SWOT-based flood descriptors - South Sudan, 2023-2025

Field name 

Description

Use Case Name 

Flooding and health care service disruption in South Sudan.

Dataset Name

SWOT-based Flood Descriptors

Dataset Description 

This dataset contains SWOT-derived flood statistic computed at specific health facilities (HF) across an area of interest spreading 189.856 km2 within South Sudan. Flood statistics were calculated using SWOT raster frames for the 2023-2025 period after a quality filtering step to remove pixels with a) degraded and bad quality measurements for water surface elevation, b) active flags in fields of "value_bad", "geolocation_qual_degraded", and "classification_qual_degraded", c) layover systematic errors above 95th percentile, d) a cross-track distance outside of 10-60 km in both sides of the swath, e) a dark water fraction greater than 0.2, and f) a water fraction lower than 0.15. Flood descriptors were calculated at square buffers around the 279 facilities, with a buffer size of 5.4x5.4km for PHCU type and 3.6x3.6 km for PHCC. Each flood descriptor includes an additional field with an estimation of the uncertainty propagated from the wse_uncert field.

Each file represents a single SWOT frame and contains one record for each health facility located within its footprint. Output files retain the original names of the corresponding SWOT frames.

Temporal Domain 

2023-2025

Spatial Domain 

South Sudan (bounding box ranging from longitude 28.5942ºE to 4.8367ºE and latitude 31.8383ºN to 9.6283ºN, in EPSG:4326).

Key Variables 

Flood fraction: Number of pixels flooded respect the total one within the buffer (0-1). Mean depth. Water surface elevation average among the identified flooded pixels. Median depth. Water surface elevation median value among the identified flooded pixels. Maximum Depth: Maximum water surface elevation value, defined as the 95^th percentile to exclude extreme erroneous values and outliers. Distance: The linear distance (meters) from the health facility (patch center point) to the pixel with the value of the 95^th percentile.

All units for flood-depth-related descriptors are meters relative to EGM2008 geoid. Each flood descriptor includes an additional field with an estimation of the uncertainty propagated from the wse_uncert field using empirical formulas and bootstrap resampling.

Data Format 

GeoJSON files.

Source Data 

Surface Water and Ocean Topography (SWOT) mission frames of L2_HR_Raster product, along with Health Facilities of HSF Master Facility List (World Health Organization) that are typically unaffected by flooding—based on VIIRS-detected flood events.

Limitations/ Assumptions 

Main limitations of this approach arise from the propagation of noise measurements throughout the workflow, the irregular SWOT revisit time, and the intrinsic characteristics of SWOT observations. Radar imaging artefacts, such as layover effects and complex scattering over heterogeneous water surfaces, can introduce errors in water surface elevation measurements. One of the main intrinsic limitations of SWOT data is the reduced data quality in the nadir (inner swath) region due to high amount of interferometric errors. Although quality filtering based on uncertainty metrics and ancillary flags were applied to mitigate these effects, residual outliers and biases may persist and propagate through the analysis, potentially affecting the estimated temporal variations.