Flood Danger Evaluation Utilizing Digital Elevation & HAND Mannequin

In early 2024, Brazil skilled heavy rainfall, notably within the south and northeast areas, resulting in floods that broken cities, destroyed crops, and induced fatalities. As local weather change will increase the frequency of maximum climate circumstances, resembling 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 Danger 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 probability 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 knowledge and computational sources.

Overview

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

Setting the Setting

The workflow used for the evaluation introduced on this research is obtainable 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 knowledge manipulation.
• RichDEM – DEM and hydrological evaluation instruments.
• Matplotlib – Information Visualization.

Information Preparation and Acquisition

For Flood Danger evaluation, listed here are the information preparations and acquisition steps:

Step 1: Information Acquisition

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

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

Step 2: Information Preparation

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

Whereas sinks are pure options, like lakes and basins, they are often created by DEM errors, resembling low decision or knowledge assortment. Due to this fact, these depressions or sinks are “crammed” throughout preprocessing to make sure clean water stream by both eradicating them or elevating their elevation in order that the hydrological modelling stays unaffected. This is a vital step in performing Flood Danger Evaluation, amongst different hydrological research.

This step will use Python libraries resembling WhiteboxTools and RichDEM to make sure the DEM is processed to characterize a clean floor appropriate for precisely calculating stream course and stream accumulation.

Additionally learn: Geospatial Evaluation for Flood Resilience

Move Route and Move Accumulation

Calculating Move Instructions

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

This research makes use of the D8 technique, which fashions stream course by discovering the steepest downslope neighbour for every pixel. The ensuing raster accommodates values between 1 and 128, indicating stream course based mostly on the steepest descent. For instance, if the steepest slope is to the precise, the pixel’s worth will probably be 1, and if it’s towards the upper-right nook, the worth will probably be 128, as proven within the diagram beneath.

Calculating Move Accumulation

As soon as the stream course has been calculated, the subsequent step is to compute the stream accumulation. Move accumulation identifies the areas the place water is more likely to acquire based mostly on the stream course. That is achieved by figuring out the variety of upstream pixels contributing to every pixel’s stream.

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

With Python, stream accumulation might be calculated with the WhiteboxTools utilizing the d8_flow_accumulation technique.

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

The selection of the brink worth relies on 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 carried out 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 characterize 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 characterize 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 technique. The ensuing raster accommodates 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 Danger With HAND Mannequin

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

Desk 1: Intervals used to categorise threat ranges.

The thresholds introduced in Desk 1 have been decided by way of empirical testing. Utilizing the NumPy bundle, class values might be assigned to completely different areas of the unique DEM, and a brand new raster file might be generated with the classification outcomes.

The thresholds introduced in Desk 1 have been decided by way of empirical testing. Utilizing the NumPy bundle, class values might be assigned to completely different areas of the unique DEM, and a brand new raster file might 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 bundle permits you to visualize the outcomes of the danger classification of the HAND raster file, as introduced in Determine 5.

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

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

Conclusion

The HAND mannequin may be very helpful for assessing the danger of inundation of areas rapidly and computationally effectively. Utilizing solely a DEM file from an space of curiosity, it’s doable to map (Flood Danger 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 obtainable right here.

References

• Nathan, Smiti & Harrower, Michael. (2023). Mapping spatial patterning of Bronze Age towers in Oman in keeping with water stream accumulation. Arabian Archaeology and Epigraphy. 34. n/a-n/a. 10.1111/aae.12237.
• Hu, Anson & Demir, Ibrahim. (2021). Actual-Time Flood Mapping on Shopper-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