Abstract
The present study aims to evaluate the performance of the impervious surface as a control (O), sandy loam substrate (SL), gravel (G), gravel with geocell layer (GGE), rosemary (R), rosemary with geocell layer (RRE), turf (T), and turf with geocell layer (TGE) in the reduction of runoff volume, time-to-start runoff (TR), runoff coefficient (C), time-to end runoff (TER), peak flow rate (PF), time to peak (TP), and time base (TB) in the laboratory dimension under three different scenarios of rainfall intensity and two different slopes using a rainfall simulator. The results revealed a significant difference between the data at the level of 5% in all cases. Generally, three rainfall scenarios for all hydrological parameters TR, TER, TP, TB, C, and PF were classified into different groups. In all cases, GGE treatment performed better than that of the rest of the test groups in reducing runoff and cumulative volume. Further, treatments O and GGE experienced the highest and lowest flow rates, respectively. For a specific scenario of rainfall intensity and slope, the value of C is the lowest for GGE treatment. Finally, the implementation of geocell in the pavements was able to delay the time to start runoff.
HIGHLIGHTS
Examining different rainfall scenarios can be effective in flood control on different lid methods.
Implementation of geocell to cover gravel, rosemary and turf can be effective in reducing floods.
Gravel with geocell layer (GGE) had the best performance in reducing floods and improving parameters affecting it.
INTRODUCTION
Urbanization and the construction of buildings, pavements, roofs, and other surfaces have limited natural infiltration, as well as evaporation and transpiration. Therefore, these changes in land use lead to the rapid production of surface runoff, which is a significant threat to urban areas where millions of people live (Saraçoğlu & Kazezyılmaz-Alhan 2023). Low-impact development (LID) is an engineering method to reduce the negative effects of urbanization and protect water resources (Demirezen & Kazezyılmaz-Alhan 2022). LID is a design philosophy that provides flood management methods, plans, and technologies to minimize negative impacts commonly associated with urban flooding, including degradation of groundwater and surface water quality, erosion, and loss of aquatic diversity (Drake et al. 2013). The purpose of LIDs implementation is to simultaneously reduce runoff and flooding and improve water quality, in addition to decreasing costs in the construction, operation, and maintenance of infrastructure (Rodrigues et al. 2021).
Infiltration trenches (IT) and vegetated filters are two LID-best management practice (LID-BMP) methods that can provide nature-based solutions to control runoff volume (RV), time-to-start runoff (TR), peak flow rate (PF), and removal of stormwater pollutants (Dadrasajirlou et al. 2023).
As part of nature, turfs (T) are low-cost structures (Deletic 2005) and the use of turfs decreases the total volume of runoff through infiltration and storage. Further, the sand structure of the infiltration trench can reduce the volume of surface runoff and the amount of PF rate and increase the groundwater supply (Fach & Dierkes 2011). The field research reviewed in the following reveals the proper effectiveness of turf and IT in reducing RV. Bäckström (2002) reported a 33% reduction in average surface runoff after observing the performance of seven turfs in urban areas of Sweden. Fassman & Liao (2009) and Rujner et al. (2016) evaluated turf performance and showed that swale implementation leads to a reduction in average surface runoff by 63.7% and 40–55%, respectively. Hunt et al. (2010) monitored the hydrological performance of a surface plant spreading filter strip and found that this strip could eliminate the outflow of 20 out of 23 rainfall events and reduce the RV by 85%. Similarly, Line & Hunt (2009) investigated the turf filter surface in 14 rainfall events and reported a reduction of RV and PF rates by 49 and 23%, respectively. In some cases, peak currents are reduced by a high order of magnitude (Winston & Hunt 2009). For example, the efficiency and effectiveness of IT and grains of sand in flood control and reduction have been mentioned in research Heilweil et al. (2015) and Barber et al. (2003). The geocell (GE) family of pavement materials is defined by constructed plastic grids whose cells can be filled with aggregate or a planting substrate for turf (Ferguson 2005; Papakos et al. 2010).
Geocells are plastic panels that form a grid of box-like cells filled with soil material (Ferguson 2005). Dreelin et al. (2006) compared the performance of an asphalt parking lot and a porous pavement parking lot of turf with geocells in Athens (Georgia, USA) during rainfall events with low rainfall duration and intensity. The results indicated that porous pavements are a suitable option for reducing runoff and some pollutants caused by small floods in clay soils. By investigating the combination of different pavements with geocell, Rosen (2013), Sato & Kojma (2018) and Wesseloo et al. (2009) found that these compounds can be effective in slope protection and runoff and erosion control. In another similar research, Song et al. (2021a) used geocell structure to strengthen soil for growing vegetation and improving slope stability. The composition of geocell and soil was investigated in two small and large sizes and two slopes under three different rainfall intensities. The results were similar to the research Rosen (2013), Sato & Kojma (2018), Song et al. (2021b) and Wesseloo et al. (2009) and highlighted the appropriate performance of the geocell. In total, limited studies have been conducted in the field of combining geocells with plants in order to control and reduce the volume of urban runoff.
In past research, it seems that no study has been done on rosemary plants in the field of flood control methods. Also, the effect that this plant can have on hydrological parameters is not seen in past studies. The creation of variable rainfall intensity during the experiment (during rainfall) based on natural rainfall has been less seen in past research in the experimental dimension. In general, studies highlight the importance of developing different methods for urban runoff and flooding control in all parts of the world. Despite conducting numerous research on IT and turf plants in the field of runoff reduction, most research is related to field studies and few studies have been conducted on a laboratory scale to carefully evaluate the hydrological parameters of LIDs such as IT and turfs. The investigation of cloudbursts shows that the rainfall intensity varies during a rainfall event such that the maximum rainfall intensity may be several times the average rainfall of that cloudburst. Considering different scenarios for variable rainfall intensity, this research first introduces the rosemary plant as a new LID to check its efficiency in the field of reducing runoff in a laboratory using a rainfall simulator. To this aim, similar tests were performed for turf pavements and IT, and the results of their hydrological parameters were compared with those of the rosemary plant. Then, we evaluated the effect of combining the mentioned LIDs with the geocell layer in reducing runoff. Given the lack of testing and comparing effective hydrological parameters with control treatments in most studies, this research seeks to compare the hydrological parameters of time-to-start runoff (TR), time-to-end runoff (TER), time-to-peak (TP), time base (TB), runoff coefficient (C), and PF rate for treatments of infiltration trench, turf, and rosemary (with and without geocell) with an impervious surface (control) and bed soil using a rainfall simulator.
METHODS AND MATERIALS
Model's explanations
Test details
Sample test . | Models abbreviation . | Slope (%) . | Number of rainfall intensity scenarios . |
---|---|---|---|
Control (without geocell) | O1 | 0 | 1 |
O2 | 5 | 1 | |
O3 | 0 | 2 | |
O4 | 5 | 2 | |
O5 | 0 | 3 | |
O6 | 5 | 3 | |
Base soil (sandy loam) | SL1 | 0 | 1 |
SL2 | 5 | 1 | |
SL3 | 0 | 2 | |
SL4 | 5 | 2 | |
SL5 | 0 | 3 | |
SL6 | 5 | 3 | |
Gravel | G1 | 0 | 1 |
G2 | 5 | 1 | |
G3 | 0 | 2 | |
G4 | 5 | 2 | |
G5 | 0 | 3 | |
G6 | 5 | 3 | |
Gravel with geocell | GGE1 | 0 | 1 |
GGE2 | 5 | 1 | |
GGE3 | 0 | 2 | |
GGE4 | 5 | 2 | |
GGE5 | 0 | 3 | |
GGE6 | 5 | 3 | |
Rosemary | R1 | 0 | 1 |
R2 | 5 | 1 | |
R3 | 0 | 2 | |
R4 | 5 | 2 | |
R5 | 0 | 3 | |
R6 | 5 | 3 | |
Rosemary with geocell | RGE1 | 0 | 1 |
RGE2 | 5 | 1 | |
RGE3 | 0 | 2 | |
RGE4 | 5 | 2 | |
RGE5 | 0 | 3 | |
RGE6 | 5 | 3 | |
Turf | T1 | 0 | 1 |
T2 | 5 | 1 | |
T3 | 0 | 2 | |
T4 | 5 | 2 | |
T5 | 0 | 3 | |
T6 | 5 | 3 | |
Turf with geocell | TGE1 | 0 | 1 |
TGE2 | 5 | 1 | |
TGE3 | 0 | 2 | |
TGE4 | 5 | 2 | |
TGE5 | 0 | 3 | |
TGE6 | 5 | 3 |
Sample test . | Models abbreviation . | Slope (%) . | Number of rainfall intensity scenarios . |
---|---|---|---|
Control (without geocell) | O1 | 0 | 1 |
O2 | 5 | 1 | |
O3 | 0 | 2 | |
O4 | 5 | 2 | |
O5 | 0 | 3 | |
O6 | 5 | 3 | |
Base soil (sandy loam) | SL1 | 0 | 1 |
SL2 | 5 | 1 | |
SL3 | 0 | 2 | |
SL4 | 5 | 2 | |
SL5 | 0 | 3 | |
SL6 | 5 | 3 | |
Gravel | G1 | 0 | 1 |
G2 | 5 | 1 | |
G3 | 0 | 2 | |
G4 | 5 | 2 | |
G5 | 0 | 3 | |
G6 | 5 | 3 | |
Gravel with geocell | GGE1 | 0 | 1 |
GGE2 | 5 | 1 | |
GGE3 | 0 | 2 | |
GGE4 | 5 | 2 | |
GGE5 | 0 | 3 | |
GGE6 | 5 | 3 | |
Rosemary | R1 | 0 | 1 |
R2 | 5 | 1 | |
R3 | 0 | 2 | |
R4 | 5 | 2 | |
R5 | 0 | 3 | |
R6 | 5 | 3 | |
Rosemary with geocell | RGE1 | 0 | 1 |
RGE2 | 5 | 1 | |
RGE3 | 0 | 2 | |
RGE4 | 5 | 2 | |
RGE5 | 0 | 3 | |
RGE6 | 5 | 3 | |
Turf | T1 | 0 | 1 |
T2 | 5 | 1 | |
T3 | 0 | 2 | |
T4 | 5 | 2 | |
T5 | 0 | 3 | |
T6 | 5 | 3 | |
Turf with geocell | TGE1 | 0 | 1 |
TGE2 | 5 | 1 | |
TGE3 | 0 | 2 | |
TGE4 | 5 | 2 | |
TGE5 | 0 | 3 | |
TGE6 | 5 | 3 |
Histogram of different rainfall intensities during the duration of rainfall, (a): Scenario 1, (b): Scenario 2, and (c): Scenario 3.
Histogram of different rainfall intensities during the duration of rainfall, (a): Scenario 1, (b): Scenario 2, and (c): Scenario 3.
Rainfall simulator

Model's structure
Plants
Rosemary
Rosemary is a plant with fragrant, evergreen, needle-like leaves and white, pink, purple, or blue flowers (de Macedo et al. 2020). This plant belongs to the mint family, which also includes many plants (de Macedo et al. 2020).
Turf
Most grasses are perennial plants, although annual grasses are available in nature. Turf is mostly propagated through seed cultivation; some types of turfs are also propagated by non-seed methods. Among other characteristics of turf is the continuous production of fresh leaves from its growing center, which is performed throughout the growing season. However, the leaves are worn out and destroyed in cases like old age. Regardless of the natural and agricultural conditions, turf grows fast in any case and it is always more than the amount of seed sprayed on the land. Thus, turf has been widely used in green spaces, because of its characteristics such as being uniform, continuous, and having fast growth.
Gravel
In this research, gravel (sand particles) was used to investigate an infiltration trench in the laboratory dimension. Gravel soil details, ≥15, 15–12, 12–9, 6–9, 4–6, and 4 mm, respectively, 100, 27.4, 35.2, 21.1, 15.2, and 1.1%.
Geocell

Tested treatments: (a) O, (b) SL, (c) G, (d) GGE, (e) R, (f) RGE, (g) T, and (h) TGE.
Tested treatments: (a) O, (b) SL, (c) G, (d) GGE, (e) R, (f) RGE, (g) T, and (h) TGE.
Statistical analysis


RESULTS AND DISCUSSION
By using a rainfall simulator, this study aims to evaluate the performance of impervious pavement, substrate soil, porous gravel, rosemary, and turf in two conditions with and without geocell in terms of TR, TER, C, PF, TP, and TB and under different rainfall intensities and slopes. To this aim, we measured the effective runoff parameters mentioned in the research (Liu et al. 2020a, 2020b).
Cumulative RV
Diagram of cumulative runoff volume in treatments O, SL, G, GE, R, RGE, T, and TGE: (a) Scenario 1, 0% slope, (b) Scenario 1, 5% slope, (c) Scenario 2, 0% slope, (d)) scenario 2, 5% slope, (e) scenario 3, 0% slope and (g) scenario 3, 5% slope.
Diagram of cumulative runoff volume in treatments O, SL, G, GE, R, RGE, T, and TGE: (a) Scenario 1, 0% slope, (b) Scenario 1, 5% slope, (c) Scenario 2, 0% slope, (d)) scenario 2, 5% slope, (e) scenario 3, 0% slope and (g) scenario 3, 5% slope.
Changes in flow rate relative to time
Hydrograph diagram in treatments O, SL, G, GE, R, RGE, T, and TGE: (a) scenario 1, 0% slope, (b) scenario 1, 5% slope, (c) Scenario 2, 0% slope, (d) scenario 2, 5% slope, (e) scenario 3, 0% slope, and (g) scenario 3, 5% slope.
Hydrograph diagram in treatments O, SL, G, GE, R, RGE, T, and TGE: (a) scenario 1, 0% slope, (b) scenario 1, 5% slope, (c) Scenario 2, 0% slope, (d) scenario 2, 5% slope, (e) scenario 3, 0% slope, and (g) scenario 3, 5% slope.
Statistical analysis
Table 2 presents the results of the factorial analysis of parameters TR, TER, TP, TB, C, and PF based on independent variables i and s and treatments O, SL, G, GGE, R, RGE, T, and TGE. The results were then categorized using Duncan's method. Table 2 indicates the factorial results of the effect of parameters i and s and sample tests on TR, TER, TP, TB, C, and PF. In all cases, there is a significant difference at the level of 5% between the data. It is worth noting that the t-student method was used for the six dependent parameters TR, TER, C, TP, TB, and PF, as the independent parameter s has been tested at two levels of 0 and 5% in this research. The results revealed no significant difference between the two slopes in the outputs in all six parameters .
Results of analysis of independent variables
. | Source . | Type III sum of squares . | df . | Mean square . | F . | Sig. . |
---|---|---|---|---|---|---|
TR | s | 239.218 | 1 | 239.218 | 48,517.408 | 0.000 |
ST | 14,024.412 | 7 | 2,003.487 | 406,341.111 | 0.000 | |
RIS | 23,520.105 | 2 | 11,760.053 | 2,385,137.408 | 0.000 | |
s * ST | 73.410 | 7 | 10.487 | 2,126.970 | 0.000 | |
s * RIS | 3.484 | 2 | 1.742 | 353.296 | 0.000 | |
ST * RIS | 3,472.517 | 14 | 248.037 | 50,306.085 | 0.000 | |
s * ST * RIS | 60.338 | 14 | 4.310 | 874.117 | 0.000 | |
Error | 0.473 | 96 | 0.005 | |||
Total | 146,921.480 | 144 | ||||
Corrected Total | 41,393.958 | 143 | ||||
TER | s | 58.650 | 1 | 58.650 | 104.797 | 0.000 |
ST | 6,359.253 | 7 | 908.465 | 1,623.265 | 0.000 | |
RIS | 71.708 | 2 | 35.854 | 64.065 | 0.000 | |
s * ST | 128.776 | 7 | 18.397 | 32.871 | 0.000 | |
s * RIS | 21.526 | 2 | 10.763 | 19.232 | 0.000 | |
ST * RIS | 286.917 | 14 | 20.494 | 36.619 | 0.000 | |
s * ST * RIS | 136.306 | 14 | 9.736 | 17.397 | 0.000 | |
Error | 53.727 | 96 | 0.560 | |||
Total | 1,523,975.950 | 144 | ||||
Corrected Total | 7,116.863 | 143 | ||||
C | s | 588.063 | 1 | 588.063 | 90,086.170 | 0.000 |
ST | 59,721.036 | 7 | 8,531.577 | 1,306,964.930 | 0.000 | |
RIS | 29.042 | 2 | 14.521 | 2,224.511 | 0.000 | |
s * ST | 112.148 | 7 | 16.021 | 2,454.292 | 0.000 | |
s * RIS | 42.012 | 2 | 21.006 | 3,217.915 | 0.000 | |
ST * RIS | 428.478 | 14 | 30.606 | 4,688.511 | 0.000 | |
s * ST * RIS | 67.868 | 14 | 4.848 | 742.632 | 0.000 | |
Error | 0.627 | 96 | 0.007 | |||
Total | 356,254.720 | 144 | ||||
Corrected Total | 60,989.273 | 143 | ||||
PF | s | 798,908.234 | 1 | 798,908.234 | 9,337,888.445 | 0.000 |
ST | 45,026,996.860 | 7 | 6,432,428.123 | 75,184,224.820 | 0.000 | |
RIS | 269,540.004 | 2 | 134,770.002 | 1,575,233.791 | 0.000 | |
s * ST | 286,231.903 | 7 | 40,890.272 | 477,938.243 | 0.000 | |
s * RIS | 54,817.821 | 2 | 27,408.910 | 320,363.889 | 0.000 | |
ST * RIS | 716,681.944 | 14 | 51,191.567 | 598,342.996 | 0.000 | |
s * ST * RIS | 221,768.369 | 14 | 15,840.598 | 185,149.844 | 0.000 | |
Error | 8.213 | 96 | 0.086 | |||
Total | 448,969,873.400 | 144 | ||||
Corrected Total | 47,374,953.350 | 143 | ||||
TP | s | 286.456 | 1 | 286.456 | 38,194.083 | 0.000 |
ST | 1,075.318 | 7 | 153.617 | 20,482.253 | 0.000 | |
RIS | 97,657.401 | 2 | 48,828.700 | 6,510,493.370 | 0.000 | |
s * ST | 719.427 | 7 | 102.775 | 13,703.374 | 0.000 | |
s * RIS | 86.805 | 2 | 43.403 | 5,787.000 | 0.000 | |
ST * RIS | 1,249.623 | 14 | 89.259 | 11,901.169 | 0.000 | |
s * ST * RIS | 840.354 | 14 | 60.025 | 8,003.370 | 0.000 | |
Error | 0.720 | 96 | 0.008 | |||
Total | 387,348.070 | 144 | ||||
Corrected Total | 101,916.103 | 143 | ||||
TB | s | 64.937 | 1 | 64.937 | 15,082.081 | 0.000 |
ST | 17,846.026 | 7 | 2,549.432 | 592,126.210 | 0.000 | |
RIS | 22,136.045 | 2 | 11,068.023 | 2,570,637.500 | 0.000 | |
s * ST | 184.278 | 7 | 26.325 | 6,114.302 | 0.000 | |
s * RIS | 32.998 | 2 | 16.499 | 3,831.984 | 0.000 | |
ST * RIS | 4,039.174 | 14 | 288.512 | 67,009.334 | 0.000 | |
s * ST * RIS | 114.619 | 14 | 8.187 | 1,901.514 | 0.000 | |
Error | 0.413 | 96 | 0.004 | |||
Total | 867,687.390 | 144 | ||||
Corrected Total | 44,418.490 | 143 |
. | Source . | Type III sum of squares . | df . | Mean square . | F . | Sig. . |
---|---|---|---|---|---|---|
TR | s | 239.218 | 1 | 239.218 | 48,517.408 | 0.000 |
ST | 14,024.412 | 7 | 2,003.487 | 406,341.111 | 0.000 | |
RIS | 23,520.105 | 2 | 11,760.053 | 2,385,137.408 | 0.000 | |
s * ST | 73.410 | 7 | 10.487 | 2,126.970 | 0.000 | |
s * RIS | 3.484 | 2 | 1.742 | 353.296 | 0.000 | |
ST * RIS | 3,472.517 | 14 | 248.037 | 50,306.085 | 0.000 | |
s * ST * RIS | 60.338 | 14 | 4.310 | 874.117 | 0.000 | |
Error | 0.473 | 96 | 0.005 | |||
Total | 146,921.480 | 144 | ||||
Corrected Total | 41,393.958 | 143 | ||||
TER | s | 58.650 | 1 | 58.650 | 104.797 | 0.000 |
ST | 6,359.253 | 7 | 908.465 | 1,623.265 | 0.000 | |
RIS | 71.708 | 2 | 35.854 | 64.065 | 0.000 | |
s * ST | 128.776 | 7 | 18.397 | 32.871 | 0.000 | |
s * RIS | 21.526 | 2 | 10.763 | 19.232 | 0.000 | |
ST * RIS | 286.917 | 14 | 20.494 | 36.619 | 0.000 | |
s * ST * RIS | 136.306 | 14 | 9.736 | 17.397 | 0.000 | |
Error | 53.727 | 96 | 0.560 | |||
Total | 1,523,975.950 | 144 | ||||
Corrected Total | 7,116.863 | 143 | ||||
C | s | 588.063 | 1 | 588.063 | 90,086.170 | 0.000 |
ST | 59,721.036 | 7 | 8,531.577 | 1,306,964.930 | 0.000 | |
RIS | 29.042 | 2 | 14.521 | 2,224.511 | 0.000 | |
s * ST | 112.148 | 7 | 16.021 | 2,454.292 | 0.000 | |
s * RIS | 42.012 | 2 | 21.006 | 3,217.915 | 0.000 | |
ST * RIS | 428.478 | 14 | 30.606 | 4,688.511 | 0.000 | |
s * ST * RIS | 67.868 | 14 | 4.848 | 742.632 | 0.000 | |
Error | 0.627 | 96 | 0.007 | |||
Total | 356,254.720 | 144 | ||||
Corrected Total | 60,989.273 | 143 | ||||
PF | s | 798,908.234 | 1 | 798,908.234 | 9,337,888.445 | 0.000 |
ST | 45,026,996.860 | 7 | 6,432,428.123 | 75,184,224.820 | 0.000 | |
RIS | 269,540.004 | 2 | 134,770.002 | 1,575,233.791 | 0.000 | |
s * ST | 286,231.903 | 7 | 40,890.272 | 477,938.243 | 0.000 | |
s * RIS | 54,817.821 | 2 | 27,408.910 | 320,363.889 | 0.000 | |
ST * RIS | 716,681.944 | 14 | 51,191.567 | 598,342.996 | 0.000 | |
s * ST * RIS | 221,768.369 | 14 | 15,840.598 | 185,149.844 | 0.000 | |
Error | 8.213 | 96 | 0.086 | |||
Total | 448,969,873.400 | 144 | ||||
Corrected Total | 47,374,953.350 | 143 | ||||
TP | s | 286.456 | 1 | 286.456 | 38,194.083 | 0.000 |
ST | 1,075.318 | 7 | 153.617 | 20,482.253 | 0.000 | |
RIS | 97,657.401 | 2 | 48,828.700 | 6,510,493.370 | 0.000 | |
s * ST | 719.427 | 7 | 102.775 | 13,703.374 | 0.000 | |
s * RIS | 86.805 | 2 | 43.403 | 5,787.000 | 0.000 | |
ST * RIS | 1,249.623 | 14 | 89.259 | 11,901.169 | 0.000 | |
s * ST * RIS | 840.354 | 14 | 60.025 | 8,003.370 | 0.000 | |
Error | 0.720 | 96 | 0.008 | |||
Total | 387,348.070 | 144 | ||||
Corrected Total | 101,916.103 | 143 | ||||
TB | s | 64.937 | 1 | 64.937 | 15,082.081 | 0.000 |
ST | 17,846.026 | 7 | 2,549.432 | 592,126.210 | 0.000 | |
RIS | 22,136.045 | 2 | 11,068.023 | 2,570,637.500 | 0.000 | |
s * ST | 184.278 | 7 | 26.325 | 6,114.302 | 0.000 | |
s * RIS | 32.998 | 2 | 16.499 | 3,831.984 | 0.000 | |
ST * RIS | 4,039.174 | 14 | 288.512 | 67,009.334 | 0.000 | |
s * ST * RIS | 114.619 | 14 | 8.187 | 1,901.514 | 0.000 | |
Error | 0.413 | 96 | 0.004 | |||
Total | 867,687.390 | 144 | ||||
Corrected Total | 44,418.490 | 143 |
Table 3 reports the classification of parameters i and for dependent data of TR, TER, TP, TB, C, and PF based on Duncan's method. Three rainfall scenarios for all hydrological parameters TR, TER, TP, TB, C, and PF were placed in different groups. For the dependent variables of TR, C, PF, and TB, eight treatments were each placed in a different group. Concerning the dependent variable TER, the performance of treatments RGE and O, treatments R and GGE, and treatments T and TGE were similar to each other. For the dependent variable TP, treatments R and SL were placed in one group of Duncan's classification.
Classification of parameters TR, TER, TP, TB, C, and PF based on the independent variables of rainfall intensity scenario and treatments with Duncan's method
. | . | . | N . | Subset . | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 . | 2 . | 3 . | 4 . | 5 . | 6 . | 7 . | 8 . | ||||
TR | RIS | 2 | 48 | 9.8958 | |||||||
3 | 48 | 30.7833 | |||||||||
1 | 48 | 40.5333 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | O | 18 | 2.0500 | ||||||||
SL | 18 | 25.3556 | |||||||||
T | 18 | 28.0333 | |||||||||
TGE | 18 | 29.6889 | |||||||||
R | 18 | 31.0222 | |||||||||
G | 18 | 32.3667 | |||||||||
RGE | 18 | 33.0278 | |||||||||
GGE | 18 | 35.0222 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |||
TER | RIS | 3.00 | 48 | 101.9021 | |||||||
2.00 | 48 | 102.4125 | |||||||||
1.00 | 48 | 103.5875 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | RGE | 18 | 96.3722 | ||||||||
O | 18 | 96.7000 | |||||||||
R | 18 | 98.0111 | |||||||||
GGE | 18 | 98.0278 | |||||||||
G | 18 | 98.7000 | |||||||||
SL | 18 | 109.0333 | |||||||||
T | 18 | 111.8722 | |||||||||
TGE | 18 | 112.3556 | |||||||||
Sig. | 0.192 | 0.947 | 1.000 | 1.000 | 0.056 | ||||||
C | RIS | 3.00 | 48 | 44.6542 | |||||||
1.00 | 48 | 45.5125 | |||||||||
2.00 | 48 | 45.6792 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | GGE | 18 | 23.3000 | ||||||||
G | 18 | 26.4944 | |||||||||
RGE | 18 | 37.5111 | |||||||||
R | 18 | 41.0111 | |||||||||
TGE | 18 | 41.7944 | |||||||||
T | 18 | 46.3556 | |||||||||
SL | 18 | 52.1000 | |||||||||
O | 18 | 93.6889 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |||
PF | RIS | 2.00 | 48 | 1,620.6500 | |||||||
3.00 | 48 | 1,663.3146 | |||||||||
1.00 | 48 | 1,725.9937 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | GGE | 18 | 1,056.7444 | ||||||||
G | 18 | 1,116.8722 | |||||||||
RGE | 18 | 1,490.6278 | |||||||||
R | 18 | 1,525.777 | |||||||||
TGE | 18 | 1,652.7333 | |||||||||
T | 18 | 1,706.0222 | |||||||||
SL | 18 | 1,821.2556 | |||||||||
O | 18 | 2,989.8556 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |||
TP | RIS | 48 | 14.5229 | ||||||||
48 | 41.0188 | ||||||||||
48 | 78.0229 | ||||||||||
1.000 | 1.000 | 1.000 | |||||||||
ST | T | 18 | 41.3556 | ||||||||
R | 18 | 42.3389 | |||||||||
SL | 18 | 42.3556 | |||||||||
O | 18 | 43.3722 | |||||||||
RGE | 18 | 44.3500 | |||||||||
TGE | 18 | 45.6778 | |||||||||
GGE | 18 | 46.3500 | |||||||||
G | 18 | 50.3722 | |||||||||
Sig. | 1.000 | 0.565 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||||
TB | RIS | 1.00 | 48 | 63.1500 | |||||||
3.00 | 48 | 71.1604 | |||||||||
2.00 | 48 | 92.5250 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | GGE | 18 | 63.0333 | ||||||||
RGE | 18 | 64.0333 | |||||||||
G | 18 | 66.3722 | |||||||||
R | 18 | 67.0111 | |||||||||
TGE | 18 | 82.6889 | |||||||||
T | 18 | 83.3611 | |||||||||
SL | 18 | 83.6944 | |||||||||
O | 18 | 94.7000 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
. | . | . | N . | Subset . | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 . | 2 . | 3 . | 4 . | 5 . | 6 . | 7 . | 8 . | ||||
TR | RIS | 2 | 48 | 9.8958 | |||||||
3 | 48 | 30.7833 | |||||||||
1 | 48 | 40.5333 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | O | 18 | 2.0500 | ||||||||
SL | 18 | 25.3556 | |||||||||
T | 18 | 28.0333 | |||||||||
TGE | 18 | 29.6889 | |||||||||
R | 18 | 31.0222 | |||||||||
G | 18 | 32.3667 | |||||||||
RGE | 18 | 33.0278 | |||||||||
GGE | 18 | 35.0222 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |||
TER | RIS | 3.00 | 48 | 101.9021 | |||||||
2.00 | 48 | 102.4125 | |||||||||
1.00 | 48 | 103.5875 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | RGE | 18 | 96.3722 | ||||||||
O | 18 | 96.7000 | |||||||||
R | 18 | 98.0111 | |||||||||
GGE | 18 | 98.0278 | |||||||||
G | 18 | 98.7000 | |||||||||
SL | 18 | 109.0333 | |||||||||
T | 18 | 111.8722 | |||||||||
TGE | 18 | 112.3556 | |||||||||
Sig. | 0.192 | 0.947 | 1.000 | 1.000 | 0.056 | ||||||
C | RIS | 3.00 | 48 | 44.6542 | |||||||
1.00 | 48 | 45.5125 | |||||||||
2.00 | 48 | 45.6792 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | GGE | 18 | 23.3000 | ||||||||
G | 18 | 26.4944 | |||||||||
RGE | 18 | 37.5111 | |||||||||
R | 18 | 41.0111 | |||||||||
TGE | 18 | 41.7944 | |||||||||
T | 18 | 46.3556 | |||||||||
SL | 18 | 52.1000 | |||||||||
O | 18 | 93.6889 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |||
PF | RIS | 2.00 | 48 | 1,620.6500 | |||||||
3.00 | 48 | 1,663.3146 | |||||||||
1.00 | 48 | 1,725.9937 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | GGE | 18 | 1,056.7444 | ||||||||
G | 18 | 1,116.8722 | |||||||||
RGE | 18 | 1,490.6278 | |||||||||
R | 18 | 1,525.777 | |||||||||
TGE | 18 | 1,652.7333 | |||||||||
T | 18 | 1,706.0222 | |||||||||
SL | 18 | 1,821.2556 | |||||||||
O | 18 | 2,989.8556 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |||
TP | RIS | 48 | 14.5229 | ||||||||
48 | 41.0188 | ||||||||||
48 | 78.0229 | ||||||||||
1.000 | 1.000 | 1.000 | |||||||||
ST | T | 18 | 41.3556 | ||||||||
R | 18 | 42.3389 | |||||||||
SL | 18 | 42.3556 | |||||||||
O | 18 | 43.3722 | |||||||||
RGE | 18 | 44.3500 | |||||||||
TGE | 18 | 45.6778 | |||||||||
GGE | 18 | 46.3500 | |||||||||
G | 18 | 50.3722 | |||||||||
Sig. | 1.000 | 0.565 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | ||||
TB | RIS | 1.00 | 48 | 63.1500 | |||||||
3.00 | 48 | 71.1604 | |||||||||
2.00 | 48 | 92.5250 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | ||||||||
ST | GGE | 18 | 63.0333 | ||||||||
RGE | 18 | 64.0333 | |||||||||
G | 18 | 66.3722 | |||||||||
R | 18 | 67.0111 | |||||||||
TGE | 18 | 82.6889 | |||||||||
T | 18 | 83.3611 | |||||||||
SL | 18 | 83.6944 | |||||||||
O | 18 | 94.7000 | |||||||||
Sig. | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Evaluation of the effective parameters in runoff
Radar chart to compare peak flow rate for different test treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and 0% slope, and (f): scenario 3 and 5% slope.
Radar chart to compare peak flow rate for different test treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and 0% slope, and (f): scenario 3 and 5% slope.
Comparison of the runoff coefficient for different test treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and slope 0%, and (f): scenario 3 and 5% slope.
Comparison of the runoff coefficient for different test treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and slope 0%, and (f): scenario 3 and 5% slope.
Comparison chart of time to start runoff and time-to-end runoff for different test treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and 0% slope, and (f): scenario 3 and 5% slope.
Comparison chart of time to start runoff and time-to-end runoff for different test treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and 0% slope, and (f): scenario 3 and 5% slope.
Figure 8 illustrates the runoff coefficient diagram for treatments O, SL, G, GGE, R, RGE, T, and TGE for the scenarios of rainfall intensity and different slopes in terms of percentage. For a specific scenario of rainfall intensity and slope, the value of C is the highest for the O treatment and the lowest for the GGE treatment. Regarding a specific rainfall intensity scenario, changing the slope from 0 to 5% causes the runoff coefficient to increase in all treatments O, SL, G, GGE, R, RGE, T, and TGE. In general, the results of the runoff coefficient in this research can be considered close to the results of research (Hunt et al. 2002; Mullaney & Lucke 2014). It can be concluded that the implementation of the geocell layer in treatments G, T, and R in all scenarios of rainfall intensity and studied slopes can reduce the runoff coefficient. In treatment O, changes in the runoff coefficient in different conditions of the tests are not large and are around 10%. The runoff coefficient for the control treatment in scenario 1 and 5% slope was higher among all the tests and its value was 0.97. Ultimately, the runoff coefficient for the GGE treatment in scenario 2 and the slope of 0% is the lowest value and equal to 0.19. In Liu et al.’s (2020a, 2020b) research, it was observed that grass vegetation can reduce the runoff coefficient between 40 and 50%, which is consistent with the present research. Dayaratne & Perera (2008) concluded that urban impervious areas are able to produce a faster hydrological response compared with the natural pervious areas, even for a low-intensity rainfall. Due to the limited initial loss, more rainfall was transferred into the concrete impervious surface runoff under more significant rainfall events and thus caused a higher runoff coefficient.
Figure 9 shows the time to start runoff (TR) and TER for treatments O, SL, G, GGE, R, RGE, T, and TGE under different test conditions. The time-to-start runoff for an experiment occurs earlier in the control treatment (O) compared to treatments O, SL, G, GGE, R, RGE, T, and TGE for all cases. The time-to-start runoff occurs later in treatments GGE, RGE, and TGE than in treatments G, R, and T under the same test conditions. In other words, at a certain rainfall intensity and slope, combining the treatments with the geocell layer allows them to perform better in delaying the time to start runoff. The time-to-start runoff for all test treatments in scenario 1 occurs later than that of scenarios 2 and 3, because the rainfall intensity changes from the lowest to the highest rainfall intensity in scenario 1, and the creation of runoff occurs later. In scenario 2, runoff occurs earlier in all investigated treatments, as the rainfall starts with the highest intensity and reaches the lowest amount of intensity with the passage of time, which makes the capacity of water infiltration in the soil to be completed faster than that of other scenarios and thus, runoff occurs earlier. Further, it is possible to create runoff faster by increasing the slope from 0 to 5% in a specific scenario for a treatment. However, the TER varies between 92 and 114 min for all treatments. In Saraçoğlu & Kazezyılmaz-Alhan (2023) and Demirezen & Kazezyılmaz-Alhan (2022), because of the larger test basin, the TER in the infiltration trench and turf treatments was reported to be longer than that of the current research. In Liu et al.’s (2020a, 2020b) research, it was shown that the grassland surface effectively delays the onset of runoff. Grassland surface represented that the time to runoff was about 25 times than that of the impervious surface. In the present research, it is between 15 and 20 times for turf and rosemary plants and between 20 and 25 times for gravel. So it was showcased as a potential storm water retention area.
Comparison chart of time to peak (TP) and time base (TB) for different test treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and 0% slope, and (f): scenario 3 and 5% slope.
Comparison chart of time to peak (TP) and time base (TB) for different test treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and 0% slope, and (f): scenario 3 and 5% slope.
Percentage of RV reduction
The results of reducing runoff volume in different treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and zero slope percentage, and (f): scenario 3 and slope 5%.
The results of reducing runoff volume in different treatments, (a): scenario 1 and 0% slope, (b): scenario 1 and 5% slope, (c): scenario 2 and 0% slope, (d): scenario 2 and 5% slope, (e): scenario 3 and zero slope percentage, and (f): scenario 3 and slope 5%.
CONCLUSION
This research sought to quantitatively evaluate the effect of rosemary, turf, and infiltration trench treatments on some parameters of the catchment basin, such as the amount of RV reduction, time to start runoff, and TER, runoff coefficient, runoff base coefficient, time to peak, and PF rate in the laboratory dimension. To this aim, 48 tests were conducted on eight treatments of control (impermeable), sandy loam, gravel, gravel with geocell layer, rosemary, rosemary with geocell layer, turf, and turf with the geocell layer in three scenarios of rainfall intensity and two slopes. By using the geocell layer in a certain treatment, the cumulative RV and the reduction percentage of the RV were the lowest and the highest in all test cases, respectively. The gravel with geocell treatment can reduce the RV by about 78%, which is the best treatment in this regard, although different types of vegetation and gravel without geocells may be preferred for normal rainfall events due to practical implementation. The presence of vegetation and gravel can also delay the time to PF rate. By comparing the turf and rosemary pavement and gravel with sandy loam pavement, it is found that the reduction percentage in RV is higher and the PF rate is less. Thus, it is recommended to use medium or low clay and silt in the implementation of sandy loam bed soil. By performing statistical analysis, it can be found that the change of slope and rainfall intensity creates a significant difference in the parameters of time to start and TER, runoff coefficient, TB, time to peak, and PF rate. Changes in the slope from 0 to 5% in all treatments caused the runoff to start a little earlier. The runoff coefficient should increase to about 5%. The present study was conducted on a laboratory scale and the dimensions and size of the plot and pond are limitations of this research. Water flow infiltration was allowed in all tests; however, its quantity was not measured. Future research is suggested to investigate the soil erosion rate and the quality of runoff with and without geocell layers under the soil conditions studied in this research. Also, it is suggested to conduct experiments on a field scale and with larger dimensions.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.