Next Article in Journal
Clonal Fidelity and Genetic Diversity of Micropropagated Hancornia speciosa Gomes (Apocynaceae) as Evaluated by Molecular Markers
Previous Article in Journal
Spatiotemporal Variation of NDVI in Anhui Province from 2001 to 2019 and Its Response to Climatic Factors
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 13
Issue 10
10.3390/f13101644
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Rainfall Physical Partitioning and Chemical Characteristics in Evergreen Coniferous and Deciduous Broadleaved Forest Stands in a High Nitrogen Deposition Region, China

by
Tao Yang
1,
Yong Li
2,
Xueying Ouyang
3,
Bo Wang
1,
Xiaomin Ge
4 and
Luozhong Tang
1,*
1
Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing 210037, China
2
Qingtian County Forestry Bureau of Zhejiang Province, Lishui 323900, China
3
Institute of Regional Agricultural Research, College of Agriculture, Nanjing Agricultural University, Nanjing 210014, China
4
Research Center for Nature Conservation and Biodiversity, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(10), 1644; https://doi.org/10.3390/f13101644
Submission received: 1 September 2022 / Revised: 4 October 2022 / Accepted: 6 October 2022 / Published: 8 October 2022
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

:
Atmospheric rainfall is one of the major sources of water and nutrient inputs in forest stands. Understanding the atmospheric rainfall partitioning and hydrochemical fluxes of forest stands is critical for forest management and monitoring regional atmospheric pollution, especially in high N deposition regions. In this study, annual rainfall collections were implemented to investigate rainfall partitioning, element concentrations, and element fluxes in an evergreen coniferous forest (ECF) stand, a deciduous broadleaved forest (DBF) stand, and open area field (OAF) in a high N deposition region, China. Rainfall in the ECF and DBF was partitioned into throughfall, stemflow, and interception loss, which accounted for 74.7%, 4.8%, and 20.5% of the gross annual rainfall in the ECF stand, respectively; and 79.8%, 5.8%, and 14.4% of the gross annual rainfall in the DBF stand, respectively. Rainfall physical partitioning chemical characteristics varied with forest stand type. The amount of throughfall and stemflow in the ECF stand was lower than that in the DBF stand; the interception loss in the ECF stand was higher than that in the DBF stand. Element concentrations and element fluxes increased as rainfall passed through forest canopies in the high N deposition region. The stemflow pH in the ECF was lower than that in the DBF stand, the concentrations of NO3−-N, Cl−, and SO42−-S in stemflow in the ECF stand were higher than those in the DBF stand, and the concentrations of K+, Na+, Ca2+, Mg2+ and NH4+-N in stemflow in the ECF stand were lower than those in the DBF stand. The inorganic N deposition was 52.7 kg ha−1 year−1 for the OAF, 110.9 kg ha−1 year−1 for the ECF stand, and 99.6 kg ha−1 year−1 for the DBF stand; stemflow accounted for 15.1% and 19.2% of inorganic N deposition in the ECF stand and the DBF stand, respectively. In the present study, given the similar rainfall characteristics and meteorological conditions, the differences in rainfall partitioning and chemical characteristics between the ECF stand and the DBF stand could largely be attributed to their differences in stand characteristics. The results of the study will facilitate a greater understanding of the atmospheric rainfall partitioning and hydrochemical fluxes of forest stands in a high nitrogen deposition region.

1. Introduction

Studying atmospheric rainfall in forests, including rainfall distribution characteristics and chemical characteristics, is relevant to monitoring regional atmospheric pollution, in addition to evaluating forest hydrology and nutrient balances, especially in high N deposition regions [1,2,3]. Although forests are usually considered to be N-deficient, anthropogenic activities have increased N deposition in some forest ecosystems [3,4]. The high N deposition in forest ecosystems may result in water and soil acidification, nutrient imbalances, and biodiversity loss [5,6]. China is one of the hotspots for N deposition in the world. A heavy regional deposition has been observed in China, and high inorganic N deposition in China’s forest stands has been documented in several publications [6,7,8]. Du et al. [8] synthesized inorganic N fluxes from 38 China’s typical forest stands from 1995 to 2010 and concluded that inorganic N deposition in bulk rainfall were in the 2.7–57.5 kg ha−1 year−1 range, with a mean of 14.0 kg ha−1 year−1; the corresponding values in throughfall were in the 3.2–111.1 kg ha−1 year−1 range, with a mean of 21.5 kg ha−1 year−1. Inorganic N fluxes in throughfall can be used as a precise estimate of inorganic N deposition when canopy uptake was not taken into consideration [8,9,10]. However, throughfall underestimates inorganic N deposition in forest due to the contribution of stemflow not being considered [11,12]. Throughfall accounts for the majority of gross rainfall, is the major source of forest soil water and plays an important role in element fluxes in forest ecosystems; although stemflow accounts for the least proportion of gross rainfall, stemflow can be transported directly to the tree roots, which could affect epiphytes and rhizosphere soil [13,14,15]. Canopy interception loss is also essential in the water budget of forest ecosystems, and previous studies have demonstrated that canopy interception loss decreases total water inputs into ecosystems, and is a considerable route of water loss in forest ecosystems [16,17,18]. Rainfall physical partitioning could vary among different forest stands, which are largely attributable to the stand characteristics (e.g., tree species, density, tree age, and canopy structure) [19,20], rainfall characteristics (e.g., intensity, velocity, and drop diameter) [21,22], and environment variables (e.g., wind and evaporation rate) [23,24].
Varying trends in rainfall chemical characteristics in forest ecosystems have been attributed to three key factors [25,26]: (1) deposits accumulated on the canopy and stem being washed by rainfall; (2) leaching of some elements from internal plant tissues by rainfall; and (3) the absorption of some elements, particles, and solutes by foliage. Additionally, the variability in rainfall chemical characteristics among different tree species stands in a region is largely attributable to the tree morphological, physiological, and epiphytic organisms characteristics [12,27]. For example, Eisalou et al. [2] and Kowalska et al. [12] have found that coniferous species tended to cause acidification of rainfall when compared to deciduous species, which caused acidity in streams of coniferous forest-dominated watersheds.
Evergreen coniferous forest (ECF) and deciduous broadleaved forest (DBF) stands are common forest stand types in China, which have important ecological and economic service functions [28,29]. In the present study, annual rainfall partitioning, element concentrations, element fluxes, and inorganic N deposition were estimated in an ECF stand, a DBF stand, and an open area field (OAF). The specific objectives of this study were: (1) to understand the changes in fluxes and chemical characteristics of rainfall after passing through the canopy of the forest stands; (2) to compare the differences in rainfall physical partitioning and chemical characteristics between the ECF stand and DBF stand.

2. Materials and Methods

2.1. Experimental Site

The ECP, DBF, and OAF were located in Xiashu Forest Farm (32°12′ N, 119°21′ E), Jiangsu Province, in the middle and lower reaches of the Yangtze River region, which is regarded as one of the high N deposition regions in China (Figure 1). The site has a typical subtropical monsoon climate, with four distinct seasons and a frost-free period of 233 days. The annual average wind speed is approximately 3.4 m s−1, with the northwest and southeast winds accounting for the majority of the winds. The annual precipitation is more than 1000 mm, and the monthly precipitation and temperature over 30 years are illustrated in Figure 2. Most storms are low-intensity storms, and the average rainfall intensity over 10 years is approximately 47 mm h−1 in the region. According to a vegetation survey conducted prior to the study, the dominant tree species in the ECF stand was Pinus taeda L. (>90%), and other species included Phyllostachys heteroclada Oliv., Ilex cornuta Lindl. et Paxt., and Lindera glauca (Sieb. et Zucc.) Bl.; the canopy cover in the ECF stand was 85%, the planting density was 720 trees per ha, and the average tree age was 35 years. The dominant tree species in the DBF stand was Quercus acutissima Carr. (>65%), and other species were Liquidambar formosana H., Celtis sinensis P., Acer buergerianum M., Ilex chinensis S., and Ilex cornuta; the canopy cover in the DBF stand was 80%, the planting density was 512 trees per ha, and the average tree age was 50 years. The other information of the forest stands including elevation, slope, direction of slope, diameter at breast height (DBH), tree height, and leaf area index (LAI) were summarized in Table 1. The LAI was estimated using a LAI-2000 analyzer (LI-COR, Inc., Lincoln, NE, USA), in the spring, summer, autumn, and winter in 2015. In addition, fertilization, irrigation, pruning, and thinning management were not carried out during the experiment.

2.2. Sample Collection

A one-year sample collection was conducted in 2015. In each forest stand, there were three 20 × 20 m repeated plots for sample collection, and the value of each measured variables for a forest stand was taken from the average of the three repeated observation plots. Bulk rainfall and throughfall samples were collected using a rainfall collector with a polyethylene funnel (27-cm diameter) mounted 1 m aboveground draining into a black polyethylene bottle (11 L, 5-cm bottleneck diameter), and a nylon mesh with 1-mm apertures was placed in each funnel to prevent the entry of tree litter. Fifteen rainfall sample collectors were randomly placed in each observation plot to sample throughfall, and throughfall depth for an observation plot was taken from the average value of the fifteen collectors. Stemflow was collected using collars (1 m above the ground) constructed from polyethylene plates that were fitted around the tree stem using a silicone sealant. Stemflow was led into a 70 L black polyethylene bottle at the bottom of the gauging tree. Ten robust and healthy dominant trees (stems straight, no broken branches, DBH ranged 19–23 cm for ECF, and DBH ranged 24–27 cm for DBF) as representative trees in each 20 × 20 m observation plot were randomly selected for stemflow collection. Stemflow depth for an observation plot was calculated by dividing the measured stemflow volume by the total projection canopy area. In addition, three repeated OAF plots (10 × 10 m) were established for bulk rainfall collection, and the value of each of the measured variables of bulk rainfall in OAF was taken from the average of the three repeated plots. Ten rainfall collectors were randomly placed in each open field plot to sample bulk rainfall, and the average value of the ten collectors was considered a measure of bulk rainfall depth for an OAF plot. Furthermore, canopy interception loss = bulk rainfall (throughfall + stemflow).

2.3. Measurement of Chemical Characteristics of Samples

The accumulated rainfall events were used for rainfall collections and dry deposition, and the rainfall events that were too small to collect would be accumulated in the collectors. At least 24 h without rain separated two collections, resulting in 23 rainfall collections during 2015. All collectors were emptied and cleaned with deionized water before each collection. The pH of samples was measured immediately using a pH meter (Mettler-Toledo, Inc., Zurich, Switzerland). Before further analysis, samples were filtered through 0.45 mm pore size filters and preserved in a −20 °C refrigerator. The concentrations of major anions, Cl−, NO3−-N, and SO42−-S, and the concentrations of major cations, K+, Na+, Ca2+, Mg2+, and NH4+-N, in samples, were measured using a HIC-6A ion chromatograph (Shimadzu, Japan). The element concentrations used for analysis in this study were the annual volume-weighted average concentrations and the annual volume-weighted average concentrations of bulk rainfall, throughfall or stemflow being estimated based on 23 rainfall collections in 2015. Then annual element fluxes were estimated according to Su et al. [27]:
Annual element fluxes (kg ha−1 year−1) = C × V/100
where C (mg L−1) is annual volume-weighted average concentration, and V (mm) is the annual bulk rainfall, throughfall or stemflow.

2.4. Data Analysis

Statistical analyses were carried out in IBM SPSS Statistics 20 (IBM, Armonk, NY, USA). The tests for normality and homogeneity of variance for the data were carried out using the Shapiro–Wilk and Levene’s tests, respectively. Interception loss, throughfall, and stemflow were also calculated as a percentage of the gross rainfall. To compare the differences in throughfall or stemflow between the ECF stand and the DBF stand, each measured variable was analyzed using one-way analysis of variance (ANOVA), with forest stand type as the factor (n = 3, significant at the p < 0.05 level).

3. Results

3.1. Rainfall Physical Partitioning

Annual gross rainfall (bulk rainfall amount) was 1763 mm in 2015 (Figure 3). There were significant differences (p < 0.05) in the annual stemflow, throughfall, and interception loss amounts between the ECF stand and the DBF stand. The throughfall and stemflow in the ECF stand were lower than that in the DBF stand. Compared with the DBF stand, there was higher interception loss in the ECF stand. Furthermore, in the ECF stand, the annual throughfall, stemflow, and interception loss amounts accounted for 74.7%, 4.8%, and 20.5% of the annual rainfall, respectively. In the DBF stand, the annual throughfall, stemflow, and interception loss in the DBF stand accounted for 79.8%, 5.8%, and 14.4% of the annual gross rainfall, respectively.

3.2. pH in Rainfall Partitions

Bulk rainfall pH during the study period was in the 5.6–8.5 range, with an average of 6.7 (Figure 4). There was no significant difference (p > 0.05) in throughfall pH between the ECF stand and the DBF stand, and the throughfall pH values during the study period in ECF and DBF were in the 4.9–7.9 and 4.5–8.3 ranges, respectively, with averages of 6.1 and 6.2, respectively. There was a significant difference in stemflow pH between the ECF stand and the DBF stand. In the ECF stand, the pH of stemflow was in the 4.1–6.0 range during the study period, with an average of 4.8; the corresponding stemflow pH in the DBF stand was in the 4.4–7.1 range during the study period, with an average of 5.3.

3.3. Element Concentrations in Rainfall Partitions

Figure 5 shows the annual volume-weighted average concentrations of the major elements in OAF, ECF and DBF during the study period. The element concentrations increased as rainfall passed through the tree canopy both in ECF and DBF during the study period. Overall, each element concentration with a rank of stemflow > throughfall > bulk rainfall. No significant difference (p > 0.05) in the concentrations of K+, Na+, Ca2+, Mg2+, NH4+-N, NO3−-N, Cl−, and SO42−-S in throughfall between the ECF and DBF was observed during the study period. However, the concentrations of K+, Na+, Ca2+, Mg2+, and NH4+-N in stemflow in the ECF stand were significantly lower than those in the DBF stand, whereas the concentrations of NO3−-N, Cl−, and SO42−-S in stemflow in the ECF stand were significantly higher than those in the DBF stand.

3.4. Annual Element Fluxes in Rainfall Partitions

The annual element fluxes in the OAF, ECF, and DBF are estimated in Table 2. The highest element flux via bulk rainfall was SO42−-S, and the element fluxes in the OAF during the study period were ranked as SO42−-S > Ca2+ > NO3−-N > Cl− > NH4+-N > Na+ > K+ > Mg2+. The fluxes of K+, Na+, Ca2+, Mg2+, Cl−, SO42−-S, NH4+-N, and NO3−-N increased as rainfall passed through the tree canopy both in ECF and DBF. Furthermore, the inorganic N deposition was 52.7 kg ha−1 year−1 in the OAF, and the inorganic N deposition in the forest stands was much higher than that in the OAF. The inorganic N deposition in the ECF stand was 110.9 kg ha−1 year−1, and throughfall and stemflow accounted for 84.9% and 15.1% of inorganic N deposition, respectively. The inorganic N deposition in the DBF stand was 99.6 kg ha−1 year−1, and throughfall and stemflow accounted for 80.8% and 19.2% of inorganic N deposition, respectively.

4. Discussion

4.1. Rainfall Physical Partitioning

Rainfall partitioning may have relevant implications in soil water dynamics, soil erosion, runoff, and water infiltration in forest ecosystems [31]. Throughfall, which accounts for the majority of gross rainfall, is the major form of atmospheric water input in forest stands. The throughfall in the ECF and DBF accounted for 74.7% and 79.8% of the gross rainfall, respectively. Conversely, stemflow often accounts for the least proportion of gross rainfall, the stemflow in ECF and DBF accounted for only 4.8% and 5.8% of the gross rainfall, respectively. Rainfall physical partitioning was affected by forest stand types, and the higher canopy interception loss for the ECF stand than the DBF stand resulted in less throughfall and stemflow in the ECF stand. Miralles et al. [32] used multisatellite observations to estimate rainfall interception loss in forest stands at the global scale and found that this flux was higher in evergreen coniferous forest stands than in deciduous broadleaved forest stands, which is consistent with our findings. Evergreen coniferous tree species often have greater LAI when compared with deciduous broadleaved tree species, and their leaves present throughout the year, which resulted in coniferous forest stands being able intercept more atmospheric deposition [33,34]. In Table 3, we reviewed the rainfall physical partitioning of evergreen coniferous and deciduous broadleaved forest stands in the literature, in which we can find a pronounced difference in rainfall physical partitioning in different stand characteristics including stand density, tree age, DBH, and tree height, in addition to tree species. There are many factors (e.g., stand characteristics, rainfall characteristics, and environment variables) affecting rainfall partitioning in forest stands [19,20,21,22,23,24]. In the present study, there were similar rainfall characteristics and meteorological conditions in each forest stand, and the difference in rainfall partitioning between the ECF stand and the DBF stand was largely attributable to their differences in stand characteristics.

4.2. Rainfall Chemical Characteristics

In the present study, pH values and concentrations of major elements among bulk rainfall, throughfall, and stemflow were different. The concentrations of major elements much increased as rainfall passed through forest canopies in the studied region, which could be attributed to heavy dry deposition interception and storage on the forest canopies. Numerous elements from dry deposition are intercepted and temporarily stored on the tree canopy, and rainwater acts as a chemical solvent that washes and leaches the elements accumulated on the canopy, resulting in higher element concentrations of rainfall in the forest stands [25,26,42]. In most studies that have explored the influence of forest ecosystems on rainfall chemical characteristics, the effects of stemflow have been excluded om consideration of its minimal amounts, which can even be negligible under low rainfall events [22]. In the present study, although stemflow accounted for the least amount of rainfall among the physical partitions, in terms of element ranking stemflow > throughfall > bulk rainfall. The higher element concentrations for stemflow could be attributed to the larger flow area and longer residence time on tree, as these factors for stemflow will favor ion exchanges [27,43]. In addition, the stemflow could be transported directly to the base of the tree, which may have potential effects on the physical and chemical properties of the soil at the base of the tree [44]. In the present study, we found that forest stand types had a significant effect on stemflow pH, and the stemflow pH in the ECF was lower than that in the DBF stand. Similar results have been reported by Eisalou et al. [2] in broadleaved and coniferous forests in Turkey, where the rainwater pH became strongly acidic in the pine stand and neutral in the oak and beech stands. Furthermore, we found that the concentrations of NO3−-N, Cl−, and SO42−-S in stemflow in the ECF stand were higher than those in the DBF stand, and the concentrations of K+, Na+, Ca2+, Mg2+, and NH4+-N in stemflow in the ECF stand were lower than those in the DBF stand. The chemical characteristics of stemflow varied across the two forest stand types examined, which is largely attributable to differences in canopy and bark morphology and biochemistry characteristics between coniferous tree species and broadleaved tree species. There may be more acid anions in coniferous tree tissues compared with broadleaved tree tissues, which would result in more anions leached by rainfall in coniferous tree species than broadleaved tree species [33]. In addition, canopies and barks of coniferous trees secrete more viscous oils, which may repel water, and, in turn, prevent the leaching of base cations from the canopy and bark [34]. Furthermore, local environment factors, element concentrations in tree tissues, active physiological, and gas exchange processes could influence the rainfall chemical characteristics in different forest stands [2,42,45].

4.3. Element Fluxes and Inorganic N Deposition

Understanding element fluxes in rainfall facilitates the assessment of nutrient cycling and the potential influence of anthropogenic pollution on forest ecosystems [46,47,48]. Although canopy interception-caused rainwater fluxes reduced in the forest stands, element fluxes increased in the forest stands due to element concentrations increasing as rainfall passed through forest canopies in the studied region. In the present study, the highest element flux via rainfall was SO42−-S and fossil fuel combustion in nearby cities could be the dominant sources of the high SO42−-S flux. Element flux in stemflow was generally lower than that in bulk rainfall, excluding the case of K+, which was largely to strong leaching of K+ from tree canopy and bark [42,49]. In the present study, the inorganic N deposition was 52.7 kg ha−1 year−1 for the OAF, 110.9 kg ha−1 year−1 for the ECF stand, and 99.6 kg ha−1 year−1 for the DBF stand. In addition, the stemflow accounted for 15.1% and 19.2% of inorganic N deposition in the ECF stand and the DBF stand, respectively, and therefore, the contribution of stemflow with regard to inorganic N deposition should not be ignored in the forest stands. The inorganic N deposition in this study was much higher than the average deposition of inorganic N in throughfall in China’s forest stands reported by Du et al. [8], which could be attributable to stemflow contribution and regional high deposition. The rapid expansion of industry and intensive agriculture, especially livestock farming and heavy fertilizer application, could be the dominant sources of the high N deposition [3,50]. Although atmospheric N deposition is a key source of N for plant growth in N-limited ecosystems [51,52], high N inputs may cause soil and water acidification, threatening both the structure and functioning of ecosystems [53,54,55]. Therefore, the effect of the high N deposition on the development of the forest ecosystems in the studied region should be taken into consideration in future research programs.

5. Conclusions

Forest stand type influenced rainfall physical partitioning and chemical characteristics. There was a higher canopy interception loss for the evergreen coniferous forest stand than for the deciduous broadleaved forest stand, which resulted in less throughfall and stemflow in the evergreen coniferous forest stand. Element concentrations and element fluxes increased as rainfall passed through forest canopies both in the evergreen coniferous forest stand and deciduous broadleaved forest stand. Although stemflow accounted for the least amounts in rainfall physical partitioning, the concentrations of major elements in stemflow were higher than those in bulk rainfall and throughfall, suggesting the contribution of stemflow in forest biogeochemical cycles should not be ignored. In the present study, given the similar rainfall characteristics and meteorological conditions, the differences in rainfall physical partitioning and chemical characteristics between the evergreen coniferous forest stand and deciduous broadleaved forest stand could largely be attributable to their differences in stand characteristics. Furthermore, forest stands with different rainfall physical partitioning and chemical characteristics may provide different ecosystem services and functioning that need further research.

Author Contributions

Investigation, Y.L., B.W., X.G. and L.T.; Methodology, L.T.; Visualization, T.Y., Y.L. and X.O.; Writing—original draft, T.Y. and X.O.; Writing—review & editing, L.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Key Research and Development Program of China (grant number 2021YFD2201202), Biodiversity Protection Project of Ministry of Ecology and Environment, China (grant number 2110404), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Owens, M.K.; Lyons, R.K.; Alejandro, C.L. Rainfall partitioning within semiarid juniper communities: Effects of event size and canopy cover. Hydrol. Process. 2006, 20, 3179–3189. [Google Scholar] [CrossRef]
  2. Eisalou, H.K.; Şengönül, K.; Gökbulak, F.; Serengil, Y.; Uygur, B. Effects of forest canopy cover and floor on chemical quality of water in broad leaved and coniferous forests of Istanbul, Turkey. For. Ecol. Manag. 2013, 289, 371–377. [Google Scholar] [CrossRef]
  3. Fang, Y.; Gundersen, P.; Vogt, R.D.; Koba, K.; Chen, F.; Chen, X.Y.; Yoh, M. Atmospheric deposition and leaching of nitrogen in Chinese forest ecosystems. J. For. Res. 2011, 16, 341–350. [Google Scholar] [CrossRef]
  4. Fenn, M.E.; Jovan, S.; Yuan, F.; Geiser, L.; Meixner, T.; Gimeno, B.S. Empirical and simulated critical loads for nitrogen deposition in California mixed conifer forests. Environ. Pollut. 2008, 155, 492–511. [Google Scholar] [CrossRef]
  5. Tomasz, G.; Thomas, S.C. Responses of Acer saccharum canopy trees and saplings to P, K and lime additions under high N deposition. Tree Physiol. 2008, 28, 173–185. [Google Scholar] [CrossRef] [Green Version]
  6. Liu, X.; Ju, X.; Zhang, Y.; He, C.; Kopsch, J.; Zhang, F. Nitrogen deposition in agroecosystems in the Beijing area. Agric. Ecosyst. Environ. 2006, 113, 370–377. [Google Scholar] [CrossRef]
  7. Liu, X.; Zhang, Y.; Han, W.; Tang, A.; Shen, J.; Cui, Z.; Vitousek, P.; Erisman, J.W.; Goulding, K.; Christie, P.; et al. Enhanced nitrogen deposition over China. Nature 2013, 494, 459–462. [Google Scholar] [CrossRef] [PubMed]
  8. Du, E.Z.; Jiang, Y.; Fang, J.Y.; Vries, W.D. Inorganic nitrogen deposition in China’s forests: Status and characteristics. Atmos. Environ. 2014, 98, 474–482. [Google Scholar] [CrossRef]
  9. Sparks, J.P. Ecological ramifications of the direct foliar uptake of nitrogen. Oecologia 2009, 159, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Fenn, M.E.; Ross, C.S.; Schilling, S.L.; Baccus, W.D.; Larrabee, M.A.; Lofgren, R.A. Atmospheric deposition of nitrogen and sulfur and preferential canopy consumption of nitrate in forests of the Pacific Northwest, USA. For. Ecol. Manag. 2013, 302, 240–253. [Google Scholar] [CrossRef]
  11. Boggs, J.L.; McNulty, S.G.; Pardo, L.H. Changes in conifer and deciduous forest foliar and forest floor chemistry and basal area tree growth across a nitrogen (N) deposition gradient in the northeastern US. Environ. Pollut. 2007, 149, 303–314. [Google Scholar] [CrossRef]
  12. Kowalska, A.; Astel, A.; Boczoń, A.; Polkowska, Ż. Atmospheric deposition in coniferous and deciduous tree stands in Poland. Atmos. Environ. 2016, 133, 145–155. [Google Scholar] [CrossRef]
  13. Swaffer, B.A.; Holland, K.L.; Doody, T.M.; Hutson, J. Rainfall partitioning, tree form and measurement scale: A comparison of two co-occurring, morphologically distinct tree species in a semi-arid environment. Ecohydrology 2014, 7, 1331–1344. [Google Scholar] [CrossRef]
  14. Carlyle-Moses, D.E.; Iida, S.; Germer, S.; Llorens, P.; Michalzik, B.; Nanko, K.; Tischer, A.; Levia, D.F. Expressing stemflow commensurate with its ecohydrological importance. Adv. Water Resour. 2018, 121, 472–479. [Google Scholar] [CrossRef]
  15. Levia, D.F.; Germer, S. A review of stemflow generation dynamics and stemflow-environment interactions in forests and shrublands. Rev. Geophys. 2015, 53, 673–714. [Google Scholar] [CrossRef]
  16. Corti, G.; Agnelli, A.; Cocco, S.; Cardelli, V.; Masse, J.; Courchesne, F. Soil affects throughfall and stemflow under Turkey oak (Quercus cerris L.). Geoderma 2019, 333, 43–56. [Google Scholar] [CrossRef]
  17. Carlyle-Moses, D.E. Throughfall, stemflow, and canopy interception loss fluxes in a semi-arid Sierra Madre Oriental matorral community. J. Arid. Environ. 2004, 58, 181–202. [Google Scholar] [CrossRef]
  18. Dunkerley, D. Measuring interception loss and canopy storage in dryland vegetation: A brief review and evaluation of available research strategies. Hydrol. Process. 2000, 14, 669–678. [Google Scholar] [CrossRef]
  19. Teklehaimanot, Z.; Jarvis, P.G.; Ledger, D.C. Rainfall interception and boundary layer conductance in relation to tree spacing. J. Hydrol. 1991, 123, 261–278. [Google Scholar] [CrossRef]
  20. Xiao, Q.; Mcpherson, E.G. Rainfall interception of three trees in Oakland, California. Urban Ecosyst. 2011, 14, 755–769. [Google Scholar] [CrossRef]
  21. Saito, T.; Matsuda, H.; Komatsu, M.; Xiang, Y.; Takahashi, A.; Shinohara, Y.; Otsuki, K. Forest canopy interception loss exceeds wet canopy evaporation in Japanese cypress (Hinoki) and Japanese cedar (Sugi) plantations. J. Hydrol. 2013, 507, 287–299. [Google Scholar] [CrossRef]
  22. Zhang, Y.F.; Wang, X.P.; Hu, R.; Pan, Y.X.; Paradeloc, M. Rainfall partitioning into throughfall, stemflow and interception loss by two xerophytic shrubs within a rain-fed re-vegetated desert ecosystem, northwestern China. J. Hydrol. 2015, 527, 1084–1095. [Google Scholar] [CrossRef]
  23. Ma, C.; Li, X.; Luo, Y.; Shao, M.; Jia, X. The modelling of rainfall interception in growing and dormant seasons for a pine plantation and a black locust plantation in semi-arid Northwest China. J. Hydrol. 2019, 577, 123849. [Google Scholar] [CrossRef]
  24. Page, T.; Chappell, N.A.; Beven, K.J.; Hankin, B.; Kretzschmar, A. Assessing the significance of wet-canopy evaporation from forests during extreme rainfall events for flood mitigation in mountainous regions of the United Kingdom. Hydrol. Process. 2020, 34, 4740–4754. [Google Scholar] [CrossRef]
  25. Parker, G.G. Throughfall and stemflow in the forest nutrient Cycle. Adv. Ecol. Res. 1983, 13, 57–133. [Google Scholar] [CrossRef]
  26. Chiwa, M.; Crossley, A.; Sheppard, L.J.; Sakugawa, H.; Cape, J.N. Throughfall chemistry and canopy interactions in a Sitka spruce plantation sprayed with six different simulated polluted mist treatments. Environ. Pollut. 2004, 127, 57–64. [Google Scholar] [CrossRef]
  27. Su, L.; Zhao, C.; Xu, W.; Xie, Z. Hydrochemical fluxes in bulk precipitation, throughfall, and stemflow in a mixed evergreen and deciduous broadleaved forest. Forests 2019, 10, 507. [Google Scholar] [CrossRef] [Green Version]
  28. Wang, B.; Wang, G.; Myo, S.T.Z.; Li, Y.; Xu, C.; Lin, Z.; Qian, Z.; Tang, L. Deforestation for agriculture temporarily improved soil quality and soil organic carbon stocks. Forests 2022, 13, 228. [Google Scholar] [CrossRef]
  29. Wang, B.; Zhu, L.; Yang, T.; Qian, Z.; Xu, C.; Tian, D.; Tang, L. Poplar agroforestry systems in eastern China enhance the spatiotemporal stability of soil microbial community structure and metabolism. Land Degrad. Dev. 2022, 33, 916–930. [Google Scholar] [CrossRef]
  30. Jia, Y.L.; Wang, Q.F.; Zhu, J.X.; Chen, Z.; He, N.P.; Yu, G.R. A spatial and temporal dataset of atmospheric inorganic nitrogen wet deposition in China (1996–2015). Sci. Data Bank 2018. [Google Scholar] [CrossRef]
  31. Janeau, J.L.; Grellier, S.; Podwojewski, P. Influence of rainfall interception by endemic plants versus short cycle crops on water infiltration in high altitude ecosystems of Ecuador. Hydrol. Res. 2015, 46, 1008–1018. [Google Scholar] [CrossRef]
  32. Miralles, D.G.; Gash, J.H.; Holmes, T.R.H.; de Jeu, R.A.M.; Dolman, A.J. Global canopy interception from satellite observations. J. Geophys. Res. 2010, 115, D16122. [Google Scholar] [CrossRef]
  33. Robertson, S.; Hornung, M.; Kennedy, V.H. Water chemistry of throughfall and soil water under four tree species at Gisburn, northwest England, before and after felling. For. Ecol. Manag. 2000, 129, 101–117. [Google Scholar] [CrossRef]
  34. Zhang, S.; Yu, J.; Pan, T.; Gao, X.; Hou, L. Difference between rainfall and throughfall chemistry for different forest stands in the Qinling Mountains, China. Hydrol. Res. 2021, 52, 523–535. [Google Scholar] [CrossRef]
  35. Dong, L.; Han, H.; Kang, F.; Cheng, X.; Zhao, J.; Song, X. Rainfall partitioning in Chinese pine (Pinus tabuliformis Carr.) stands at three different ages. Forests 2020, 11, 243. [Google Scholar] [CrossRef] [Green Version]
  36. Aydın, M.; Sen, S.G.; Celik, S. Throughfall, stemflow, and interception characteristics of coniferous forest ecosystems in the western black sea region of Turkey (Daday example). Environ. Monit. Assess. 2018, 190, 316. [Google Scholar] [CrossRef]
  37. Staelens, J.; Schrijver, A.D.; Verheyen, K.; Verhoest, N.E.C. Rainfall partitioning into throughfall, stemflow, and interception within a single beech (Fagus sylvatica L.) canopy: Influence of foliation, rain event characteristics, and meteorology. Hydrol. Process. 2008, 22, 33–45. [Google Scholar] [CrossRef]
  38. Mużyło, A.; Valente, F.; Domingo, F.; Llorens, P. Modelling rainfall partitioning with sparse Gash and Rutter models in a downy oak stand in leafed and leafless periods. Hydrol. Process. 2012, 26, 3161–3173. [Google Scholar] [CrossRef]
  39. Rodrigo, A.; Àvila, A.; Rodà, F. The chemistry of precipitation, throughfall and stemflow in two holm oak (Quercus ilex L.) forests under a contrasted pollution environment in NE Spain. Sci. Total. Environ. 2003, 305, 195–205. [Google Scholar] [CrossRef]
  40. Carlyle-Moses, D.E.; Price, A.G. An evaluation of the Gash interception model in a northern hardwood stand. J. Hydrol. 1999, 214, 103–110. [Google Scholar] [CrossRef]
  41. Price, A.G.; Carlyle-Moses, D.E. Measurement and modelling of growing-season canopy water fluxes in a mature mixed deciduous forest stand, southern Ontario, Canada. Agric. Forest Meteorol. 2003, 119, 69–85. [Google Scholar] [CrossRef]
  42. Tian, Y.; Haibara, K.; Chang, S.X.; Toda, H.; Fang, S. Acid deposition strongly influenced element fluxes in a forested karst watershed in the upper Yangtze River region, China. For. Ecol. Manag. 2013, 310, 27–36. [Google Scholar] [CrossRef]
  43. André, F.; Jonard, M.; Ponette, Q. Effects of biological and meteorological factors on stemflow chemistry within a temperate mixed oak-beech stand. Sci. Total Environ. 2008, 393, 72–83. [Google Scholar] [CrossRef]
  44. Wang, X.P.; Wang, Z.N.; Berndtsson, R.; Zhang, Y.F.; Pan, Y.X. Desert shrub stemflow and its significance in soil moisture replenishment. Hydrol. Earth Syst. Sci. 2011, 15, 561–567. [Google Scholar] [CrossRef] [Green Version]
  45. Limpert, K.; Siegert, C. Interspecific differences in canopy-derived water, carbon, and nitrogen in upland oak-hickory forest. Forests 2019, 10, 1121. [Google Scholar] [CrossRef] [Green Version]
  46. Horowitz, A.J. Determining annual suspended sediment and sediment-associated trace element and nutrient fluxes. Sci. Total Environ. 2008, 400, 315–343. [Google Scholar] [CrossRef]
  47. Dawoe, E.K.; Barnes, V.R.; Oppong, S.K. Spatio-temporal dynamics of gross rainfall partitioning and nutrient fluxes in shaded-cocoa (Theobroma cocoa) systems in a tropical semi-deciduous forest. Agrofor. Syst. 2018, 92, 397–413. [Google Scholar] [CrossRef]
  48. Sen, I.S.; Peucker-Ehrenbrink, B. Anthropogenic disturbance of element cycles at the Earth’s surface. Environ. Sci. Technol. 2012, 46, 8601–8609. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Arthur, M.A.; Fahey, T.J. Throughfall chemistry in an Engelmann spruce-subalpine fir forest in north central Colorado. Can. J. For. Res. 1993, 23, 738–742. [Google Scholar] [CrossRef]
  50. Cao, R.; Chen, S.; Yoshitake, S.; Ohtsuka, T. Organic and inorganic nitrogen deposition in an urban evergreen broad-leaved forest in central Japan. Atmos. Pollut. Res. 2021, 12, 488–496. [Google Scholar] [CrossRef]
  51. Tabatabai, M.A.; Laflen, J.M. Nitrogen and sulfur content and pH of precipitation in Iowa. J. Environ. Qual. 1976, 5, 108–112. [Google Scholar] [CrossRef]
  52. Jung, K.; Chang, S.X. Four years of simulated N and S depositions did not cause N saturation in a mixedwood boreal forest ecosystem in the oil sands region in northern Alberta, Canada. For. Ecol. Manag. 2012, 280, 62–70. [Google Scholar] [CrossRef]
  53. Jung, K.; Chang, S.X. Soil and tree chemistry reflected the cumulative impact of acid deposition in Pinus banksiana and Populus tremuloides stands in the Athabasca oil sands region in western Canada. Ecol. Indic. 2013, 25, 35–44. [Google Scholar] [CrossRef]
  54. Yesmin, L.; Gammack, S.M.; Sanger, L.J.; Cresser, M.S. Impact of atmospheric N deposition on inorganic- and organic-N outputs in water draining from peat. Sci. Total Environ. 1995, 166, 201–209. [Google Scholar] [CrossRef]
  55. Phoenix, G.K.; Emmett, B.A.; Britton, A.J.; Caporn, S.J.M.; Dise, N.B.; Helliwell, R.; Jones, L.; Leake, J.R.; Leith, I.D.; Sheppard, L.J. Impacts of atmospheric nitrogen deposition: Responses of multiple plant and soil parameters across contrasting ecosystems in long-term field experiments. Glob. Chang. Biol. 2011, 18, 1197–1215. [Google Scholar] [CrossRef]
Forests 13 01644 g001 550
Figure 1. Spatial distribution of atmospheric inorganic nitrogen wet deposition in China from 2011 to 2015, and the location of the field site in Jiangsu Province, China. Data of atmospheric inorganic nitrogen wet deposition reproduced from Jia et al. [30], with permission from Science Data Bank, 2018. The Dataset is provided by National Ecosystem Science Data Center, National Science & Technology Infrastructure of China (http://www.nesdc.org.cn; accessed on 1 September 2022).
Figure 1. Spatial distribution of atmospheric inorganic nitrogen wet deposition in China from 2011 to 2015, and the location of the field site in Jiangsu Province, China. Data of atmospheric inorganic nitrogen wet deposition reproduced from Jia et al. [30], with permission from Science Data Bank, 2018. The Dataset is provided by National Ecosystem Science Data Center, National Science & Technology Infrastructure of China (http://www.nesdc.org.cn; accessed on 1 September 2022).
Forests 13 01644 g001
Forests 13 01644 g002 550
Figure 2. Average precipitation and temperature over 30 years at the experimental site. Data from Chinese Central Meteorological Station.
Figure 2. Average precipitation and temperature over 30 years at the experimental site. Data from Chinese Central Meteorological Station.
Forests 13 01644 g002
Forests 13 01644 g003 550
Figure 3. Annual amounts and percentages in rainfall partitions in the ECF and DBF stands in 2015. The vertical error bars are standard deviations; different small letters indicate significant differences in annual amount in rainfall partitioning between the ECF stand and the DBF stand at the 0.05 level. ECF: evergreen coniferous forest; DBF: deciduous broadleaved forest.
Figure 3. Annual amounts and percentages in rainfall partitions in the ECF and DBF stands in 2015. The vertical error bars are standard deviations; different small letters indicate significant differences in annual amount in rainfall partitioning between the ECF stand and the DBF stand at the 0.05 level. ECF: evergreen coniferous forest; DBF: deciduous broadleaved forest.
Forests 13 01644 g003
Forests 13 01644 g004 550
Figure 4. Annual average pH of bulk rainfall, stemflow, and throughfall. The data from 23 rainfall collections in 2015. Solid and black dashed lines, box boundaries, and bars represent median and mean values, 25th and 75th, and 5th and 95th percentiles of all data, respectively. * indicates significant differences in pH between the ECF and DBF at the 0.05 level; ns, not significant in pH between the ECF stand and the DBF stand at the 0.05 level. OAF: open area field; ECF: evergreen coniferous forest; DBF: deciduous broadleaved forest.
Figure 4. Annual average pH of bulk rainfall, stemflow, and throughfall. The data from 23 rainfall collections in 2015. Solid and black dashed lines, box boundaries, and bars represent median and mean values, 25th and 75th, and 5th and 95th percentiles of all data, respectively. * indicates significant differences in pH between the ECF and DBF at the 0.05 level; ns, not significant in pH between the ECF stand and the DBF stand at the 0.05 level. OAF: open area field; ECF: evergreen coniferous forest; DBF: deciduous broadleaved forest.
Forests 13 01644 g004
Forests 13 01644 g005 550
Figure 5. Annual volume-weighted average concentrations of major elements in the OAF, ECF, and DBF. The data from 23 rainfall collections in 2015. The vertical error bars are standard deviations; * indicates significant differences in element concentrations between the ECF stand and the DBF stand at the 0.05 level; ns, not significant in element concentrations between the ECF stand and the DBF stand at the 0.05 level. OAF: open area field; ECF: evergreen coniferous forest; DBF: deciduous broadleaved forest.
Figure 5. Annual volume-weighted average concentrations of major elements in the OAF, ECF, and DBF. The data from 23 rainfall collections in 2015. The vertical error bars are standard deviations; * indicates significant differences in element concentrations between the ECF stand and the DBF stand at the 0.05 level; ns, not significant in element concentrations between the ECF stand and the DBF stand at the 0.05 level. OAF: open area field; ECF: evergreen coniferous forest; DBF: deciduous broadleaved forest.
Forests 13 01644 g005
Table
Table 1. The characteristics of the evergreen coniferous forest (ECF) and deciduous broadleaved forest (DBF) in 2015.
Table 1. The characteristics of the evergreen coniferous forest (ECF) and deciduous broadleaved forest (DBF) in 2015.
ECFDBF
Elevation (m)120170
Slope12°14°
Direction of slopeESES
DBH (cm)21.325.6
Tree height (m)10.012.4
Leaf area index on March 152.10.4
Leaf area index on June 212.42.3
Leaf area index on September 32.62.6
Leaf area index on December 152.61.6
Table
Table 2. Annual element fluxes in the OAF, ECF, and DBF in 2015. OAF: open area field; ECF: evergreen coniferous forest; DBF: deciduous broadleaved forest.
Table 2. Annual element fluxes in the OAF, ECF, and DBF in 2015. OAF: open area field; ECF: evergreen coniferous forest; DBF: deciduous broadleaved forest.
Stand TypesK+Na+Ca2+Mg2+Cl−SO42−-SInorganic N
NH4+-NNO3−-NTotal
(kg ha−1 year−1)
Element fluxes in OAF4.95.159.53.113.290.312.740.052.7
ECF
Throughfall33.67.656.37.123.7130.118.475.794.1
Stemflow5.70.85.60.84.117.84.012.816.8
Element fluxes in ECF39.38.461.97.927.8147.922.488.5110.9
DBF
Throughfall42.08.766.48.322.5125.614.066.580.5
Stemflow9.02.08.51.23.619.55.713.419.1
Element fluxes in DBF51.010.774.99.526.1145.119.779.999.6
Table
Table 3. Rainfall physical partitioning characteristics of evergreen coniferous forest stands, and deciduous broadleaved forest stands in different locations.
Table 3. Rainfall physical partitioning characteristics of evergreen coniferous forest stands, and deciduous broadleaved forest stands in different locations.
Forest TypeDominant Tree SpeciesLocationStand Density (Trees ha−1)Tree Age (Years)DBH (cm)Tree Height (m)Throughfall (%)Stemflow (%)Interception (%)References
Evergreen coniferous
Pinus taedaChina7203521.31074.74.820.5This study
Chamaecyparis obtusaJapan15264119.216.865.39.125.5Saito et al. [21]
Cryptomeria japonicaJapan11074127.422.667.96.625.5Saito et al. [21]
Pinus tabuliformisChina10174016.111.378.80.820.4Dong et al. [35]
Pinus tabuliformisChina6345020.615.674.11.124.8Dong et al. [35]
Pinus tabuliformisChina4346030.420.766.70.632.8Dong et al. [35]
Pinus tabuliformisChina120017157.275.40.723.9Ma et al. [23]
Pinus nigraTurkey––35.5–69.82.627.7Aydın et al. [36]
Pinus sylvestrisTurkey––37.6–73.95.920.2Aydın et al. [36]
Deciduous broadleaved
Quercus acutissimaChina5125025.612.479.85.814.4This study
Fagus sylvaticaBelgium––6830717.921Staelens et al. [37]
Quercus pubescensSpain828–21.311.182.62.614.8Mużyło et al. [38]
Quercus cerrisItaly–582719.579.60.6719.7Corti et al. [16]
Quercus cerrisItaly–603121.778.80.3920.8Corti et al. [16]
Quercus ilexSpain2127–11.3675.52.721.9Rodrigo et al. [39]
Quercus ilexSpain1753–126.472.15.322.6Rodrigo et al. [39]
Quercus ruba, Acer saccharumCanada513––12–1576.44.319.3Carlyle-Moses and Price [40]
Quercus ruba, Acer saccharumCanada442––12–1577.53.718.8Price and Carlyle-Moses [41]
Italicized values are calculated from original articles.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Yang, T.; Li, Y.; Ouyang, X.; Wang, B.; Ge, X.; Tang, L. Rainfall Physical Partitioning and Chemical Characteristics in Evergreen Coniferous and Deciduous Broadleaved Forest Stands in a High Nitrogen Deposition Region, China. Forests 2022, 13, 1644. https://doi.org/10.3390/f13101644

AMA Style

Yang T, Li Y, Ouyang X, Wang B, Ge X, Tang L. Rainfall Physical Partitioning and Chemical Characteristics in Evergreen Coniferous and Deciduous Broadleaved Forest Stands in a High Nitrogen Deposition Region, China. Forests. 2022; 13(10):1644. https://doi.org/10.3390/f13101644

Chicago/Turabian Style

Yang, Tao, Yong Li, Xueying Ouyang, Bo Wang, Xiaomin Ge, and Luozhong Tang. 2022. "Rainfall Physical Partitioning and Chemical Characteristics in Evergreen Coniferous and Deciduous Broadleaved Forest Stands in a High Nitrogen Deposition Region, China" Forests 13, no. 10: 1644. https://doi.org/10.3390/f13101644

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop