Urban runoff pdf

The CAZ is targeted and delimited by the London planning strategies for ca 10 years and is the subject of specific planning objectives and goals, which are related to the urban metabolism see e. Greater London Authority , The location of the trees are based on discussions with Greater London Authority mainly planned around sealed surfaces. The trees are assumed to be all deciduous broadleaved and the soil surface to change from impermeable to highly permeable. The study area does not correspond to any water management unit or hydrological sub-catchment, but was chosen to allow the output to be combined with other models that are based on rectangular grids, such as air quality models.

Each UMT represents a specific land cover type with specified reservoir characteristics. Not included in the model as presented in this paper are anthropogenic factors, such as watering gardens and leakage of water supply and sewerage pipes. In London leakage of pipes is a significant term in the water balance, i.

The greener city planning scenarios are compared to a baseline PA0: CAZ land cover in with attention to evapotranspiration, soil water shortages, and intensity of runoff peaks.

The SIMGRO results show that if the number of trees or other green areas are increased, the natural soil water storage for the simulated year of will limit the evapotranspiration rate during a part of the year. If insufficient soil water is available during hot periods, evaporation will be limited and as a result the potential cooling effect by urban green will not be realised. To maintain maximum cooling, either additional irrigation water, or a greater soil water storage capacity to retain precipitation is needed.

That effects of such measures are dependent on time of year and influence one another. In the summer months, water shortages resulting from the combined alternative PA4 becomes larger than the permeable street PA2 alternative. As noted above, the addition of trees in both PA1 and PA4 requires additional water to maximise evapotranspiration.

To support decision making in planning processes, spatial representation of areas at risk, or not complying to a certain threshold value either represented by a physical variable, or an index, are desirable. These areas are expected to be associated with warmer temperatures because of reduced cooling. These changes in risk levels can be directly related to the land cover changes of the alternatives. These simulations show see Figure In this study, alternative strategies for a greener city, the CAZ of London, UK, were compared with a baseline and evaluated focussing on the effects of an increase of actual evapotranspiration and a decrease of intensity in rainfall generated runoff peaks.

The risk maps see Figure If the goal is to use evaporation increases to lower air temperature for human comfort, not only the evaporation rate but also the meteorological conditions will be important e. Obviously, the greatest opportunity for change is in the areas with a large fraction of sealed surfaces. However, to make such a shift possible in these areas will mean a drastic land use change from built-up to vegetated surfaces, or open water. To implement such alternatives sufficient water needs to be available at the appropriate time.

This may require irrigation. Not surprisingly, the quality of the results are highly dependent on the quality of the input data forcing data, model parameters and models used. It is essential that the models are extensively evaluated to ensure that they are capable of performing appropriately for both current conditions and proposed PA.

Thus the measurements of evaporation see Chapter 5 for details and evaluation of these processes e. Chapter 9 , are critical to not only energy, but also to water exchange related processes.

Models, such as presented in this chapter, allow for a rapid assessment of different PA, while simultaneously providing insights in the dynamic processes and interactions between different components of the hydrological cycle in the urban environment. As such these models can aid in the identification of potential problem areas associated with different urban PA and support planning processes at the relatively large scale of settlements and master plans.

The role of soil in the generation of urban runoff: development and evaluation of a 2D model. Journal of Hydrology, , - Berthier, E. Comparison of two evapotranspiration schemes on a sub-urban site. Chrysoulakis, N.

Landscape and Urban Planning, , - Diaper, C. Wolf, Morris, B. Dupont, S. Parameterization of the urban water budget with the Submesoscale Soil Model. Journal of Applied Meteorology and Climatology, 45, - Modeling soil water dynamics in the unsaturated zone - state of the art. Journal of Hydrology, , 69 - Managing risks and increasing resilience. Grimmond C. Urban water balance: 1. A model for daily totals. Water Resources Research, 22, - Grimmond, C. An evapotranspiration-interception model for urban areas.

Water Resources Research, 27, - Geoscientific Model Developments Discussion, 7, - Jia, Y. It also couples these outputs with impact models to project effects on hazardous substance increase or decrease. There are few LUCC models targeting land use change prediction in shrinking cities, while most of them are mainly focus on urban growth prediction. These gaps in the research are the primary focus of this study.

In this research, ask, what effects can VLRG through smart decline help decrease pollutant loads in legacy cities undergoing excessive vacant lands amounts? With flood occurrence frequency increasing in legacy cities with a large degree of brownfields, vacant lands, toxic release sites, and outdated sewer systems, this research is both timely and necessary. To understand the potential urban contamination reduction services that VLRG can provide to legacy cities, is essential to effectively support planning or management through smart decline for shrinking cities.

This paper sheds light on the long-term impacts of smart decline on stormwater management and urban contamination levels and is important to land use decision-makers, whose policies support urban regeneration and multi-exposure risk evaluations for urban populations. To understand the VLRG effects on stormwater runoff reduction and nonpoint source pollution mitigation, we evaluate a legacy city with a declining population, excessive amounts of vacant land, and one which also faces flood issues.

The city of St. Based on population change from to , St. Louis is ranked as a top 3 shrinking U. Additionally, the numerous small and oddly shaped vacant parcels in St. Louis make it a unique study area. From to , the area of vacant land in St. The geographic location of St. Louis also makes it vulnerable to flooding see Fig.

Louis is nestled below the confluence of the Missouri and the Mississippi rivers, two of the largest and most powerful rivers in the U.

Combined with the Meramek river, these three rivers nearly completely encircle the city. Historically, the Great Flood of occurred on the Missouri and Mississippi rivers, ravaging nearly the entire city of St.

From to , St. Louis experienced 12 severe flood events and the frequency of flooding also has increased recently St. Louis County, Missouri, The pollutants in the resultant stormwater runoff, such as chemicals, oil, suspended solids, and disease-causing bacteria, primarily come from industrial sites, parking lots, streets, vacant lands, and unkempt infrastructure in declining neighborhoods.

Unlike domestic wastewater, the polluted stormwater directly enters the water system without any treatment. The methodological approach follows three primary steps: 1 application of the CLUE-S model to predict future land uses of St.

Louis by ; 2 development and application of regreening proportions for different smart decline scenarios; and 3 calculation of stormwater runoff and the nonpoint source pollutant loads under each different scenario. Parcel data from to was obtained from the City of St. Louis Open Data City of St. Louis, MO Open Data, The parcel data contains detailed information such as zoning codes, land use codes, land vacancy status, land value, and land improvement value.

There are 24 types of zoning codes and land use types in the data. Land use types were condensed to 7 classifications — single-family, multi-family, industrial area, commercial area, green space, vacant land, and other land uses.

Green space comprises forest parks, public gardens, golf courses, water areas, and other outdoor entertainment areas. Vacant lands, according to the datasets, were defined as any place with a vacant status, regardless of whether it has building structure on the land.

In addition, others land uses include institutional facilities, governmental services, and transportation utilities. In St. As noted, although the area of vacant land in St. Louis has declined, the number of vacant parcels is increasing. Moreover, VLRG offers an alternative to the traditional method of directing stormwater runoff simply into sewers and rivers. As noted, in this research, we adopted the CLUE-S model to forecast future land use and generate different vacant regreening scenarios.

The CLUE is a dynamic model which integrates environmental modeling with geographical information systems. Because the CLUE model was initially developed for study areas with a large extent, such as national and continental levels, the model has been modified to adapt to finer-scaled extent now called the CLUE-S model Verburg et al.

There are two processes which are required to be completed in the spatial module. One is to select the land use with the highest preference. To determine preferred land use for each location, it is necessary to consider the suitability of the location itself, land use conversion elasticity, and the competitive advantage of land use types Hu et al.

To determine the suitability of the land use location, the logistic regression results of each land use type are required to be entered into the CLUE-S model. Its equation is as follows:. Where Pi is the probability of a grid cell to have a certain type of land use.

X1 represents one of the driving factors. The values of conversion elasticity should be in the range from 0 easy to convert to 1 cannot be converted.

Land use types with a low capital investment can be easily shifted to other types if there are few demands, and vice versa. During each iteration, the competitive advantage can be modified based on the balance of the allocated area and the land demand. If the allocated area is not equal to the land demand, the competitive advantage of different land use types will be modified so that the land use with unmet demand will have higher preference.

Another process in the spatial module is to check if the conversion is allowed. Spatial policies and restrictions need to be defined in the CLUE-S model to allow for certain areas to remain unchanged, influencing the pattern of the land use change.

In addition, the conversion matrix is used to understanding the land use temporal characteristics, that is how possible the land use type of the area can be changed to another land use type. For example, it is almost impossible to convert agriculture land into forest, while it is very easily to convert it to grassland. Thus, the agriculture land to forest conversion should be value 0, and the agriculture land to grassland conversion should be value 1. The non-spatial module needs to be assisted by other specific models, like the Markov chain model, to calculate the land demands for all land use types each year.

The results of the non-spatial module are treated as direct input of the land allocation module. We used the Markov chain model to predict temporal land use change for using historical data from to After calculating the land demands for baseline scenar, the land use demands were adjusted according to the requirements of VLRG scenarios as described in future scenarios development section.

They are used as a stochastic model to project land use changes from one status to another status between discrete time periods. The probabilities of the land transition are assumed to be no memory, that is, the future state of land use depends only on the current state of land use, not the previous state Iacono et al.

The Markov chain model predicts the quantity of future land use in the way that is subject to the nature of land use transition. Land use change can be viewed as a multi-directional process. In theory, a given piece of land may transfer from one type of land use to any other at any time. In the above, p xy indicates the transition probability from previous xth land use status to future yth land use status.

The row of the probability matrix must add up to one, as below:. To determine the preferred land use for each location, the relationship of location driving factors and different land uses was considered. All driving factors were selected based on proven models to represent the complexity of the land use system, covering physical, demographic, socioeconomic, and proximity aspects.

The driving factors of general land use types came from a proven model used by Zheng et al. Using these factors, they successfully simulated changes in commercial, residential, industrial, green space, vacant, and other land uses in urban renewal areas of Hong Kong. To compensate the lack of driving factors for vacant land change in the model of Zheng et al.

Relative operating characteristic ROC , kappa, quantity disagreement, allocation disagreement, and overall agreement were used of calibrate the model. ROC is utilized to evaluate the performance of the logistic regression model. In this study, we used and land use data to predict land use in and then compared the predicted land use in with the actual land use in The kappa statistic is the most widely used method to measure the magnitude of agreement between observations Cohen, The mathematical expression of the kappa statistic is following:.

Where p o represents the actually observed agreement and pe indicates the expected chance agreement. A prefect agreement gives a kappa value of 1. However, since the kappa statistic can sometimes be misleading for practical applications, Pontius and Millones recommended researchers to replace kappa indices with a more straightforward and useful calculation process that provides two components of disagreement between maps — quantity disagreement and allocation disagreement.

To ensure the reliability of this research, we utilized both kappa statistic, quantity disagreement, and allocation disagreement measurements. The formulas for quantity disagreement, allocation disagreement, and overall agreement are as follows Newman et al. The L-THIA model was used to estimate the long-term average annual runoff and nonpoint source pollution for each different scenario.

The L-THIA, developed by Purdue University, sheds light on the hydrologic impacts of the land use change for planners and natural resource managers. In order to prevent catastrophic flooding and improve public health, it is critical to understand how land use changes have an impact on surface runoff, ground water recharge, and pollution loading.

The inputs of the L-THIA model include location, type and size of land uses, and hydrologic soil type. The outputs of the L-THIA are composed of two components; one is stormwater runoff results and the other is nonpoint source pollutant results. The following formula shows its equation:.

Where CN is curve number, which is found from the table and ranges from 0 to High curve numbers indicate high runoff potential Golberg, When S is known, we can calculate the direct runoff. The mathematic expression for runoff is. Where Q is direct runoff acre-ft , and P is precipitation inch. Based on the stormwater runoff results and coefficients associated with different land use types, the nonpoint source pollutant results are estimated Lim et al.

The average annual NPS pollution for 15 pollutants are calculated based on an amount of runoff and event mean concentration EMC. During the simulation period, the annual average pollutant loads of the entire study area are calculated.

The Markov chain model was used to predict the land demand for the baseline scenario in Before using the Markov chain model, the land conversion probability from to must be provided. Based on this historical land conversion probability, we used Markov chain model to calculate the probability of land conversion in see Table 1 , which can be multiplied by land use to calculate the land demand under the baseline scenario in The land requirement of VLRG scenarios were calculated based on the demand of the baseline scenario with adjustments according to different land requirement of each VLRG scenario see Table 2.

In the spatial analysis module of the CLUE-S model, land use conversion elasticity, driving factors of location, the conversion matrix, and spatial restriction settings are required as inputs.

The conversion elasticity of the baseline scenario was calculated based on historical trends from to From the results, vacant land has the lowest elasticity among all land use types, due to its low capital investment. For VLRG scenarios, the vacant land utilization should be larger than the baseline scenario; therefore, we further reduced the elasticity of vacant land to simulate a smart decline strategy, which manages vacant land through transforming it into green space.

Because there are no restrictions on conversion between each land use type, the values in the conversion matrix are tabulated as 1. In terms of the spatial restriction, forest parks and historical district were considered unchangeable under any circumstances. Table 3 lists the estimated coefficients for the selected spatial factors and the different land use types related to their different driving factors. Table 4 shows the ROC, kappa value, quantity disagreement, allocation disagreement, and overall agreement of each land use type.

All of the values of the ROC are above 0. Each kappa value falls within the range of 0. There are some minor quantity and allocation disagreements, but all of them are within an acceptable range. ROC, kappa, quantity disagreement, allocation disagreement, and overall agreement output. The land use patterns of the four scenarios for were simulated after running the calibrated CLUE-S model with different land demands and conversion elasticity see Fig. From the results of the simulation scenarios see Fig.

The conversion rate of vacant land to green space increases as the percentage of regreening increases under the VLRG scenario type see Fig. Additionally, there are still Louis was input as a first step to help assess runoff amounts. For next step, there are eight land use types and four soil types A, B, C, and D available to choose from. The soil type A means the soil is has high infiltration, whereas type D represents impermeable soil. Louis is type C. Therefore, we used type C soil to represent the soil type for each land use.

Also, because vacant land, green space, and others land use types are not options in the L-THIA, we used other similar land use types as substitutes. For example, we use the low-density residential land uses to replace vacant land, the commercial land use to replace others, and the forest to replace green space.

Table 7 shows the runoff results of vacant land transition under different scenarios in Both the runoff and pollutant loads within the runoff significantly decreased with more vacant lands converted to green spaces see Table 8.

In all the VLRG scenarios, all the nonpoint source pollutants significantly decrease except Chromium. Nonpoint source pollutant results per ha of vacant transition under different scenarios in We combined the CLUE-S and the Markov chain prediction models with the L-THIA performance model in this research to forecast the environmental impacts of land use changes related to urban contamination levels both spatially and temporally.

The parameters of the prediction models were drawn from historical land use data from to and we validated models by comparing simulated land use with actual land use in ROC, kappa, quantity disagreement, allocation disagreement, and overall agreement outputs all indicated an acceptable or above reliability.

Results show that smart decline has positive direct and indirect impacts on flood mitigation and pollutant removal.

Directly, it is shown that, as more vacant lands are converted to green spaces, stormwater burdens on the sewer system are lessened, resulting in a significant reduction in the stormwater runoff and non-point source pollutants. The reduction of non-point source pollutants is due not only to the decrease in stormwater runoff but also to the capability of increased vegetation to help remediate soil contamination.

From the results, we noticed that while all three VLRG scenarios have positive impacts on urban runoff and contamination levels, these levels do not decrease proportionally as the increase in the VLRG rate. In our study, we found that all the nonpoint source pollutants significantly decrease through VLRG. Fine particulates and dissolved pollutants can be trapped and removed by being adsorbed into the filter medium, being up-taken by plants or microbial components of the plant-soil environment as stormwater infiltrates through the planted filter media Read et al.

In the CLUE-S model, in order to balance land supply spatial analysis module and land demand non-spatial analysis module , it may modify the competitive advantage of different land use types so that the land use with unmet demand will have higher preference.

As the land demand for green space increases in each VLRG scenario, the transition rate of vacant land uses to other land uses is influenced. In addition to the direct impacts of regreening vacant lots, indirectly, VLRG through smart decline can play a large role in vacant land reduction and reuse.

The unappealing and unregulated aesthetics associated with most vacant land can make such lots a hotspot for illegal dumping. While the effects of illegal dumping on urban contamination levels take long periods of time to present themselves, there are myriad of potential environmental risks.

Because of no built-in monitoring system, illegal dumping, unlike regulated sanitary landfills, may contaminate soil and water, destroy wildlife habitat, and increase negative human health issues. Both the people who violate the law can be affected by the risks of illegal dumping, as well as all those who share the environment with them. Through our simulation results, we see an obvious effect on vacant land reduction. Without regreening interventions, the vacancy issue is more likely to get worse and, as the literature shows, have a negative ripple effect on the surrounding areas, leading to issues related to urban decline Newman et al.

As a result, more people, especially upper- and middle-class residents, often relocate to other places in search of a higher-quality living environment. Inversely, successful VLRG can reduce the negative externalities associated with vacant land, help stabilize declining neighborhoods, boost economic markets, reduce violent crimes, mitigate the impacts of a changing climate, and, as shown in this research, decrease pollutant loads in stormwater runoff.

This research indicates that effects of smart decline on urban runoff contamination, both in the short-and long-term, can significantly improve health of residents in shrinking legacy cities. Through applying smart decline to clean up brownfields, improve land infiltration, make up for inadequate drainage system, and strengthen water treatment, all immediate health effects downing, physical injuries, and animal bites and secondary health effects water contamination and waterborne diseases.

Reducing peak stormwater runoff can decrease fatal drowning and injuries caused by flash flooding. Physical injuries usually caused by sharp debris and wildlife contained in floodwaters can also be lessened.

Even after floods, the unstable building condition and broken electrical power cables can cause injuries, even death, during the cleanup process Du et al.

The concentrations of pollutants can cause stress for aquatic organisms especially during the first few minutes of the first major rainstorm of the winter season, the "First Flush". This problem becomes worse with population growth and urbanization because such activities alter natural water processes and create more impervious surface areas. Because urban runoff is nonpoint source pollution, no single source, work to prevent it must be far-reaching and cover many different aspects.

As part of the sanctuary's Management Plan "WQPP Action Plan I: Implementing Solutions to Urban Runoff", seven strategies are outlined, each having a number of activities that when completed will result in a coordinated program addressing urban runoff. In many cases the work being done by the cities for MS4 permits also addresses the Urban Runoff Action Plan strategies.

Residents play an especially important role in connecting actions on their property to water quality in the sanctuary. Educational efforts are wide ranging and include helping individual citizens to understand what they can do to minimize their own impacts and improve urban runoff. Resource Protection.