A decision-making tool is applied when multiple criteria need consideration. The goal is to evaluate the significance of different criteria and integrate them into a dataset. This method is used to examine various factors that influence flood risk, and to rank areas according to their combined risk profile.

This process calculates the climate normal precipitation for a desired region, representing the long-term average precipitation over a defined 30-year period. The climate normal is calculated using historical precipitation data gathered over this period. The process involves aggregating the data to calculate monthly and annual averages, providing a baseline for evaluating climate events and year-to-year variability. In this script, the climate normal is computed for the period from 1991 to 2020, aligning with the climatological standard normals updated every decade. Once the climate normal is established, the script categorizes recent precipitation levels into three classes based on their relationship to this normal: below normal, within normal range, and above normal. This classification provides valuable insights into current precipitation patterns compared to long-term averages, aiding in the assessment of potential deviations or anomalies in the region's weather conditions.

The Topographic Wetness Index, commonly referred to as TWI, is a scientific tool used as an indicator to depict the influence of local geographical features on the direction and accumulation of runoff flow. This index is physically-based, meaning it derives from measurable properties of the natural world. Its role in indicating the distribution of water is critical as it provides an understanding of how water moves across a given landscape. TWI is recognised for its substantial correlation with various soil attributes, these include horizon depth, the percentage of silt, the content of organic matter, and the presence of phosphorus. Each of these attributes has a significant impact on the overall quality and health of the soil, which in turn affects the vegetation and wildlife it supports. One of the primary uses of the Topographic Wetness Index is in the study of the effects of spatial scale on hydrological processes. This involves an examination of how changes in the scale of observation can influence the understanding and interpretation of the movement of water. By doing this, it allows for a more detailed understanding of how different scales can provide different insights, which is crucial in the field of hydrology. Another key use of TWI is to identify flow paths for geochemical modelling. This means it can help to determine the most likely routes that chemical substances will take as they move through a landscape. This is particularly useful in understanding and predicting patterns of chemical distribution, which can be vital in a range of scenarios from agricultural planning to environmental conservation.

The process that we are elaborating upon here is indeed a thorough and meticulous one, designed to calculate the distances from various rivers and floodplains located within a specified region. This is achieved through an intricate series of steps that carefully consider the geographical aspects of the region in question. The data that is utilized in the execution of this process is primarily predicated upon highly detailed river data and exhaustive information related to floodplains. This data serves as the backbone of the process, providing a reliable foundation for accurate calculations.

The data is carefully curated and trimmed to the region of interest to ensure accuracy and relevance. Additionally, cumulative cost calculations are used to determine the distance between land pixels and river or floodplain pixels.

This function operates by retrieving a comprehensive set of historical flood data for a specific region that the user has determined. Its main operation involves the examination and identification of any significant flood events that may have occurred within a pre-determined buffer distance from the specified region. If the function identifies any flood events within this buffer distance, it generates a detailed and accurate image. This image contains visual representations of the flooded areas, which are distinctly marked with the numerical value of 3 for easy identification and analysis. However, in the absence of any flood events within the defined buffer distance, the function has a default return. It generates an image where all the pixel values are uniformly set to 0, indicating that there are no notable flood incidents within the buffer distance from the specified region.

The Hydrologic Soil Profile, a key concept in environmental science and related studies, is essential to our understanding of the world beneath our feet. This concept deals with the categorisation of different types of soil, taking into account their unique water absorption and retention capabilities. Every type of soil has its own unique properties and characteristics, and the Hydrologic Soil Profile helps us to understand these differences. By classifying soils based on their ability to absorb and retain water, we can gain valuable insights into how different soil types behave under various conditions. This classification system plays a pivotal role in understanding and predicting how various soil profiles respond to different levels of precipitation. This can be particularly crucial during periods of heavy rainfall or drought. For example, soils with high water retention capabilities may become waterlogged during rainy periods, while those with poor water retention may struggle to maintain moisture levels during a drought. Furthermore, comprehending the Hydrologic Soil Profile can greatly aid in effective water management and conservation efforts. By understanding how different soils respond to varying levels of precipitation, we can make more informed decisions about water usage and conservation. This can ultimately lead to more sustainable practices and contribute to the broader goal of environmental preservation.

The Hydrologic soil profile groups are the following:

In our flood risk assessment tool, we have chosen to implement thresholding that is user-defined and hard-coded in order to ensure it has a global impact. This approach was chosen as we wanted the thresholds to be universally applicable, regardless of the specific region being analysed.

After thorough research and analysis, we have established the following thresholds:

The final output is a visual representation that colour codes the regions based on their calculated risk. The regions are classified as:

Flood risk areas New Version - green (low), yellow (medium), red (high)