Hydrological Properties of Rill Erosion on a Soil from a Drought-Prone Area during Successive Rainfalls as a Result of Microorganism Inoculation

Abstract

Soil and water loss is one of the most severe kinds of land degradation, particularly in drought-vulnerable regions. It diminishes fertility and increases natural catastrophes, such as floods, landslides, sedimentation, drought, and economic, social, and political issues. The current study explores the efficacy of individual and combination cyanobacteria and bacteria inoculation on runoff production from plots generated by rill erosion on soil from the Marzanabad drought-prone region, northern Iran, and exposed to five successive rainfalls with three days intervals. Experiments were conducted on mid-sized plots with dimensions of 6 × 1 m, three replications, and a 30% slope during simulated rains at the lab with an intensity of 50 mm h⁻¹ and a duration of 30 min. Also, excess runoff of about 2.180 L min⁻¹ was introduced to the plots to promote rill formation. Because none of the treated plots created runoff during the design rainfall, the expected circumstances were subject to continuous rainfall until runoff was generated. Compared to the control plots, statistical analysis indicated that the study treatments had a significant (p < 0.01) lower influence on hydrological components during the initial rainfall event. The highest performance was obtained in the combination inoculation of cyanobacteria and bacteria in successive rainfalls (i.e., first to the fourth event), which reduced runoff volume and coefficient by 35.41, 45.34, 26.35, and 36.43%, respectively. During subsequent rainfalls, the bacteria and combination treatment of cyanobacteria and bacteria did not vary substantially (p = 0.94) on the study components. As a result, after consecutive rainfall events, runoff volume dropped by 20.79, 22.15, 12.83, and 15.87%, and the runoff coefficient reduced by 20.80, 22.15, 12.84, and 15.88%. The cyanobacteria treatment diminished the study components only after the initial rainstorm event. The current study’s findings underscored the need to minimize water loss in the early phases of erosion in drought-sensitive regions where soil and water conservation is a vital task.

1. Introduction

Water erosion is an environmental challenge and a threat to soil resources, which leads to reduced fertility, soil erosion, and land production capacity [1,2,3,4]. Several techniques have been utilized for soil and water conservation. The use of soil amendment and stabilizers has been introduced as a primary, environmental, and economical tool for soil erosion management and runoff generation [5]. In this regard, the beneficial impact of soil microorganism inoculation on runoff inhibition and infiltration has already been shown [6,7,8,9,10,11,12,13]. However, the impact of soil microorganisms on water loss during consecutive rainfalls is yet to be studied. However, when studying different stages of water erosion, such as surface, interrill, and rill erosion, due to their complex impact on land degradation, it is very important to explain appropriate and efficient methods for managing and conserving soil and water resources. Additionally, rill erosion accounts for 50 to 70% of water erosion in various world regions [14,15]. Rill erosion is formed due to flow concentration and is considered a transitional stage from surface erosion to rill and gully erosions [11,16,17,18]. Generally, various rainfall characteristics, including intensity, duration, frequency, and erosivity [3,19,20,21], hydraulic flow characteristics such as shear stress [22,23], depth and velocity [11,14,24,25,26], as well as rill characteristics including depth, length, width, and density affect soil and water loss [11,14,27]. In this case, the behavior of rill development during successive rainfalls due to significant changes in soil components and its hydrological response is of considerable importance.

In this context, Erpul et al. (1999) [28] employed 40 rainstorm events on clay loam and silt loam soils in laboratory settings. They showed that the runoff and soil loss were more in subsequent events compared to the first rainfall. Sadeghi et al. (2016) [29] studied the effects of vinasse amendment on runoff and soil erosion components in sandy clay loam soil during successive rainfalls. According to the findings, applying varied quantities of vinasse significantly influenced (p < 0.01) runoff start time, runoff volume, and runoff coefficient for both study intensities. The runoff and sediment transport mechanisms showed a pattern with many peaks during protracted rainfall events and a single or two peaks during intermittent rainfall events, according to Zhang et al. (2022) [18]. Compared to immediate rainfall intensity, peak flow, and sediment output rates showed a 1–12 min latency. Jafarpoor et al. (2022a) [11] examined the effectiveness of soil endemic cyanobacteria on rill erosion at a mid-sized plot scale and under laboratory settings following a review of comparable studies. According to the findings, cyanobacteria inoculation decreased sediment concentration, runoff volume, runoff coefficient, and soil loss by 42, 64, 82, and 62%, respectively. Additionally, it boosted infiltration volume by 74%, demonstrating the treated soil’s strong water-retention ability. Jafarpoor et al. (2022b) [12] looked at the effects of inoculating soil cyanobacteria in marl soil on changes in hydrologic components. The findings showed that even when the rainfall occurred for prolonged periods, cyanobacterization via exopolysaccharide secretion delayed runoff formation and successfully suppressed runoff components. The current study also confirmed the beneficial impact of cyanobacterization on improving infiltration and the possible storage of water beneath the soil, which points to the beneficial use of cyanobacteria for controlling hydrologic components. The effects of cyanobacterial and bacterial soil inoculation on soil loss from soil susceptible to rill erosion at mid-sized plots during successive rainfalls were assessed by Sadeghi et al. (2023a) [30]. According to the findings, the first rainstorm at the control treatment caused the most considerable soil loss of 30,810.70 g. Contrarily, soil loss was considerably (p < 0.01) reduced (by 99.65%, 99.91%, and 100.00%, respectively) by solo and combination inoculation treatments of cyanobacteria and bacteria with the release of polysaccharides and the associated stability of aggregates in comparison to the control treatment.

It is, therefore, implied from the review of kinds of literature that bioengineering methods are known as one of the control methods in the early erosion stages [11,12,27,29,31,32,33]. Soil microorganisms have recently been certified as one of the most effective and successful bioengineering strategies for minimizing soil erosion and water loss [34,35,36]. Soil microorganisms, including cyanobacteria, bacteria, mosses, lichens, and fungi [37,38], increase the roughness [39], nitrogen fixation [40,41], carbon sequestration [41,42]), infiltration [6,9,10,11,12,30,43] and runoff control, and reduce soil loss [6,8,30,36,43] by secreting polysaccharide and improving the soil structure to hold more water. By secreting polysaccharide substances, microorganisms cause soil aggregate adhesion and runoff absorption and will eventually balance the runoff. Cyanobacteria and bacteria can operate at pH between four and 11, temperatures of 10 to 55 °C, and drought-tolerant conditions [44,45]. By participating in element cycles in the soil, microorganisms provide nutrients for other soil microorganisms [8,46]. Given that the positive role of microorganisms in controlling rill erosion components has only recently been confirmed, in the form of an increase at the beginning of the rill formation and a decrease in the length, width, depth, and density of the formed rills [11,12,30,43], it is expected that this type of microorganism can be used to control and reduce the destructive effect of successive rainfall on runoff generation by rill erosion. Therefore, the goal of this study was to see how individual and mixed inoculation of cyanobacteria and bacteria at mid-sized plots improved hydrological components (i.e., runoff volume, runoff coefficient, infiltrated and seepage water) in rill erosion of an erosion-prone soil in the Marzanabad region of West Mazandaran Province, northern Iran, from consecutive rains. The present study accordingly tried to assess the effectiveness of elongation of inoculated soil microorganisms in controlling runoff characteristics in rill erosion-induced plots during consecutive rainfalls. A comprehensive study of the microorganisms’ effect on runoff generation, infiltration, and water seepage in consecutive rainfalls is innovative in the current study. The results of the current study can be used in the management of soil and water resources in erosion-prone areas. The results can be employed for hydrologic modeling during successive rainfall events.

2. Research Methodology

2.1. Soil Analysis

The soil sample for the study was taken at Marzanabad, Mazandaran Province, northern Iran, in an erosion-prone region. The climate in the region was semi-arid and cold. Based on data from Kojour and Nowshahr stations from 1978 to 2010, the mean annual precipitation and temperature were 432 mm and 12 °C, respectively [11,12]. pH and EC of the studied soil were 7.94 and 125 μS cm⁻¹, respectively. Sand, silt, and clay contents were also 64.71, 16.86 and 18.43%, respectively, representing sandy loam soil texture. The bulk density of the soil was 1.16 g cm⁻³, and the amount of carbon, nitrogen, and phosphorus in the soil was 0.14%, 0.08%, and 6.57 ppm, respectively [30,43].

2.2. Selection and Supply of Cyanobacteria and Bacteria for Inoculation

The soil sample was transferred to the laboratory in this investigation to select and prepare cyanobacteria and bacteria [11,12]. Accordingly, the cyanobacteria and bacteria were identified using Bold’s Basal Medium (BBM) medium and solid Tryptic Soy Agar (TSA), respectively [47,48]. We used Nostoc sp., Oscillatoria sp., and Lyngbya sp. from cyanobacteria and Bacillus subtilis and Azotobacter sp. from bacteria with successful performance in previous studies for the same soil [36]. Finally, Bold Basal’s Medium (BBM) and Luria Broth (LB) broth media for cyanobacteria and bacteria were, respectively, used to prepare the necessary biomass required for inoculation [36,49]. To inoculate microorganisms on the experimental plots’ surface, 9 L with 10¹² coliform forming units (CFU) of desired microorganisms were used for each treatment [30,43].

2.3. Experimental Section Plot Preparation

At the Rainfall and Erosion Simulation Laboratory of Tarbiat Modares University, Noor City, Mazandaran Province, Iran, medium-sized plots with dimensions of 6 × 1 × 0.5 m and a slope of 30% were used in this study. Rainfall simulation included a 4000-l water tank with 7 BEX-typed nozzles 3/8 S24 W per line (3 lines) with high simulation capability [50]. According to the intensity–duration–frequency curves of rainfall events at the nearest Kojour and Nowshahr weather stations with a 30-year return period, a rainfall intensity of 50 mm h⁻¹ with a 30-min duration and five consecutive rainfalls with three-day intervals were considered for rainfall simulation [30,43] (Sadeghi et al., 2023 a,b). In all experiments, an extra runoff of 2.0 ± 0.32 L min⁻¹ was introduced from the top section of the plot at the onset of runoff commencement to form concentrated flow for rill formation [11,12,51]. The measurement was prolonged for 30 min after runoff generation [52]. Experiments were performed for four treatments consisting of (I) control, (II) cyanobacteria, (III) bacteria, and (IV) cyanobacteria + bacteria in three replications and five rainfall events with an interval of three days according to the conditions of the region (i.e., four treatments × five rainfall events × three replications = 60 events). In this case, the effectiveness of microorganism treatments was assessed 40 days after inoculation, as reported by Sadeghi et al. (2023b) [43].

2.4. Hydrological Components Measurement

Following the commencement of surface runoff, the runoff volume was measured with 20 L buckets every 2 min during each rainstorm event [11,12]. The runoff coefficient (RC) was also determined for each plot by dividing the volume of runoff by the amount of rainfall (Equation (1)) [43].

$$\mathrm{RC}=\frac{\mathrm{R}\mathrm{u}\mathrm{n}\mathrm{o}\mathrm{f}\mathrm{f} \mathrm{v}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{e}}{\mathrm{R}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{a}\mathrm{l}\mathrm{l} \mathrm{v}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{e}}$$

(1)

In this regard, the extra runoff added to the experimental plots was subtracted from the runoff volume in the collection containers to calculate the runoff coefficient. At the same time, the amount of seepage water from beneath the plots was measured using plastic sheets placed below the plots up to 24 h after the end of the experiment and almost completely stopped. The amount of incoming water was calculated by calculating the rainfall duration and excess runoff. The water in the soil was also calculated by calculating the input water (i.e., rain) and the water in the soil during the previous rainfall. In this regard, water evaporation from the soil surface during the period between two rainfalls was ignored [30].

2.5. Analytical Statistics

The statistical comparison of research treatments, i.e., the influence of soil microorganisms on runoff commencement time, runoff volume, and runoff coefficient, was made using the SPSS 22 software package and two-way ANOVA and Duncan test. In addition, the General Linear Model (GLM) was used to statistically examine the individual and interaction effects of the variables studied. Figure 1 shows the flow chart of the experimental steps.

3. Results

3.1. Runoff Volume

The results collected runoff volume through the experimental setup shown in Figure 2 and graphically demonstrated in Figure 3 in different treatments during successive rainfall ascertained that in the initial event, runoff volume in the control treatment, individual and combined cyanobacteria and bacteria treatments were 109.10, 0.60, 1.19, and 0.00 L, respectively.

3.2. Runoff Coefficient

The statistical analyses of microorganism inoculation during successive rainfalls on the volume of infiltrated and seepage water in experimental plots in the study treatments are presented in

Table 1. GLM test results for determining individual and interaction effects of consecutive rainfalls and inoculated treatments on runoff components.

Treatment	Source
Runoff volume (L)	Rainfall	4	14,547.32	15.50	0.00
	Treatment	3	16,361.16	17.43	0.00
	Treatment × Rainfall	12	2807.26	2.99	0.01
Runoff coefficient (%)	Rainfall	4	2058.99	12.93	0.00
	Treatment	3	7700.87	48.37	0.00
	Treatment × Rainfall	12	531.57	3.34	0.00
Infiltration water (L)	Rainfall	4	3.329	5.25	0.00
	Treatment	3	3.481	5.49	0.00
	Treatment × Rainfall	12	0.824	1.30	0.257
Seepage water (L)	Rainfall	4	4013.90	9.23	0.00
	Treatment	3	55,165.53	126.81	0.00
	Treatment × Rainfall	12	199.62	0.46	0.88

. The results related to runoff coefficient during successive rainfalls in Table 1 and Figure 4 showed that in the initial rainfall event, runoff coefficients in control, individual, and combined study treatments by cyanobacteria and bacteria were 45.76%, 0.42%, 0.83%, and 0.00%, respectively.

3.3. Infiltration and Seepage Water

The results of the start time of seepage from the study plots during successive rainfalls are also depicted in

Table 2. Start time (min) of seepage water in successive rainfalls in experimental treatments.

Rainfall Event	Plot Number	Control	Cyanobacteria	Bacteria	Cyanobacteria + Bacteria
Initial rainfall	1	0.00	0.00	17.00	17.92
	2	0.00	0.00	15.28	15.60
	3	0.00	0.00	14.13	18.47
Mean		0.00	0.00	15.47	17.33
SD		0.00	0.00	1.44	1.52
CV (%)		0.00	0.00	9.34	8.79
1st rainfall	1	0.00	2.00	1.33	3.33
	2	0.00	5.00	1.90	2.33
	3	0.00	6.00	2.75	2.95
Mean		0.00	4.33	1.99	2.87
SD		0.00	2.08	0.71	0.50
CV (%)		0.00	48.04	35.85	17.59
2nd rainfall	1	2.00	2.42	2.00	1.78
	2	5.00	5.00	1.92	2.50
	3	2.00	2.83	3.25	2.00
Mean		3.00	3.42	2.39	2.09
SD		1.73	1.39	0.75	0.37
CV (%)		57.74	40.58	31.21	17.63
3rd rainfall	1	1.00	1.33	1.42	1.58
	2	5.00	8.33	2.33	2.10
	3	4.00	4.17	1.83	1.50
Mean		3.33	4.61	1.86	1.73
SD		2.08	3.52	0.46	0.33
CV (%)		62.45	76.37	24.50	18.87
4th rainfall	1	1.8	1.80	0.33	0.87
	2	2.00	2.47	2.17	0.57
	3	2.60	2.68	1.33	1.48
Mean		2.13	2.32	1.28	0.97
SD		0.42	0.46	0.92	0.46
CV (%)		19.52	19.84	72.15	47.64

.

Table 3. Hydrologic balance during successive rainfalls in control plots.

Rainfall Event	Plot Number	Entrance (L)	Runoff		Seepage Water		The Remaining Water in the Plot from the Current Rainfall		The Water in the Plot from the Previous Rainfall
			Volume (L)	Ratio (%)	Volume (L)	Ratio (%)	Volume (L)	Ratio (%)	Volume (L)
Initial rainfall	1	239.60	129.56	54.07	0.00	0.00	110.04	45.93	0.00
	2	240.41	111.76	46.49	0.00	0.00	128.65	53.51	0.00
	3	234.25	85.98	36.70	0.00	0.00	148.27	63.30	0.00
Mean		238.09	109.10	45.76	0.00	0.00	128.99	54.24	0.00
SD		3.35	21.91	8.71	0.00	0.00	19.12	8.71	0.00
CV (%)		1.41	20.08	19.03	0.00	0.00	14.82	16.05	0.00
1st rainfall	1	239.22	111.75	46.71	0.00	0.00	127.47	53.29	110.04
	2	236.86	111.45	47.05	0.00	0.00	125.41	52.95	128.65
	3	238.89	86.85	36.24	0.00	0.00	152.31	63.76	148.27
Mean		238.32	103.26	43.34	0.00	0.00	135.06	56.66	128.99
SD		1.28	14.45	6.15	0.00	0.00	14.97	6.15	19.12
CV (%)		0.54	13.99	14.18	0.00	0.00	11.08	10.85	14.82
2nd rainfall	1	236.59	146.61	61.97	55.00	23.25	34.98	14.79	237.51
	2	238.79	85.95	35.99	73.40	30.74	79.44	33.27	254.06
	3	239.74	79.14	33.01	113.80	47.47	46.80	19.52	300.58
Mean		238.37	103.90	43.66	80.73	33.82	53.74	22.52	264.05
SD		1.62	37.14	15.93	30.08	12.40	23.03	9.60	32.70
CV (%)		0.68	35.75	36.48	37.26	36.67	42.85	42.62	12.38
3rd rainfall	1	236.88	169.83	71.69	77.50	32.72	−10.45	−4.41	272.49
	2	240.79	61.49	25.54	87.00	36.13	92.30	38.33	333.50
	3	239.46	79.27	33.10	80.00	33.41	80.19	33.49	347.38
Mean		239.04	103.53	43.44	8150	34.09	54.01	22.74	317.79
SD		1.99	58.10	24.76	4.92	1.80	56.15	23.41	39.84
CV (%)		0.83	56.12	56.98	6.04	5.30	103.96	104.16	12.54
4th rainfall	1	236.26	181.21	76.70	45.01	19.05	10.04	4.25	262.04
	2	233.78	61.95	26.50	72.00	30.80	99.83	42.70	425.80
	3	238.65	90.03	37.72	97.60	40.90	51.02	21.38	427.57
Mean		236.23	111.06	46.97	71.54	30.25	53.63	22.78	371.80
SD		2.44	62.35	26.35	26.30	10.93	44.95	19.26	95.06
CV (%)		1.03	56.14	56.09	36.76	36.14	83.82	84.58	25.57

,

Table 4. Hydrologic balance during successive rainfalls in plots treated by cyanobacteria.

Rainfall Event	Plot Number	Entrance (L)	Runoff		Seepage Water		The Remaining Water in the Plot from the Current Rainfall		The Water in the Plot from the Previous Rainfall
			Volume (L)	Ratio (%)	Volume (L)	Ratio (%)	Volume (L)	Ratio (%)	Volume (L)
Initial rainfall	1	143.25	1.80	1.26	94.00	25.63	202.23	55.14	0.00
	2	143.25	0.00	0.00	97.26	24.76	220.21	56.05	0.00
	3	143.25	0.00	0.00	105.00	26.28	251.22	62.78	0.00
Mean		143.25	0.60	0.42	98.75	25.55	224.55	58.02	0.00
SD		0.00	1.04	0.73	5.65	0.76	24.78	4.23	0.00
CV (%)		0.00	173.21	173.21	5.72	2.99	11.04	7.29	0.00
1st rainfall	1	236.98	161.59	68.19	75.00	31.65	0.39	0.16	202.23
	2	238.31	144.08	60.46	55.47	32.28	38.76	16.26	220.21
	3	241.85	93.10	38.49	79.70	32.95	69.05	28.55	251.22
Mean		239.05	132.92	55.71	70.06	29.29	36.07	14.99	224.55
SD		2.52	35.58	15.40	12.85	5.25	34.41	14.24	24.78
CV (%)		1.05	26.77	27.65	18.34	17.93	95.40	94.95	11.04
2nd rainfall	1	238.03	181.98	76.45	60.38	25.37	−4.33	−1.82	202.62
	2	236.74	180.58	76.28	66.80	28.22	−10.64	−4.49	258.97
	3	238.50	115.40	48.39	77.60	32.54	45.50	19.08	320.27
Mean		237.76	159.32	67.04	68.26	28.71	10.18	4.25	260.62
SD		0.91	38.04	16.15	8.70	3.61	30.75	12.91	58.84
CV (%)		0.38	23.88	24.10	12.75	12.58	302.15	303.35	22.58
3rd rainfall	1	236.97	191.76	80.92	66.96	28.26	−21.75	−9.18	198.29
	2	237.07	198.05	83.54	69.45	29.30	−30.43	−12.84	248.33
	3	239.22	148.46	62.06	105.00	43.89	−14.24	−5.95	365.77
Mean		237.75	179.42	75.51	80.47	33.81	−22.14	−9.32	270.80
SD		1.27	27.00	11.72	21.28	8.74	8.10	3.44	85.97
CV (%)		0.53	15.05	15.52	26.44	25.86	−36.59	−36.94	31.75
4th rainfall	1	236.74	161.18	68.08	99.00	41.82	−23.44	−9.90	189.11
	2	238.41	164.19	68.87	83.00	34.81	−8.78	−3.68	235.49
	3	237.69	149.43	62.87	65.00	27.35	23.26	9.79	359.82
Mean		237.61	158.27	66.61	82.33	34.66	−2.99	−1.27	261.47
SD		0.84	7.80	3.26	17.01	7.24	23.88	10.06	88.27
CV (%)		0.35	4.93	4.90	20.66	20.88	−799.65	−794.90	33.76

,

Table 5. Hydrologic balance during successive rainfalls in plots treated by bacteria.

Rainfall Event	Plot Number	Entrance (L)	Runoff		Seepage Water		The Remaining Water in the Plot from the Current Rainfall		The Water in the Plot from the Previous Rainfall
			Volume (L)	Ratio (%)	Volume (L)	Ratio (%)	Volume (L)	Ratio (%)	Volume (L)
Initial rainfall	1	143.25	0.00	0.00	340.00	53.74	292.68	46.26	0.00
	2	143.25	111.76	46.49	180.00	49.44	180.52	49.58	0.00
	3	143.25	85.98	36.70	85.00	22.19	298.09	77.81	0.00
Mean		143.25	109.10	45.76	0.00	41.79	257.10	57.88	0.00
SD		0.00	21.91	8.71	0.00	17.11	66.37	17.34	0.00
CV (%)		0.00	20.08	19.03	0.00	40.95	25.82	29.95	0.00
1st rainfall	1	236.64	55.01	23.25	165.00	69.73	16.63	7.03	292.68
	2	235.78	130.13	55.19	95.00	40.29	10.65	4.52	180.52
	3	238.79	60.24	25.23	165.00	69.10	13.55	5.67	298.09
Mean		237.07	81.79	34.55	141.67	59.71	13.61	5.74	251.10
SD		1.55	41.94	17.90	40.41	16.82	2.99	1.26	66.37
CV (%)		0.65	51.28	51.80	28.53	28.16	21.97	21.89	25.82
2nd rainfall	1	237.64	73.12	30.77	184.00	77.43	−19.48	−8.20	309.31
	2	236.83	100.61	42.48	125.00	52.78	11.22	4.74	191.17
	3	238.17	68.93	28.94	94.00	39.47	75.24	31.59	311.64
Mean		237.55	80.89	34.06	134.33	56.56	22.33	9.38	270.71
SD		0.67	17.21	7.35	45.72	19.26	48.33	20.30	68.89
CV (%)		0.28	21.28	21.57	34.03	34.05	216.45	216.44	25.45
3rd rainfall	1	238.17	64.24	26.97	141.00	59.20	32.93	13.83	289.83
	2	236.83	118.46	50.02	168.00	70.94	−49.63	−20.96	202.39
	3	238.88	88.04	36.86	118.00	49.40	32.84	13.75	386.88
Mean		237.96	90.25	37.95	142.33	59.85	5.38	2.21	293.03
SD		1.04	27.18	11.56	25.03	10.78	47.64	20.06	92.29
CV (%)		0.44	30.11	30.47	17.58	18.02	885.50	909.31	31.49
4th rainfall	1	235.54	71.30	30.27	151.00	64.11	13.24	5.62	322.76
	2	236.74	132.08	55.79	130.00	54.91	−25.34	−10.70	152.76
	3	238.17	76.94	32.30	90.00	37.79	71.23	29.91	419.72
Mean		236.82	93.44	39.46	123.67	52.27	19.71	8.27	298.41
SD		1.32	33.58	14.18	30.99	13.36	48.61	20.44	135.14
CV (%)		0.56	35.94	35.95	25.06	25.56	246.62	246.95	45.28

and

Table 6. Hydrologic balance during successive rainfalls in plots treated by cyanobacteria + bacteria.

Rainfall Event	Plot Number	Entrance (L)	Runoff		Seepage Water		The Remaining Water in the Plot from the Current Rainfall		The Water in the Plot from the Previous Rainfall
			Volume (L)	Ratio (%)	Volume (L)	Ratio (%)	Volume (L)	Ratio (%)	Volume (L)
Initial rainfall	1	143.25	0.00	0.00	529.00	85.72	81.29	13.17	0.00
	2	143.25	0.00	0.00	534.00	93.70	5.83	1.02	0.00
	3	143.25	0.00	0.00	315.00	39.82	454.39	57.44	0.00
Mean		143.25	0.00	0.00	459.33	73.08	180.50	23.88	0.00
SD		0.00	0.00	0.00	125.02	29.08	240.17	29.69	0.00
CV (%)		0.00	0.00	0.00	27.22	39.79	133.06	124.35	0.00
1st rainfall	1	242.80	71.49	29.44	160.00	65.90	11.31	4.66	81.29
	2	238.50	91.92	38.54	208.00	87.21	−61.42	−25.75	5.83
	3	238.41	36.69	15.39	122.00	51.17	79.72	33.44	454.39
Mean		239.90	66.70	27.79	163.33	68.09	9.87	4.11	180.50
SD		2.51	27.92	11.66	43.10	18.12	70.58	29.60	240.17
CV (%)		1.05	41.87	41.97	26.39	26.61	715.11	719.37	133.06
2nd rainfall	1	239.31	60.11	25.12	140.00	58.50	39.20	16.38	92.60
	2	240.99	81.44	33.79	155.00	64.32	4.55	1.89	−55.59
	3	239.60	28.81	12.02	110.00	45.91	100.79	42.07	534.11
Mean		236.97	56.79	23.65	135.00	56.24	48.18	20.11	190.37
SD		0.90	26.47	10.96	22.91	9.41	48.74	20.35	306.77
CV (%)		0.37	46.62	46.35	16.97	16.73	101.17	101.17	161.14
3rd rainfall	1	238.88	91.42	38.27	136.00	59.93	11.46	4.80	131.80
	2	240.27	63.97	26.62	154.00	64.09	22.30	9.28	−51.04
	3	236.97	73.36	30.96	88.00	37.14	75.61	31.91	634.90
Mean		238.71	76.25	31.95	126.00	52.72	36.46	15.33	238.55
SD		1.66	13.95	5.89	34.12	13.96	34.34	14.53	355.21
CV (%)		0.69	18.30	18.42	27.08	26.49	94.19	94.8	148.90
4th rainfall	1	236.35	80.88	34.22	170.00	71.93	−14.53	−6.15	143.26
	2	237.88	49.40	20.77	178.00	74.83	10.48	4.41	−28.74
	3	237.40	81.52	34.34	88.00	37.07	67.88	28.59	710.51
Mean		237.21	70.60	29.78	145.33	61.27	21.28	8.95	275.01
SD		0.78	18.36	7.80	49.81	21.01	42.25	17.81	386.83
CV (%)		0.33	26.01	26.20	34.27	34.29	198.59	−198.99	140.66

further show the changes in the components of hydrologic balance, viz. runoff, seepage, and infiltration in the experimental plots in the control and inoculated treatments.

4. Discussion

4.1. Runoff Volume

The study treatments had 99.45%, 98.26%, and 100% reductions in runoff volumes compared to the control treatment. By forming a biocrust and secreting polysaccharide adhesive, soil microorganisms stick to the soil aggregates and decrease the runoff generation. Previous studies [8,11,12,30,34,36,38,43,44,53] have confirmed the decrease in runoff volume due to the inoculation of microorganisms. In the first rainfall, runoff volume in control, individual, and combined cyanobacterial and bacterial study treatments was 103.26, 132.92, 81.79, and 66.70 L, respectively. The control treatment has decreased by 5.35% compared to the initial rainfall. Moreover, in the second rainfall, untreated and individual and combined treatments of cyanobacteria and bacteria increased by 0.62%, 19.86%, and decreased by 1.10% and 14.86%, respectively. In the third rainfall event, the runoff volume of control and individual and cyanobacteria + bacteria treatments decreased by 0.36% and increased by 12.62%, 11.57%, and 34.27%, respectively, compared to the previous event. Finally, the runoff volume in the fourth rainfall in control, individual, and combined treatments by cyanobacteria and bacteria increased by 7.27%, decreased by 11.79%, increased by 3.53%, and decreased by 7.41%, respectively, compared to the previous event. However, the volume of runoff in control and individually inoculated treatments of cyanobacteria and bacteria from the initial to the fourth rainfall in the experimental plots with rills increased by about 1.80%, 26,278.33%, and 4817.89%, respectively. In the combined treatment of cyanobacteria and bacteria, because there was no runoff generation in the initial rainfall event and the first rainfall was the initial event that runoff was observed, the runoff volume from the first to the fourth rainfall increased by 5.85%.

The results of the statistical test (Table 1 and Figure 5) verified that the role of successive rainfalls and treatments and the interaction effect of rainfalls and treatments on the total runoff volume were significant (p < 0.01). In this regard, a group of independent variables into homogeneous subgroups showed that the runoff volume in the initial rainfall event significantly differed from other rainfalls (p < 0.01). However, there was no discernible change in consecutive rainfalls. The individual cyanobacterial treatment was non-significantly different from the control treatment (p > 0.05). However, the individual bacterial and combined treatment of cyanobacteria and bacteria significantly differed from the control at a 99% confidence level. According to erosion research, individual cyanobacteria treatments showed a non-significant (p > 0.05) influence on runoff volume compared to the control treatment. Individual bacterium treatment, on the other hand, had a significant effect (p < 0.01). Also, the combined inoculation by cyanobacteria and bacteria has been more appropriate due to its synergistic properties in decreasing runoff. In this sense, the current study’s findings differ from those of previous studies [18,20,28] but were consistent with Montenegro et al. (2013) [54] and Sadeghi et al. (2016) [29] on the significant impact of successive rainfalls on reducing runoff volume. In this regard, Yin et al. (2023) [55] showed that using biochar in successive rainfalls in karst areas increased the surface runoff in the first rainfall and decreased the runoff in subsequent rainfall. The amount of leakage runoff also increased in successive rainfalls.

4.2. Runoff Coefficient

In comparison with the untreated plots, individual and combined inoculated treatments of cyanobacteria and bacteria were reduced by 99.08%, 98.19%, and 100%, respectively, which agrees with Jafarpoor et al. (2022a,b) [11,12]. In the first event, the runoff coefficient in control, individual, and combined treatments of cyanobacteria and bacteria was 43.34%, 55.71%, 34.56%, and 27.79%, respectively. The control treatment had a reduction of 5.29% compared to the previous rainfall. Also, in the second rainfall in control, individual and combined treatments by cyanobacteria and bacteria compared to the previous event increased by 0.74%, 20.34%, and decreased by 1.45% and 14.90%, respectively. In the third rainfall, control, individual, and combined inoculated cyanobacteria and bacteria treatments decreased by 0.50% and increased by 12.63%, 11.42%, and 31.10%, respectively, compared to the previous event. In addition, in the fourth rainfall, control, individual, and combined inoculated by cyanobacteria and bacteria treatments increased by 3.31%, decreased by 11.79%, increased by 3.98%, and 6.79% compared to the previous event. The linearity of the flow due to the continuity of rainfall and excess runoff has been an apparent reason for the guidance of runoff to the output of experimental study plots during successive rainfalls. On the other hand, the runoff coefficient in control and individual study treatments of cyanobacteria and bacteria from the initial to fourth rainfall has increased by about 2.64%, 15,759.52%, and 4654.22%, respectively. In the combined treatment of cyanobacteria and bacteria, because there was no runoff in the initial rainfall event and the first event was the initial rainfall event that runoff was observed, the runoff coefficient from the first to the fourth rainfall has increased by about 7.16%.

Table 2 and Figure 6 present statistical analysis verifying that the individual and interaction effects of rainfalls and treatments on runoff coefficient were significant (p < 0.01) (Figure 6). In this regard, grouping independent variables into homogeneous subgroups showed that the initial rainfall event significantly differed from other rainfalls (p < 0.01). However, no significant difference was observed in subsequent rainfalls. Also, individual inoculated cyanobacteria treatment significantly differed from the control treatment (p > 0.05). However, individual bacteria combined with cyanobacteria and bacteria treatments at the 99% confidence level significantly differed from the control. Sadeghi et al. (2016) [29] confirmed the significant influence of successive rainfalls on the runoff coefficient. In other words, microorganism inoculation is influential in reducing the runoff coefficient by reducing the runoff generation.

4.3. Infiltration and Seepage Water

The results of the statistical analyses shown in Figure 7 verified that the effect of successive rainfalls and treatments on the total volume of infiltration was significant (p < 0.00), and the interaction effect of successive rainfalls and treatment was insignificant (p < 0.257). Regarding the determinant variables, grouping the independent variable into homogeneous subgroups showed that this variable had a significant difference (p < 0.01) in the initial rainfall with other rainfalls. However, no significant difference was observed in the subsequent consecutive rainfalls. Regarding different treatments, the individual and combined treatment of cyanobacteria and bacteria had a significant difference (p < 0.05) from the control treatment.

The analysis of the results related to the total volume of infiltration during successive rainfall showed that in the initial rainfall, the volume of infiltration in the control treatment, individual and combined treatments of cyanobacteria and bacteria was 128.99, 108.36, 105.77, and 38.89 L, respectively. In the first rainfall, the infiltration volumes in control, individual, and combined treatments of cyanobacteria and bacteria were 135.06, 36.07, 13.61, and 9.87 L, respectively. In addition, in the second rainfall of control and individual and combined cyanobacteria and bacteria treatments, they were 53.74, 10.18, 22.33, and 48.18 L. In the third rainfall, the amounts of infiltration of control and inoculated treatments individually and combined with cyanobacteria and bacteria were 54.01, 22.14, 5.38, and 36.46 L. Finally, the volumes of infiltration in the fourth rainfall in the control and individual and combined treatments of cyanobacteria and bacteria was 53.63, −2.99, 19.71 and 21.28 L, respectively.

On the other hand, the results of the statistical test and Figure 8 showed that the individual effect of successive rainfall and treatment on the volume of seepage water was significant (p < 0.000), and the interaction effect of successive rainfall and treatment was insignificant (p < 0.88). Regarding the seepage water variables, grouping the independent variable into homogeneous subgroups showed that this variable had a significant difference (p < 0.05) in the initial rainfall only with the fourth successive rainfall. The first rainfall was significantly different from the second and third rainfall events (p < 0.05), but no significant change was observed in comparison with the initial and fourth rainfall (p < 0.05). Regarding the different treatments, the combined treatment of cyanobacteria and bacteria had a significant difference from the control at the 99% confidence level. However, the individual treatment of cyanobacteria and bacteria had a non-significant difference (p = 0.99) compared to the control treatment.

The analyses of the results related to the total volume of seepage water during successive rainfalls showed that the control treatment had no seepage water in the initial and first rainfall, and it was observed in the second, third, and fourth rainfalls. In this regard, the seepage water for the initial rainfall, in the individual treatment of bacteria and the combination of cyanobacteria and bacteria were 44.04 and 62.13 L, respectively, which is consistent with the results of [6,9,10,11,12] regarding the effect of microorganisms on the infiltration rate. In the first rainfall, in the individual and combined treatments of cyanobacteria and bacteria, 70.06, 141.67, and 163.33 L of precipitation were converted into seepage water, respectively. In addition, in the second rainfall, the amount of seepage water for the individual and combined treatments of cyanobacteria and bacteria were found to be 68.26, 134.33, and 135 L, respectively, while in the control plot, the amount of seepage water was measured as 80.73 L. In the third rainfall, 81.50, 80.47, 142.33, and 126 L of seepage water were in the control, individual, and combined study treatments of cyanobacteria and bacteria, respectively. Finally, in the fourth rainfall, the amount of seepage water in the control, individual, and combined treatments of cyanobacteria and bacteria were measured as 54.71, 82.33, 123.67, and 145.33 L. The current research is compatible with the authors’ findings in [11,12,30,43] regarding the impact of microbe inoculation on boosting infiltration. In general, the inoculation of microorganisms can penetrate the rainfall entering the soil, and despite reducing the runoff volume, it has increased the amount of seepage water. In this regard, microorganism inoculation under natural conditions can increase the volume of subsurface water and even infiltration into underground aquifers. In other words, microorganisms’ inoculation can prevent water loss through surface runoff evaporation.

5. Conclusions

The current study investigated the effect of runoff generation during successive rainfalls in mid-sized plots with rill erosion from inoculation of soil microorganisms, including cyanobacteria, bacteria, and cyanobacteria + bacteria, as well as a control treatment, on prone-erosion soil of Marzneabad–Kendallus to an initial rainfall and four consecutive rainfall events with a three-day interval on prone-erosion soil of Marzneabad–Kendallus. The following is the conclusion of the current research in response to the main research question:

• Each soil microorganism inoculation treatment regulated the rilling process during the first rainfall event, finally abolishing runoff-generating circumstances.
• After rill formation and flow linearization, the cyanobacterial treatment had no significant effect compared to the control treatment.
• The bacterial and combined cyanobacteria and bacteria treatments were influential and significant in controlling runoff components to rill erosion in successive rainfalls.
• Using soil microorganisms is an efficient biological strategy for providing crucial ecosystem services, including recharging groundwater and ensuring water safety while enhancing ecosystem restoration in erosion-prone locations.

Considering the laboratory limitation of the present study, more endeavors under the laboratory and field conditions are needed to confirm the present results confidently. However, it is suggested that decision-makers, managers, and planners, particularly in arid and semi-arid regions with high vulnerability to drought consequences, can use the study findings to identify appropriate soil and water conservation methods using soil microorganism-based biological methods.