Water-level changes in the water-level fluctuating zone (WLFZ) promoted soil and plants to release nutrients into the water, threatening the water health in the reservoir. Plant restoration in the WLFZ is also an important way to reduce the nutrient release in order to select plants that can effectively reduce the release of soil nutrients under changing water levels. This study conducted a flooding experiment to reveal the difference in the change in soil physico-chemical properties and microbial communities with various plants under different water-level conditions. The flooding experiment began at the end of September 2020 and was planted with three dominant plants common to reservoirs, namely Cynodon dactylon, Alternanthera philoxeroides, and Acorus calamus. Our study found the three common dominant plants along the reservoir, and C. dactylon had a good adsorption capacity for nitrogen and phosphorus when it was flooded with shallow water, decreasing soil nutrients during the drying period. After a wetting–drying cycle, there was an obvious and significant (p <  0.05) divergence among soil microbial community structures between N0 and D1, D2, and D3, respectively. This study could provide sufficient reference information for plant selection and the assessment of nutrient release of WLFZ in restoration work.

  • The effects of flooding on soil physical and chemical properties were investigated.

  • The changes in the microbial community structure under flooding stress were identified.

  • Suggestions on the operation and management of the reservoir were provided.

Building reservoirs can alleviate regional water scarcity and energy shortages. However, the periodic changes in water level create the water-level fluctuating zone (WLFZ) with an elevation of several meters or even tens of meters on both sides of the reservoir area (Li et al. 2019a). The microorganism in the WLFZ directly affects the self-purification ability of water quality (Wang et al. 2020; Li et al. 2022). The content and form of nitrogen and phosphorus in the soil in the WLFZ also determine the releasing flux of nitrogen and phosphorus into the reservoir water, thus affecting the water quality of the reservoir (Xu et al. 2020; Huang et al. 2022). As a transition zone between terrestrial and aquatic ecosystems, the WLFZ was sensitive to be altered by the external environment (e.g., rainfall, growth and death of coastal plants, and variation in water levels). With the influence of these environmental factors, therefore, the ecosystem of the WLFZ became more and more vulnerable (Song et al. 2019; Liu et al. 2020).

Previous studies had shown that after the construction of a dam, the changes in hydraulic retention time and microbiological and physico-chemical properties of water could affect the existent forms of nutrients and soil structure in the WLFZ (Ye et al. 2019; Yu et al. 2019). Xu et al. (2011) found that after the completion of Three Gorges Reservoir (TGR), the hydraulic retention time in the tributaries rose from 25 to 538 days, resulting in a significant cyclical impact on reservoir water quality. In addition, Shen et al. (2022) studied the changes in soil physical–chemical properties in the WLFZ of TGR from 2008 to 2013, finding that periodic changes in the water level significantly reduced the content of soil available phosphorus (P) and nitrogen (N). In particular, the periodic wet–dry cycle of WLFZ could cause a shift from large pores (>100 μm) to medium and small pores (<100 μm) in soil aggregates (Zhang et al. 2018). With decreasing water table elevation, the microstructure of these aggregates also altered from loose and porous to compact and less porous, thus reducing the ability of soil to store nutrients (Zhang et al. 2018).

Furthermore, due to water-level changes, the vegetation variation in the WLFZ also affected the geochemical cycles and the release of nitrogen and phosphorus in soils (Doran & Zeiss 2000; Chaparro et al. 2012; Eze et al. 2021). For example, variation in the water level could cause the original vegetation around the reservoir to wither and die as it cannot withstand the stress of prolonged flooding. The dead vegetation continued to be degraded. This process promoted the release of nutrients such as N, P, and organic matter, which were supposed to be partly absorbed by the soil (Wang et al. 2020; Chen et al. 2021). Moreover, during the operation of the reservoir, the plant community would be in reverse succession towards flood-tolerant herbaceous plants, such as Cynodon dactylon, Alternanthera philoxeroides, Acorus calamus, and other hygrophytes (Xu et al. 2021). As illustrated by many field surveys and laboratory simulations, these dominant plants in the WLFZ would die or be reborn in response to changes in the water level, with a continuous effect on soil physical–chemical properties in both the WLFZ and overlying water at certain time scales (Liu et al. 2017a; Ge et al. 2021). For example, Jian et al. (2018) carried out a continuous observation of N, P, and other nutrient matters in the TGR for 10 years, indicating that the nutrient matters released from decaying vegetation in the WLFZ were the main sources of water in the early stage of the reservoir.

Meanwhile, the nutrients released from soils and plants in the WLFZ would continue to dissolve into the water, and the excessive nutrients in the water may result in eutrophication, threatening the health of aquatic ecology in the reservoir (Wang et al. 2022). The construction of reservoirs is still the preferred solution to alleviate the water shortage in many countries and regions, so it is necessary to clarify the impact of water-level changes in the ecological environment of the subsidence zone (Yang et al. 2012). Yet many researchers focused on the effects of water levels with a depth of 10, 20, or even more meters (Li et al. 2019a, 2019b, 2019c; Shen et al. 2022). The water level of the reservoir did not change significantly at a certain time, but there were various reasons for minor fluctuations of ±1 m of water level at short times. Although it had no significant impact on the operation of the reservoir, the frequent fluctuations of water level at the water–air–soil interface would lead to frequent changes in physico-chemical properties (e.g., light and oxygen content). Thus, it was necessary to investigate the ecological changes in the WLFZ with a low submergence depth (less than 1 m).

This study conducted a flooding experiment to select three dominant plants of the reservoir (C. dactylon, A. philoxeroides, and Acorus calamus) for testing their normal growth with various flooding treatments (20, 50, and 1 m). We aimed to understand (1) the material cycle process between soil and plants in the WLFZ and (2) the changes in soil microbial community structure in the WLFZ after a wet–dry cycle. These would provide a scientific basis for water conservancy management departments.

Experimental design

This study selected three plants, C. dactylon (CD), A. philoxeroides (AP), and Acorus calamus (AC), which were dominant in the WLFZ of the Shiquan Reservoir on the Han River (Xu et al. 2021). Planting containers were 50 × 100 × 72 cm boxes, containing 25 kg of soil, ensuring that the soil was about 30 cm in thickness and evenly distributed. The CD was grown from seed and 50 seeds were sown evenly in each box. All plants were watered regularly before starting the experiment. The control group received basic watering operations to ensure normal plant growth. Three treatments were carried out as follows: 20 cm flooding (D1), 50 cm flooding (D2), and 1 m flooding (D3); the flooding height was calculated from the part above the ground (Figure 1). The soil samples were collected six times during the experiment.

Collection and analysis of samples

The flooding experiment began at the end of September 2020. Soil samples of each treatment were collected in Oct and Dec 2020 and Mar, May, Jul, and Sep 2021. The soil samples were air-dried for a fortnight in darkness and sieved through 100 mesh. Then, the total nitrogen (TN), ammonium nitrogen (NH4+-N), and nitrate nitrogen (NO3-N) were determined by the Kjeldahl method using the automatic Kjeldahl azotometer. The dichromate oxidation method was used to analyze soil organic carbon (SOC), and total phosphorus (TP) was determined by molybdenum-blue colorimetry (Ball & Williams 1968). Total organic carbon (TOC) measured with a TOC analyzer (Shimadzu, TOC-L, Japan) was used to represent the dissolved organic carbon (DOC) content of soil extracts in this study (Li et al. 2019a, 2019b, 2019c; Li et al. 2023).

Soil microbial community structure in each treatment was determined using the 16S rDNA sequence. Microbial gene DNA was extracted from 3 g of the well-mixed soil using the CTAB method, the quality of the DNA extraction was checked by agarose gel electrophoresis, and the quantity of DNA was quantified using a UV spectrophotometer (Logue et al. 2016). The bacterial 16S rDNA V3 and V4 regions were also amplified in a 25 μL polymerase chain reaction (PCR) mixture containing 50 ng of template DNA, 12.5 μL of Phusion Hot start flex 2X Master Mix, 2.5 μL of forward primer, and 2.5 μL of reverse primer (Walters et al. 2016). The primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′) were used, and the PCR amplification was prepared as follows: initial denaturation at 97 °C for 40s; 35 cycles at 97 °C for 15 s, 54 °C for 15 s, 70 °C for 45 s; and a final extension at 70 °C for 10 min (Takai & Horikoshi 2000). Meanwhile, PCR products were purified from AMPure XT Beads (Beckman Coulter Genomics, Danvers, MA, USA) and Quantitative Qubit (Invitrogen, USA). PCR amplification products were detected using 2% agarose gel electrophoresis, while purification was performed using the AMPure XT beads purification kit. Then, 2 × 250 bp double-end sequencing was performed using the NovaSeq 6000 sequencer with the NovaSeq 6000 SP Reagent Kit (500 cycles) (Gou et al. 2016).

Data analysis

The raw data of soil microorganisms were stitched using overlap, and quality control and chimera filtering were performed to obtain high-quality clean data. Then, the data were length filtered and denoised by qiime dada2 denoise-paired (https://qiime2.org/), and the class Operational Taxonomic Units (OTUs) were constructed using the concept of amplicon sequence variants (ASVs) to obtain the final feature table of ASVs as well as feature sequences for further diversity analysis, species taxonomic annotation, and difference analysis. Statistical analyses were calculated with R v3.4.4, and data visualization was made using ‘ggplot2’ R-package v3.2.0. and Venn Diagram.

The soil physical and chemical data in this study were expressed in terms of mean and standard deviation. The significance test was carried out using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA). All the data graphs were treated with Origin 2019 (OriginLab, Northampton, MA, USA).

The effects of the wet–dry cycle on soil nitrogen

Figure 2 represents the changes in soil nitrogen concentration for all treatment groups (p < 0.05). The uppermost and lowermost lines represent the maximum and minimum values, respectively. The three lines in the box represent, from top to bottom, the first quartile, the median, and the third quartile. Figure 2(a)–2(c) shows the changes in soil nitrogen in CK after 1 year of wet–dry cycle. Total nitrogen (TN) concentration in CK decreased continuously during a wet–dry cycle, and the NO3-N concentration increased in both wetting and drying periods. In contrast, NH4+-N concentration decreased, whereas the decrease rate was greater in the drying period than in the wetting period.
Figure 1

Diagram of the experimental design for this study.

Figure 1

Diagram of the experimental design for this study.

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Figure 2

Changes of TN (a, d, g, j), NO3-N (b, e, h, k), and NH4+-N (c, f, i, l) in soils with four treatments after 1 year of flooding. The left side of the red line represents the inundation period, while the right side represents the fall dry period. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wqrj.2022.125.

Figure 2

Changes of TN (a, d, g, j), NO3-N (b, e, h, k), and NH4+-N (c, f, i, l) in soils with four treatments after 1 year of flooding. The left side of the red line represents the inundation period, while the right side represents the fall dry period. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wqrj.2022.125.

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The variations of N concentration in soil with CD are shown in Figure 2(d)–2(f). The concentration of TN fluctuated around 1.0 g kg−1. As observed in Figure 2(e)–2(f), NO3-N and NH4+-N concentrations varied with different patterns: NO3-N concentration increased in both wetting and drying stages, and especially in the drying cycle, the concentration increased rapidly from 3.89 to 6.66 mg kg−1. In contrast, NH4+-N concentration continued to decrease during the wetting period, and increased in the first month in the drying period, then rapidly decreased from 6.81 to 2.99 mg kg−1.

The changes in soil N concentration in AC are shown in Figure 2 (g)–2(i). TN concentration decreased from 1.11 to 1.04 g kg−1 during the wetting period but fluctuated around 1.00 g kg−1 during the drying period. The NO3-N concentration decreased and then increased from 4.29 to 6.63 g kg−1 during the wetting stage but decreased from 6.29 to 5.25 mg kg−1 during the drying period. The NH4+-N concentration showed a continuous decreasing trend from the wetting stage to the drying stage, varying from 5.86 to 2.64 mg kg−1.

Figure 2(j)2 shows the variations of soil N in AP. The TN concentration decreased from 1.12 g kg−1 to 1.06 mg kg−1. NO3-N concentration increased from 3.84 to 13.2 mg kg−1 during the wetting period and decreased to 3.45 mg kg−1 at the beginning of the drying period, and fluctuated around 3.50 ± 0.21 mg kg−1 throughout the drying stage.

Waterlogging could open the crystal lattice of soil minerals, release ammonium nitrogen, and inhibit the mineralization and nitrification of nitrogen (Fierer & Schimel 2002; Gao et al. 2020). In this study, the variation in the NO3-N concentration in the wetting–drying cycle showed that shallow water flooding inhibited nitrification to some extent, but nitrification still played a dominant role. Therefore, NH4+-N concentration was higher in the wetting stage and the decrease rate was slow. In the drying stage, NH4+-N was rapidly oxidized by increasing oxygen and NO3-N also increased faster than in the wetting stage. The TN concentration in AC was slightly lower than in CK, and the concentration of NO3-N and NH4+-N decreased at the early stage of wetting, which may be because the AC, as a submergence tolerant plant, could maintain normal physiological activities in shallow water and accelerate the formation of adventitious roots and stem growth to obtain external oxygen (Yang et al. 2012; Ye et al. 2020; Lin & Lin 2022).

The effects of wet–dry cycle on soil phosphorus

The mean value changes of soil TP with four treatments are shown in Figure 3 (p < 0.01). The TP concentration in CK decreased from 0.271 to 0.244 g kg−1 in the wetting period. The variation in the drying period was very small and decreased by 0.03 g kg−1 (Figure 3(a)). The variation in TP concentration in AC was similar to the CK and decreased from 0.255 to 0.243 g kg−1 (Figure 3(c)). In addition, both CD and AP had the same variation trend in TP concentration.
Figure 3

Changes of TP in soils with four treatments after 1 year of flooding: (a) CK, (b) CD, (c) AC, and (d) AP. The left side of the red line represents the inundation period and the right side represents the desiccation period. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wqrj.2022.125.

Figure 3

Changes of TP in soils with four treatments after 1 year of flooding: (a) CK, (b) CD, (c) AC, and (d) AP. The left side of the red line represents the inundation period and the right side represents the desiccation period. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wqrj.2022.125.

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The TP concentration in all four groups decreased rapidly during the wetting period and increased in the first month of the drying period. The results showed that the increase rate in the CK was much higher than the others. These phenomena would be because the roots of aquatic plants and hygrophytes absorbed phosphorus (Zhang et al. 2013; Yin et al. 2020; Zhu et al. 2020).

The effects of wet–dry cycle on soil organic carbon

The changes in soil DOC concentration in the four groups are shown in Figure 4 (p < 0.001). The variation trend of DOC concentration in the four groups showed a similar trend: it decreased first and then increased in the wetting period and then continued to decrease in the drying period.
Figure 4

Variation in soil DOC inundation-fall dry for four treatments: (a) CK, (b) CD, (c) AC, and (d) AP. The left side of the red line represents the inundation period and the right side represents the desiccation period. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wqrj.2022.125.

Figure 4

Variation in soil DOC inundation-fall dry for four treatments: (a) CK, (b) CD, (c) AC, and (d) AP. The left side of the red line represents the inundation period and the right side represents the desiccation period. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wqrj.2022.125.

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As illustrated in Figure 5, changes in SOC concentration followed the same pattern as the change in DOC. The SOC concentration (g kg−1) in CK, CD, AC, and AP decreased from 20.1, 18.7, 19.6, and 19.0 to 18.0, 17.5, 17.9, and 18.7, respectively.
Figure 5

One-year variation in SOC inundation-fall dry for four treatments: (a) CK, (b) CD, (c) AC, and (d) AP. The left side of the red line represents the inundation period and the right side represents the desiccation period. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wqrj.2022.125.

Figure 5

One-year variation in SOC inundation-fall dry for four treatments: (a) CK, (b) CD, (c) AC, and (d) AP. The left side of the red line represents the inundation period and the right side represents the desiccation period. Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wqrj.2022.125.

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There was a significant correlation (p < 0.001) between DOC and SOC concentrations in this study, which was consistent with the findings of Huang et al. (2019). Kaiser et al. (2004) found that the variation rules of DOC and SOC were consistent: the DOC concentration in the soil without additional DOC depends on the organic matter content of the soil. Therefore, SOC concentration was one of the important factors determining DOC concentration when the soil was under inundation (Liu et al. 2017a, 2017b; Li et al. 2019a, 2019b, 2019c). While the soil was submerged, the SOC concentration decreased substantially as it was released into overlying water and consumed by microorganisms and plants. As the inundation time increased, plants decayed and organic matter from the plants was released into the soil, causing an increase in SOC, and microbial activity during this process also caused an increase in DOC concentration (Fierer & Schimel 2002; Nsabimana et al. 2021; Xiao et al. 2022). The plants and microbial activity increased, which consumed the amounts of SOC and DOC, so the concentration of SOC and DOC was decreased during the drying period.

In our preliminary field investigation, we found that the dominant plants around the study area were C. dactylon, A. philoxeroides, and Acorus calamus (Jian et al. 2018). This finding was the same as that in some locations in the TGR (Xu et al. 2021). In contrast, previous researchers had focused on the effect of plants on soil nutrients in the case of inundation at a depth of ten or even tens of meters. They found that plants with a well-developed root system, like C. dactylon, would have more retention of nutrient salts in the soil. However, we found that when the inundation depth was less than 1 m, the capacity of retention phosphorus by C. dactylon was higher than A. philoxeroides, and that of Acorus calamus was the highest. This difference is determined by the physiological characteristics of the plants. As an invasive weed originally from South America, A. philoxeroides could grow rapidly in shallow water. It had been found that A. philoxeroides could partially survive under a depth of 2 m (Akman et al. 2012; Fan et al. 2015). C. dactylon has a fast antioxidant system and a well-developed root system to defend against flooding stress (Tan et al. 2010). However, Acorus calamus, a water-holding plant, could transport the oxygen produced by photosynthesis to the roots through its own well-developed aeration system (Rizalihadi & Safiana 2015). This process can enhance the root system's ability to secrete oxygen and provide oxygen to the microorganisms around the roots (Armstrong et al. 2000). In addition, its root system is more developed and stronger than the other two plants. In summary, the inherent differences among plants cause them to have different growth states after submergence, which affects soil physico-chemical properties and microbial community structure.

Effects of inundating at different depths on soil microbial communities

After 1 year of the experiment, the differences in soil microbial ASVs in four different treatments are shown in Figure 6. A total of 1,762,903 valid sequences were measured in N0, D1, D2, and D3, and 11,835, 13,823, 15,651, and 15,983 ASVs were obtained through cluster analysis, respectively. It can be observed that there were 2,316 identical ASVs in four groups, with 4,890, 5,892, 8,143, and 8,794 ASVs peculiar to them, respectively. There were 558 identical ASVs between N0 and D1; N0 and D2 had 350 identical ASVs; N0 and D3 had 439 identical ASVs; D1 and D2 had 807 identical ASVs; D1 and D3 had 700 identical ASVs; and D2 and D3 had 1,205 identical ASVs. This indicated that within a certain flooding range, the species of soil microorganisms increased with flooding depth.
Figure 6

Venn diagram of ASV distributions of soil microorganisms for the four treatments (N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm).

Figure 6

Venn diagram of ASV distributions of soil microorganisms for the four treatments (N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm).

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The index of both Shannon's diversity and Chao1 in the microbial community was calculated (Figure 7). The Shannon's diversity index of N0, D1, D2, and D3 was 10.65, 10.81, 10.89, and 10.95, respectively (Figure 7(a), p < 0.005), and the Chao1 index was 2,968, 3,475, 3,924, and 4,013, respectively (Figure 7(b), p < 0.001). This suggested that the abundance and diversity of microorganisms increased in all the treatments compared to the control, and the variety of microorganisms increased with increasing water depth. The community composition of microorganisms was further analyzed for Beta diversity using principal component analysis (PCA) (Figure 8). The results showed that the first principal component contributed 65.4% and the second principal component contributed 6.79%, with the two factors representing 72.19% of the total variation. Moreover, in the two-dimensional coordinate system constructed with these two principal components as the coordinate axes, the distribution of N0 varied dramatically from the three groups of D1, D2, and D3. It indicated that there are differences in the microbial community structure in N0 and D1, D2, and D3, while there was less variability in the microbial community structure in the three flooding treatments.
Figure 7

Index of Shannon's diversity and Chao1 richness of soil microorganisms in four treatments (N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm).

Figure 7

Index of Shannon's diversity and Chao1 richness of soil microorganisms in four treatments (N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm).

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Figure 8

PCA of soil microorganisms in four groups: N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm.

Figure 8

PCA of soil microorganisms in four groups: N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm.

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In addition, ASVs of each group were classified in SILVA Release 138 and the NT-16S database for species classification and subsequent analysis, and were annotated to 64 phyla, 116 orders, 217 families, 399 families, and 634 genera.

The top 20 species of each sample at the phylum and the genus level were selected for analysis (Figures 9 and 10). The results of phylum level analysis indicated that the dominant species in all treatments were Proteobacteria and Acidobacteria, the abundance of Proteobacteria was 26.86% in N0 and 38.21, 37.18, and 37.98% in D1, D2, and D3, respectively. The abundance of Acidobacteria in N0, D1, D2, and D3 were 22.61, 20.54, 16.97, and 19.04%, respectively (Figure 9). It indicated that Proteobacteria increased and Acidobacteria decreased when the soil was inundated. In addition, Actinobacteria, Gemmatimonadetes, and Firmicutes also showed an obvious decrease in relative abundance.
Figure 9

The top 20 species of each sample of soil microorganism at the phylum level for the four treatments: N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm.

Figure 9

The top 20 species of each sample of soil microorganism at the phylum level for the four treatments: N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm.

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Figure 10

The top 20 species of each sample of soil microorganism at the genus level for the four treatments: N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm.

Figure 10

The top 20 species of each sample of soil microorganism at the genus level for the four treatments: N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm.

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The results of genus-level analysis showed that two genera with the highest biomass abundance in all treatments were Acidobacteria_unclassified and Betaproteobacteri_unclassified (Figure 10). Acidobacteria_unclassified had the lowest abundance (19.54%) in D2, while there was no change in the other three groups with abundances of 25.28, 24.17, and 25.76% in the other three groups, respectively. Betaproteobacteri_unclassified was the most abundant in D1 with 13.11%, and 8.94, 9.95, and 9.71% in N0, D2, and D3, respectively. However, the third most abundant in N0 was Gemmatimonadetes_unclassified (10.85%), and those of the other three groups were Proteobacteria_unclassified (8.35–8.67%). It was noteworthy that Nitrospira, which was not present in N0, accounted for 1.41, 1.95, and 2.23% in D1, D2, and D3, respectively. The clustering results showed great variability between the microorganisms in N0 and D1, D2, and D3, with similar microbial compositions in D1, D2, and D3, but showed the most similar microbial composition between D2 and D3.

The species that accounted for 95% of the total abundance of microorganisms in the four treatments at the genus level were clustered, and the numerical magnitude was indicated by color shades. The result of cluster analysis was presented in a dendrogram and heat map (Figure 11). It can be observed that the abundance of different bacteria differed in different treatments. For example, Acidobacteria_unclassified, Actinobacteria, Gemmatimonadetes_unclassified, and Firmicutes_unclassified had a high abundance in N0, which was in sharp contrast to their proportion in D1, D2, and D3. In contrast, Gammaproteobacteria_unclassified, Nitrospira, and some unclassified bacteria (e.g., unclassified) were present in large numbers in the soils of D1, D2, and D3. The abundance of soil microorganisms in N0 and D1, D2, and D3 was clearly varied at the genus level. Besides, there was also differentiation among the soil microorganisms in D1, D2, and D3 treatments.
Figure 11

Genus-level heat map of soil microbial abundance for the four treatments (N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm).

Figure 11

Genus-level heat map of soil microbial abundance for the four treatments (N0: blank treatment; D1: flooded 20 cm; D2: flooded 50 cm; D3: flooded 100 cm).

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The Shannon's diversity index and the Chao1 index of the microbial community showed that flooding increased soil microbial species and biomass, and it complicated the community structure (Chen et al. 2020). This is different from the results of some previous studies on microorganisms in wetlands in the WLFZ (Rui et al. 2009; Wang et al. 2019; Han et al. 2021). This may be attributed to the fact that the depth of flooding in this study was shallow, and the water level would be lowered or increased at times due to evaporation and rain, putting the soil in a state where oxygen exists. In contrast, in an oxygen-rich state, both anaerobic and aerobic bacteria in the soil would multiply. It has been found that under flooding conditions, when the soil water content is high, material exchange will occur with the water body, thus affecting microbial diversity (Zhao et al. 2011; Wang et al. 2016). Meanwhile, Nitrospira, which appeared in the flooding treatment groups, belongs to a type of nitrobacteria, which can oxidize nitrite to nitrate. This explained why soil nitrate nitrogen shows an increasing trend during the flooding period, while its presence indicates that the amount of oxygen is sufficient for the activity of some aerobic microorganisms within 1 m of flooding (Wang et al. 2016; Du et al. 2020). Based on the variation of soil physical and chemical properties, as well as microbial community structure, the biochemical processes at the soil–water interface in the 1 m submerged area under water may be positive.

In summary, Acorus calamus was a suitable plant for planting at 1-m depth, but its growth was inhibited due to lack of water after drying. It was also considered that C. dactylon can survive when flooded and can grow well during drought periods, and A. philoxeroides was an invasive weed plant. So, we suggested that the mixed planting of Acorus calamus and C. dactylon at 1-m water depth will have better results for protecting soil health.

This study investigated the effect of water level on soil physico-chemical properties and microbial communities, as well as plant growth. Our results showed that C. dactylon had a good adsorption capacity for nitrogen and phosphorus when it was flooded with shallow water, leading to the decrease of soil nutrients during the drying period. Acorus calamus, being a water-holding plant, grew well when it was flooded, but its growth was inhibited due to lack of water after drying. A. philoxeroides died and degraded when it was flooded, leading to the increase of nitrogen and phosphorus and other nutrients; but after drying, it regenerated and consumed nutrients again. The soil microbial biomass and species increased with increasing water depth when being subjected to inundation within 1 m. After a wetting–drying cycle, there was an obvious divergence in soil microbial community structure between N0 and D1, D2, and D3.

This work was supported by the Joint Foundation of Shaanxi Province (No. 2019JLM-59), the Water Conservancy Science and Technology Project of Shaanxi Province (2022slkj-8), and the National Natural Science Foundation of China (No. 51979236).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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