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MethodsX
MethodsX
MethodsX
2215-0161
Elsevier

S2215-0161(24)00357-1
10.1016/j.mex.2024.102905
102905
Environmental Science
A simple method to assess flood regulation supply in urban lawns
Pereira Paulo pereiraub@gmail.com
a⁎
Inacio Miguel a
Kalinauskas Marius a
Pinto Luis a
Barcelo Damia b
Bogunovic Igor c
a Environmental Management Laboratory, Mykolas Romeris University, Vilnius, Lithuania
b Department of Chemistry and Physics, University of Almería, Spain
c Faculty of Agriculture, University of Zagreb, Svetosimunska 25, Zagreb 10000, Croatia
⁎ Corresponding author. pereiraub@gmail.com
13 8 2024
12 2024
13 8 2024
13 1029051 5 2024
12 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Floods have an important impact on life and loss of goods. Urban green spaces are crucial to mitigating flood impact. However, their capacity to prevent floods depends on their condition, especially in areas highly affected by human activities such as lawns. Here, we developed a simple method to assess flood regulation using soil penetration resistance as a proxy and tested it on an urban lawn in Vilnius (Lithuania) in winter. We developed an experimental design using an app for collecting data and working with it in a GIS environment. To understand their spatial relations, geostatistical (e.g., semi-variogram model and ordinary kriging mapping) and spatial statistics ((Moran's global autocorrelation index and Cluster and Outlier Analysis (Anselin Local Moran's I)) tools were applied. The preliminary results from the tested method showed that the lawn studied has different capacities to retain floods due to the management practices. Nevertheless, it is essential to be applied in different soil moisture conditions since flood regulation (soil penetration resistance) can be variable throughout the year.• A novel method was developed to estimate flood regulation using soil penetration resistance as a proxy;

• An urban lawn was used to test the method and identify areas with low and high capacity for flood regulation;

• The method quickly assesses lawn flood retention capacity in different environments.

Graphical abstract

Image, graphical abstract

Keywords

Flood regulation
Soil penetration resistance
Urban lawns
Geostatistics
Spatial statistics
Method name

Flood Regulation Supply Proxy
==== Body
pmcSpecifications tableSubject area:	Environmental Science	
More specific subject area:	Soil science, ecosystem services, nature-based solutions	
Name of your method:	Flood Regulation Supply Proxy	
Name and reference of original method:	N/A	
Resource availability:	Tyd-2 Digital Soil Hardness Tester Soil Penetrometer QGISField ArcGIS Pro	

Background

Floods are responsible for substantial losses of lives and goods in urban areas [1]. It is expected that in the future, due to climate change, extreme precipitation events and growing urbanization will increase it. In cities, urban green spaces (UGS) (e.g., urban forests, gardens, wetlands, lawns) supply a critical number of ecosystem services (ES), including flood regulation [2]. UGS are essential to retain water and reduce the overland flow. Nevertheless, UGS's effectiveness depends on the type and the management conducted. For instance, in urban areas, lawns are amongst the most disturbed by human activities (e.g., management with tractors, tracks, and recreation), reducing their ability to regulate floods [3].

Infiltration tests (e.g., ring infiltrometer or mini-disk) are well-known methods for identifying the time that water takes to be absorbed by the soil. The faster the time, the higher the capacity [4]. Rainfall simulator methods are also used to estimate the overland flow [5]. Nevertheless, infiltration tests and rainfall simulators are extremely time-consuming [6,7]. Other proxies can be used to estimate the soil water infiltration, such as bulk density. However, bulk density analysis also requires laboratory procedures that can consume time [8]. To have a high number of samples in a short period, soil penetration resistance measurements using a penetrometer can be a good method to identify the soil's ability to infiltrate and retain water. Soil penetration resistance is typically used to measure compaction and soil strength [9]. It has been used to assess the soil resistance for root penetration [10], nest construction [11], vegetation development [12] and rodent burrows [13]. Previous works observed that soil penetration is negatively correlated with soil water content [14,15], infiltration [[16], [17], [18]] and soil water holding capacity [19,10]. Therefore, soil penetration resistance can be a good proxy for flood retention since compaction reduces water infiltration and increases overland flow. Also, soil penetration resistance measurements are relatively more straightforward, and a substantial amount of data can be sampled quickly. This is important in urban areas, especially in areas with high compaction, such as lawns [20]. Previous works measured soil penetration resistance in urban parks [21]. However, they should have considered lawns and a detailed protocol developed or mapping exercise crucial to identify spatial differences. Therefore, in this work, we developed a method to assess the capacity of lawns to regulate floods, using soil penetration resistance as a proxy and mapped it using geostatistical methods.

Method details

An urban lawn was selected in Vilnius (Lithuania) to test the method (Fig. 1A and B). The studied soils are classified as Anthrosols [22] and are subjected to frequent management by the municipality using a light tractor (axle load below 2 t). An experimental area of 9447.85 m2 was established in this urban lawn (Fig. 1C). Subsequently, a fishnet with 10 × 10 m was created using the ArcGIS pro “Create Fishnet” tool.1 A dot was placed in the centre of each square, corresponding to the sampling point (Fig. 2). The shapefile created with the points was clipped using the ArcGIS pro “Clip” tool2 to select only the points that were located inside the studied area. A total of 260 points were selected.Fig. 1 Study area (a) Lithuania location, (B) study area location and (C) Sampled area.

Fig 1

Fig. 2 Framework developed.

Fig 2

To develop the fieldwork sampling, the clipped fishnet was uploaded to the QGIS field (Qfield) app3 and a field to measure soil penetration resistance with five entrances was created (Fig. S1A). Soil penetration resistance measurements were conducted using a Tyd-2 Digital Soil Hardness Tester Soil Penetrometer (NANBEI Instrument Limited, China). The specifications of the device are shown in Table 1. As a field sampling scheme, (1) sample in the middle, (2) upper left corner, (3) upper right corner, (4) lower left corner and (5) lower right corner (Fig. 2). The penetrometer was inserted into the soil at 10 cm deep, and then the strength (kg/cm2) needed to insert the sensor was recorded in each subpoint. The values observed were introduced in the Qfield app. Once the sampling point is measured, the app changes the colour to green. If only some of the 5 entrances are filled, the sampling point is coloured in yellow (Fig. S1B), also represented in the map (Fig. S1C). The average of each sampling point corresponds to the average of 5 points. One thousand three hundred soil penetration resistance measurements were done on two consecutive (March 6 and 7, 2024) days with similar meteorological conditions. No rain occurred during sampling time. The sampling collection scheme at each point is shown in Fig. 2.Table 1 Tyd-2 Digital Soil Hardness Tester Soil Penetrometer characteristics (Source: https://nanbei-china.en.made-in-china.com/product/oZRAnQTyvJpN/China-Tyd-2-Digital-Soil-Hardness-Tester-Soil-Penetrometer.html).

Table 1Model	TYD-2	
Performance	Semi-automatic	
Weight	0–100 Kg	
Customized	Customized	
Maximum Load	50 kg (Kg,Ng and Ib Three Units Can Be Automaticall)	
Accuracy	±0.5 %	
Power: Charging Power Supply	220 V/AC;Continuous Operating Time of Battery:6∼8 H	
Calibration Range	Full-Scale Calibration	
Ambient Humidity	≤ 80 %	
Screen	LCD Display	
Storage	896 Set Values	
Transport Package	Aluminium Box and Carton	
Trademark	NANBEI	
HS Code	9,031,809,090	
Frequency	50±0.5 Hz	
Function	Tester	
Power Source	Battery	
Product Name	Portable Soil Hardness Tester	
Resolution	0.1 kg	
Measuring Depth	0∼450 mm	
Stability	0.2UV/ °C(0–60 °C);Null Drift:≤ 0	
Ambient Temperature	0∼+60 °C	
Allowable Overload	150 %	
Backlight	with Backlight Function	
MOQ	1set	
Specification	50×30×35 cm	
Origin	Zhengzhou, China	
Production Capacity	2000 Month	

Once the data was collected, it was taken from Qfield and exported to the ArcGIS Pro environment in shapefile format for statistical and spatial analysis (Fig. 2). ArcGIS Pro exported the shapefile data to a table and converted it to a CSV file for statistical analysis in JASP 0.18.3.4 Previous to modelling data, several descriptive statistics were assessed. Normal data distribution was assessed using the Shapiro-Wilk test (p > 0.05). Once the statistical analysis was conducted, spatial analysis was carried out in ArcGIS Pro (Fig. 2). Data interpolation is conducted using geostatistical methods (ordinary kriging),5 flowing the formula6:(1) Z¯(S0)=∑i=1Nλ1Z(Si)

Where Z(si) = the measured value at the ith location, λi = an unknown weight for the measured value at the ith location, s0 = the prediction location and N = the number of measured values6. In the current work, the ordinary kriging method was selected to map soil penetration resistance. Previous to mapping, the semi-variogram7 was modelled following the formula:(2) γ(Si,Sj)=1/2var(Z(Si)−Z(Sj)

Where “si and sj, are close to each other in terms of the distance measure of d(si, sj), you expect them to be similar, so the difference in their values, Z(si) - Z(sj), will be small. As si and sj get farther apart, they become less similar, so the difference in their values, Z(si) - Z(sj), will become larger."8 Several variogram parameters were assessed, such as the nugget effect (measurement error), range (distance where the model flattens and there is no spatial correlation) and partial-sill (sill minus the nugget effect).9 Multiple model types were tested (Circular, Spherical, Tetraspherical, Pentaspherical, Exponential, Gaussian, Rational Quadratic, Hole Effect, K-Bessel, J-Bessel and Stable). Geostatistical model accuracy was evaluated using the cross-validation method. The principle of this method is to use the “leave one out” sampling point and estimate this point using the remaining. Further, the estimated value is compared with the observed [23] (Fig. 2). Root-Mean-Square Error (RMSE) and r2 were used to assess geostatistical model accuracy.10

Several spatial statistics were also conducted to understand data patterns. Moran's I global spatial autocorrelation analysis was carried out to identify if data were dispersed, random or clustered. Z-scores higher than 1.96 show a significant clustered pattern, while z-scores lower than −1.96 show a significant dispersed pattern. Z-scores between −1.96 and 1.96 show a random pattern11 [24]. Moran's I scatterplot was also assessed to identify low values surrounded by low values (low-low cluster), low values surrounded by high values (low-high outlier), high values surrounded by low values (low-high outlier) and high values surrounded by high values (high-high cluster) [25]. The clusters and outlier's spatial distribution were conducted using the Cluster and Outlier Analysis (Anselin Local Moran's I) method in ArcGIS pro.12 Moran's I global spatial autocorrelation and Cluster and Outlier Analysis (Anselin Local Moran's I) used the inverse distance method to conceptualise spatial relationships, as distance method the Euclidean distance and a row standardization (Fig. 2).

The descriptive statistics obtained from soil penetration resistance measurements are shown in Table 1, Table 2. The mean value was 8.937 kg/cm2, and the distribution ranged from 3.360 to 15.672 kg/cm2. Data followed the normal distribution (Shapiro Wilk p = 0.285); therefore, data can be modelled (Table 2; Fig. 3A). Data normality is a prerequisite to be modelled using geostatistical methods [26,27]. The criteria used to model the semi-variogram (Fig. 3B) are shown in Table 3. The best-fitted model was the Gaussian, and the nugget effect was reduced (3.279). This shows some small-scale errors related to the small distance variation. The partial sill was 2.366, and the range was 3.864 m, showing no spatial autocorrelation after this distance. Soil penetration resistance spatial distribution (Fig. 3C) showed that the highest values were located in the southeast parts of the plot. At the same time, the lowest was observed in some areas in the middle and northwest of the area of interest. The areas where soil penetration resistance was high retained less water; the opposite is expected in the areas where the values are the lowest. Although low, there is some spatial heterogeneity in the capacity of the studied lawn for flood regulation. Cross-validation results (Table 4) showed that the RMSE was reduced (2.099), and the r2 was 0.67 and significant at a p < 0.001. This indicates that there is good validation. Moran's spatial autocorrelation index showed that the values were clustered (z-score value = 9.711, p < 0.001). Moran's I scatterplot is shown in Fig. 4A and identified that several points had low values surrounded by low values (low-low cluster) and low values surrounded by high values (low-high outlier). Only 3 points were considered as low-high outlier. A positive r2 (0.42, p < 0.05) showed that the highest spatial lag and the highest high values clustered. Most of the sampling points were not considered as outliers or clustered. Low-low cluster sampling points were located in the northwest and the middle of the plot, and this shows that in this area, soil penetration resistance was low, and, therefore, flood retention is likely to be more effective. The opposite was identified in the southeast area, where the low-high outliers were observed. Flood retention is higher in these points than in the surrounding ones (Fig. 4B).Table 2 Soil penetration resistance descriptive statistics.

Table 2	Soil penetration resistance (kg/cm2)	
Median	8.959	
Mean	8.937	
Standard Deviation	2.487	
Inter Quartile Range	3.224	
Skewness	0.114	
Standard Error of Skewness	0.155	
Kurtosis	−0.389	
Standard Error of Kurtosis	0.308	
Shapiro-Wilk test	0.993	
P-value of Shapiro-Wilk test	0.285	
Range	12.312	
Minimum	3.360	
Maximum	15.672	
25th percentile	7.252	
50th percentile	8.959	
75th percentile	10.476	

Fig. 3 (A) Soil penetration resistance data histogram, (B) Semi-variogram, (C) Ordinary Kriging mapping, (D) Observed vs Predicted soil hardness. Note: The number above histogram figure show the number of cases in each class.

Fig 3

Table 3 Geostatistical and variogran model results.

Table 3Geostatistics		
Kriging method	Ordinary Kriging	
Trend type	None	
Searching neighborhood	Smooth	
Smoothing factor	0.2	
Major and minor semi-axis	29.647	
Angle (°)	0	
	
Variogram		
	
Number of lags	20	
Lag size	4.046	
Nugget effect	3.279	
Model type	Gaussian	
Range	3.864 m	
Partial sill	2.366	

Table 4 Cross validation results.

Table 4Mean	−0.002	
Root-Mean-Square Error (RMSE)	2.099	
Mean Standardized	−0.0009	
Root-Mean-Square Standardized	1.003	
Average Standard Error	2.088	
r2	0.67, p < 0.001	

Fig. 4 Soil penetration resistance (A) Morans global autocorrelaton scatterplot and (B) Cluster and Outlier Analysis (Anselin Local Moran's I).

Fig 4

Method limitations

Soil penetration resistance can be used as a quick method to identify the areas with high and low capacity to retain floods. The method can be applied in other areas important to retain water (e.g., forests) or areas vulnerable to overland flow (e.g., agricultural areas). Future work can be done in these areas. One of the main advantages of the method used in this study is that it is fast to get data and reduces time-expensive laboratory work, such as bulk density, soil water retention curves [28], pressure plate, dew point method, simplified evaporation method, or hanging-water column [29]. Compared to other field methods, such as mini-disk infiltration or double-ring infiltrometer, the time to perform these experiments can be extremely high, especially if the soil is near a saturation point or is hydrophobic [30]. However, these methods can be more accurate. They are challenging to apply when we want to map flood regulation since many measurements are needed, and the time needed to invest in this can be prohibitive. Therefore, using a soil penetrometer can provide reliable information about an area's flood regulation capacity. Nevertheless, some aspects need to be considered when this method is applied. For instance, many measurements are needed to identify soil penetration resistance with accuracy and spatial patterns, even in reduced areas like the one in this work. Penetration resistance depends on soil water content, bulk density, soil texture, soil structure and soil organic matter, making this property highly spatially variable [31]. Another important aspect is that soil penetration resistance depends significantly on the soil type [32]. In our case, the soils were classified as Anthrosols with many artefacts of human origin (e.g., plastics or concrete). Soil texture also has implications for soil penetration resistance. Clay-rich soils had higher soil penetration resistance values than sandy ones [33]. Nevertheless, in soils with vertic properties, the application of this method may be limited since, during dry periods, the swelling and shirking during wet and dry cycles [34]. This process creates cracks that can be a pathway for water infiltration. Soil hydrophobicity may also affect the soil infiltration process, especially when soils are dry and rich in sand and organic matter [35]. Therefore, caution should be taken when applying the method presented, especially during the dry season. Soil humidity also affects penetration resistance. The penetration resistance is low when the soils are saturated [36]. This means that seasons also have implications for soil penetration resistance. Finally, management changes soil penetration resistance. For instance, soil penetration resistance increases in areas frequently managed with tractors [37]. The study presents a simple methodology to estimate the areas where flood regulation is high or low. Nevertheless, it is crucial to consider that this can vary according to the season and to have a complete picture of lawn flood regulation capacity. It is important to conduct measurements in different seasons. Further work needs to validate this with infiltration measurements, mainly if the work is conducted on different types of soils, seasons, and land uses.

Ethics statements

The work did not involve human beings.

The work did not involve animals.

The work did not involve data collected from social media platforms.

CRediT authorship contribution statement

Paulo Pereira: Conceptualization, Methodology, Data curation, Writing – original draft. Miguel Inacio: Visualization, Writing – review & editing. Marius Kalinauskas: Visualization, Writing – review & editing. Luis Pinto: Visualization, Writing – review & editing. Damia Barcelo: Writing – review & editing. Igor Bogunovic: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Image, application 1

Data availability

Data will be made available on request.

Acknowledgments

This work was supported by the project Monetary valuation of soil ecosystem services and creation of initiatives to invest in soil health: setting a framework for the inclusion of soil health in business and in the policy making process (InBestSoil) (10.13039/501100007601 Horizon Europe ) Grant agreement ID: 101091099 .

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.mex.2024.102905.

1 https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/create-fishnet.htm.

2 https://pro.arcgis.com/en/pro-app/3.1/tool-reference/analysis/clip.htm.

3 https://qfield.org/.

4 https://jasp-stats.org/.

5 https://pro.arcgis.com/en/pro-app/latest/help/analysis/geostatistical-analyst/understanding-ordinary-kriging.htm.

6 https://pro.arcgis.com/en/pro-app/3.1/tool-reference/3d-analyst/how-kriging-works.htm.

7 https://pro.arcgis.com/en/pro-app/latest/help/analysis/geostatistical-analyst/modeling-a-semivariogram.htm.

8 https://desktop.arcgis.com/en/arcmap/latest/extensions/geostatistical-analyst/semivariogram-and-covariance-functions.htm.

9 https://pro.arcgis.com/en/pro-app/3.1/help/analysis/geostatistical-analyst/understanding-a-semivariogram-the-range-sill-and-nugget.htm.

10 https://pro.arcgis.com/en/pro-app/latest/help/analysis/geostatistical-analyst/performing-cross-validation-and-validation.htm.

11 https://pro.arcgis.com/en/pro-app/3.1/tool-reference/spatial-statistics/what-is-a-z-score-what-is-a-p-value.htm.

12 https://pro.arcgis.com/en/pro-app/3.1/tool-reference/spatial-statistics/cluster-and-outlier-analysis-anselin-local-moran-s.htm.
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