In January 2024, a severe weather event ravaged large swaths of Brazil, with the southern and northeastern regions bearing the brunt of the impact. Heavy rainfall caused widespread flooding, leaving a trail of destruction in its wake and claiming lives. As local weather patterns shift in response to global climate changes, the likelihood of extreme events like devastating droughts and catastrophic floods is expected to rise, making it crucial to develop proactive contingency plans and conduct thorough risk assessments to mitigate the impact of these occurrences.
A Python and Jupyter-based pocketbook workflow facilitates flood danger assessments in rural and small-town settings within a northeastern Brazilian state, focusing on effective emergency planning and response strategies. The process commences by leveraging a digital elevation model (DEM), ultimately culminating in a visualization of flood risk assessment across diverse regions via the Topographic Above Nearest Drainage (HAND) methodology. The strategy aims to assess flood risk in urban cores promptly, leveraging limited data and computational resources.
Overview
- To accurately assess flood inundation danger, one must first procure high-quality Digital Elevation Model (DEM) data that captures the terrain’s topography. This can be achieved by consulting online repositories such as NASA’s Shuttle Radar Topography Mission (SRTM), the National Geophysical Data Center (NGDC), or the United States Geological Survey (USGS).
- To set the programming setting with the required packages for the evaluation, first ensure that your Python environment is properly configured by installing necessary packages through pip or conda. For instance, to install a specific package named ‘package_name’, execute the following command in your terminal:
- DEM data processing and preprocessing involve several steps to ensure accurate drainage extraction. These include:
Data quality assessment and filtering;
Removing no-data values or null pixels;
Applying spatial filters, such as median or mean, to reduce noise and improve data quality;
Correcting for atmospheric effects, such as topographic shadows;
Ensuring the DEM has sufficient resolution and accuracy for drainage extraction; -
To accurately assess the risk of flooding in different regions using a HAND mannequin, it’s essential to categorize areas into five labels: “extremely high danger”, “high danger”, “moderate danger”, “low danger”, and “very low danger”.
Setting the Surroundings
The workflow employed in this research, as presented, is readily available. The workflow is a Jupyter notebook that runs on Python 3.8 and utilizes the following packages:
- Numpy – array manipulation.
- Geospatial Data Evaluation and Manipulation with Whitebox Tools.
- GDAL – Geospatial information manipulation.
- RichDEM: A suite of DEM and hydrological evaluation instruments for spatial analysis and modeling applications.
- Matplotlib – Knowledge Visualization.
Knowledge Preparation and Acquisition
Flood risk assessment requires a systematic approach, involving the following information preparation and acquisition steps:
Step 1: Knowledge Acquisition
Acquiring comprehensive elevation data is the inaugural step in fostering a sense of wonder and exploration in the world of curiosity. This study leverages high-quality topographic data from the publicly accessible Forest and Buildings Eliminated Copernicus Digital Elevation Model (FABDEM) dataset. The FABDEM is a globally comprehensive topographic dataset that mitigates the effects of construction and tree canopy height artefacts present in the Copernicus GLO-30 Digital Elevation Model, providing an accurate representation of terrain morphology. The information is provided with an accuracy of 1 arc second, which corresponds to approximately 30 meters on the equator, covering the entire globe.
The focus of this study lies in the northeastern region of Brazil. The DEM file encompasses a 1° by 1° area, spanning from 6°S 39°W to 5°S 38°W, referencing the World Geodetic System 1984 (EPSG: 4326) coordinate system. The illustration of this area is presented below in Figure 1.
Within Brazil’s most parched biome, a highlighted space is nestled. Characterized by sporadic and infrequent rainfall patterns, this region experiences precipitation in short intervals, typically occurring only a few times within a 12-month period. Despite being a normal year by all accounts, 2024 saw an extraordinary and intense bout of rainfall in this region, resulting in far-reaching consequences that resonated globally?
Step 2: Knowledge Preparation
The information preparation process involves populating the DEM file’s sink structures. Sinks refer to regions where elevation values indicate a sense of melancholy; in other words, they represent pixels or units of pixels that have neighboring pixels with higher elevation. Water is collected in sinks as an alternative to flowing in traditional hydrological assessments.
While sinks may appear as innocuous features akin to lakes and basins, they are often generated by digital elevation model (DEM) inaccuracies, mirroring poor data quality or collection decisions. To ensure accurate hydrological modeling, subsequent depressions or sinks are filled during preprocessing, either by removing them or raising their elevation to allow for unobstructed water flow. This crucial step in flood danger evaluation stands out among various hydrological research studies.
Using Python libraries akin to NumPy and SciPy, we’ll process the DEM to ensure it’s characterized in a way that enables precise calculations of flow routes and accumulation.
Additionally learn:
Movement Course and Movement Accumulation
Calculating Movement Instructions
The next step involves determining the path of circulation for each pixel in the image. This process generates a novel raster image where each pixel’s value corresponds precisely to the circulation pathway. Three key strategies exist: D8, the Number of Movement Courses (MFC), and D-Infinity (DINF).
Using the D8 method, this study identifies flow paths by determining the steepest downhill neighbour for each pixel. The resulting raster contains pixel values ranging from 1 to 128, representing the optimal path calculated by the steepest-descent algorithm. If the steepest slope points to the right, the pixel’s value could be 1, while a slope oriented towards the top-right corner would yield a value of 128, as depicted in the accompanying diagram.
Calculating Movement Accumulation
Once the circulation route has been computed, the next step involves calculating the circulation accumulation? Movement accumulation identifies the regions where water is more prone to collect based on the flow pathway. The desired output quality is ensured by accurately determining the diverse upstream pixels that collectively contribute to each pixel’s spread.
The resulting raster displays the cumulative flow values at each pixel location, where the value of each pixel corresponds to the total accumulated flow at that point. Pixels with unusually high accumulation values often represent streams, rivers, or drainage networks, which gather runoff from multiple upstream regions. Inversely, pixels exhibiting low accumulation values indicate regions evoking the characteristics of ridges or elevated terrain, where minimal water accumulation is evident, as depicted in Figure 3.
Using Python and the WhiteboxTools library, the calculation of circulation-based accumulation can be accomplished via the d8_flow_accumulation method.
By leveraging the circulate accumulation raster, it is possible to identify areas characterizing watercourses, including rivers, streams, and drainage systems. Pixels exceeding a predetermined threshold are classified as part of the streaming community, utilizing a buildup value system.
The choice of a suitable Brinkworth number relies heavily on various factors, including the hydrological conditions of the study area and the digital elevation model (DEM) used. In this region, a predominantly semi-arid climate prevails, with a Digital Elevation Model (DEM) having a spatial resolution of 30 meters. After conducting extensive trials and refining the process, a threshold value of 15 was ultimately determined to be optimal for effectively identifying larger-scale drainage networks.
HAND Mannequin Evaluation
The HAND (Hydrologic Area Near Dryness) methodology, initially introduced by , serves as a reliable technique for assessing the likelihood of an area experiencing flood inundation. To achieve this, professionals employ a Digital Elevation Model (DEM) in tandem with a stream community raster, calculating the deviation of each DEM pixel from its corresponding value in the stream community dataset by measuring the absolute difference between the two values. The resulting image features a novel raster where each pixel value corresponds to the vertical distance between its elevation and that of the nearest drainage level, as depicted in Figure 4.
Pixel values in the subsequent HAND (Height Above Nearest Drainage) raster represent the relative elevation above the nearest drainage level. Higher values indicate zones farther away from the drainage, which are significantly less prone to flooding, whereas lower values pinpoint locations closer to the drainage, increasing their susceptibility to flooding.
The HAND (Height Above Nearest Drainage) raster was created using the WhiteboxTools Python library and the elevation above stream technique. This subsequent grid encompasses a range of pixel values spanning from 0 to 330 metres, depicting the elevation of each DEM pixel relative to its nearest drainage point within the study area.
What’s the probability that floodwater poses an immediate threat?
Pixels from the HAND (Height Above Nearby Drainage) raster can inform danger zones, where lower values suggest an increased risk of flooding compared to areas with higher elevations. The following desk outlines the thresholds (in meters) for categorizing areas according to varying levels of danger.
What intervals are employed to stratify perilous zones?
| Danger Stage | Threshold (m) | Class Worth |
|---|---|---|
| Very Excessive | 0 – 1 | 5 |
| Excessive | 1 – 2 | 4 |
| Medium | 2 – 6 | 3 |
| Low | 6 – 10 | 2 |
| Very Low | 10 | 1 |
The thresholds established in Desk 1 were determined through empirical testing and validation. By leveraging the NumPy package, it is possible to assign class values to distinct regions of a unique digital elevation model (DEM), ultimately generating a new raster file that reflects the classification results.
Thresholds introduced in Desk 1 were determined through rigorous empirical testing. By leveraging the capabilities of the NumPy package, it is possible to assign class values to distinct regions of a unique DEM, thereby generating a novel raster file featuring the classification results.
Outcomes and Discussions
After processing is complete, it’s now necessary to visualize the results and draw meaningful inferences. This package deal enables visualization of the outcome predictions from the risk classification model applied to the HAND raster file, as depicted in Figure 5.
To raise awareness of which areas are particularly vulnerable to flooding, the GDAL Python library can be employed to convert the labeled array into a GeoTIFF file for further analysis and visualization. The file can be subsequently uploaded into GIS software programs like QGIS to visualize the higher-risk zones, as depicted in Figure 6 below.
In Determinant 6, a rural landscape is depicted with a small city situated at its center. On the correct side of the determination, high-risk areas highlighted in yellow and extremely high-risk areas highlighted in purple are designated for identification purposes. The stream community, depicted in a calming blue hue, provides a visual representation of the regions proximal to the waterways susceptible to flooding.
Conclusion
The HAND mannequin is particularly useful for swiftly and accurately assessing the risk of flooding in areas, allowing for efficient computational assessments. By leveraging a DEM file exclusively from an area of interest, it is possible to create detailed maps that accurately identify flood-prone zones and enable the development of effective contingency plans aimed at minimizing the devastating effects of inundation. The workflow outlined in this study possesses broad applicability across diverse areas and scenarios, yielding significant benefits for civil safety organizations.
References
- Nathan, Smiti & Harrower, Michael. (2023). Assessing the spatial arrangement of Bronze Age towers in Oman in relation to water circulation patterns. Arabian Archaeology and Epigraphy. 34. n/a-n/a. 10.1111/aae.12237.
- Hu, Anson & Demir, Ibrahim. (2021). Flood mapping in real-time utilizing consumer-aspect network techniques with the Handheld Automated Neural Network Device (HAND). Hydrology. 8. 65. 10.3390/hydrology8020065.
- Top-Down, a novel terrain model for hydrologic applications, Journal of Hydrology (2011), doi: 10.1016/j.jhydrol.2011.03.051
- Lindsay, J.B. (2023). Introducing WhiteboxTools: A Comprehensive Guide to the Command-Line Interface and Python API for Efficient Geospatial Processing. Whitebox Geospatial Inc. Retrieved from .
- Barnes, R. (2023). Rich DEM: A High-Performance Terrain Evaluation Library for Digital Elevation Models? Retrieved from .
- Esri. (2024). Understanding Drainage Techniques. ArcGIS Professional. Retrieved from .
Often Requested Questions
Ans. The existing investigation employed FABDEM, opting for a threshold of 30 metres. While low-resolution digital elevation models (DEMs) can be beneficial for larger regions, they tend to oversimplify terrain complexities, leading to inaccuracies during the stream extraction process and, subsequently, incorrect classification of areas susceptible to flooding risks.
Ans. We investigated diverse approaches to determine the optimal threshold value for extracting streams from communities in our research. While higher threshold values yield more compact drainage areas, resulting in a drastically altered stream community compared to satellite imagery? Therefore, selecting a suitable threshold is crucial for identifying a drainage network that accurately reflects the water patterns depicted in the imagery, thereby fostering a deeper understanding of the world’s complexities.
Ans. The hand mannequin highlights areas of exceptionally high risk (yellow) and extremely high risk (purple), underscoring the heightened vulnerability to flooding due to their proximity to the drainage system. Notwithstanding, determining a space as flooded necessitates taking into account factors such as land use, precipitation patterns, and historical flood data.
Ans. The proposed workflow enables real-time mapping of flood-vulnerable regions, thereby augmenting situational awareness, disaster mitigation efforts, and swift emergency responses. The HAND (Height Above Nearest Drainage) mannequin provides a practical, scalable solution utilising only DEM data and open-source Python libraries, making it an excellent choice for regions with limited technological resources to assess flood risks reliably?
