Flash Flood Susceptibility Map (Sub-Basin Level)

[kapildadheech]

Ticket Contents

Description

Flash flood susceptibility mapping at the sub-basin level helps identify areas prone to sudden flooding events. This information is crucial for hazard assessment, disaster management, and planning mitigation strategies. Using GEE, raster layers of flash flood susceptibility can be generated, vectorized, and integrated into Micro Watershed (MWS) and sub-basin-level reports.

Goals

Goals

• Compute flash flood susceptibility for sub-basins using topographic, hydrological, and land use/land cover datasets.
• Generate raster layers representing susceptibility at sub-basin scale.
• Vectorize raster outputs to create sub-basin-level polygons.
• Publish raster and vector outputs as Earth Engine assets with metadata.
• Enable visualization and analysis of flash flood hazard patterns.
• Generate MWS reports highlighting areas of high susceptibility.

Expected Outcome

Expected Output

• Raster dataset showing flash flood susceptibility per sub-basin.
• Vectorized sub-basin polygons with attributes:
  • Susceptibility class (low, moderate, high)
  • Area (km²)
  • Relevant metrics (slope, flow accumulation, land cover)
• Published Earth Engine assets (raster + vector) with metadata.
• GEE visualizations showing susceptibility patterns across sub-basins.
• MWS-level report summarizing flash flood susceptibility.
• Validation report confirming coverage, accuracy, and proper classification.

Acceptance Criteria

Acceptance Criteria

Data Acquisition

• Input datasets (DEM, LULC, rainfall, soil type) must be preprocessed and clipped to sub-basin boundaries.
• Resolution standardized for sub-basin analysis.
• Flow accumulation and hydrological networks computed.

Raster Computation

• Flash flood susceptibility raster computed per sub-basin using weighted indicators (slope, land cover, rainfall intensity, drainage density).
• Entire study area covered without gaps.
• Classification thresholds documented (low, moderate, high susceptibility).

Vectorization

• Raster outputs converted to sub-basin polygons using reduceToVectors() in GEE.
• Each polygon includes:
  • Susceptibility class
  • Area (km²)
  • Relevant metrics
• Polygons must align with sub-basin boundaries.

Asset Publishing

• Raster and vector datasets published as Earth Engine assets.
• Metadata includes source datasets, resolution, processing date, and classification methodology.

Quality & Validation

• Coverage check: all sub-basins included.
• Accuracy check: susceptibility classes validated against historical flood records.
• Attribute check: all polygons include class, area, and metrics.
• GEE visualization confirms correct spatial distribution of susceptibility.

Implementation Details

Implementation Details

Data Sources

• DEM (e.g., SRTM 30m)
• LULC datasets
• Rainfall data (e.g., CHIRPS, IMD)
• Soil type and texture maps
• Sub-basin boundary shapefiles

Processing

• Compute hydrological parameters: slope, flow accumulation, drainage density.
• Apply weighted model for flash flood susceptibility per pixel.
• Generate raster outputs for entire study area and clip to sub-basins.

Vectorization & Publishing

• Convert raster outputs into sub-basin polygons using reduceToVectors().
• Include attributes: class, area, slope, flow accumulation, LULC metrics.
• Upload raster and vector layers as EE assets with metadata.

Visualization

• Color-coded raster and vector layers in GEE (low = green, moderate = yellow, high = red).
• Overlay sub-basin boundaries for hazard assessment.

Validation

• Compare outputs with historical flood events.
• Spot-check polygons for correct attribute classification.
• Generate validation report documenting coverage, accuracy, and attribute completeness.

Mockups/Wireframes

No response

Product Name

KYL

Organisation Name

C4GT

Domain

No response

Tech Skills Needed

Python

Organizational Mentor

@amanodt @ankit-work7 @kapildadheech

Angel Mentor

No response

Complexity

Medium

Category

Backend

[abhishek-8081]

Greetings Mentors,

I'm Abhishek Kumar, currently an LFX Mentee with CNCF working on the KubeEdge Ianvs project. I'm interested in contributing to this flash flood susceptibility mapping task.

Through my LFX mentorship, I've gained extensive experience in debugging complex systems, optimizing Python environments, and building automated CI/CD pipelines. My work involves processing large-scale datasets, resolving dependency issues, and ensuring robust validation across distributed systems. I also have a strong foundation in data structures, algorithms, and geospatial data processing.

I'm confident I can implement the weighted susceptibility model using GEE, generate the required raster and vector outputs at sub-basin scale, and deliver comprehensive validation reports. This disaster management initiative aligns well with my interest in applying technology for social impact.

Looking forward to contributing to this important hazard assessment project!

Best regards,
Abhishek Kumar

[aaditeshwar]

Thanks @abhishek-8081 for your interest. Please see my comment on another thread #234 (comment). The approach for flash flood susceptibility can follow the same approach as for flood susceptibility maps. Perhaps you can join hands with @sunilkumar2170 to build a combined MWS vector asset with floods and flash floods susceptibility. Will be helpful to also understand the underlying papers and outline the difference between floods and flash floods.

[aaditeshwar]

Hi all, Do please join the discord channel and googlegroup linked from this announcement of the CoRE stack innovation challenge, and do participate in the challenge too: https://core-stack.org/core-stack-innovation-challenge-1st-edition/. Our first community call on Friday 3-4pm will be very useful to get started.

[aaditeshwar]

Hi @abhishek-8081 , It seems like we did not share the main paper with you https://www.nature.com/articles/s44304-025-00121-3. We've been having weekly calls to discuss various ongoing contributions. Can you join the next call and take us through your methodology and implementation so far. Please do go over the recordings https://core-stack.org/core-stack-innovation-challenge-1st-edition/ and join the Google group https://groups.google.com/u/2/g/core-stack-dev and discord channel https://discord.gg/FpMArraM2X. We'll meet next Friday at 3pm. Please let us know if you can join.

[amit-spatial]

Hi @abhishek-8081 ,

We'll have our dev call at 3pm on Friday as usual. There has also been a lot of interest on some issues we had created on github to add new datasets and pipelines to the CoRE stack. So we wanted to put out some guidelines to make this experience enriching and productive for all of us.

Many of the issues we had created and a wishlist of datasets we had published some time back are very domain specific and need to be technically correct before they can be included in the stack. For context, any of the layers currently on the CoRE stack that required some kind of geospatial algorithms or machine learning went through several months of testing, sometimes years, to examine the outputs carefully, understand when and where the models are performing well or not, how to handle missing data (satellite data can have cloud cover and needs specific methods to address this), and so on.

So if you are keen to pick up such issues then please share your intermediate progress with us during the dev calls. Help us understand how you are approaching the issue, show us the results, discuss problems, and it'll become much easier then to absorb your contributions. If you can inform us in advance about anything specific you'd like to discuss then we can also arrange for somebody who understands that area really well to also come on the call. Amongst so many research scholars and students who have contributed to the CoRE stack, there is a lot of expertise on LULC, computations on DEM layers, tree cover analysis, rainfall, drought, indexes like NDVI, census data, commodity pricing data, agriculture marketing, etc.

We are working hard to put up a developer website asap with all required documentation in one place, but here are two MUST read links before you begin to take up any substantial tasks.

Contribute datasets, pipelines and tools

Core-Stack Innovation Challenge

See you in our developer calls!

Zoom Link
Meeting ID: 856 5729 0380
Passcode: 317250

[ayushk687]

@kapildadheech PLEASE ASSING THIS ISSUE TO ME