During the last decade, water sensitive urban design (WSUD) has become more and more accepted. However, there is not any simple tool or option available to evaluate the influence of these measures on the local water balance. To counteract the impact of new settlements, planners focus on mitigating increases in runoff through installation of infiltration systems. This leads to an increasing non-natural groundwater recharge and decreased evapotranspiration. Simple software tools which evaluate or simulate the effect of WSUD on the local water balance are still needed. The authors developed a tool named WABILA (Wasserbilanz) that could support planners for optimal WSUD. WABILA is an easy-to-use planning tool that is based on simplified regression functions for established measures and land covers. Results show that WSUD has to be site-specific, based on climate conditions and the natural water balance.

INTRODUCTION

If surfaces are sealed, runoff from urban areas increases while groundwater recharge and evapotranspiration are reduced significantly (e.g. Fletcher et al. 2013). As a result, the hydrological regime, the morphology and ecology of urban streams, the groundwater level, as well as the urban climate, are affected. Therefore, water sensitive urban design (WSUD) has become more and more accepted as a general principle in urban drainage planning during the last decade (Fletcher et al. 2014). WSUD focuses on finding site-specific planning solutions which take the local climate into account and restore the natural hydrological cycle. Further advantages are that WSUD (green infrastructure) requires smaller sewer systems which can result in lower life cycle costs than conventional urban drainage systems (grey infrastructure; US EPA 2013). Furthermore, by reducing surface runoff, the risk of urban flooding is decreased (Qin et al. 2013).

Actually, stormwater control measures (SCMs) used for WSUD often focus on reducing surface runoff because they are primarily designed to increase infiltration. This leads to a non-natural groundwater recharge and may cause groundwater mounding problems while evapotranspiration is still low (e.g. Goebel et al. 2004, 2007; Fletcher et al. 2013). To restore the hydrological cycle, surface runoff, groundwater recharge and actual evapotranspiration have to be taken into account. Among others, Messer (2013) has demonstrated that the natural water balance depends on land use, topography, soil, and climate conditions. Thus, to reduce the influence of sealing and to re-establish pre-development hydrology, WSUD has to take these site-specific conditions into consideration.

These challenges of WSUD have motivated researchers and practitioners to develop a variety of simulation and decision support tools (Lerer et al. 2015). Recent reviews by Jayasooriya & Ng (2014) and Lerer et al. (2015) focus on ‘Tools for Modelling of Stormwater Management and Economics of Green Infrastructure Practices’ and on ‘A Mapping of Tools for Informing Water Sensitive Urban Design Planning Decisions’, respectively. The tools can be grouped (cp. Lerer et al. 2015) into models which (i) quantify the effects of measures (e.g. Stormwater Management Model 5.0 (SWMM); Rossman 2010), (ii) select the best location for measures (e.g. DayWater; Jin et al. 2008) or (iii) select a most effective measure (e.g. SUSTAIN; Lee et al. 2012).

More advanced tools like UWOT (Makropoulos et al. 2008), SUDSLOC (Viavattene & Ellis 2013), UrbanBeats (Bach et al. 2015), or Dance4Water (Urich et al. 2013) are combined tools for WSUD that do not only focus on water quantity but also consider economic, human, and bio-physical aspects.

In Germany, there are several technical guidelines and policy requirements which necessitate strategies for storm-water management (e.g. Wasserhaushaltgesetz (WHG) 2009). The water policy in Germany (WHG 2009) puts the focus on SCMs such as infiltration, retention and detention of storm runoff instead of conventional sewer systems. The developed WABILA model (Wasserbilanzmodell) is a tool to compare the water balance of individual plots or development areas to pre-development or natural conditions. It specifically addresses the needs of the new German guideline DWA-A 102 (2015) for urban stormwater management, which will emphasize, amongst other topics, WSUD for new and retrofitted developments and aims to keep the hydrological cycle close to the pre-developed or natural condition. Especially in the early stages of urban planning, detailed data are not available, but the basic concepts of WSUD are fixed in city planning for smaller catchment areas. For these concepts, a simple water balance tool is needed that already operates with a few input parameters and mean annual climate data. None of the above mentioned tools is able to work with these limited input data.

It is, therefore, the aim of this study to develop a simple model using regression functions in order to estimate the average annual water balance of different surfaces and SCMs, respectively. The model has to be representative for the broad range of different site and climate conditions in Germany and similar countries.

METHODOLOGY

WABILA model

WABILA concept

The fundamental WABILA idea is to have a good tool that can be used to compare the water balance of individual plots given different levels of development to pre-development or natural conditions. It supports WSUD and the selection of SCMs to meet the requirements of the local water cycle in the early planning phase. WABILA needs only a few, generally available input parameters and describes site-specifically the mean annual values of surface runoff, groundwater recharge and evapotranspiration as portions of the rainfall. These partitioning factors are described by system functions for different impervious areas (roofs, porous pavements, etc.), SCMs (infiltration areas, green roofs, stormwater harvesting, etc.) and basic local conditions (soil, mean annual rainfall, mean annual evapotranspiration). The system functions calculate the partitioning factors as mean annual values.

As climate input data only mean annual values of precipitation and potential evaporation are needed, which can be taken from ‘Hydrologischer Atlas von Deutschland’ (Hydrological Atlas of Germany) (HAD; http://geoportal.bafg.de/mapapps/resources/apps/HAD/index.html). The HAD has a 1 km² grid and the grid values are calculated by Separate word as inter-polation of point measurements. For the calculation of the partitioning factors for infiltration measures, the hydraulic conductivity and the soil characteristics are required as input parameters. Further input parameters of the system functions are explained in the section ‘system functions’.

The water balance can be written as: 
formula
1
 
formula
2
 
formula
3
with P precipitation (mm/a), ETa actual evapotranspiration (mm/a), GWN groundwater recharge (mm/a), RD surface runoff (mm/a), v (-) partitioning factor for evapotranspiration, g (-) partitioning factor for groundwater recharge, a (-) partitioning factor for surface runoff.

Climate datapool

For the derivation of system functions, the annual rainfall distribution of Germany is analysed using the HAD. Additionally, the HAD provides data for potential and actual evapotranspiration, total runoff, and groundwater recharge for the anthropogenic environment of the current state which are used as conditions for undeveloped areas (cf. Wessolek et al. 2008; Uhl et al. 2013) in the case that no regional water balance model is available. The HAD data consider the timespan from 1961 to 1990.

In sum, 40 different climate stations (precipitation and evapotranspiration) representing the German climate conditions (Figures 1 and 3) were selected. The duration of the time series varies between seven and 20 years.
Figure 1

Mean annual values of the 40 selected climate stations.

Figure 1

Mean annual values of the 40 selected climate stations.

Process simulation

The effect of WSUD measures on the water balance can be evaluated with urban drainage models (e.g. SWMM; Rossman 2010) or urban soil vegetation atmosphere models (SVAT; cp. Lemonsu et al. 2007). Both types of models require detailed input data, model parameters, and time series for rainfall and evapotranspiration. Following the review of Jayasooriya & Ng (2014), EPA's Stormwater Management Model 5.0 (SWMM) is ‘the most sophisticated tool in modelling stormwater quality, quantity, and GI performance’.

SWMM calculates the water balance of impervious areas with a depression storage and simulates the green roofs or vegetative swales with a soil moisture model. Porous pavements were represented with a low impact development element (LID, column ‘SWMM model’ in Table 1). In the current version, WABILA only simulates the water balance of impervious surfaces and SCMs (Table 1). Interaction between impervious and pervious areas is neglected. The model also does not consider the groundwater level or aquifer.

Table 1

| Model parameters for different surface covers and WSUD measures

   Partitioning factor
 
    
      Runoff Groundwater recharge Evapotranspiration     
Type ID Specification SWMM model Parameter sources 
Roof steep roof, flat roof, gravel roof, retention roof f(P, ETp, Sp) 1-a subcatchment with 100% imperviousness Rossman (2010)  
green roof f(P, ETp, h, kf, WKmax1-a LID – bio-retention cell FLL (2008)  
Road, sidewalk asphalt, pavement f(P, ETp, Sp) 1-a subcatchment with 100% imperviousness Rossman (2010)  
porous pavements f(P, ETp, Sp, FA, h) f(P, ETp, Sp, FA, kf, h) f(P, ETp, Sp, FA, kf, WKmax, h) subcatchment and LID bio-retention cell DIN 4220 (2008); Illgen (2009)  
Infiltration infiltration area f(P, BASf(P, ETp, BASf(P, ETp, BASLID – bio-retention cell DWA-A 138 (2005)  
infiltration swale 1-gA-vA f(P, ETp, kf, BAS,Mf(P, ETp, kf, BAS,MLID – bio-retention cell DWA-A 138 (2005)  
swale-trench-element f(P, kf, BAS,Mf(P, ETp, kf, BAS,Mf(P, ETp, kf, BAS,MLID – bio-retention cell DWA-A 138 (2005)  
swale-trench-system f(P, ETp, kf, BAS,M, qdrf(P, ETp, kf, BAS,Mf(P, ETp, kf, BAS,M, qdrLID – bio-retention cell DWA-A 138 (2005)  
Rainwater harvesting  f(P, ETp, VSp, VBr, VBw)  f(P, ETp, VSp, VBr, VBw) storage with controlled pumps DIN 1989 (2002)  
   Partitioning factor
 
    
      Runoff Groundwater recharge Evapotranspiration     
Type ID Specification SWMM model Parameter sources 
Roof steep roof, flat roof, gravel roof, retention roof f(P, ETp, Sp) 1-a subcatchment with 100% imperviousness Rossman (2010)  
green roof f(P, ETp, h, kf, WKmax1-a LID – bio-retention cell FLL (2008)  
Road, sidewalk asphalt, pavement f(P, ETp, Sp) 1-a subcatchment with 100% imperviousness Rossman (2010)  
porous pavements f(P, ETp, Sp, FA, h) f(P, ETp, Sp, FA, kf, h) f(P, ETp, Sp, FA, kf, WKmax, h) subcatchment and LID bio-retention cell DIN 4220 (2008); Illgen (2009)  
Infiltration infiltration area f(P, BASf(P, ETp, BASf(P, ETp, BASLID – bio-retention cell DWA-A 138 (2005)  
infiltration swale 1-gA-vA f(P, ETp, kf, BAS,Mf(P, ETp, kf, BAS,MLID – bio-retention cell DWA-A 138 (2005)  
swale-trench-element f(P, kf, BAS,Mf(P, ETp, kf, BAS,Mf(P, ETp, kf, BAS,MLID – bio-retention cell DWA-A 138 (2005)  
swale-trench-system f(P, ETp, kf, BAS,M, qdrf(P, ETp, kf, BAS,Mf(P, ETp, kf, BAS,M, qdrLID – bio-retention cell DWA-A 138 (2005)  
Rainwater harvesting  f(P, ETp, VSp, VBr, VBw)  f(P, ETp, VSp, VBr, VBw) storage with controlled pumps DIN 1989 (2002)  

P precipitation (500–1,700 mm/a); ETP potential evapotranspiration (450–700 mm/a); Sp storage height (1: 0.1–10 mm, 3: 0.6–3 mm, 4: 0.1–4.2 mm); h thickness (2: 40–500 mm, 3: 50–100 mm); kf conductivity (2: 18–100 mm/h, 4: 6–180 mm/h, 6: 14–3,600 mm/h, 7/8: 3.6–36 mm/h); WKmax max. water capacity (0.3–0.6); FA, void space portion (2–30%); BAS relative size of infiltration area (3.6–111%); BAS,M relative size of swale area (6: 2.3–26%, 7: 3.4–27%); qDr throttle runoff rate (1–10 l/s/ha); VSp specific storage volume (10–200 mm); VBr specific water demand (0–5 mm/d); VBw specific irrigation area (0–5).

First simulations identified sensitive parameters for each simulation model. For these sensitive parameters, a range was defined based on literature, manufacturers’ instructions and technical guidelines (cp. sources in Table 1).

For those ranges, 1,000 parameter combinations were generated using Latin Hypercube or Monte Carlo Sampling. For each of the defined surface covers and WSUD measures, 40,000 long-term simulations with durations between seven and 20 years were carried out (1,000 parameter combinations and 40 climate stations). Based on mean annual simulation results, ‘partitioning factors’ for surface runoff (a), groundwater recharge (g) and evapotranspiration (v) were calculated. They are defined as the entire proportion of the mean annual precipitation, by normalizing them with the annual precipitation. The sum of the three partitioning factors a, g and v is 1 (cf. Equation (3)).

System functions

The partitioning factors are expressed by non-linear multiple regression functions including the main influencing variables. These system functions were derived from the data-pool of SWMM model results by using the statistic software tool R and R Studio. The quality of the regression function is determined by using QQ-plots, distribution of residuals and, R2. Plausibility checks of the coefficients and the functions were undertaken.

The residuals between the system functions and the SWMM simulations results are normally distributed. R² values range from 0.2 to 0.99 with a median of 0.93. Low R² values of the regression function for the partitioning factor for runoff (a) are present, if the 40,000 SWMM results show very small ranges, e.g. between 0.01 to 0.05. In this case, absolute deviations were analysed because relative deviations are very high (e.g. value of 0.03 is predicted by a regression function and the SWMM result is 0.01). Therefore, the absolute deviations are much more informative than relative values.

The available system functions for different surface covers and WSUD measures are listed in Table 1. Default values for the input variables of the regression functions are supporting conceptual planning in early project phases. Mean annual precipitation, potential evapotranspiration and soil conductivity have to be inserted as site-specific parameters.

Software tool WABILA

Finally, the regression functions were applied in the software tool WABILA (Figure 2). In WABILA, the water balance of several planning scenarios is directly compared to the water balance of the existing conditions. The water balance of the natural conditions can be taken from HAD or calculated using the tool GWNeu (Messer 2013) which is included in the WABILA software.
Figure 2

Screenshot of the WABILA software for WSUD.

Figure 2

Screenshot of the WABILA software for WSUD.

In the future, WABILA will also be available as an ESRI ArcMap Add-In. WABILA will be used as part of the new German planning guideline DWA-A 102 (2015) for stormwater management. An English version is currently under development. WABILA can be made available upon request by contacting the first author.

Case studies

Locations and climate conditions

Artificial case studies are given for two different development scenarios (residential area and business park) in three locations in Germany (Figure 3). All six scenarios are development areas built on pervious surfaces. By using the three locations, the impact of climate conditions on planning WSUD is identified. The first scenario (‘residential area’) contains single-family-houses with steep roofs and 40% covered area. The ‘business park’ scenario contains flat roofs and 80% covered area. Climate conditions are listed in Table 2. All three locations have identical land use but vary in climate conditions and soil conductivity. Meteorologic conditions, as well as the basic data for calculation, are taken from the HAD (e.g. Uhl et al. 2013).
Figure 3

Mean annual precipitation in Germany and locations of the case studies (precipitation data source: HAD).

Figure 3

Mean annual precipitation in Germany and locations of the case studies (precipitation data source: HAD).

Table 2

Climate conditions and water balance for non-urban areas at three German locations

  ETp Conductivity 
Location mm/a mm/a mm/h – – – 
Berlin 505 752 14 0.13 0.23 0.64 
Muenster 807 461 23 0.20 0.24 0.56 
Rosenheim 1,120 630 19 0.36 0.23 0.41 
  ETp Conductivity 
Location mm/a mm/a mm/h – – – 
Berlin 505 752 14 0.13 0.23 0.64 
Muenster 807 461 23 0.20 0.24 0.56 
Rosenheim 1,120 630 19 0.36 0.23 0.41 

P precipitation; ETp potential evapotranspiration.

Development

The areas were distributed in an interior development and in a net building area (Table 3). For the residential areas, three different stormwater management scenarios were developed and calculated with WABILA: (i) conventional separate sewer system, (ii) infiltration of entire storm runoff with infiltration swales and (iii) combination of measures to approximate the local water balance (‘optimisation’) for each of the three locations.

Table 3

Distribution of areas for the two case studies

  Residential area
 
Business park
 
Area Surface cover Area (%) Surface cover Area (%) 
Interior development Roads Asphalt Asphalt 
Sidewalks Pavement 5,5 Pavement 
Parking lots Pavement Pavement 
Roadside vegetation Green area Green area 
Net building area Roofs Steep roofs 35 Flat roofs 45 
Paths, access roads Pavement Pavement 
Parking lot, garage, carport Pavement Pavement 
Gardens, green areas Green area 46 Green area 20 
  Residential area
 
Business park
 
Area Surface cover Area (%) Surface cover Area (%) 
Interior development Roads Asphalt Asphalt 
Sidewalks Pavement 5,5 Pavement 
Parking lots Pavement Pavement 
Roadside vegetation Green area Green area 
Net building area Roofs Steep roofs 35 Flat roofs 45 
Paths, access roads Pavement Pavement 
Parking lot, garage, carport Pavement Pavement 
Gardens, green areas Green area 46 Green area 20 

For the business park case study, also three solutions are analysed: (i) conventional separate sewer system, (ii) green roofs for all buildings and (iii) combination of measures to approximate the local water balance (‘optimisation’) for each of the three locations. The surface covers and the selected WSUD measures for the scenario ‘optimisation’ are listed in Table 4. The selected measures are explained in the result section. The optimisation was done in ‘good planning practice approach’ taking into account planning options, legal implications, as well as functional and operational constraints for WSUD tools.

Table 4

WSUD for the scenario ‘optimisation’ for the different areas, locations and case studies

  Residential area
 
Business park
 
  Berlin Rosenheim Muenster Berlin Rosenheim Muenster 
Roads – – – – – – 
Sidewalks ppb ppb ppb ppb – ppb 
Parking lots ppb ppb ppb ppb – ppb 
Roofs rha & stec rha & swale rha & swale green roofs & swale green roofs & swale green roofs & swale 
Paths, access roads ppb ppb ppb – – – 
Parking lot, garage, carport green roofs green roofs green roofs – – – 
  Residential area
 
Business park
 
  Berlin Rosenheim Muenster Berlin Rosenheim Muenster 
Roads – – – – – – 
Sidewalks ppb ppb ppb ppb – ppb 
Parking lots ppb ppb ppb ppb – ppb 
Roofs rha & stec rha & swale rha & swale green roofs & swale green roofs & swale green roofs & swale 
Paths, access roads ppb ppb ppb – – – 
Parking lot, garage, carport green roofs green roofs green roofs – – – 

arh rainwater harvesting (domestic use and irrigation).

bpp pervious pavers.

cste swale trench element.

Finally, the deviation from natural, pre-developed conditions for the partitioning factors a, g and v were calculated for the WABILA results.

All locations and management solutions were simulated with SWMM and WABILA. The comparison of SWMM and WABILA results is used for the validation of the WABILA model. The comparison is carried out by using absolute deviations for the partitioning factors of a, g and v. 
formula
4
 
formula
5
 
formula
6
Absolute deviations are selected, because a, g and v values are ranging from 0 to 1. The presentation of relative deviations is misleading, particularly because of very low values close to 0, since very small absolute deviations result in enormous relative deviations. As validation criteria we have chosen the following levels for the absolute deviations:
  • <0.01: very good

  • 0.01–0.025: good

  • 0.025–0.05: moderate

  • 0.05–0.10: acceptable

  • 0.1: not acceptable

RESULTS

Residential area

For SWMM and WABILA, in sum, nine simulation runs were conducted (three locations and three scenarios). Both models have eight different areas (Table 3) and for scenario ‘optimisation’ different SCMs (Table 4). SWMM and WABILA model results for the three scenarios and three locations are compared in Figure 4. The scenarios ‘conventional’ and ‘infiltration’ have very low absolute deviations in the partitioning factors (less than 0.02). Only the optimisation scenario shows deviations between 0.020 and 0.044 for the locations ‘Rosenheim’ and ‘Berlin’. These results are due to the climate conditions in Rosenheim and Berlin. Compared to the distribution of mean annual precipitations used for the development of WABILA (cp. Figure 1), Berlin and Rosenheim have larger deviations from average German climate conditions (Table 2) which result in slightly higher differences between WABILA and SWMM.
Figure 4

Comparison of SWMM and WABILA results for partitioning factors for a, g, v and e (withdrawal for rainwater harvesting) for the three locations and scenarios.

Figure 4

Comparison of SWMM and WABILA results for partitioning factors for a, g, v and e (withdrawal for rainwater harvesting) for the three locations and scenarios.

Figure 5 presents the deviation of the partitioning factors to the natural water balance of the three locations (Table 2). The different climate conditions of these locations are reflected in the deviations to the natural water balance. Although the measures are identical, the two solutions (conventional and infiltration) lead to different partitioning factors with regard to the locations.
Figure 5

Residential area absolute deviation between partitioning factors for different stormwater management concepts and pre-developed state.

Figure 5

Residential area absolute deviation between partitioning factors for different stormwater management concepts and pre-developed state.

The results after the optimisation indicate that WSUD has to be site-specific. Climate conditions in Berlin, for example, require solutions and measures which primarily increase the evapotranspiration. Compared to Berlin, the WSUD in Rosenheim is simpler. Sealing is less important because the natural water balance already has a high runoff (Table 2). Infiltration measures result in a partitioning factor ‘a’ with a very small deviation ranging between ±0.05. The one-sided treatment results in an increased infiltration. Deviations of the partitioning factor ‘v’ are the same as for the conventional urban drainage system. Because of this fact, an optimisation has to focus on evapotranspiration. As a result, sidewalks, parking lots and access roads are constructed with pervious pavers that increase the infiltration, as well as the evapotranspiration compared to non-permeable pavers (Table 4; see also Starke et al. 2011). The main reason for this is the soil material in the gaps or chambers of the pavers acting as a permeable storage layer. Rainwater harvesting devices for irrigation are implemented in Berlin, as well. Runoff flows to a swale-trench-element where the water is stored and infiltrates slowly. Garages are covered with green roofs. The rainwater harvesting in Rosenheim also substitutes the fresh water import, because stored water is used as service water for toilet flushing and washing clothes. In Muenster, roof runoff is used for irrigation, toilet flushing and washing clothes. Storage runoff infiltrates in vegetative swales. Green roofs are installed on garages. The larger deviation to the natural conditions of v in Muenster compared to the other two locations is based on the smaller potential evapotranspiration (Table 2).

Business park

The results of validation are shown in Figure 6. The deviation of SWMM and WABILA results for the three locations and scenarios is less than 5% even if treatment trains are used, which were not explicitly considered during WABILA development. Very low absolute deviations are reached for the conventional scenario (less than 2%). The deviation of portioning factor g is 0 for the ‘conventional’ and the ‘green roof’ scenarios because these two solutions do not have any infiltration measures or areas which infiltrate. Just for the ‘optimisation’ scenario swales are used in order to reduce surface runoff and increase groundwater recharge. Deviations in Rosenheim and Berlin are comparable to those of the residential area and are larger than for Muenster.
Figure 6

Comparison of SWMM and WABILA results for partitioning factors for a, g, v and e (withdrawal for rainwater harvesting) for the three locations and scenarios.

Figure 6

Comparison of SWMM and WABILA results for partitioning factors for a, g, v and e (withdrawal for rainwater harvesting) for the three locations and scenarios.

Figure 7 demonstrates the results for the three locations and different SCMs. The examples show that the pre-developed state of the water balance is not reached with the measures used. The absolute deviation for the conventional urban drainage system compared to the residential area is, because of higher imperviousness, much higher. Runoff value ‘a’ is between 0.38 and 0.47. Compared to the residential area example, runoff increases while groundwater recharge and evapotranspiration decrease. The influence of green roofs is significant. The runoff is reduced and evapotranspiration rises. For optimisation, it is necessary to install measures such as infiltration trenches that raise the groundwater recharge (cp. Table 4). In Berlin and Muenster, the sidewalks and parking lots are constructed with porous pavers and green roof runoff infiltrates in vegetative swales.
Figure 7

Business park absolute deviation between partitioning factors for different stormwater management concepts and pre-developed state.

Figure 7

Business park absolute deviation between partitioning factors for different stormwater management concepts and pre-developed state.

The flat roofs of the business park enable the installation of green roofs, thus resulting in smaller deviations for v. In sum, the deviation between the optimisation scenario and the pre-developed state is smaller in comparison to the residential area with steep roofs.

DISCUSSION

The water balance of the analysed scenarios showed that surface runoff (factor ‘a’) can be kept very close to the natural water balance by established SCMs like infiltration trenches. The current stormwater management solutions mostly use infiltration and storages which reduce runoff and increase infiltration. However, the results show that this affects very high groundwater recharges at some locations. Infiltration trenches do not increase the evapotranspiration significantly compared to natural ground. Pervious pavers contribute to higher evapotranspiration compared to non-pervious pavers. Green roofs are the most effective measure to raise the evapotranspiration.

The variation of the partitioning factors at different locations highlights the influence of the climate conditions. The developed regression functions allow to calculate the partitioning factors for runoff, infiltration and evapotranspiration for a large variety of WSUD solutions and local climate and soil conditions. The validation results of WABILA show very low deviations to the SWMM model. WABILA was validated by comparing results to the SWMM model. Absolute deviations range between −0.05 and 0.04 with a median of 0. When using the above defined quality criteria for the WABILA validation, 64% reach very good results, 18% good, and 18% moderate results.

The model setup of WABILA needs only a few generally available parameters. The case studies of this paper show in contrast to Jayasooriya & Ng (2014) that process models are not mandatory when focussing on the mean annual water balance. This allows us to integrate the needs of the water balance in the early design process of urban developments.

CONCLUSION

The developed software tool WABILA is a regression-based model for calculating the urban water balance with only a few, generally available parameters. The tool is designed to support planners at an early stage of city and water management planning. By using mean annual precipitation and potential evapotranspiration, it is possible to consider site-specific climatic effects which lead to different SCMs to meet the requirements of the local water cycle.

The case studies point out that it is less complicated to ensure groundwater recharge and decrease runoff to natural rates but it is still difficult to restore evapotranspiration to natural rates. New innovative technologies are necessary to raise the evapotranspiration. These technologies will be integrated in an ongoing research project.

ACKNOWLEDGEMENTS

The research work and software development are part of the joint research project ‘Die Stadt als hydrologisches System im Wandel’ (SAMUWA) and is supported by the German Federal Ministry of Education and Research (BMBF, FKZ 033L071J, Fördermaßnahme INIS, Förderschwerpunkt NAWAM). We would like to thank the two anonymous reviewers for their valuable comments.

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