One Ecosystem :
Research Article
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Corresponding author: Thea Wübbelmann (thea.wuebbelmann@hereon.de)
Academic editor: Joachim Maes
Received: 06 Jun 2022 | Accepted: 29 Aug 2022 | Published: 16 Sep 2022
© 2022 Thea Wübbelmann, Laurens Bouwer, Kristian Förster, Steffen Bender, Benjamin Burkhard
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
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Increasing urbanisation in combination with a rise in the frequency and intensity of heavy rain events increase the risk of urban flooding. Flood Regulating Ecosystem Services (FRES) address the capacity of ecosystems to reduce the flood hazard and lower damage. FRES can be estimated by quantification of supply (provision of a service by an ecosystem) and demand (need for specific ES by society). However, FRES for pluvial floods in cities have rarely been studied and there is a gap in research and methods on FRES supply and demand quantification.
In this study, we assessed FRES of an urban district in the City of Rostock (Germany) for a one-hour heavy rainfall event using the hydrological model LEAFlood. The hydrological model delivered the FRES supply indicators of soil water retention and water retained by canopies (interception). An intersection of the potential demand (based on indicators of population density, land reference value, monuments and infrastructure) and the modelled surface water depth revealed the actual demand. Comparing the actual demand and supply indicated the budget of FRES to identify unmet demand and supply surplus.
Results show highest mean FRES supply on greened areas of forests, woodlands and green areas, resulting in a supply surplus. Whereas, sealed areas (paved surface where water cannot infiltrate into the soil), such as settlements, urban dense areas, traffic areas and industry, have an unmet demand resulting from low supply and relatively high actual demand.
With the hydrological model LEAFlood, single landscape elements on the urban scale can be evaluated regarding their FRES and interception can be considered. Both are important for FRES assessment in urban areas. In contrast to flood risk maps, the study of FRES gives the opportunity to take into account the contribution of nature to flood regulation benefits for the socio-economic system. The visualisation of FRES supply and demand balance helps urban planners to identify hotspots and reduce potential impacts of urban pluvial flooding with ecosystem-based adaptations.
supply and demand, unmet demand, mismatch, hydrological modelling, LEAFlood
The sixth report of working group I of the Intergovernmental Panel on Climate Change (IPCC) (
To assess ES, the matrix method is a widly known and simple method that classifies ES, based on land-use classes from 0 to 5 (
Additionally, the indicator framework developed by
Although a more comprehensive picture is given by quantitative models, which are increasingly being used in ES research (
The use of hydrological models, instead, is more complex, but more accurate in its depiction of reality. Depending on the research question, different hydrological models can be used (
The MAES-Indicators, as well as many methods based on models that determined FRES, focus on fluvial floods and on gauged catchment areas (
Flood regulating supply only has a societal value and turns into an ES if there is an according demand (
After evaluating FRES supply and demand, a budget analysis can be applied to identify mismatches of ES supply and demand to discover unmet demand besides the benefiting areas with a supply surplus (
Therefore, the main aim of this paper is to fill the research gap of a comprehensive FRES assessment of natural supply and demand and their mismatches at the urban scale with a focus on heavy precipitation events. Accordingly, we applied the methodological approach to an exemplary area and heavy precipitation event. We tested indicators of soil water storage and interception for FRES supply using the hydrological Model LEAFlood that is based on the Catchment Modelling Framework (CMF). After we identified FRES supply areas, we carried out a comprehensive FRES demand analysis that takes into account different demand types. Finally, we conducted an analysis at the urban scale to uncover the unmet demand.
We, therefore, address the following research questions:
The basis for the following analysis was the hydrological model LEAFlood. The model was designed by
In the following, the results of the model and other spatial data used in the FRES analysis were analysed with ArcGIS Pro 2.8 from ESRI and Python 3.7. For the FRES analysis, we partly followed the approaches of existing studies (
The study area covers partially the city districts Hansaviertel, Reutershagen and Köpeliner-Tor-Vorstadt in Rostock (northeast Germany) at the estuary of the River Warnow at the Baltic Sea (Fig.
Due to its proximity to the Baltic Sea, the climate in Rostock is mild-maritime. The annual mean temperature is 9.2°C (1981-2010). The annual precipitation sum is 730 mm with a maximum monthly precipitation of around 70 mm in July (
The study area with a size of 4.5 km² is located in the southwest of Rostock (Fig.
The hydrological model requires an appropriate dataset of meteorological, land use, soil and elevation information. The main meteorological input dataset is precipitation. We used the data of the DWD climate station Rostock-Warnemünde at one minute resolution (
Other required meteorological data that are used if (canopy) evaporation is activated, are the minimum and maximum temperature (
Spatial data of the land use includes soil-sealing information (
The FRES demand analysis also required a set of spatial data. In addition to the land-use data, which was used to identify the traffic infrastructure (
For the hydrological modelling, we used LEAFlood (Landscape vEgetAtion and Flood model) (
LEAFlood adopts CMF features to create the geometry, based on polygon cells out of GIS shapefiles on the spatial resolution that is required for adequate hydrological modelling on the city scale. Most models designed for urban areas focus on urban drainage with a simplified representation of vegetation (
The geometry for our study was created on the basis of an irregular polygon shapefile consisting of 4750 cells with an average size of approximately 1000 m², in order to best possibly resolve relevant urban landscape elements on the one hand side and numerical stability on the other. The canopy closure, which defines the amount of through-fall and canopy interception, was given by the quotient of the projected canopy area and cell area. Each tree was assigned a Leaf Area Index (LAI) value and an interception capacity, based on its species-specific attributes using the datasets of
LEAFlood uses a one soil layer approach, assuming that only the upper layer is relevant for infiltration and that percolation does not play a role due to the time delay. The used infiltration approach is Green-Ampt, which is an approximate theory adaptation of the Darcy equation (
Process/ Parameter |
Setting |
Interception |
Rutter Interception Through-fall Canopy Evaporation |
Infiltration
Layer depth Saturated conductivity (Ksat) Porosity Theta_x _b Porosity decay Saturated depth |
Green-Ampt Brooks Cores Retention Curve 0.5 m 0.3 m/d (base value) 0.3 [-] 0.2 [-] 8 [-] 0.2 m-1 1 m |
Surface Run-off |
Kinematic wave |
A higher degree of sealing, therefore, resulted in a lower Ksat value (Table
The roughness coefficient Manning's n and the saturated conductivity (Ksat) defined for each land use class.
Land use |
Manning's n [s*m-1/3] ( |
Saturated Conductivity [m/day] |
Urban dense areas |
0.2 |
0 |
Settlements |
0.12 |
0.015 |
Industry |
0.12 |
0 |
Traffic area |
0.03 |
0.006 |
Green area |
0.05 |
0.29 |
Woodland |
0.14 |
0.3 |
Forest |
0.15 |
0.3 |
Water |
0.03 |
0.015 |
The FRES analysis was undertaken with Python and ArcGIS Pro using an intersection of spatial information of population, economy, land use and hydrological model results. Fig.
Term |
Definition |
Indicators |
Other studies |
Used Supply |
ES supply indicates the provision of a service by an ecosystem ( |
Interception capacity [mm] + Soil water capacity [mm] Difference between Maximum and initial depth (t0) = Supply [mm] converted into relative scale 0-1 |
There is no synonym from other studies, such as the vulnerability and risk approach, since they do not consider flood regulating elements, but are focused on the flooding itself. |
Potential demand |
An ecosystem only provides ES if there is a demand by society or other stakeholder. Therefore, the demand is the need of an ES by society or other stakeholders ( |
Population Density [people/100 km²] Monuments [-] Land reference value [€] Infrastructure [-] (for details see Table 4) |
In other concepts or approaches, the terms of vulnerability and exposure ( |
Flood hazard |
Flood hazard indicates the surface flooding. The indicator is the modelled surface water depth. |
Surface water depth [mm] converted into relative scale 0-1 |
In vulnerability and flood risk assessments, flooding is referred to as hazard and is the potential occurrence of an event ( |
Actual demand |
The actual demand resulting from an intersection of the potential demand and flood hazard. It is the potential demand that was actually used for this single rainfall event (flood hazard). Therefore, the potential demand turns into a actual demand. |
Product of potential demand and flood hazard |
The actual demand can be understood as the risk and results of the function of vulnerability, exposure and hazard ( |
Budget |
ES budget results from the difference of FRES actual demand and supply. It indicates the mismatches of supply and demand as benefiting areas with a supply surplus and unmet demand areas, where the FRES is not sufficient to balance the amount of precipitation ( |
Difference of supply and flood hazard |
Other concepts do not consider the used regulating storage capacities and balance to examine the sufficiency of FRES supply. |
The FRES supply indicators were the soil water depth and the intercepted water depth on the canopies. They were defined by the difference of the maximum over the time and the initial water column at the first time step. The values were derived from the output of the hydrological model. The total supply resulted from the sum of both indicators (Fig.
The supply and its indicators were individually classified into a relative scale from 0 to 1 by dividing the water depth of the cell by the maximum of all cells. Thereby, 0 to 0.2 indicates a very low supply, 0.2 to 0.4 a low supply, 0.4 to 0.6 a medium supply, 0.6 to 0.8 a high supply and 0.8 to 1.0 a very high supply with the 1.0 as maximum supply, according to the suggested 0 to 5 classification by
For the demand analysis, we used the proposed indicators of the INTEK project in Rostock (
Five different indicators were selected, covering different potential demand types of population density [people/100 m²], cultural heritage [-], economy by the land-reference value [€] and the infrastructure sectors [-] (see Table
Sector |
Population |
Cultural Heritage |
Economy |
Infrastructure |
|
Indicator |
Population density [people/100m²] |
Monuments [-] |
Land reference value [€] |
Critical Infrastructure [-] |
Traffic [-] |
Scaling |
converted into relative scale 0-1: Value of scale divided by maximum of all cells |
1.0: monuments |
converted into relative scale 0-1: Value of scale divided by maximum of all cells |
1.0: hospitals, fire stations, schools, care facility, disabled institutions |
0.6: station, main streets, railway tracks 0.4: streets 0.2: ways |
The actual demand is understood to be the area that has a potential demand or need for flood protection (for instance, by population or economy) and that was actually flooded by the observed event, according to our hydrological simulation. This means that, if an area is flooded and has a potential demand, this turns into an actual demand. Accordingly, this indicator resulted from the intersection of potential demand and flood hazard (Fig.
In order to quantify, map and visualise the mismatches between actual demand and supply, a FRES budget map was created. The budget resulted from the spatial overlay and difference of the total supply and the actual demand of FRES (see Fig.
The main results are analysis and maps for all three FRES-components – supply, demand and budget. The supply map includes the two indicators of soil water and interception, as well as the total supply (see chapter FRES supply). The demand map shows the potential and actual demand, as well as the flood hazard (see chapter FRES demand). The mismatch of supply and actual demand is displayed in the budget map and is analysed in the following chapter 'FRES budget'. It indicates the unmet demand and benefitting/ supply surplus areas. In addition, the table in the Suppl. material
The mean stored water depth of soils was highest on forest, woodlands and green areas land uses with 2.5 mm (see Suppl. material
Over the entire study area, the surface water depth was higher than the supply by interception and soil. The surface flooding reached from ~ 16 mm on settlements, forests and urban dense areas and up to 90 mm on terrain depressions of water land-uses. Traffic areas were flooded with an average water depth of 30 mm.
The indicators for the FRES supply were the water depth of interception and soil. Each indicator and the total supply, which resulted from the sum of interception and soil water depth, were converted into relative scales from 0 to 1, respectively. The maximum interception depth was 7 mm, soil water depth was 3 mm and the total supply in one cell reached a maximum of 10 mm. Fig.
In general, green areas, such as forests, parks and woodlands, had the highest supply. A very high supply was provided by forests with ~ 0.9. Both the supply through interception and through the soil were very high. The supply on green areas and woodland were mainly provided by soil (very high), while the supply by interception on this areas was low (0.2 to 0.3).
Traffic areas had a low supply, which resulted from a low supply by interception (0.3), while the supply by soil was very low. The other sealed areas had a very low supply, which was also due to the very low supply by interception (0.15).
Over the entire area, the interception supply was low (~ 0.3), the soil supply medium (~ 0.5) and the total supply low (~ 0.3) on a relative scale. However, the results also showed, if a canopy were present, the absolute amount of interception storage was higher than the soil storage.
The demand components of potential demand, flood hazard and actual demand are displayed in Fig.
The potential demand (Fig.
The flood hazard (Fig.
The actual demand (Fig.
The budget map (Fig.
Greened spaces, such as forests, green areas and woodlands, had an average supply surplus. Thereby, forests had a very high supply and low actual demand, which resulted in a medium supply surplus (~ 0.55). Green areas and woodlands were exposed to a medium supply (> 0.4) and a low actual demand. On average, there was a very low supply surplus on green areas (0.1) and a low supply surplus (~ 0.2) for woodlands.
On the contrary, sealed spaces were indicated with an unmet demand. While the supply was low on nearly all land-uses, the actual demand was low or medium (traffic areas, urban dense areas). This resulted in a very low unmet demand for settlements, industry, traffic areas and urban density areas.
In total, the study area had a low supply and low to medium actual demand. Therefore, the budget was calculated with a very low unmet demand of -0.1.
The results showed local pluvial FRES supply and demand that were quantified and mapped in an exemplary urban area and heavy precipitation event. These results indicated that vegetation plays an important part in flood regulation, if the soils are saturated or sealed and, thus, should be considered in urban FRES assessments. The intercepted values of maximum 7 mm are comparable to the measurements and model results of other studies (
Over the entire study area, the surface water depth was found to be deeper than the water depth of the total supply. To counteract the high water levels on the surfaces, more storage by ecosystems can be provided (e.g. infiltration or interception). Since we investigated a single event, even changing intial conditions, such as a lower saturated depth, could lead to more supply capacity. Furthermore, we did not consider the sewerage system in the hydrological modelling, as the focus was on the possible contribution of natural ecosystems to the regulation of pluvial floods. Neglecting the drainage system is a limitation, which might overestimate the actual demand, but does not influence the FRES supply. At this point, we accept this limitation, since the study focuses on rare events of high intensities that potentially cause pluvial flooding that typically exceeds the capacity of urban drainage systems - as observed during the considered event in 2011 (
By using hydrological models for FRES-assessment, it is possible to take different rainfall events and initial conditions into account. Thus, the results of the actual FRES demand and budget analysis are only valid for a specific event. However, this also gives the opportunity to test different scenarios and replicate real events with different initial conditions to get a bandwith of possible impacts. Designed events and ideal (drier) soil conditions, for example, could lead to an improbably high supply and are far from reality. The total capacity would be determined rather than the actual used flood regulating capacity available to the population .
CMF fills the gap of flexible and modular hydrological modelling structure that the community is asking for (
Besides the vegetation-related hydrological processes, we considered the infiltration with the Green-Ampt approach and the kinematic surface run-off. For infiltration, we only looked at the upper soil layer and did not consider percolation (water flow from the unsaturated to the saturated soil zone), ( because of a time delay, most of the water infiltrates into the upper layer during an short rainfall event
The advantage of hydrological modelling, especially of LEAFlood, for valuing FRES, is its flexibility. Depending on the available data and the research question, the complexity of the model can be adapted and extended by the processes, input data or resolution. For instance, we used a simple soil approach with regard to the spatial distribution because detailed information about soil texture distribution was unavailable. Urban soils are highly heterogeneous, which is why a dense measurement network is necessary for detailed soil mapping. In addition to the enormous measurement effort, it is difficult to obtain according permissions. Therefore, the existing level of detail is sufficient for the research question and, even with the simpler approach, good conclusions can be drawn about FRES.
A calibration of the ungauged urban study area in Rostock is not possible because of missing field measurements, which is common for urban areas since these are not demarcated catchments on this scale (
The results showed that the interception by vegetation has a large share of the total FRES supply, which is particularly true when the soil is highly saturated. This confirms the statement by
In addition to the common processes of interception and infiltration, smaller landscape and green infrastructure elements, such as green roofs, have great potential to contribute to flood regulation (
Flood regulating demand should not only be roughly estimated by land-use or population density, as it is often used in other studies (
We defined the protected assets of population, economy, cultural and infrastructure as potential demand, by arguing that all vulnerable areas and activities have a demand for flood protection regardless of whether they are actually exposed to the hazard. The actual demand results from the areas of potential demand that would be flooded. Therefore, it must be noted that the actual demand calculated here is only valid for the selected precipitation event and its initial conditions. For a more comprehensive assessment, other possible extreme precipitation events and initial conditions need to be considered.
A mismatch analysis of FRES demand and supply is important to identify priority areas for adaptation with an unmet demand, which is necessary, for instance, for adaptation planning (
Furthermore, the budget analysis can be strongly influenced by site-specific and short-term aspects. This is particularly true for this study, where event-based modelling was used. The results are valid for a one-hour event with a total amount of 22 mm with a saturated soil, as has already been observed in Rostock. A less saturated soil at the beginning could increase the FRES supply and consequently the supply surplus in green-related areas. However, no improvement is expected for areas with a high unmet demand since these areas are mainly highly sealed. Whereas, with a prolonged or more intense rainfall, the proportion of land with unmet demand is expected to increase.
We would like to emphasise here that the results of the mismatch analysis do not constitute a flood hazard map. Rather, it serves as an input dataset in the FRES analysis and as an indicator of hazard, which in turn, is the output of the modelling. Unlike the Flood Framework Directive (
Since CMF is a modular python package, it is possible to connect it with other models, including models from other disciplines (
In terms of ES research, it is interesting to compare the results obtained in this study with the well-known and frequently used ES matrix method, based on land-use classifications (
It has already been mentioned that demand is multidisciplinary (
So far, we did not consider future climate and land-use scenarios. For urban planning, the method would be an interesting approach to test adaptation measures in terms of their FRES supply functionality under changing climate conditions.
Cities are, in particular, vulnerable to pluvial flood events caused by heavy precipitation. The prediction of FRES on the city scale is an important tool for flood risk assessment to value the contribution of natural (or near-natural) structures and processes to flood regulation and the benefits for demanding factors, such as society, economy or culture. This study proposes an approach for the quantification of FRES supply, demand and their mismatches in urban areas for short-term heavy precipitation events.
FRES supply was estimated by the soil water and canopy interception, based on the LEAFlood model. It could be shown that interception has a high FRES supply in soil water saturated or sealed areas and is, therefore, an important indicator to be considered in FRES assessment on the urban scale. Green spaces, such as forests or parks, had high FRES supply, whereas sealed areas had a low FRES supply.
We argued that an area used in a certain way has a demand for protection against pluvial flooding, since pluvial flooding can happen everywhere. Therefore, the approach to investigate the flood regulating effects cannot be reduced only to single areas which are actually flooded. With the terminology of potential and actual demand, we could consider a general demand that is always asked by different sectors of society and economy and the demand for single flood events, when the potential demand turned into an actual demand. The potential demand was conducted by considering multiple actors of economy, population, infrastructure, critical infrastructure and monuments. In our analysis, monuments and critical infrastructure had a high impact on the total potential demand. Therefore, a demand analysis, solely based on land use classification, is not sufficient. Afterwards, the actual demand was defined by a function of the hazard and potential demand. The subsequent budget analysis of supply and actual demand indicated unmet demand for the entire study area. While greened areas had a supply surplus, sealed areas and, in particular, industry, urban dense areas and traffic areas had an unmet demand. Even the existing street trees could not compensate the unmet demand over traffic land-uses. In general, the water retained by the soil and interception, which represented the supply, was smaller over the entire study area than the surface water, which was the indicator for the hazard.
The visualisation of mismatches in maps with indicators is an essential tool for urban planning and flood risk management. Compared to the flood risk approach, the concept of ES for flood regulation has the advantage that also the supply side of flood risk reduction is considered. In the case of ecosystem-based adaptation, the ES concept can estimate the contributions of nature to flood regulation and their benefits to the socio-economic system. This can support city planners in making sustainable decisions in order to avoid long-term consequences of ecosystem loss.
For urban areas, a catchment area-based model is not sufficient, because of the spatial and temporal scale, as well as the involved considered processes. Instead of the catchment scale, it is more important to be able to identify the flood regulation supply capacities of single landscape elements and to include vegetation related hydrological processes, which are both considered by LEAFlood. In general, ungauged urban areas face the problem of lack of data for calibration and validation for hydrological models. However, previous studies could prove the model performance of LEAFlood in urban areas regarding run-off and interception. Therefore, it can be classified as a suitable hydrological model for quantifying and assessing FRES on urban scale for heavy precipitation events.
The authors would like to thank Philipp Kraft for his technical support on questions concerning the model construction. Many thanks also go to Claudia Dworczyk for discussions on the methodology and to Angie Faust for checking the language of the manuscript. Data on topology was kindly provided by the City of Rostock and the State of Mecklenburg-Vorpommern.
Literature review: Wübbelmann
Study design: Wübbelmann, Bouwer, Burkhard
Modelling: Wübbelmann, Förster
Writing: Wübbelmann
Review: Bouwer, Förster, Bender, Burkhard
Supervision: Bender, Burkhard
The authors declare no conflict of interest.