disruption duration
* Electricity service outage duration
* Extreme precipitation intensity
* Flooded area extent
* Landfill leachate contamination load
* Municipal solid waste leakage rate

== Key People ==
* Jiayi Song
* Zhiyu Shao
* Ziyi Zhan
* Lei Chen
* Xinyi Shen

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== Key Associated People ==
* '''Jeffrey Blay''' — North Carolina Agricultural and Technical State University [Dataset contributor; High]
* '''Rohit Mukherjee''' — Columbia University [Dataset contributor; High]

Inclusion reflects material contribution to the scientific understanding of this damage signal; it does not imply review, endorsement, or affiliation with SIGNAL Earth.
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== Sources ==
* [https://doi.org/10.1016/j.dib.2025.112347 Inundation2Depth: A multi-source dataset for floodwater depth estimation in urban areas] — Data in Brief, 2026. [Dataset; Supporting; High]
* [https://arxiv.org/abs/2604.23066 Urban Flood Observations (UFO): A hand-labeled training and validation dataset of post-flood inundation] — arXiv preprint, 2026. DOI: 10.48550/arXiv.2604.23066. [Dataset; Supporting; High]
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