StormTac Web is a stormwater and recipient (receiving water) model. It is today, after the start of operation in 2000 and with continuous updates, a fully functioning Web application. The model uses input data forms and presents results in a flowchart and in automatically generated reports.
The model is used as a tool for action planning in urban water management, and is suitable for water quantity and quality calculations within watersheds (catchment areas). It integrates processes of runoff, transport, recipient, treatment and flow detention.
Unique model properties
The unique properties of the model especially refer to the aspects below:
• It is simple to use and consists of an overall system presentation from a flowchart of the entire watershed system (using clickable boxes and input forms in the flowchart). A changed input from the flowchart results in presented changed outputs within the flowchart (watershed system).
• The parameters and methods are continuously being updated with more input data.
• It requires little input data and no large manual or comprehensive education (help notes are included in the file), however a short manual that let you begin quickly (“within one hour”) with the calculations is included.
• It integrates watershed and runoff properties with treatment/detention facilities and impacts on receiving waters; all in one model.
• It includes databases with continuously updated precipitation data, runoff coefficients, concentration data and reduction efficiencies.
• It includes a fine specification of both urban and rural land uses (>100 land uses, increasing with new updates) and a large set of nutrients and pollutants (around 80 pollutants, among them substances included in the WFD, the number of pollutants increasing with new updates).
The main methodology has been reviewed internationally through scientific papers and a doctoral thesis. The methods are described in more details in documents and publications presented under Downloads.
The model has been developed to automate the calculations by using land use specific standard values. It is best suited for long-term predictions. Site specific yearly precipitation data and rain intensities can be used.
Runoff water flow is calculated from precipitation data and land use specific runoff coefficients and areas. Pollutant load rate is quantified from calculated flow and from standard concentrations.
The standard concentrations are estimated empirically from a large set of flow proportional field sampling data, which contributes to their general applicability. These are tabled as standard, minimum and maximum values. The data are available in StormTac database and can be downloaded under Downloads.
Base flow, base flow concentrations and loads are calculated using specific coefficients (infiltration rates and coefficients for leakage/connection into ditches, lakes and stormwater sewers) and standard concentrations for base flow (different for different land uses, from measured base flow concentrations). As is the case with standard concentrations and runoff coefficients for runoff, the base flow coefficients and concentration data can also be changed by the user. These data are available in StormTac database and can be downloaded under Downloads.
StormTac Web includes a large amount of sub models and equations for the design of different stormwater facilities. The user can choose between a relatively detailed or a quick and simple design. The resulted dimensions by using different methods and by changing parameter values can easily be reviewed and compared. Examples of included design parameters are runoff coefficients, land use areas, facility permanent water depth, water depth of detention volume, slope, design rain depth, outflow, emptying time and reduction efficiency.
The design methods have been employed for a large number of case studies from pre-studies to final detailed construction drawings.
The employed equations are presented in the flowchart when hovering the mouse over the presented data.
The method used in StormTac Web to calculate the annual pollution load from catchments is based on the product of the annual pollutant concentrations of various land use and annual flow, where the annual flow in turn is calculated from the product of the annual rainfall, area and volume runoff coefficient. The method allows the direct calibration against the flow and concentration, and it is peer reviewed (Larm, 2000). Examples of other models which basically use the same method are SWMM, Storm, Mike and MUSIC.
Observations including Huber (1980), EPA (1983) and Driscoll et al. (1989) usually show that the stormwater concentration is poorly or not at all correlated with stormwater flow or stormwater volume, which indicates that it is sufficient to assume that concentration is constant, according to Novotny (1995). As the pollution load is the product of concentration and flow, load is normally well correlated with the flow regardless of whether the concentration is correlated to flow or not. If the load is linearly proportional to the flow, the assumption that the concentration is constant is valid and so-called “standard concentrations” from different land use can be used. If not, you need to use some kind of function between the concentration and flow (Novotny, 1995). A large number of data from river basins in the United States in the 1980s were used and the pollution load was calculated in different models. The two most significant variables for the calculation proved to be the total annual rainfall and basin area. For some of the models were also among other variables impermeable area and land use significant (Novotny, 1995; Sing, 1995).
Encouragingly, rainfall and runoff functions show good potential for predicting pollutant loads (Francey, 2010).
Required input data
The model requires very little input data. Watershed area (ha) per land use (e.g. residential area, roads and woodland) is the only obligatory input data. Information on the traffic intensity (vehicles/day) is needed if studying the loads from larger roads within the catchment area. The area and volume of the receiving water are needed for estimating allowable loads. The included databases help to make more accurate analyses by letting you change other input data such as precipitation, runoff coefficients and water depths or slopes of facilities.
The model parameters can be calibrated to measured data to ensure that site specific conditions are being considered. In such cases further input data consist of measured flow, precipitation, rain intensity and sampled concentration (mg/l or μg/l) in stormwater, base flow and/or the receiving water.
An increased urbanization and climate effects may cause an increasing number of floods. StormTac Web can calculate the capacity of the transport system and required detention volumes for the design rain return time and rain duration, including the implementation of climate factors.
StormTac Web quantity calculations include:
• The quantification of yearly average water flows (yearly runoff volumes of stormwater, base flow and groundwater) and runoff flows during average rain events.
• Calculation of design flows for different return times, including climate factors.
• Design of stormwater transport systems, e.g. sewers, ditches and channels.
• Calculation of the flow capacity for new designed transport systems, as well as for existing systems.
• Design of stormwater flow detention facilities, e.g. dry/wet ponds and detention basins.
Metals and nutrients are examples of pollutants in stormwater that may cause toxic and eutrophic effects in the receiving waters. StormTac Web is the tool that can be of great use in the development of a more sustainable stormwater management.
StormTac Web can be used as a simple-to-use forecast tool (and as such requiring little input data) for water quality and Action plans for stormwater and surface water, e.g. to be used within the EU Water Framework Directive (WFD). It includes a large number (>80) of substances, of which several are included in the WFD.
StormTac Web quality calculations include:
• The quantification of yearly average pollutant concentrations and loads in the discharge points and from different land uses.
• Comparison of measured concentration data to calculated values.
• Identification of the largest pollutant sources and discharge locations to a recipient, presenting loads from different land uses and loads from different materials (such as copper roofs) if these specific areas have been set up as input.
• Presentation of data from up to 99 sub watershed areas in each project, to be used in e.g. Action plans for stormwater management in a whole municipality or for different lakes or water courses.
• Design of stormwater treatment facilities (e.g. areas and volumes of wet ponds, constructed wetlands, biofilters, swales, ditches, underground filter basins, underground detention basins and filter strips) regarding used criteria for stormwater concentrations in the discharges and/or allowable loads and surface water quality criteria.
• Calculation of treatment reduction efficiencies (% or in- and outlet concentrations and loads) for designed or existing facilities for site specific and more reliable calculations, such as the effects of inlet and outlet concentrations, the share of water vegetation, flow detention, hydraulic efficiency (length:width ratio etc.) and temperature.
• Setting up water and mass balances for receiving waters (lakes, sea bays and water courses), including calculation of net internal loading from the sediments (kg/year) or net sedimentation load to the sediments (kg/year).
• Calculation of required treatment load to reach allowable (acceptable) loads to the receiving waters, considering water quality criteria in the receiving waters (μg/l), as those stated in the WFD.
• Calculation of the new concentrations in the receiving water after reduced load after a designed treatment facility or after changed land use in the watershed area, e.g. after a planned residential area on an existing woodland area. Comparison to water quality criteria, presenting need for more treatment (larger facility, different facilities or more facilities in other sub watershed areas).
Land use specific concentrations
Pollutant calculations in StormTac Web is based on land use specific concentrations. The normal approach to classify urban sites for estimating stormwater characteristics is based on land use. This approach is generally accepted because it is related to the activity in the watershed, plus many site features are generally consistent within each land use. Two drainage areas with the same size, percentage of imperviousness, ground slope, sampling methods, and stormwater controls will produce different stormwater concentrations if the main activity in one watershed is for example an automobile manufacturing facility (industrial land use) while the other is for example a shopping centre (commercial land use). Previous studies indicated that there are significant differences in stormwater constituents for different land use categories (Pitt et al., 2004). One question to be addressed here is however if there is a different classification method that better describes stormwater quality, possibly by also considering such factors as geographical area, season, percentage of imperviousness, watershed area, type of conveyance, controls in the watershed, sampling method, and type of sample compositing, and possible interactions between these factors. Maestre (2005b) has shown that ignoring the non-detected observations can adversely affect the mean, median and standard deviations of the dataset (Maestre and Pitt, 2005).
The Nationwide Urban Runoff Program (NURP) (EPA, 1983) concluded that concentrations for different land uses were not significantly different, so all their data were pooled into a single category. The National Stormwater Quality Database (NSQD, version 1.1, 2004) is unique in that detailed descriptions of the test areas and sampling conditions are being tabulated. This project also involved extensive quality assurance/quality control evaluations of these data; and performing statistical analyses and summaries of these data. The very large number of samples represented in the NSQD resulted in statistically significant differences of land use concentrations being identified. The NSQD is much more representative of a broader range of land uses, while almost all of the NURP data was obtained from residential areas (Pitt and Maestre, 2005). Statistical analyses found significant differences for land use categories for all pollutants. This is notable because National Urban Runoff Program (NURP) findings showed no significant differences in urban runoff concentrations as a function of common urban land uses (EPA, 1983), likely because they had few data from non-residential areas.
The National Stormwater Quality Database (NSQD) is an urban stormwater runoff characterization (quality) database developed under the direction of Dr. Robert Pitt, P.E., of the University of Alabama and the Center for Watershed Protection, under support from the U.S. Environmental Protection Agency. Originally released in 2001, followed by several updates by Dr. Pitt and Dr. Alexander Maestre (also at University of Alabama), it has moved to a companion project to the International Stormwater BMP Database. The updated database (Pitt, 2015) includes data from the databases NURP, BMP Database and NSQD. It consists of compiled measured data from 10 000 events throughout the US. Mean data from the automatic flow proportional samples from the updated database have been calculated for the different land uses and have been compiled in StormTac database, used among other studies for estimating the land use specific standard concentrations in StormTac Web.
So the conclusion is that many of the constituents do have significant concentration differences by land uses, shown by analysing the large set of data from the NSQD database. Statistical ANOVA analyses for all land use categories (using SYSTAT) found significant differences for land use categories for all pollutants.
The open space COD concentrations are the lowest, and the freeway COD concentrations are the largest for most of the data range.
Total Kjeldahl Nitrogen (TKN), copper, lead, and zinc observations are lowest for open space areas, as for most constituents (Pitt and Maestre, 2005).
Freeway locations generally had the highest median values, except for phosphorus, nitrates, fecal coliforms, and zinc.
Industrial and institutional sites had the highest reported zinc concentrations (Pitt, Maestre and Morquecho, 2004).
One of the conclusions of the final NURP report was that the event mean concentrations (EMCs) of stormwater constituents were described by lognormal distributions. This finding has been re-evaluated, with the conclusion that not all stormwater constituents were adequately described by lognormal distributions (Van Buren, 1997; Beherra, 2000). Most of the stormwater constituents, however, can be assumed to follow a lognormal distribution with little error (Maestre and Pitt, 2004).
Description and use of standard concentrations
Standard concentration data can be downloaded, and the corresponding file presents standard, minimum and maximum concentrations for different urban and rural land uses. The standard concentrations should only be used when the stormwater pollutant load from the studied land use is considered to be of average quantity, else values closer to the presented min- or max-values should be used. The background colors in the tables indicate the level of uncertainty, based on the number of data values and their uncertainties.
Observe that when using runoff coefficients and standard concentrations for runoff, only the runoff (stormwater) part is calculated. In StormTac Web the base flow part is also calculated, see method description above and a separate table for base flow concentrations in the same data file.
The standard concentrations for each land use in StormTac Web are mainly based on long-term flow proportional sampling and refer to annual average. References for these samplings are compiled in the StormTac data base.
To assemble a standard concentration an overall assessment is made of statistical data, time trends, calibrations based on case studies and comparisons of data from similar land uses. A standard concentration is therefore not always representing a median value from the flow proportional samplings presented in StormTac database. Some of the references presented in the database consists of several case studies, whereby these references may have been weighted higher compared to other references. For some land uses, no flow proportional sampling is available. In these cases, the standard concentration values are partly based on calibration against case studies and/or balanced against other equivalent land uses where credible data is available.
For some few specific land uses, the only data available is from grab samples. Grab samples are not comprehensive and often tend to underestimate the concentration. This is considered when using such data. See below under the headline “Comparison between calculated and concentration data” for the differences between grab samples and flow proportional sampling.
The standard concentrations are updated continuously. When new reliable data for a land use is implemented in the database, data for other comparable land uses is being revaluated. The standard concentrations are also calibrated to consider different time trends, see below. Therefore, in the StormTac data base, the standard values are not always a median value based on flow proportional sampling but may be a combination of the above described methodology.
Use of standard concentrations
The calculations can be performed for both small and large areas, e.g. different sub areas in a multi-family housing area (e.g. roof, courtyard, local street, parking and park area) or for a whole municipality or a whole river basin divided into sub-basin areas where the areas consist of entire residential areas, forest areas, thoroughfares, etc. Which kind of land use characterization to use depends on the substances to be investigated and the amount of data available for these from different land use in the area, and the purpose for the calculations.
If you only calculate the pollutant load from a forest area to be exploited into a multi-family residential area, it is recommended to calculate for the entire forest area before exploitation and then calculate for the entire multi-family area. If the stormwater from the entire multi-family housing area is planned to be led to a stormwater treatment facility downstream, it is also recommended not to divide the area into sub areas.
If you need to calculate the load for different local measures in the multi-family housing area, you need to divide the area into different areas that are led to different local facilities (PMBs, SUDs etc), and in areas where the stormwater is not planned to be treated.
For detailed calculations, site-specific standard concentrations should be used where values are adjusted between the default standard, minimum and maximum values. StormTac Web contains factors (0-10) that will be used to calculate land use specific concentrations closer to minimum or maximum values in the database, depending on the site-specific conditions. A factor 5 indicates normal conditions for the land use, while a factor <5 indicates that the levels are reduced towards the minimum value and a factor> 5 towards the maximum value in the database. Copper roofs in a residential area can increase the copper concentration closer to the maximum value. For more densely populated residential areas and more polluting industrial areas, values closer to the maximum are also used. In areas where it is planned to limit the use of copper and zinc as building materials in e.g. roofs, hangers, dumpers, rails and lamp posts, a lower factor of e.g. factor 3 can be used.
StormTac Web also has features that describe pollutant content as a function of traffic intensity, indicating the traffic intensity for the transit routes to be calculated.
The seasonal variations for the example residential data are not as obvious as were geographical variations, except that the bacteria values appear to be lowest during the winter season and highest during the summer and fall (a similar conclusion was obtained during the NURP, EPA 1983, data evaluations) (Pitt, Maestre and Morquecho, 2004; Pitt and Maestre, 2005).
The influence of location on measured stormwater concentrations was studied by Hernandez et al (2013) for SS, oil, Cu, COD and Zn. The locations in America, Asia and Europe had generally no significant influence on the concentrations of the studied substances, with exception of the event mean Zn concentrations being higher in Asia than in America and Europe. This implies that the land use specific standard concentrations can be used in these three continents but to increase the Zn data when the application is used in Asia. Further studies will be investigated regarding these and other substances, as well as for other locations.
First flush refers to an assumed elevated load of pollutants discharged in the first part of a runoff event. First flush effect was not present in all the land uses, and certainty not for all constituents. The first flush effect has been observed more often in small catchments than in large catchments (Thompson et al, 1995, cited by WEF and ASCE 1998) (Maestre and Pitt; Pitt, Maestre and Morquecho, 2004).
It is expected that peak concentrations generally occur during periods of peak flow (and highest rain energy). On relatively small paved areas, however, it is likely that there will always be a short period of relatively high concentrations associated with washing off of the most available material near the beginning of the runoff event (Pitt 1987; Pitt, Maestre and Morquecho, 2004).
The example investigation of first flush conditions indicated that a first flush effect (increased concentrations at the beginning of an event) was not present in all the land uses, and certainly not for all constituents. Commercial and residential areas were more likely to show this phenomenon, especially if the peak rainfall occurred near the beginning of the event. It is expected that this effect will be more likely to occur in a watershed with a high level of imperviousness, but the data indicated first flushes less than 50% of the time for the most impervious areas.
Groups of constituents showed different behaviour for different land uses. All the heavy metals evaluated showed higher concentrations at the beginning of the event in the commercial land use category. Similarly, all the nutrients show a higher concentration in the residential land use except for total nitrogen and ortho-P. This phenomenon was not found in the bacteria analyses. None of the land uses showed a higher number of colonies during the beginning of the event. Conventional constituents showed elevated concentrations in commercial, residential and institutional land uses (Maestre and Pitt, 2004).
Relationships of common pollutants such as suspended solids, phosphorus, fecal coliforms,
and total zinc concentrations for different rain depths show little variation, implying there is
no strong “first flush” effect at stormwater outfall locations. About 70% of the constituents
in the commercial land use category, about 60% of the constituents in the residential,
institutional and the mixed (mostly commercial and residential) land use categories, and
about 45% of the constituents in the industrial land use category, had first flushes. In contrast,
no constituents were found to have first flushes in the open space category. COD, BOD5,
TDS, TKN, and Zn had first flushes in all areas (except for the open space category). In
contrast, turbidity, pH, fecal coliforms, fecal strep., total N, dissolved and ortho-P showed no
statistically significant first-flushes in any category (Pitt and Maestre, 2005; Pitt, Maestre and Morquecho, 2004).
The conflict with TKN and total N implies that there may be other factors involved in the identification of first flushes besides land use. If additional paired data becomes available during later project periods, it may be possible to extend this analysis to consider rain effects, drainage area, and geographical location (Maestre and Pitt, 2004). If first flush effects are present, manual sampling may likely miss these more concentrated flows due to delays in arriving at the site to initiate sampling (Maestre and Pitt, 2005).
Comparison between calculated and measured concentration data
The standard concentrations for different land uses are calibrated continuously against measured concentrations from areas with the same type of land use. This is done partly for individual land use, such as sampling directly downstream of a residential area or from a stormwater well (gully pot, drain inlet) from a road with a certain measured traffic intensity. Secondly, they are carried out from different large (sub)watershed areas through calibration. This has been carried out and are carried out regularly for areas in the size of a roof area that may not be greater than 100 m2 (0.01 ha) to areas greater than 10 km2 (> 1 000 ha) from which there is good flow proportional sampling (Larm, 2000). This means that certain reductions of concentrations in technical and natural transport systems are included, see below.
For measured concentrations to be comparable to the standard concentrations, they must be taken with automatic flow proportional sampling during longer periods (several months in different seasons to one or more years). The standard concentration data are calibrated continuously against flow proportional samples from different types of case studies.
If calculated concentrations are higher than measured concentrations, it can often be explained that the sampling has not been taken flow-proportional over a long period of time. Grab samples usually yield significantly lower levels than the average stormwater levels contain. A larger proportion of samples of inleaking groundwater and connecting drainage water is taken than the flow-weighted mixture contains on average. It can be very large difference. The grab samples may show up to 10 times lower pollutant concentration (also depending on analyzed substance) than the corresponding flow-weighted sample. If you take grab samples or time-weighted samples and start these manually, the risk is high to miss the highest concentrations in the first part of the runoff (first flush), due to delays in arriving at the site to initiate sampling (Pitt and Maestre, 2005). The samples will then not be sufficiently representative of a yearly flow which should be based on long-term volume-weighted samples.
The mean values better represent long‐term mass discharges than median values in the case of measuring stormwater from flow proportional sampling (Pitt, 2011) and in the case of measuring grab samples from water courses (HVMFS, 2013). Median values artificially reduce the effects of the periodic unusually high concentrations that do occur in stormwater (Pitt, 2011). However, median values are recommended for representing longer periods of grab sampling in lake and sea water (HVMFS, 2013).
Another explanation that calculated concentrations may be higher than measured concentrations is that there may be greater retention in the transport systems upstream than included in the specified land uses and their standard concentration data. There may also be existing treatment facilities upstream that may not be included in the calculations. In such cases, these need to be simulated to get a more site-specific calculation.
Differences between calculated and measured concentrations may also be due to differences in building materials, traffic intensities, etc. If, for example, there are copper roofs in the area, so the employed standard concentrations need to be increased to take this into account. In this way, more site-specific calculations are obtained.
Pollutant load (kg/year) and pollutant concentrations to and in surface waters (streams, rivers, lakes and sea bays) cannot be directly compared to calculated concentrations in the emission points of stormwater (and baseflow) to the receiving surface waters. In surface waters, a dilution occurs, atmospheric deposition is added to its surface and processes in the surface water affect the amounts and concentrations. There may be either a net retention on an annual basis or a net leakage (release) of contaminants from the sediments to the water mass in the surface water. These processes affect the calculated load and concentration in the outlet from the receiving water. The model takes these processes into account and calculates an output pollutant load and concentration from the surface water, which are presented in the recipient section of the result report. Thus, the calculated load and concentration in the outlets from the catchment areas to a recipient cannot be directly compared to the calculated concentration in the recipient and the load out of the recipient.
Explanations why flow proportional sampling should be used instead of grab sampling on stormwater:
– Measured concentrations and flow vary greatly during each runoff event and between events.
– Measured concentrations from grab samples do not represent a yearly average concentration value and are generally lower than the measured concentrations based on flow proportional sampling.
– Baseflow normally contains much lower pollutant concentrations than the stormwater.
– The reduction efficiency is underestimated if calculated from grab samples.
StormTac Web has been used for example in the following case studies, where * indicates that calibration or comparison to measured data has been performed: Nybohov*, Stockholm (residential); Essingeleden*, Stockholm (road); Sätra*, Stockholm (residential); Lake Flaten*, Salem (residential); Flemingsbergsviken*, Huddinge (mixed); Tyresö municipality (mixed); Upplands Väsby municipality* (mixed); Lake Edsviken and Lake Norrviken, Sollentuna (mixed), Lidingö municipality* (mixed); Karlstad municipality (mixed); Fittja, Botkyrka (residential); Reykjavik, Iceland (residential); Kaliningrad, Russia (road) and Lake Titicaca, Peru and Bolivia (mixed).
Calibration to measured data has also been performed for a large number of roads and treatment facilities where StormTac Web has been used.
Francey M. (2010). Characterising urban pollutant loads. PhD thesis, Monash university.
Hernandez J.R., Valeri V.C.A., Barrera A.H.F. and Fresno D.C. (2013). Relationship between urban runoff pollutant and catchment characteristics. Journal of irrigation and drainage engineering. October 2013.
HVMFS (2013). Swedish Agency for Marine and Water Management (2013:19).
Larm T. (2000). Watershed-based design of stormwater treatment facilities: model development and applications. PhD Thesis, Dep Civil & Environmental Engineering, KTH, Stockholm, Sweden.
Maestre A. and Pitt R.E. (2005). Identification of Significant Factors Affecting Stormwater Quality Using the NSQD. Draft
Maestre A. and Pitt R.E. (2004). Stormwater quality descriptions using the three parameter lognormal distribution. Draft.
Maestre A. and Pitt R.E. Nonparametric Statistical Tests Comparing First Flush and Composite Samples from the National Stormwater Quality Database.
Novotny V. (1995). Non point pollution and urban stormwater management. Volume 9.
Sing V.P. (1995). Environmental hydrology.
Pitt R.E. and Maestre A. (2005). Stormwater quality as described in the National Stormwater Quality Database (NSQD). 10th International Conference on Urban Drainage, Copenhagen/Denmark, 21-26 August 2005.
Pitt R.E., Maestre A. and Morquecho R. (2004). The National Stormwater Quality Database (NSQD, version 1.1) February 16, 2004.
Pitt R.E. (2011). The National Stormwater Quality Database, Version 3.1. March 8, 2011