Processes

Processes provided by Rana

Every process in Rana is designed to streamline your workflow. Select your inputs, configure parameters, and let Rana handle the heavy lifting — whether you’re creating a DEM, generating boundary conditions, or launching a rainfall-runoff simulation.

Rana has the following processes available:

Recently Ran processes

In recently ran processes you’ll find the processes ran in the project by any user in that project.

In Process Runner the input and output of the process is stated
In Status are the log messages during the running of the process
In Results is a preview of the result

Analyse flood duration

This process analyses a Rana flood simulation scenario and computes how long each pixel stays flooded above a chosen water depth threshold. The scenario input is expected to contain a Rana gridadmin file (.h5), a simulation results file (.nc), and a DEM raster (.tif).

The process first generates water depth rasters for all available simulation timesteps. It then compares those rasters against the configured threshold and sums the elapsed time for each pixel where the water depth exceeds that threshold. The result is a flood duration raster in hours, together with the intermediate water depth rasters used for the analysis.

The Input of This Process

Rana Scenario, a scenario archive or scenario directory.

Water Depth Threshold (meters), the minimum water depth that counts as flooded. Default: 0.01 m.

The Output of This Process

Results directory containing:

Flood duration raster (flood_duration.tif):
- GeoTIFF raster with flood duration in hours.
- Each flooded cell contains the total time that the water depth was above the configured threshold.
- Cells that never exceed the threshold are written as NoData with value -9999.
Water depth rasters (rasters/):
- Intermediate GeoTIFF rasters generated for each available timestep in the scenario.
- Filename pattern: waterdepth_<hours>h_step<timestep>.tif.

Technical Documentation

NoData (-9999): the cell never exceeded the threshold.
Higher duration values: the cell remained inundated for a longer part of the simulation.

The exact duration values depend on the timestamps available in the simulation results.

Assess Building flood risk

This process analyzes flood risk for individual buildings by combining simulation results with building footprint data. It calculates which buildings are vulnerable to flooding and provides detailed risk assessments based on simulated water levels and building characteristics.

Example result:

The process takes simulation results (scenario data) and building vector data as inputs, then determines flood impact for each building.

The Input of This Process

Scenario

A scenario file containing simulated water level data from previous flood simulation processes. This provides the time series water levels needed to assess flood impact on buildings.

Buildings Vector File

A vector file containing building footprints with the following requirements:

Supported formats: Shapefile, GeoJSON, or other OGR-compatible formats
Must contain single polygon geometries representing individual buildings
Must have a defined coordinate reference system (CRS)
Must contain only a single layer

Calculation Method

Basic Method (DGBC): Provides general flood risk assessment without considering building floor levels. Every building is buffered with 2 meters. Based on water depths around the buildings, buildings are classified as follows:

Table 5 Flood risk classifications
Water depth around the building	Classification	Description
0m	Class 0	No Flood Risk
0-0.1 m	Class 1	Very Low Risk
0.1-0.15 m	Class 2	Low Risk
0.15-0.2 m	Class 3	Moderate Risk
0.2-0.3 m	Class 4	High Risk

The Output of This Process

Flood Risk Results

A vector file (.gpkg) containing the original building footprints enhanced with flood risk analysis results. Each building polygon includes calculated flood risk metrics based on the simulation data and selected calculation method.

Technical Documentation

Building Vector Requirements

The buildings vector file must meet specific criteria:

Single layer containing building footprint polygons
Defined coordinate reference system with appropriate projection
Single polygon geometries only (no multi-polygons or other geometry types)

Flood Risk Calculation

The process analyzes simulation results against building locations to determine:

Which buildings are affected by flooding
Flood depth at building locations
Risk assessment based on water levels and building characteristics

The calculation considers the temporal aspects of flooding from the scenario data to provide comprehensive flood impact analysis for each building in the dataset

Calculate drainage pattern

Extract the main overland drainage routes from a digital elevation model. The drainage pattern gives immediate, visual insight in the hydrological behaviour of your study area. Each streamline also contains a list of attributes such as the Strahler order and the distance to outlet, that can be used for further analysis.

A minimum catchment area [%] can be specified as a percentage of the DEM. A smaller number will result in more streamlines being returned by the algorithm. With a larger number, only the streamlines draining a larger area will be returned.

For a high-detail result, set the minimum catchment area to a small percentage, e.g. 0.1%. If you only want to see the most important drainage pathways, choose a larger percentage, e.g. 10%, the default. How does this work? Using 10%, drainage paths that drain at least 10% of the valid DEM area will be shown, while drainage paths draining a smaller contributing area will not be shown.

The Input of This Process

Digital elevation model, a raster file containing elevation data.
Minimum catchment area, Defines the minimum size of the contributing area as a percentage of the total DEM area for each streamline. Streamlines with contributing areas below this value will not be included in the result.

The Output of This Process

The output is a vector layer of streamlines. The vector has attributes:

Technical Documentation

This analysis tool chains a number of algorithms from Whitebox Geospatial:

Breach depressions least cost
Fill depressions
D8 pointer
D8 flow accumulation
Extract streams
Raster streams to vector
Vector stream network analysis

The result is copied to a geopackage layer with easier to understand field names. Gaussian smoothing is applied to the lines. The smoothing does not affect the first and last vertex of each line, i.e. topology is preserved.

The output geopackage has the following columns

Table 6 Output fields
Column (renamed)	Type	Description
total_upstream_channel_length	Length (m)	Total length of all stream channels upstream of this link. Used as a proxy for upstream drainage area.
max_upstream_distance	Distance (m)	Maximum flow-path distance from this link to the most distant upstream channel head (longest upstream path).
outlet_id	Integer ID	Identifies which outlet/watershed this link belongs to. All links draining to the same outlet share this value.
horton_order	Stream order	At each confluence, the dominant upstream branch inherits the current order all the way to its headwater. Non-dominant branches restart from order 1.
strahler_order	Stream order	Headwaters get number 1. Order increases by 1 when two equal-order streams join.
shreve_magnitude	Magnitude	Headwaters get number 1. At each confluence, magnitudes sum. A link’s value equals the total number of headwater sources upstream of it.
hack_order	Stream order	The main stem gets number 1. Direct tributaries get number 2, tributaries to those get number 3, and so on.
distance_to_outlet	Distance (m)	Distance along the stream network from this link to the watershed outlet/mouth.
nr_downstream_nodes	Count	Number of downstream confluence nodes between this link and the outlet.
is_outlet	Boolean (0/1)	Whether this link is the most downstream segment draining to the watershed outlet.
downstream_link_id	Integer ID	The immediately downstream stream link. -1/NoData for outlet links.
is_main_stream	Boolean (0/1)	Whether this link is part of the main stem (the dominant path from headwater to outlet).
tributary_id	Integer ID	Unique identifier per tributary branch. All links belonging to the same tributary share this value.

Calculate floor levels

Calculate floor levels for individual buildings based on their surrounding elevation data. This process is used in flood risk assessments to accurately determine which buildings may be exposed to flooding. It combines building footprint data with a digital elevation model (DEM) to compute representative ground surface elevations around each building.

The Input of This Process

Digital Elevation Model (DEM), an elevation raster (GeoTIFF), can be extracted from dataset from the ‘elevation’ category in the catalogue. Any buildings outside the DEM extent will not receive a floor level.
Buildings Vector layer, a polygon vector layer containing building footprints (e.g., dataset extract from the ‘building’ category in the catalogue).
Buffer Distance (default: 1 meter), radius around each building footprint used to sample elevation values. Standard methodology uses 1 meter.
Percentile Statistic (default: 75), statistical percentile (0-100) to compute floor level from sampled elevations.

The Output of This Process

Buildings vector with floor level, a GeoPackage file with the original building geometries and an additional floor_level column containing ground surface elevations around each building.

Technical Documentation

For each building, the process performs the following steps:

Creates a buffer zone around the building footprint at the specified distance (default: 1 meter)
Extracts all elevation values from the DEM that fall within the buffer zone
Calculates the specified percentile (default: p75) of the sampled elevation values
Assigns the computed percentile value as the floor_level for that building

For specialized applications, the buffer distance and percentile can be modified:

Increasing the buffer distance captures a larger neighborhood around the building, useful for areas with coarser DEM resolution. Decreasing the buffer provides a tighter estimate closer to the building footprint.
Using p100 (maximum) provides the highest elevation in the buffer, as used in the Lizard DGBC method. Using lower percentiles (e.g., p50) produces lower floor level estimates.

Calculate road accessibility

Calculate the accessibility of roads after a flood event. Roads are classified as Accessible when there is little to no flooding (e.g. less than 10 cm), Inaccessible when a deep inundated area has to be passed (e.g. more than 30 cm), or Limited accessibility in between these values.

It is recommended to use a roads dataset that has separate lines from junction to junction, the line being on the centerline of the road.

The Input of This Process

Roads, a vector layer containing the centrelines of the roads to be analysed. Each feature should represent a single road segment (junction to junction). Long segments will be split automatically according to Maximum road length.
Water depth, a raster file containing water depths in metres, in a projected (metric) coordinate system.
Maximum passable depth [meters] (default 0.1). Roads with a 90th-percentile water depth at or below this value are classified as Accessible.
Impassable depth threshold [meters] (default 0.3). Roads with a 90th-percentile water depth at or above this value are classified as Inaccessible. Roads in between are classified as Limited accessibility.
Maximum road length [meters] (default 200). Road segments longer than this value are split into shorter pieces before analysis to prevent memory issues.

The Output of This Process

The output is a vector layer of roads (lines), with the accessibility class and the statistics that were used for the analysis.

The accessibility is stored in the output dataset (column “accessibility”) as a numeric value:

1: full accessibility
0.5: limited accessibility
0: no accessibility

Technical Documentation

The algorithm works as follows:

Long roads are cut into segments of ‘Maximum road length’.
Roads are buffered by 1 meter on both sides (half a 2-meter road width, with round endcap style).
Statistics (minimum, several percentiles, maximum, active pixel count) are sampled from the water depth raster for each buffered road line. Note that the dry pixels (water depth = 0) are not included in the calculation of the percentiles.
The percentage of wet area is calculated as the surface of the active pixels (pixels with water depth > 0) relative to the total area of the buffered road.
The road accessibility is determined using the 90th percentile water depth (p90). First, roads with less than 5% wet area are automatically classified as Accessible. For roads with 5% or more wet area:
- If p90 ≤ 0.1 m (maximum passable depth): Accessible
- If 0.1 m < p90 < 0.3 m (impassable threshold): Limited accessibility
- If p90 ≥ 0.3 m: Inaccessible
The results will be written to the fields “min”, “p10”, “p25”, “p50”, “p75”, “p90”, “max”, “percentage_wet”, and “accessibility”. If a field with this name already exists, values will be overwritten.

Calculate storage curves

Calculate storage curves for areas of interest based on a HCC schematisation. A storage curve describes the relationship between water level and stored volume for a given area. This is useful for understanding the capacity of sewerage systems and determining how much water can be stored at different water levels within each polygon.

The process uses the schematisation (and optionally its associated DEM) to generate a computational grid, identifies the relevant sewerage nodes (pipes, weirs, orifices) within each polygon of the study area, and computes a cumulative volume for a range of water levels.

The Input of This Process

Vector layer, a polygon vector layer containing the areas of interest for which to compute storage curves.
Schematisation, an HCC schematisation containing the sewerage network definition.
Minimum water level (meters), optional. The lowest water level at which to start computing volumes. When not set, the minimum bottom level of the relevant nodes is used.
Maximum water level (meters), optional. The highest water level at which to stop computing volumes. When not set, the maximum bottom level of the relevant nodes (plus a margin) is used.
Interval (meters), the water level step size between computed points on the storage curve. Default: 0.5 m.

The Output of This Process

Storage curves GeoPackage, a GeoPackage containing the original study area features with storage curves stored as a related table (using the GeoPackage Related Tables Extension). Each feature is linked to a series of (water level, volume) pairs that together form the storage curve for that area.

Technical Documentation

The process performs the following steps:

Downloads the study area vector layer and schematisation (with optional DEM).
Generates a computational grid (gridadmin.h5) from the schematisation using threedigrid-builder.
For each polygon in the study area, identifies all sewerage nodes (pipes, weirs, orifices with sewerage=1) whose coordinates fall within the polygon.
Generates hydraulic tables (tables.h5) using threedi-tables, which contain pre-computed volume and surface area increments per node.
For each polygon, iterates over the specified water level range and sums the cumulative volume across all relevant nodes at each water level.
Writes the result as a GeoPackage with the Related Tables Extension (RTE), linking each feature to its storage curve data.

Compute elevation difference

This process calculates the difference between two elevation rasters. It takes a reference raster and a raster to compare, computes compare - reference, and writes the overlapping result as a GeoTIFF raster.

This process is useful for quantifying terrain or model elevation changes between two datasets on the same grid.

The Input of This Process

Elevation reference raster, the baseline elevation raster.
Elevation raster to compare, the elevation raster that is compared against the baseline.

The Output of This Process

Elevation difference raster, a raster showing the per-pixel elevation difference for the overlapping area of the inputs.

Technical Documentation

Requirements

Both rasters must use the same pixel size.
Both rasters must use the same pixel tilt.
Both rasters must use the same CRS.

Calculation Method

The process computes compare - reference in blocks to limit memory usage. If the rasters do not share the same extent, only the overlapping extent is processed. Input nodata values are treated as 0, and the output nodata value is set to 0.

Compute land use difference

This process compares two land use rasters and shows where a selected land use category has increased or decreased between the reference and the comparison raster.

This process is useful for detecting land use transitions between two time periods. For example, identifying where agricultural land has been converted to residential or nature areas.

The Input of This Process

Land use reference raster, the baseline land use raster (e.g. an older year).
Land use raster to compare, the land use raster that is compared against the baseline (e.g. a more recent year).
Land use category, the category to analyse for change (Agricultural, Living, Industry, Nature, Water, or Paved).

The Output of This Process

The output directory contains three rasters:

land_use_change.tif, a raster showing where the selected category has changed:
- 0: unchanged (used as nodata, rendered as transparent)
- 1: category decreased (present in reference, absent in comparison)
- 2: category increased (absent in reference, present in comparison)
reference_grouped.tif, the reference raster reclassified into the six land use categories.
compare_grouped.tif, the comparison raster reclassified into the six land use categories.

Technical Documentation

Land use categories

Raw pixel values from the source land use map (based on BAG, BGT, TOP10NL, and BRP) are grouped into six categories: Agricultural (1), Living (2), Industry (3), Nature (4), Water (5), and Paved (6). Unknown values are treated as Other / no data (0).

Requirements

Both rasters must use the same pixel size.
Both rasters must use the same pixel tilt.
Both rasters must use the same CRS.

Calculation method

Processing is done in blocks to limit memory usage. If the rasters do not share the same extent, only the overlapping area is processed. Input nodata values are treated as 0 (no land use), and the output nodata value is 0.

Compute Max Water Depth

This process calculates the maximum water depth raster from a Rana HCC scenario file. It takes a scenario archive as input, extracts the required simulation files, computes the maximum water depth over time, and outputs a raster file with the result.

This process is useful for identifying the highest flood depths reached during a simulation, helping you assess flood impact and support risk analysis.

The Input of This Process

HCC Scenario file, a Rana scenario archive containing simulation results.

The Output of This Process

Max water depth raster, a raster showing per pixel the maximum water depth reached during the simulation. Higher values indicate deeper inundation.

Technical Documentation

Requirements

The scenario archive must contain a GridAdmin file (.h5).
The scenario archive must contain a results file (.nc).
The scenario archive must contain a DEM file (.tif).

Calculation Method

The process computes maximum water depth using HCC depth calculation on the scenario input files.

Data Handling

The scenario archive is downloaded and extracted before processing.
The result is written as a GeoTIFF raster (max_waterdepth.tif).

Compute water depth difference

This process calculates the difference in water depth between two raster files representing water depth data. It takes a reference water depth raster and a comparison raster as inputs, computes the difference (comparison minus reference) for their overlapping area, and outputs a new raster file with the result.

This process is useful for analyzing changes in water depth over time or between different scenarios, helping you identify areas where water levels have increased or decreased.

The Input of This Process

Water depth reference raster: The baseline water depth raster for comparison.
Water depth raster to compare The water depth raster to compare against the reference.

The Output of This Process

Water depth difference raster: A raster showing the calculated difference in water depth between the two input rasters. Positive values indicate areas where water depth increased, while negative values indicate areas where water depth decreased.

Technical Documentation

Requirements

Both input rasters must have the same pixel size and coordinate reference system
Rasters should have overlapping geographic areas for meaningful comparison

Calculation Method

The process computes the difference as: comparison raster - reference raster

Data Handling

Areas with no data are treated as zero water depth
If rasters have different extents, only the overlapping area is processed

Create 2D Flood Model

Create a flood model for your study area, ready for use in assessment of flooding due to intense rainfall (pluvial flooding). This Rana model is also a reliable foundation for further analyses: integrated urban drainage assessments, coastal and/or fluvial flooding studies, and more. Simply open it in the Rana Desktop Client to have the full range of expert modelling options at your fingertips.

The Input of This Process

Study area, the study area defines the physical boundaries for the model. Output of the ‘Define study area’ process is guaranteed to be a valid input.
Model schematisation name, this name will determine how the model schematisation is saved.
Elevation raster, select a dataset from the Catalog or your own file.
Infiltration raster (optional), select a dataset from the Catalog or your own file, or leave empty. If left empty, a default infiltration raster will be derived.
Friction raster (optional), select a dataset from the Catalog or your own file, or leave empty. If left empty, a default friction raster will be derived.
Add boundary storage zone (default: True), add a 50m wide and deep boundary storage zone around the project area to ensure realistic water flow at the edges.

The Output of This Process

The output is a 2D hydrodynamic model that allows you to simulate rainfall, infiltration, runoff, overland flow, ponding and flooding. This output becomes the basis for rainfall and flow analysis simulations in follow-up processes.

Technical Documentation

Study area requirements

Outputs of the process Define study area is guaranteed to be a valid input for this process. If you use another file as input, it has to meet these requirements:

It contains a single layer with a single polygon in it.
It has a projected coordinate reference system, with meters as unit.
The polygon is within the Netherlands.

Model schematisation

The model is configured as a pure 2D rainfall-driven model with 1D flow disabled, 2D flow and 2D rain are enabled. Friction is modelled using the Manning equation, either with a constant coefficient of 0.03 or a friction raster if one was provided. The minimum cell size is calculated automatically from the study area to target a maximum of 50,000 computation cells. A buffer of 50 meters will be added to the study area in case selected. DEM pixels in this buffer will be given a value of 50 m below the lowest value in the DEM to ensure that water can flow out of the study area, into this buffer. This ensures a realistic water flow in the study area and prevents artificial flooding at the edges of your study area.

Expert level technical settings are visible in the table below.

Table 7 Information about model settings
Setting	Value	Description
Friction type	Manning (2)	Friction method used in the simulation.
Friction coefficient	0.03	Constant friction value, used when no friction raster is provided.
Friction coefficient file	friction.tif	Used when a friction raster is provided.
Minimum cell size	Calculated	Derived from the study area size, targeting a maximum of 50,000 cells.
1D flow	Disabled
2D flow	Enabled
2D rain	Enabled

Time step settings

Table 8 Time step settings
Setting	Value
Computation time step	30 s
Output time step	300 s (5 min)
Minimum time step	0.01 s
Time step stretch	Disabled

Numerical settings

Table 9 Numerical settings
Setting	Value
Friction shallow water depth correction	3
Water level gradient limiter 2D	0
Slope cross-sectional area limiter 2D	3
Slope friction limiter 2D	1
Slope thin water layer limiter	0.1
Nested Newton	Disabled
Max degree Gauss-Seidel	5

Table 10 Initial conditions
Setting	Value
Initial water level	-99.0 m

Simple infiltration

Only added when an infiltration raster is provided. Implemented as described in the Simple Infiltration documentation.

Aggregate settings

Aggregate settings are required for the Water Balance Tool and are implemented by adding these settings to the schematisation.

Create 3D water level data

Convert simulation results to a water level layer in a format that can be used in 3D platforms (OGC 3D Tiles).

Note

The image above shows 3D visualisation in 3rd party software. Rana only provides the OGC 3D Tiles, other software is required to be able to visualise them.

Visualizing water levels in 3D can help stakeholders make better decisions regarding flood risks, infrastructure planning, and water management strategies. OGC 3D tiles make this possible. The format efficiently handles large datasets, allowing users to view and interact with 3D models without performance issues.

The Input of This Process

Scenario, a simulation result from which the water level data will be read. Use for example the ‘Simulate rainfall’ process to create this file.
Interval (in minutes), time gap between each frame (or snapshot) of water levels in the 3D tiles. A shorter interval results in smoother animations but generates more data and larger files. Default: 60 minutes.

The Output of This Process

An OGC 3D Tiles file with water level surfaces. This file can be viewed in GIS platforms or web viewers to visualize the simulation results.

Create Basic Hydrodata

Add essential hydrological datasets to your project.

The process extracts elevation, soil, and land cover raster datasets for your study area and adds them to your project. These layers allow you to quickly conduct a hydrological scan of your study area.

Select your study area and the datasets you need, and the outputs will be automatically aligned to the correct spatial projection for seamless integration into your project.

The Input of This Process

Study area, the study area defines the geographic extent for data extraction.
Elevation Dataset (optional), select an item from the elevation category in the Catalog.
Soil Dataset (optional), select an item from the soil category in the Catalog.
Land use Dataset (optional), select an item from the land use category in the Catalog.
Land Cover Dataset (optional), select an item from the land cover category in the Catalog.
Cell size (in meters) (default: 0.5), each grid cell represents a square on the ground. Minimum: 0.5 m.

The Output of This Process

The outputs are raster files that cover your whole study area.

Digital elevation model (DEM)

Each pixel represents the elevation in meters above mean sea level (m MSL) at that location. A DEM is crucial for hydrological analyses like water flow, slope calculation, and flood modelling.

Soil type

Each pixel represents the soil type for that location. This is important for understanding water infiltration and retention in hydrology studies.

The soil types are stored as whole numbers; see source data for an overview of which number corresponds to which soil type.

Land use (functional)

Each pixel represents a land use class (e.g., urban, forest, agricultural). This classification by function helps to understand what is going on in your study area. It can also be used to estimate the damage (costs) of flooding: e.g. cropland may have a different damage curve than grassland.

The land use types are stored as whole numbers; see source data for an overview of which number corresponds to which land use category.

Land cover (physical appearance)

Each pixel represents the physical appearance of the land (e.g. paved or unpaved), which affects hydrological processes like runoff, evaporation, and storage.

The land cover types are stored as whole numbers; see source data for an overview of which number corresponds to which land cover category.

Technical Documentation

Source Data

Digital elevation model

Digital terrain model (DTM) of the Netherlands, based on Algemeen hoogtebestand Nederland 4, preprocessed for hydrological analysis. At the location of buildings (according to the Basisregistratie Adressen en Gebouwen), the elevation is set to the estimated floor level, calculated as the 75th percentile of all elevation values directly adjacent to the building.

At other locations where the original dataset had gaps due to water bodies, parked cars, dense vegetation, etc., values have been spatially interpolated.

Soil type

This dataset is based on the Bodemfysische eenhedenkaart 2012 (BOFEK). Soil types are classified according to the Policy Analysis for the Water management of the Netherlands (PAWN) classification; see the table below. The dataset is spatially interpolated to fill nodata gaps in the original dataset, e.g. in urban areas.

Table 11 Soil types (PAWN classification)
#	Soil type
1	Veengrond met veraarde bovengrond
2	Veengrond met veraarde bovengrond, zand
3	Veengrond met kleidek
4	Veengrond met kleidek op zand
5	Veengrond met zanddek op zand
6	Veengrond op ongerijpte klei
7	Stuifzand
8	Podzol (Leemarm, fijn zand)
9	Podzol (zwak lemig, fijn zand)
10	Podzol (zwak lemig, fijn zand op grof zand)
11	Podzol (lemig keileem)
12	Enkeerd (zwak lemig, fijn zand)
13	Beekeerd (lemig fijn zand)
14	Podzol (grof zand)
15	Zavel
16	Lichte klei
17	Zware klei
18	Klei op veen
19	Klei op zand
20	Klei op grof zand
21	Leem

Land use (functional)

This dataset is a contiguous raster on a 0.5 m resolution for the entire Netherlands, that represents functional land use.

Both land cover and land use are constructed from a combination of publicly available Dutch land use datasets: Basisregistratie Adressen en Gebouwen, Basisregistratie Grootschalige Topografie (BGT), Basisregistratie Topografie (BRT): TOP10NL, and Basisregistratie Gewaspercelen (BRP).

The classes used in this dataset are given below.

Table 12 Land use (functional) classes
#	Land use
1	Veengrond met veraarde bovengrond
2	woonfunctie
3	celfunctie
4	industriefunctie
5	kantoorfunctie
6	winkelfunctie
7	kas
8	logiesfunctie
9	bijeenkomstfunctie
10	sportfunctie
11	onderwijsfunctie
12	gezondheidszorgfunctie
13	overige gebruiksfunctie
14	niet ingevuld
15	woongebied
16	bedrijventerrein
17	dagrecreatief terrein
18	verblijfsrecreatief terrein
19	sportterrein
20	begraafplaats
21	volkstuinen
22	glastuinbouw
24	overig
25	snelweg
26	regionale_weg
27	verkeerseiland
28	lokale_weg
29	overige wegdelen
30	vliegveld
31	spoorbaan
32	transformatorstation
33	opslagtank
34	bezinkbak
35	bassins
40	groenvoorziening
41	overig gras/groen
42	gras
43	bos / natuur
44	berm
45	spoorberm
50	water
51	binnenwater
52	buitenwater
55	aardbeien_op_stelling
56	aardbeien_open_grond
57	aardperen
58	agrarisch gras en veevoeders
59	akkerbouw
60	andijvie
61	appelen
62	asperges
63	augurk
64	blasrammenas
65	blauwebessen
66	bloembollen
67	bloembollen en sierteelt
68	bloemkool
69	boerenkool
70	boom en heesterkweek
71	bos en haagplanten
72	bospeen
73	braak
74	broccoli
75	bruinebonen
76	buxus
77	chinesekool
78	cichorei
79	consumptieaardappelen
80	courgette
81	cranberry
82	engels raaigras
83	erwten
84	frambozen
85	fruitteelt
86	gerst
87	granen
88	grasland natuurlijk
89	graszoden
90	groente in open grond
91	haver
92	hennep
93	ijsbergsla
94	kapucijners
95	kersen
96	kersen zuur
97	kerstbomen
98	klaver
99	knoflook
100	knolselderij
101	knolvenkel
102	komkommer
103	koolraap
104	koolrabi
105	koolzaad
106	kruiden
107	laanbomen
108	luzerne
109	mais corncob
110	mais energie
111	mais korrel
112	mais snij
113	mais suiker
114	miscanthus
115	natuur
116	notenbomen
117	paksoi
118	pastinaak
119	peren
120	peulen
121	pompoen
122	pootaardappelen
123	prei
124	pronkbonen
125	pruimen
126	rabarber
127	radijs
128	rietzwenkgras
129	rode bieten
130	rodebessen
131	rodekool
132	rogge
133	rozen
134	schorseneren
135	selderij
136	sierconiferen
137	sierheesters en klimplanten
138	sla
139	sojabonen
140	sperziebonen
141	spinazie
142	spitskool
143	sportveld
144	spruitjes
145	suikerbieten
146	tarwe
147	trek en besheesters
148	triticale
149	tuinbonen
150	uien
151	valeriaan
152	vaste planten
153	vlas
154	voederbieten
155	waspeen
156	water
157	weidehooi
158	wijndruiven
159	winterpeen
160	witlof
161	wittekool
162	wortelpeterselie
163	zetmeelaardappelen
164	zwartebessen
165	voetgangersgebied
166	fietspad
167	duin
167	akkerland
167	fruitkwekerij
168	ov baan

Land cover (physical appearance)

Table 13 Land cover (physical appearance) classes
#	Land cover
1	Veengrond met veraarde bovengrond
1	dak
2	zand
3	half verhard
4	erf
5	gesloten verharding
6	onverhard
7	open verharding
8	groenvoorziening
9	naaldbos
10	gras
11	grasland
12	struiken
13	natuurterrein
14	boomteelt
15	duin
16	heide
17	bouwland
18	houtwal
19	loofbos
20	rietland
21	fruitteelt
22	gemengd bos
23	braakland
26	moeras
27	kwelder
28	waterberm
29	water
30	overige
254	water

Create friction raster

Create a friction raster from a land cover map. The friction value represents the Manning’s roughness coefficient for surface water flow, derived from the land cover class.

The Input of This Process

Land cover raster, select a land cover raster from your files.

The Output of This Process

Friction raster, a GeoTIFF raster with Manning’s roughness coefficients derived from the land cover classes.

Technical Documentation

The classes of the land cover map are translated using the table below to the Manning’s roughness coefficient.

Physical land use input

The input classes of the land cover map are listed below, together with their Manning’s roughness coefficient:

Table 14 Manning’s roughness coefficient per land cover class
Class ID	Name	Manning’s n (s/m^(1/3))
1	Roof	0.058
2	Sand	0.03
3	Semi-paved	0.03
4	Yard	0.03
5	Closed pavement	0.013
6	Unpaved	0.03
7	Open pavement	0.016
8	Green roof	0.03
9	Trees	0.058
10	Grass	0.03
11	Agriculture	0.03
12	Forest	0.058
13	Shrubs	0.058
14	Heather	0.058
15	Dunes	0.05
16	Bare soil	0.058
17	Gravel	0.034
18	Natural	0.058
19	Wetland	0.058
20	Floodplain	0.058
21	Other natural	0.058
22	Other	0.058
23	Unknown built	0.058
26	Infrastructure	0.058
27	Rail	0.058
28	Beach	0.03
29	Tidal flat	0.026
30	River bed	0.03
253	Unknown	0.03
254	Water	0.026

Create infiltration raster

Create an infiltration rate raster from soil and land use maps. The infiltration rate is retrieved by multiplying the soil infiltration rate with a factor derived from the land use.

The Input of This Process

Soil raster, select a soil raster from your files.
Land cover raster, select a land cover raster from your files.

The Output of This Process

Infiltration raster, a GeoTIFF raster with infiltration rates derived from the soil and land cover classes.

Technical Documentation

Soil map input

The table that maps the soil map to the infiltration rate.

Table 15 Infiltration rate per soil class
Class ID	BOFEK Name	Infiltration Rate (mm/day)
1	Peat soil with humified topsoil	480
2	Peat soil with humified topsoil, sand	480
3	Peat soil with clay cover	120
4	Peat soil with clay cover on sand	480
5	Peat soil with sand cover on sand	480
6	Peat soil on immature clay	120
7	Drift sand	480
8	Podzol (low in loam, fine sand)	480
9	Podzol (slightly loamy, fine sand)	480
10	Podzol (slightly loamy, fine sand over coarse sand)	480
11	Podzol (loamy boulder clay)	120
12	Enkeerd soil (slightly loamy, fine sand)	120
13	Beekeerd soil (loamy fine sand)	120
14	Podzol (coarse sand)	480
15	Loam	480
16	Light clay	120
17	Heavy clay	120
18	Clay over peat	120
19	Clay over sand	120
20	Clay over coarse sand	120
21	Silt / Loess	120

Physical land use input

The input classes of the land use map are listed below, together with their infiltration factor.

Table 16 Infiltration factor per land use class
Class ID	Name	Infiltration Factor
1	Roof	0
2	Sand	1
3	Semi-paved	0.5
4	Yard	0.25
5	Closed pavement	0
6	Unpaved	1
7	Open pavement	0.025
8-30	Green and natural areas	1
253	Unknown	1
254	Water	0

Define Study Area

Create a valid study area polygon for your project. It extracts one or more polygons from a layer, merges them into one and (optionally) adds a buffer around it.

The resulting polygon can be used as “study area” input in other processes related to hydrological modelling, data analysis, or visualization.

For your convenience, Rana provides datasets of administrative boundaries that you can choose from. You may also use any other polygon layer from your project.

The Input of This Process

Area, select your study area. You can draw on the map, choose from standard administrative boundaries (e.g. municipalities), or upload a custom polygon file.
Buffer (in meters) (default: 0), add a buffer around the selected area to include features near the borders.

The Output of This Process

A geopackage file with one layer that contains one polygon.

Technical Documentation

If the input data has a coordinate reference system (CRS) with units that are not meters, the output polygon is reprojected to the appropriate UTM zone projection.

If multiple polygons are selected, they will be dissolved into a single polygon. It is required that the input polygons are not disjoint.

Source Data

The municipalities layer is sourced from PDOK, “Bestuurlijke Gebieden 2024”.

Delineate basins

Delineate basins (watersheds) from a digital elevation model. Basins are defined as areas that drain to the same edge pixel. The basins can be used to help you understand the hydrological functioning of your study area, or to subdivide a large area in smaller, hydrologically separate study areas.

The Input of This Process

Digital elevation model, a raster file containing elevation data.

The Output of This Process

The output is a vector layer of basin polygons.

Technical Documentation

This analysis tool chains a number of algorithms from Whitebox Geospatial:

Breach depressions least cost
Fill depressions
D8 pointer
Basins

The result is vectorized and copied to a geopackage layer “basins”. Gaussian smoothing is applied to the polygons. The smoothing preserves topology (creates no gaps or overlaps).

Extract dataset

Import a dataset (for instance a digital elevation model) into your project. Select your study area and the dataset you need, and the output will be automatically aligned to the correct spatial projection for seamless integration into your project.

The Input of This Process

Dataset, select a Catalog item that is to be extracted.
Study area, the study area defines the geographic extent for data extraction.
Resolution (default: “default”), pixel size of the output raster. Use “default” to use the native resolution of the source dataset, or specify a custom resolution in meters.

The Output of This Process

Extracted file, the extracted geospatial file, clipped and aligned to your study area.

Generate D-Hydro Freeboard

Calculates corrected freeboard (drooglegging) values. Combining model outputs with storage node effects, and filters out boezem (lake/buffer water) areas.

The Input of This Process

Boezem (Lake / Buffer Water)Shapefile / GeoPackage
- Boezem (lake/buffer water) polygons to exclude from analysis.
- Source: D-Hydro model input or water management area definitions.
- Required: Polygon geometry representing water bodies.
Calculation Points (WITH storage nodes)GeoPackage
- Calculation points with water levels from D-Hydro model (WITH storage nodes).
- Source: D-Hydro FlowFM output (_his.nc files).
- Required columns: ‘t_end’ (water level), geometry (point locations).
Water Levels WITHOUT Storage NodesCSV
- Water levels without storage node effects.
- Source: D-Hydro FlowFM output (separate run without storage nodes).
- Format: Skip first row (metadata), column ‘Water level - mesh1d_nNodes: mean (m)’.
Freeboard CSV (WITHOUT storage)CSV
- Model freeboard values at calculation points (calculated WITHOUT storage nodes).
- Source: D-Hydro FlowFM output (run without storage nodes).
- Formula: Good freeboard = freeboard_without_storage + (wl_without_storage - wl_with_storage)
- Format: Skip first row (metadata), column ‘Freeboard - mesh1d_nNodes: mean (m)’.

The Output of This Process GeoPackage with corrected freeboard values:

Layer ‘freeboard_winter’: Corrected freeboard (fb_cor = freeboard + waterstand).
Layer ‘freeboard_zomer’: Empty layer (placeholder for future summer calculations).
Points in boezem areas set to -9999 (nodata).

Prepare Digital Elevation From AHN

Extract and process Dutch elevation data (AHN - Actueel Hoogtebestand Nederland) to prepare a digital elevation model with calculated floor levels for buildings in your study area.

This process downloads the specified AHN dataset, fills missing elevation values, calculates floor levels around building footprints, and produces a final elevation raster suitable for hydrological modeling.

The Input of This Process

Elevation The Digital Terrain Model without surface features such as vegetation and structures.

Study Area A polygon vector dataset defining the geographic extent for elevation data extraction. This ensures all processed data corresponds to your specified area of interest.

Resolution (default: “default”) Pixel size of the output raster. Use “default” to use the native resolution of the source dataset, or specify a custom resolution in meters.

Decimal Precision (default: 2) The number of decimal places for rounding interpolated elevation values.

Buildings Vector File A vector file containing building footprints with polygon geometries representing individual buildings. Supported formats include Shapefile, GeoJSON, or other OGR-compatible formats. If your file contains multiple layers, you can specify which layer contains the building footprints.

Buffer Distance (default: 1 meter) Radius around each building footprint used to sample elevation values for floor level calculation.

Percentile Statistic (default: 75) The statistical percentile (0-100) used to determine floor level. For example, 75 means the floor level is set such that 75% of the buffered area is below this elevation.

The Output of This Process

A GeoTIFF raster file containing the digital elevation model with integrated floor level data for buildings, clipped to your study area boundaries. The floor level represents the estimated ground surface elevation around each building, which is essential for accurate flood risk assessment and water flow modeling.

Technical Documentation

The process performs several sequential steps:

Data Validation: Validates that the study area polygon and buildings vector file are properly formatted
AHN Extraction: Downloads and extracts the specified AHN dataset clipped to study area boundaries
Data Interpolation: Fills missing or null elevation values using interpolation techniques to create a continuous DEM
Floor Level Calculation: For each building, buffers the footprint, samples elevation values within the buffer, and computes the percentile-based floor level
Rasterization: Converts calculated floor level values back onto the DEM raster
Final Processing: Clips the result to study area boundaries and optimizes file size
The elevation raster uses a metric coordinate reference system, which is validated during processing. All interpolated values are rounded to the specified decimal precision for consistent data.

Simulate Rainfall

This process allows you to simulate constant rainfall scenarios for one model in your project. The simulation starts with a rain period where a constant rain intensity is applied across the entire model. After the rain period ends, the simulation continues with a dry period, allowing you to observe water retention, runoff, and drainage behavior. This type of simulation is essential for analyzing how the terrain and landscape respond to sustained rainfall events, helping identify potential flooding risks and evaluating water management strategies.

The Input of This Process

Model, a rana model schematisation, for example created by the ‘Create 2D flood model’ process.
Simulation duration (in minutes), total duration of simulation, covering both rainfall and dry period. Default: 120 minutes.
Rain duration (in minutes), controls how long the simulation applies rainfall. Default: 60 minutes.
Rain intensity (in mm/h), rain intensity directly affects the amount of water entering the model. Default: 80 mm/h.

Processed Simulation Results

Max water depth, in meters relative to the surface (DEM). The exact moment that this max water depth occurs can vary between cells.
A scenario result, that can be readily used in a publication.
Detailed NetCDF files, that can be used for further analysis in the Rana Desktop Client.

Raw Simulation Results

These files can be used for advanced analysis in the Rana Desktop Client. These files contain the water levels, volumes, flow velocities, discharge, and other relevant variables for each output time step of the simulation (e.g. every 5 minutes). For more information, see Rana Documentation.

A model schematisation may have multiple revisions; the Rana model of the latest revision is used in the simulation. A Rana model for the latest revision will be generated if it does not exist yet.

If multiple simulation templates exist for the Rana model, the most recent one will be used.

Simulate rainfall events: constant

This process allows you to simulate a constant rainfall event, used as a stress test in the Netherlands. These events are determined by RIONED.

The Input of This Process

Model, a rana model, for example created by the ‘Create 2D flood model’ process.

Rainfall event, select a historical rainfall event.

Processed Simulation Results

Max water depth, in meters relative to the surface (DEM). The exact moment that this maximum water depth occurs can vary between cells.
A scenario result, that can be readily used in a publication.
Detailed NetCDF files, that can be used for further analysis in the Rana Desktop Client.

Raw Simulation Results

These files can be used for advanced analysis in the Rana Desktop Client. These files contain the water levels, volumes, flow velocities, discharge, and other relevant variables for each output time step of the simulation (for example, every 5 minutes). For more information, see Rana Documentation.

Technical Documentation

A schematisation may have multiple revisions; the Rana model of the latest revision is used in the simulation. A Rana model for the latest revision will be generated if it does not exist yet.

If multiple simulation templates exist for the Rana model, the most recent one will be used.

Overview constant rainfall events

Table 17 Constant Rainfall events
Constant rainfall event name	Total rainfall (mm)	Rainfall event duration / Time series duration (h)	Return period (y)
DPRA Event 70 mm	70	1	200
DPRA Event 90 mm	90	1	500
DPRA Event 160 mm	160	2	2000

DPRA Event 70 mm

70 mm

DPRA Event 90 mm

90 mm

DPRA Event 160 mm

160 mm

Simulate rainfall events: historical

This process allows you to simulate a historic rainfall event for one schematization in your project.

The Input of This Process

Model, a rana model, for example created by the ‘Create 2D flood model’ process.

Rainfall event, select a historical rainfall event.

Processed Simulation Results

Max water depth, in meters relative to the surface (DEM). The exact moment that this maximum water depth occurs can vary between cells.
A scenario result, that can be readily used in a publication.
Detailed NetCDF files, that can be used for further analysis in the Rana Desktop Client.

Raw Simulation Results

These files can be used for advanced analysis in the Rana Desktop Client. These files contain the water levels, volumes, flow velocities, discharge, and other relevant variables for each output time step of the simulation (for example, every 5 minutes). For more information, see Rana Documentation.

Technical Documentation

A schematisation may have multiple revisions; the Rana model of the latest revision is used in the simulation. A Rana model for the latest revision will be generated if it does not exist yet.

If multiple simulation templates exist for the Rana model, the most recent one will be used.

Overview historical rainfall events

Table 18 Historical storm hydrographs
Historical rainfall event name	Total rainfall (mm)	Rainfall event duration (h)	Time series duration (h)	Timestep (min)	Year measurements	Return period	Source
Deelen	129.6	2 hours and 50 minutes	2 hours and 50 minutes	5	2014	T=1000	Rioned
Marknesse	111.0	2 hours and 20 minutes	2 hours and 50 minutes	5	2003	T=500	Rioned
Herwijnen	93.6	2 hours and 30 minutes	2 hours and 50 minutes	5	2011	T=500	Rioned
Cabauw	73.3	2 hours and 30 minutes	2 hours and 50 minutes	5	2005	T=200	Rioned
Maastricht	48.8	2 hours and 30 minutes	2 hours and 50 minutes	5	2014	T=100	Rioned
De Bilt	36.2	1 hour and 10 minutes	2 hours and 50 minutes	5	2016	T=20	Rioned
Copenhagen	134.5	1 hour and 50 minutes	1 hour and 50 minutes	10	2011	T>2000	DMI / Arnbjerg-Nielsen et al.
Limburg	200.4	48 hours	49 hours	60	2021	T=1000	Stowa

Details Historical Event Deelen

Details Historical Event Marknesse

Details Historical Event Herwijnen

Details Historical Event Cabauw

Details Historical Event Maastricht

Details Historical Event De Bilt

Details Historical Event Copenhagen

Details Historical Event Limburg

In July 2021, Limburg experienced an extreme rainfall event that led to significant flooding, primarily caused by a storm depression named ‘Bernd.’ This unprecedented rainfall resulted in the Meuse and Geul rivers overflowing, damaging thousands of homes and infrastructure, and causing estimated losses of 350 to 600 million euros. The event was characterized by record-breaking precipitation levels, making it one of the most severe flooding incidents in the region’s history.

The source of the Copenhagen event is from the Danish Institute of Meteorology, and was accessed using their API using the following URL: https://opendataapi.dmi.dk/v2/metObs/collections/observation/items?stationId=05735&parameterId=precip_past10min&datetime=2011-07-02T17:10:00Z/2011-07-02T18:50:00Z&limit=11 (16-1-2026).

The source of the Limburg event can be found here https://klimaatadaptatienederland.nl/hulpmiddelen/overzicht/handreiking-bovenregionale-stresstesten/ when clicking the PDF (17-03-2026).

Simulate rainfall event: standard events NL

Simulate Rainfall Event: Standard Events (Netherlands) This process allows you to simulate a standard rainfall event used in the Netherlands. These are C2100 rainfall events from RIONED for one Rana model in your project.

The Input of This Process

Model, a rana model, for example created by the ‘Create 2D flood model’ process.

Rainfall event, select a C2100 rainfall event.

Processed Simulation Results

Max water depth, in meters relative to the surface (DEM). The exact moment that this maximum water depth occurs can vary between cells.
A scenario result, that can be readily used in a publication.
Detailed NetCDF files, that can be used for further analysis in the Rana Desktop Client.

Raw Simulation Results

These files can be used for advanced analysis in the Rana Desktop Client. These files contain the water levels, volumes, flow velocities, discharge, and other relevant variables for each output time step of the simulation (for example, every 5 minutes). For more information, see Rana Documentation.

Technical Documentation

A schematisation may have multiple revisions; the Rana model of the latest revision is used in the simulation. A Rana model for the latest revision will be generated if it does not exist yet.

If multiple simulation templates exist for the Rana model, the most recent one will be used.

Overview standard rainfall events

Table 19 Standard Dutch rainfall events
Rainfall event name	Total rainfall (mm)	Rainfall event duration / time series duration (h)	Timestep (min)	Return period (y)	Location peak (e: early, l: late)
Standard rainfall event 01	10.5	1 hour and 15 minutes	5	T=0.25	e
Standard rainfall event 02	10.5	1 hour and 15 minutes	5	T=0.25	l
Standard rainfall event 03	14.4	1 hour and 15 minutes	5	T=0.5	e
Standard rainfall event 04	14.4	1 hour and 15 minutes	5	T=0.5	l
Standard rainfall event 05	16.8	1 hour and 15 minutes	5	T=1	e
Standard rainfall event 06	16.8	1 hour and 15 minutes	5	T=1	l
Standard rainfall event 07	19.8	1 hour	5	T=2	e
Standard rainfall event 08	19.8	1 hour	5	T=2	l
Standard rainfall event 09	29.4	1 hour	5	T=5	e
Standard rainfall event 10	35.7	45 minutes	5	T=10	e

Simulate rainfall painter event

This process allows you to simulate a rainfall event using a rainfall event generated by Rainfall Painter for one model in your project. The rainfall is uniformly spread over the model.

The Input of This Process

Model, a rana model schematisation to run the simulation on.
Rainfall file, a CSV export from Rain Canvas Studio with columns Time (hr) and Intensity (in/hr). The CSV may contain comment lines starting with # and a header line, as produced by Rain Canvas Studio.

Processed Simulation Results

Max water depth, in meters relative to the surface (DEM). The exact moment that this max water depth occurs can vary between cells.
A scenario result, that can be readily used in a publication.
Detailed NetCDF files, that can be used for further analysis in the Rana Desktop Client.

Raw Simulation Results

These files can be used for advanced analysis in the Rana Desktop Client. These files contain the water levels, volumes, flow velocities, discharge, and other relevant variables for each output time step of the simulation (e.g. every 5 minutes). For more information, see Rana Documentation.

A model schematisation may have multiple revisions; the Rana model of the latest revision is used in the simulation. A Rana model for the latest revision will be generated if it does not exist yet.

If multiple simulation templates exist for the Rana model, the most recent one will be used.