Flood Threat Evaluation Utilizing Digital Elevation & HAND Fashions

In early 2024, Brazil skilled heavy rainfall, significantly within the south and northeast areas, resulting in floods that broken cities, destroyed crops, and brought about fatalities. As local weather change will increase the frequency of utmost climate circumstances, comparable to droughts and floods, contingency planning and threat evaluation have gotten more and more essential for managing such occasions.

This text presents a Python and Jupyter Pocket book workflow for Flood Threat evaluation in rural and small-city areas of a northeastern Brazilian state. The method begins with a digital elevation mannequin (DEM) and ends with a visualization of the chance of inundation of various areas utilizing the Peak Above Nearest Drainage (HAND) mannequin. The tactic goals to evaluate flood threat in inner-city areas in actual time and with little information and computational sources.

Overview

• How do you discover and obtain DEM information for flood inundation threat evaluation?
• How do you set the programming setting with the mandatory packages for the evaluation?
• How do information preparation and pre-processing steps for DEM information for drainage extraction be carried out?
• Tips on how to use the HAND mannequin to determine the danger of inundation of various areas and label them as “very excessive threat”, “excessive threat”, “reasonable threat”, “low threat”, and “very low threat”.

Setting the Surroundings

The workflow used for the evaluation offered on this research is offered right here. The workflow is a Jupyter Pocket book that runs on Python 3.12 and makes use of the next packages:

• Numpy – array manipulation.
• Whiteboxtools – Geospatial evaluation and manipulation.
• GDAL – Geospatial information manipulation.
• RichDEM – DEM and hydrological evaluation instruments.
• Matplotlib – Knowledge Visualization.

Knowledge Preparation and Acquisition

For Flood Threat evaluation, listed here are the info preparations and acquisition steps:

Step 1: Knowledge Acquisition

Step one is buying elevation information for the world of curiosity. This research makes use of DEM information from FABDEM (Forest and Buildings Eliminated Copernicus DEM), that are publicly out there on the College of Bristol’s web site [1]. FABDEM is a worldwide elevation map that removes constructing and tree top biases from the Copernicus GLO-30 Digital Elevation Mannequin (DEM). The info is supplied at a decision of 1 arc second (roughly 30 meters on the equator) for the whole globe.

As beforehand talked about, this research’s space of curiosity is Brazil’s northeast area. The DEM file covers a 1º-by-1º space, starting from 6ºS 39ºW to 5ºS 38ºW, utilizing the WGS84 coordinate system (EPSG: 4326). This area is illustrated in Determine 1 under.

The highlighted space lies inside certainly one of Brazil’s most arid biomes. It’s characterised by an irregular and sparse rainfall sample, with rain sometimes falling only some months per 12 months. Nonetheless, in 2024, this area suffered from an unusually excessive quantity of rainfall concentrated over a brief interval, considerably impacting the world.

Step 2: Knowledge Preparation

The info preparation consists of filling the DEM file’s sinks. Sinks are areas the place elevation information kinds a melancholy; in different phrases, they’re pixels or units of pixels which have neighbouring pixels with larger elevation. In hydrology evaluation, water is collected in sinks as an alternative of flowing.

Whereas sinks are pure options, like lakes and basins, they are often created by DEM errors, comparable to low decision or information assortment. Subsequently, these depressions or sinks are “stuffed” throughout preprocessing to make sure easy water circulate by both eradicating them or elevating their elevation in order that the hydrological modelling stays unaffected. This is a crucial step in performing Flood Threat Evaluation, amongst different hydrological research.

This step will use Python libraries comparable to WhiteboxTools and RichDEM to make sure the DEM is processed to signify a easy floor appropriate for precisely calculating circulate route and circulate accumulation.

Additionally learn: Geospatial Evaluation for Flood Resilience

Circulate Path and Circulate Accumulation

Calculating Circulate Instructions

The following step entails figuring out the circulate route for every pixel within the DEM. This creates a brand new raster the place every pixel’s worth represents the circulate route. There are three strategies: D8, A number of Circulate Path (MFD), and D-Infinity (DINF).

This research makes use of the D8 methodology, which fashions circulate route by discovering the steepest downslope neighbour for every pixel. The ensuing raster comprises values between 1 and 128, indicating circulate route primarily based on the steepest descent. For instance, if the steepest slope is to the fitting, the pixel’s worth can be 1, and if it’s towards the upper-right nook, the worth can be 128, as proven within the diagram under.

Calculating Circulate Accumulation

As soon as the circulate route has been calculated, the following step is to compute the circulate accumulation. Circulate accumulation identifies the areas the place water is more likely to acquire primarily based on the circulate route. That is achieved by figuring out the variety of upstream pixels contributing to every pixel’s circulate.

The output is a brand new raster the place the worth of every pixel represents the entire collected circulate at that location. Pixels with excessive accumulation values sometimes correspond to streams, rivers, or drainage networks as they collect circulate from a number of upstream areas. Conversely, pixels with low accumulation values point out areas comparable to ridges or elevated terrain with minimal water accumulation, as illustrated in Determine 3.

With Python, circulate accumulation could be calculated with the WhiteboxTools utilizing the d8_flow_accumulation methodology.

Utilizing the circulate accumulation raster, it’s potential to determine areas representing watercourses, comparable to rivers, streams, and drainage networks. That is achieved by making use of a threshold to the buildup values, the place any pixel with a worth above the brink is taken into account a part of the stream community.

The selection of the brink worth is determined by a number of components, such because the hydrological circumstances of the research space and the decision of the DEM. On this case, the area is predominantly semi-arid, and the DEM has a spatial decision of 30 meters. After some trial and error, a threshold worth of 15 was thought-about best suited for capturing broader drainage networks.

HAND Mannequin Evaluation

HAND (Peak Above Nearest Drainage) evaluation, first launched by NOBRE et al. (2011), is a technique used to evaluate an space’s susceptibility to flood inundation. That is completed by using a DEM (Digital Elevation Mannequin) and a stream community raster to find out how excessive every DEM pixel is above the stream community’s nearest pixel. The result’s a brand new raster the place every pixel worth represents the vertical distance between the pixel’s elevation and the elevation of the closest drainage level, as illustrated in Determine 4.

The pixel values within the ensuing HAND raster signify the relative top above the closest drainage level. Increased values point out areas farther from the drainage, that are much less more likely to flood, whereas decrease values signify areas nearer to the drainage, making them extra vulnerable to flooding.

This evaluation generated the HAND raster utilizing the WhiteboxTools Python library and the elevation_above_stream methodology. The ensuing raster comprises pixel values starting from 0 to 330 meters, representing the peak of every DEM pixel above the closest drainage level within the research space.

Classifying Flood Threat With HAND Mannequin

Based mostly on the pixel values from the HAND raster, threat lessons could be outlined, with decrease values indicating a better threat of inundation in comparison with areas with higher elevation. Desk 1 under presents the thresholds (in meters) used to categorise areas into completely different threat ranges.

Desk 1: Intervals used to categorise threat ranges.

The thresholds offered in Desk 1 had been decided via empirical testing. Utilizing the NumPy package deal, class values could be assigned to completely different areas of the unique DEM, and a brand new raster file could be generated with the classification outcomes.

The thresholds offered in Desk 1 had been decided via empirical testing. Utilizing the NumPy package deal, class values could be assigned to completely different areas of the unique DEM, and a brand new raster file could be generated with the classification outcomes.

Outcomes and Discussions

With all of the processing steps accomplished, it’s time to visualise the outcomes and attain some conclusions. The Matplotlib Python package deal lets you visualize the outcomes of the danger classification of the HAND raster file, as offered in Determine 5.

To higher perceive which areas are extra liable to flooding, the GDAL Python library can be utilized to export the labeled array as a GeoTIFF file. This file can then be loaded into GIS software program, comparable to QGIS, to visualise the higher-risk areas, as illustrated in Determine 6 under.

In Determine 6, a rural space is proven with a small metropolis situated within the centre. On the fitting facet of the determine, high-risk areas (highlighted in yellow) and really high-risk areas (highlighted in pink) are indicated. The stream community, represented in blue, exhibits the extent of areas close to the streams susceptible to inundation.

Conclusion

The HAND mannequin may be very helpful for assessing the danger of inundation of areas shortly and computationally effectively. Utilizing solely a DEM file from an space of curiosity, it’s potential to map (Flood Threat evaluation) areas liable to inundation and facilitate the event of contingency plans to mitigate the impacts. The workflow proposed within the current research can be utilized for various areas and conditions, and it’s advantageous for civil safety businesses.

The Jupyter Pocket book with all of the steps, detailed explanations, and necessities is offered right here.

References

• Nathan, Smiti & Harrower, Michael. (2023). Mapping spatial patterning of Bronze Age towers in Oman based on water circulate accumulation. Arabian Archaeology and Epigraphy. 34. n/a-n/a. 10.1111/aae.12237.
• Hu, Anson & Demir, Ibrahim. (2021). Actual-Time Flood Mapping on Consumer-Facet Internet Techniques Utilizing HAND Mannequin. Hydrology. 8. 65. 10.3390/hydrology8020065.
• Nobre, A.D., Cuartas, L.A., Hodnett, M., Rennó, C.D., Rodrigues, G., Silveira, A., Waterloo, M., Saleska, S., Peak above the Nearest Drainage, a hydrologically related new terrain mannequin, Journal of Hydrology (2011), doi: 10.1016/j.jhydrol.2011.03.05.1
• Lindsay, J.B. (2023). Introduction to WhiteboxTools: A Person Information for the WhiteboxTools Command-Line Interface and Python API. Whitebox Geospatial Inc. Retrieved from Whiteboxgeo.
• Barnes, R. (2023). RichDEM: Excessive-performance Terrain Evaluation Library for Digital Elevation Fashions. Retrieved from RichDEM — Excessive-Efficiency Terrain Evaluation.
• Esri. (2024). Understanding Drainage Techniques. ArcGIS Professional. Retrieved from Understanding drainage methods—ArcGIS Professional.

Often Requested Questions