1. INTRODUCTION
The process of urbanization makes it necessary for cities to develop as their population grows. This process consists of the construction of houses and infrastructure, which leads to a reduction in the natural infiltration capacity of the soil through the transformation of permeable surfaces that reduce the flow of water into impermeable surfaces that must be served by a rainwater drainage system.
Rainwater runoff caused by impermeable surfaces associated with urbanization has become a major source of water pollution and can cause street flooding and watershed degradation (^{Meng and Hsu, 2019}).
Traditional construction techniques and increased urbanization contribute to reduced soil absorption capacity and rainwater storage through waterproofing, which results in an increased risk of flooding, pollution transport and overloaded stormwater piping systems (^{Berndtsson et al., 2019}).
Because of this, there is an urgent need for better rainwater management practices. In urban areas in France, for example, dry infiltration basins are used to reduce the volume of water in downstream networks and limit the discharge of pollution into surface water. These basins can also promote urban development in areas far from existing networks or in natural outlets and improve urban locations where basins can be used as parks or playgrounds. Lower costs and recharging of groundwater are other potentially attractive aspects of appropriate management practices (^{Dechesne et al., 2004}).
Infiltration basins are stormwater control measures that capture, temporarily store and gradually infiltrate secondary runoff into the ground, thereby reducing the net runoff volume (^{Natarajan and Davis, 2016}).
To implement this practice, it is necessary to determine the hydrodynamic behavior of the water in the soil. For this purpose, many authors such as ^{Oliveira et al. (2018)}; ^{Santos et al. (2012)}, ^{Bagarello et al. (2014)}, ^{Lassabatère et al. (2010)}, and ^{Souza et al. (2008)} make use of the Beerkan method proposed by ^{Lassabatère et al. (2006)}. Knowledge of these hydrodynamic soil parameters is directly linked to understanding the process of water infiltration into the soil.
^{Oliveira and Soares (2017)} determined the saturated hydraulic conductivity at nine distinct points in an area susceptible to flooding in the city of Recife using the Beerkan method. The results obtained from the infiltration curves can be used in a variety of modeling or forecast studies, for periods of long or short duration, that estimate the water balance in the locality (rain x infiltration), the cumulative water capacity in the soil, and the performance with regard to urban drainage.
The objective of this study was to estimate potential infiltration in public squares near flood-prone areas in a flood-prone Brazilian city, and to determine how much these squares can contribute to the infiltration of rainwater into the soil, thereby reducing the risk of flooding.
2. MATERIALS AND METHODS
The research was carried out in the city of Recife, Pernambuco, which has a total area of 218,435 km² and had an estimated population of 1,633,697 inhabitants in 2017. The city has a total of 413 public squares, and 159 critical flooding points catalogued by EMLURB (Urban Maintenance and Sanitation Company). The locations of the squares and critical flooding points are shown in Figure 1.
This study focused on eight of the total number of squares, all of which were located near critical flooding points and were not waterproofed. They are Dom Miguel Valverde Square, with an area of 2075 m² and a permeable soil rate of 74.40%; Dr. José Vilela Square, with a total area of 2206 m² and a permeable soil rate of 56.98%; Dr. Fernando Figueira Square, with 4921 m² total area and a permeable soil rate of 59.09%; Entroncamento Square, with a permeable soil rate of 38.80% and a total area of 5125 m²; Nossa Senhora da Boa Viagem Square, with an area of 8101 m² and a permeable soil rate of only 12.39%; Jardim São Paulo Square, which has an area of 5675 m² and a permeable soil rate is 68.22%; SUDENE Square, with an area of 4316 m² and 67.81% of permeable soil; and Várzea Square with 5809 m² total area and 27.80% permeable soil (Figure 2).
In the surveyed squares, it was observed that only three of them presented non-sealed soil area less than 50% of their total area.
2.1. Field Studies and Sample Collection
At each of the public squares studied, simple ring infiltration assays and sample collections were performed nine times to determine the particle-size distribution curves.
The simple ring infiltration test is performed by inserting a 15 cm diameter metal cylinder into the ground to a depth of approximately 1 centimeter and then pouring a known volume of water into the cylinder that is sufficient enough to form a layer of water on the surface of the soil inside the infiltrator. When the sheet of water has dried and the soil inside the infiltrator has become exposed, the same volume of water is poured again. During the test, the time elapsed between the additions of water was measured and the process was repeated until the relationship between the volume of water infiltrated and the time became constant.
The samples collected to determine the particle-size distribution curves were taken to a laboratory, where the sample preparation procedures were carried out. The particle-size distribution was obtained using the screening and sedimentation processes defined in NBR 7181 (^{ABNT, 2016}).
2.2. Beerkan Method
The Beerkan methodology is performed through infiltration and particle-size distribution tests, and is used to determine local hydrodynamic parameters in the field. In this method, the soil water-retention curve θ (h) and the hydraulic conductivity K(θ) are described, respectively, by the model defined by Van ^{Genuchten (1980)}, represented in Equation 1, and the model defined by ^{Brooks and Corey (1964)}, shown in Equation 2.
Where θ is the volumetric moisture [cm^{3}.cm^{-3}], θ _{ r } and θ _{ s } are the residual and saturated volumetric masses [cm^{3}.cm^{-3}], respectively; h is matrix potential [mm]; h _{ g } is a scale value of h; m and n are shape parameters; K _{ s } is the saturated hydraulic conductivity of the soil [mm.s^{-1}]; and η is the shape parameter for the hydraulic conductivity curve.
Other equations necessary for the use of the BEST method are presented in the studies by ^{Souza et al. (2008)} and ^{Santos et al. (2012)}.
3. RESULTS AND DISCUSSION
Graphs were generated from the soil particle-size data determined from the collected samples. The average curves for each public square studied are shown in Figure 3. It is important to note that there were no great variations between them.
Along with the particle-size distribution, the corresponding fractions of clay, silt, and sand were determined for each soil sample, and this was used to indicate the soil textural classes at the studied locations (Table 1).
It can be seen that Jardim São Paulo Square varied the greatest when compared to the other squares, especially with regard to the fraction of medium- and coarse sand corresponding to the region of the graph with particle diameter between 0.200 mm and 1.000 mm.
In Table 1, the average shape parameters of the retention curve and hydraulic conductivity curve are shown, along with the soil textural classes.
Square | Textural Class | ρ_{d} (g/cm^{3}) | m | n | η | Ks (mm s^{-1}) | hg (mm) |
---|---|---|---|---|---|---|---|
Dom Miguel Valverde Square | Loamy Sand | 1.520 | 0.141 | 2.335 | 9.828 | 0.014 | -0.399 |
Dr. José Vilela Square | Sandy Loam | 1.654 | 0.126 | 2.289 | 9.963 | 0.030 | -0.734 |
Dr. Fernando Figueira Square | Loamy Sand | 1.546 | 0.149 | 2.356 | 9.118 | 0.016 | -0.441 |
Entroncamento Square | Loamy Sand | 1.670 | 0.203 | 2.521 | 7.287 | 0.025 | -1.173 |
N. S. da Boa Viagem Square | Sandy Loam | 1.534 | 0.125 | 2.286 | 9.999 | 0.029 | -0.350 |
Jardim São Paulo Square | Sandy Loam | 1.653 | 0.133 | 2.306 | 9.551 | 0.017 | -0.654 |
SUDENE Square | Loamy Sand | 1.486 | 0.153 | 2.362 | 8.656 | 0.084 | -0.350 |
Várzea Square | Sandy Loam | 1.525 | 0.125 | 2.286 | 10.187 | 0.001 | -0.111 |
Note that for the Loamy Sand textural class, Dom Miguel Valverde Square presented the lowest values calculated for parameters m and n, while for the Sandy Loame class, the lowest values calculated for parameters m and n were obtained at Nossa Senhora da Boa Viagem Square and Várzea Square.
It can be emphasized that calculated parameters (m, n, and η) agree satisfactorily among the soils with the same textural classes in studies carried out by ^{Souza et al. (2008)} and ^{Santos et al. (2012)}.
It is possible to perceive the coherence between the parameters obtained by ^{Souza et al. (2008)} and the values determined in Table 1. The values for parameters m, n, and η were 0.13, 2.31, and 9.54, respectively for soils of the loamy sand texture, while sandy loam soils presented the following values: 0.07, 2.16, and 15.67.
The saturated hydraulic conductivity, K _{ s } , is affected by the texture of the soil, but depends mainly on its structure. Soils having a larger average particle diameter tend to be better conductors of water.
^{Souza et al. (2008)} estimated and analyzed the soil hydraulic properties through infiltration experiments applying BEST, discovering an average K _{ s } value of 0.03 mm.s^{-1} for soils in the loamy sand textural class. For soils having a textural class of sandy loam, they obtained average K _{ s } values of 0.13 mm.s^{-1} and 0.02 mm.s^{-1}. ^{Santos et al (2012)} also identified mean K _{ s } values of 0.144 mm.s^{-1} and 0.073 mm.s^{-1} for soils in the loamy sand textural class and mean K _{ s } values of 0.060 mm.s^{-1} and 0.224 mm.s^{-1} for soils in the sandy loam textural class. The K _{ s } values presented in Table 1 are therefore in accord with the results obtained in previous studies.
3.1. Retention curves and hydraulic conductivity of soil
The hydraulic conductivity (Figure 4A) and the retention curves (Figure 4B) were elaborated for each of the squares studied, following the determination of the shape parameters and normalization parameters.
Due to the similarity between the soil textural classes, it can be seen that the conductivity curves present similar aspects. Várzea Square was found to have the lowest hydraulic conductivity while Entroncamento Square had the highest hydraulic conductivity, very similar to that found for the remaining squares.
In the retention curves, it is possible to observe that the soils vary only slightly in moisture content and have a small retention capacity. Even so, it is possible to perceive that the sandy loam soils have slightly higher retention than the loamy sand soils. The squares having soil of textural class loamy sand presented a mean difference in humidity of 0.16 cm³ cm^{-3} between h = 1 mm and h = 10 mm, while the squares with soil of textural class sandy loam presented a mean difference in humidity of 0.13 cm³ cm^{-3} between h = 1 mm and h = 10 mm.
These results were also observed by ^{Santos et al (2012)}, who noted that the greater proximity of the particles in the sandy loam soils causes absorption and capillarity to be more intense and that these soils consequently retain more water than loamy sand soils.
3.2. Discussion
From the results obtained, it was possible to determine the infiltration capacity of each of the squares studied and, with that, determine the amount of rain water that each absorbs. The maximum rainfall intensity for Recife, PE determined by ^{Silva Junior and Silva (2016)}, for a return time of two years and duration of 60 minutes, is 47.44 mm h^{-1}, a value then used for each of the squares to establish the volume of surface runoff from the impermeabilized areas the square could receive and absorb, in order to reduce overloading of the drainage system.
Dom Miguel Valverde Square had an infiltration capacity of 50.9 mm h^{-1}. Taking into account its total area of permeable soil, it has the capacity to absorb 78.616 m³ of rainwater. During a rainfall event of intensity 47.44 mm h^{-1}, it is possible for 73.247 m³ of rainwater to infiltrate. The square could therefore still receive 5.369 m³ of surface flow from its surroundings.
Dr. José Vilela Square has an infiltration capacity of 108.8 mm h^{-1}, which allows it to absorb a total of 136.818 m³ of rainfall. During precipitation with an intensity of 47.44 mm h^{-1}, 59.632 m³ of rainwater can infiltrate. This square could therefore still receive 77.186 m³ of surface runoff from nearby impermeable areas (Table 2).
Square | Infiltration rate mm h^{-1} | Ground area not waterproofed m^{2} | Absorption capacity m^{2} | Max. rainfall Int.* mm h^{-1} | Infiltration m^{3} | Able to infiltrate m^{3} |
---|---|---|---|---|---|---|
Dom Miguel Valverde Square | 50.9 | 1544 | 78.616 | 47.44 | 73.247 | 5.369 |
Dr. José Vilela Square | 108.8 | 1257 | 136.818 | 47.44 | 59.632 | 77.186 |
Dr. Fernando Figueira Square | 56.3 | 2906 | 163.896 | 47.44 | 137.860 | 26.035 |
Entroncamento Square | 90.3 | 1989 | 179.793 | 47.44 | 94.358 | 85.435 |
N. S. da Boa Viagem Square | 104.1 | 1004 | 104.595 | 47.44 | 47.629 | 56.966 |
Jardim São Paulo Square | 60.0 | 3872 | 232.564 | 47.44 | 183.687 | 48.877 |
SUDENE Square | 302.8 | 2927 | 886.562 | 47.44 | 138.856 | 747.705 |
Várzea Square | 3.9 | 1615 | 6.4527 | 47.44 | 6.4527 | 0 |
*Maximum rainfall intensity.
The infiltration capacity of Dr. Fernando Figueira Square is 56.3 mm h^{-1}, allowing it to infiltrate up to 163.896 m³ of rainwater. A rainfall intensity of 47.44 mm h^{-1} would produce a total infiltration of around 137.860 m³ of rainwater in the square, making it is possible to infiltrate another 26.035 m³ of surface runoff.
Entroncamento Square has the capacity to infiltrate 90.3 mm h^{-1}, or a total of 179.793 m³ of rainwater. In a rainfall event of intensity 47.44 mm h^{-1}, 94.358 m³ of rainwater is infiltrated into the soil, allowing for the square to receive 85.435 m³ of water from its surroundings.
Nossa Senhora da Boa Viagem Square has the capacity to infiltrate at a rate of 104.1 mm h^{-1}, allowing for a total of 104.595 m³ of infiltrated rainwater. With a rainfall intensity of 47.44 mm h^{-1}, 47.629 m³ of rainwater will infiltrate into the soil, allowing the square to potentially receive 56.966 m³ of surface runoff from its surroundings.
Jardim São Paulo Square has an infiltration capacity of 60.0 mm h^{-1}, allowing for a total of 232.564 m³ of infiltrated rainwater. Thus, during rainfall of intensity 47.44 mm h^{-1}, 183.687 m³ of rainwater will infiltrate into the square, allowing for the infiltration of another 48.877 m³ of surface runoff.
SUDENE Square has an infiltration capacity of 302.8 mm h^{-1}, allowing for a total of 886.562 m³ of infiltrated rainwater. In a rainfall event of 47.44 mm h^{-1}, 138.856 m³ of rainwater will be infiltrated, meaning that the square could still receive a total of 747.705 m³ of surface runoff from the surrounding impermeable areas.
Várzea Square has an infiltration capacity of only 3.9 mm h^{-1}, allowing for a total of only 6.452 m³ of infiltrated rainwater. In a rainfall with an intensity of 47.44 mm h^{-1}, the square will infiltrate its maximum capacity of 6.452 m³ of rainwater, generating a surface runoff of 70.162 m³, making it impossible to receive any surface runoff from its surroundings that would contribute to flood reduction.
4. CONCLUSIONS
The determination of the infiltration capacity at each of the squares studied made it possible to observe that all of them, with the exception of Várzea Square, have the capacity to absorb a quantity of rainwater superior to that which falls directly on the squares themselves.
In squares that have little area free of construction, soils exhibit characteristics such that, were the area of non-sealed soil, it would contribute to infiltration.
Although it does not have the capacity to infiltrate rainwater from surface runoff, the structure of Várzea Square may be modified to enable its contribution. For example, squares may be left lower than roads so that rainwater can be temporarily stored in this area and thus reduce the overhead on the existing drainage system.
The squares could be adapted into micro-infiltration basins, or into retention basins as in the case of Várzea Square, which would contribute directly to the reduction of the existing drainage system by the reduction of runoff.
It is clear that public squares have a great capacity to contribute favorably to the reduction of flooding in large urban centers, which are constantly subject to damage and losses resulting from floods.