Molecular Composition of Stream Dissolved Organic Matter in Cool-Temperate Forest Headwaters with Landslides, Northern Japan

Abstract

Vegetation and subsequent ecosystem services can recover over time in forest headwaters devastated by massive disasters. However, in cold regions, their recovery rates are typically slow and often imperceptible, which makes it difficult to evaluate how much ecosystem services have recovered. This study targeted dissolved organic matter (DOM), which plays a central role in biogeochemical processes in forest ecosystems, and aimed to examine whether vegetation conditions affect the quality of stream DOM from cool-temperate forest headwaters in northern Japan. To achieve this, hydrological observations and stream water sampling were conducted monthly from May to December 2021 in three small forest catchments with different landslide coverage. Dissolved organic carbon (DOC) concentration in stream water was measured, and the molecular composition of DOM was analyzed using ultrahigh-resolution mass spectrometry and compared among the three catchments. The peak-intensity-weighted average aromaticity index (AI_wa) increased with DOC concentration. We found that AI_wa was the highest in the undisturbed catchment, followed by the catchments with landslide coverages of 16% and 52% at a given DOC level. These results indicate that the quality of DOM could dramatically change depending not only on DOC concentration but also on vegetation disturbance in cool-temperate forest headwaters.

1. Introduction

The increasing frequency of heavy rainfalls and large earthquakes in recent years has caused massive disasters, such as landslides associated with topographic changes in forest headwaters [1,2,3]. These disasters have altered water and biogeochemical cycles in forests and have reduced ecosystem services, such as water recharge and purification, as well as prevention of sediment runoff, resulting in several adverse effects on human infrastructure [4]. Therefore, the restoration of forest headwaters is an urgent issue to be addressed in the areas damaged by massive disasters.

On the other hand, it is difficult to percept the recovery of forest ecosystems and their services over time after a disturbance, regardless of whether it is caused by natural disasters or human activity. Especially in cold regions, the growth of vegetation is relatively slow and soil freezing during winter and snow-melt runoff during spring are associated with soil erosion, which make it difficult for vegetation to re-establish itself after a disturbance [5]. Therefore, it remains unclear how long and how much the recovery of vegetation and subsequent ecosystem services can be expected in cold-region forests. How much forest ecosystems and their services recover over time after a disturbance needs to be judged comprehensively based on several observations to evaluate the factors, such as vegetation recovery, stabilization of stream flow conditions, reduction of sediment runoff, water chemistry, and so on [6,7,8]. However, conducting these observations is laborious and expensive, and it is difficult to judge the recovery of forest ecosystems based on these observations. Therefore, a simple and comprehensible indicator is required to evaluate the recovery of forest ecosystems.

Dissolved organic matter (DOM), which includes fulvic acid and humic-like substances, plays a central role in biogeochemical cycles in forest ecosystems. This is because DOM forms complexes with metals and nutrients and transports them from soils to watercourses [9,10,11,12,13,14]. DOM is also utilized by microorganisms as a source of energy [15,16]. Additionally, it affects the color, acidity, and chemistry of the water that passes through forests [17,18,19,20,21]. Humic substances in forest soils originate from plant litter and affect the amount of DOM in stream water; thus, it is predicted that vegetation conditions are related to the molecular composition and functions of stream DOM. Water chemistry in a headwater stream is the product of the natural ecosystem, the control of which can be altered by disturbances [22]. Therefore, a detailed evaluation of the quality of stream DOM could be a useful indicator of the recovery of forest ecosystems. However, few studies have examined the molecular composition of stream DOM as an indicator of the recovery of forest ecosystems after disturbances induced by massive disasters, due to the limitations of analytical techniques.

Ultrahigh-resolution mass spectrometry, i.e., Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), allows us to detect hundreds and thousands of DOM molecular species contained in natural water, such as rainwater, stream water, and lake water [23,24,25,26,27]. A molecular formula can also be assigned to each mass peak detected and classified into each of biomolecular classes, such as lignin-like molecules, based on the hydrogen-to-carbon (H/C) and oxygen-to-carbon ratios (O/C) [28]. Additionally, the aromaticity index (AI), which is calculated from a molecular formula, can be used to deduce the aromatic structures inherent to plant-derived phenolic compounds [29]. It is predicted that losses of vegetation and soils caused by disturbances, such as landslides, reduce phenolic compounds contained in DOM in forest headwaters. Thus, FT-ICR-MS analyses can be applied to stream DOM in multiple forest catchments with different landslide coverage to detect differences in the abundance of phenolic compounds between different vegetation conditions in forests.

This study aimed to examine whether vegetation conditions were reflected in the molecular species of stream DOM in cold-region forest catchments by using FT-ICR-MS that allows evaluating the quality of DOM in detail. To achieve this, hydrological and hydrochemical observations were conducted in three small forest catchments with different landslide coverages in northern Japan.

2. Materials and Methods

2.1. Site Description

This study was conducted in three forested headwater catchments (i.e., A, B, and C catchments; Figure 1), located in an upstream area of the Habiu River Basin in Iburi Subprefecture, northern Japan (42°46′ N, 141°58′ E; 120–200 m a.s.l.). This region was severely affected by the 2018 Hokkaido Eastern Iburi earthquake, which resulted in a large number of slope failures and landslides over a wide area [30]. The area and the landslide coverage were 1.1 ha and 0% in the A catchment, 1.5 ha and 16% in the B catchment, and 0.7 ha and 52% in the C catchment, respectively [31]. The failure depth of the landslide area ranged from 0.7 to 1.1 m in B catchment and from 0.4 to 1.6 m in C catchment. The humus layer was absent and weathered soils were exposed on landslide scars in the B and C catchments. The average slope gradients of the A, B, and C catchments were 33°, 33°, and 25°, respectively. The underlying bedrock in the three catchments consisted of Neogene sedimentary rock. Soils were composed of humus layers (i.e., andosol, 0–0.5 m deep), pyroclastic deposits (i.e., tephra, 0.5–2.0 m deep), and weathered bedrock layers (2.0–2.6 m deep). The climate is classified as cool temperate [32], and the dominant forest vegetation is a mixture of secondary deciduous forests, which consists mainly of Betulaceae and Fagaceae, such as Japanese white birch (Betula platyphylla) and mizunara (Quercus crispula), and conifer forests, such as Japanese larch (Larix kaempferi) and Todo fir (Abies sachalinensis). The average annual precipitation and air temperature from 1992 to 2021 were 1040 mm y⁻¹ and 7 °C, respectively [33].

2.2. Hydrological Observations and Water Sampling

A triangular-notch weir was installed at the end of each catchment (Figure 1) [31,33]. Water levels were recorded at 10 min intervals using a capacitance water level gauge (SE-TR/WT500, TruTrack, Christchurch, New Zealand) at each weir. The actual water level and runoff volume at each weir were also measured monthly using a metal ruler and a measuring cylinder, respectively.

Soil samples were collected from a soil depth of 0–5 cm at 18 locations within each catchment in October 2021. Stream water was manually sampled monthly from May to November 2021. The water samples were collected in PFA bottles (Big Boy, AS ONE Corporation, Osaka, Japan) and transported to the laboratory. They were then filtered through pre-combusted glass fiber filters with a nominal pore size of 0.7 μm (Whatman GF/F, GE Healthcare, Chicago, IL, USA) into pre-combusted amber glass bottles, and stored at a temperature of 4 °C in the dark until analysis.

2.3. Chemical and Data Analyses

The soil samples were oven-dried at 105 °C for two hours, sieved through a 1-mm mesh, and then ground to powder using a mortar. The carbon content of the ground soil samples was measured using an elemental analyzer (vario MAX CNS, Elementar, Langenselbold, Germany). Dissolved organic carbon (DOC) concentrations in water samples were measured using the combustion catalytic oxidation method (TOC-V_CPH, SHIMADZU Corp., Kyoto, Japan). The molecular composition of dissolved organic matter (DOM) was analyzed using electrospray ionization coupled to high-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The filtered water samples were solid-phase extracted (SPE) using the sorbent of styrene divinylbenzene polymer (Bond Elut PPL SPE cartridges, Agilent, Santa Clara, CA, USA) according to the procedure recommended by Dittmar et al. [34] to increase DOM concentration and remove inorganic salts. The SPE samples were diluted with ultrapure water and methanol to yield a final sample composition of 50/50 (v/v) of water to methanol. Then they were injected into the FT-ICR mass spectrometer (solariX 7T, Bruker Daltonics Inc., Billerica, MA, USA) using a syringe pump (infusion rate: 180 µL h⁻¹). All samples were analyzed in the negative ion mode. Ions were accumulated in a hexapole for 0.1 s before they were transferred to the ICR cell, and the 32 transients collected using an 8 M Word time domain were co-added. All spectra were externally calibrated using the Tuning Mix standard (Bruker Daltonics Inc., Billerica, MA, USA) and internally calibrated using fatty acids [35]. Mass lists were generated using a signal-to-noise ratio cut-off of five. Isotope peaks were removed from the list. Additionally, the m/z peaks derived from ultrapure water were removed. Molecular Formula Calculator (ver. 1.0; ©NHMFL, 1998) was used to assign an expected molecular formula for each m/z value with a mass accuracy ≤ 1 ppm. The targeted m/z values ranged from 150 to 500 and were used to detect differences in the molecular composition among the three catchments. The following conditions were used for formula assignment: C = 0–∞; H = 0–∞; O = 0–∞; N = 0–4; S = 0–2; P = 0–1 [36,37,38]. After the formula assignment, some formulas not likely to be observed in natural water were eliminated based on the rules described in Kujawinski and Behn [39] and Wozniak et al. [40].

To identify which biomolecular class each molecular compound belonged to, a van Krevelen diagram was used based on the elemental ratios of the expected molecular formulas, i.e., the oxygen-to-carbon (O/C) and hydrogen-to-carbon ratios (H/C). Each molecular compound was divided into seven biomolecular classes, i.e., lipids, protein-like molecules (proteins), aminosugars/carbohydrates (As/Ch), unsaturated hydrocarbons (UH), lignin-like molecules (lignins), tannin-like molecules (tannins), and condensed aromatic structures (CASs) for descriptive purposes, based on the protocol described by Ide et al. [23,24,26], with slight modifications. Additionally, based on the number of atoms in the molecular formulas assigned, the double bond equivalent (DBE) and the aromaticity index (AI) of each molecular compound were calculated as follows [29]:

DBE = 1 + C − O − S − 0.5H (1)

AI = DBE/(C − O − S − N − P) (2)

In Equation (2), if DBE ≤ 0 or (C − O − S − N − P) ≤ 0, then AI = 0. In this study, to compare the DBE, AI, O/C, and H/C among the water samples from the three catchments, these molecular parameters were expressed as peak-intensity-weighted average (wa) values [41].

Soil carbon content, DOC concentration, and the number of DOM molecular species detected were compared among the three catchments using one-way analysis of variance (ANOVA). Tukey’s post-hoc tests were used to compare sample means. Differences in the two-variable relationship, such as DOC concentration versus runoff, among the catchments were tested using analysis of covariance (ANCOVA). Jaccard similarity coefficients were calculated to examine how many molecular species were the same between water samples. Then, the two-dimensional ordination of non-metric multidimensional scaling (NMDS) for different samples was obtained for all molecular species detectable by FT-ICR-MS by calculating the Jaccard’s distance between samples. Permutational multivariate analysis of variance (PERMANOVA) was used to examine whether molecular species significantly differed among the catchments. Statistical analyses in this study were conducted using R (version 4.1.3) [42].

3. Results

The average carbon content in surface soils was significantly two-times higher in the A catchment than in the other two catchments (Figure 2a; Tukey’s test, p < 0.05). Dissolved organic carbon (DOC) concentration in stream water was significantly higher in the A catchment than in the B catchment (Figure 2b; Tukey’s test, p < 0.05), and tended to be higher in the A catchment than in the other two catchments at a given runoff level (Figure 3a; ANCOVA, p < 0.1).

On the other hand, the relationship between the number of molecular species in stream dissolved organic matter (DOM) and runoff did not significantly differ among the three catchments (Figure 3b; ANCOVA, p = 0.67). The number of molecular species inherent to each catchment on average accounted for more than 60% of the total molecular species in all catchments, whereas it did not significantly differ among the three catchments (Figure 4; one-way ANOVA, p = 0.20). Reflecting this, molecular species in stream DOM significantly differed among the three catchments (Figure 5; PERMANOVA, p < 0.05).

The total number of molecular species did not significantly differ among the three catchments (one-way ANOVA, p = 0.71), and the molecular species classified into lignin-like molecules (lignins) accounted for a major proportion of the total molecular species in all catchments (Figure 6). On the other hand, the total number of molecular species was positively correlated with DOC concentration in just the A catchment (Figure 7a; r = 0.98, p < 0.001). Additionally, the numbers of molecular species classified into protein-like molecules (proteins), lignins, tannin-like molecules (tannins), and compounds with condensed aromatic structures (CASs) were positively correlated with DOC concentration in the A catchment (Figure 7c,f–h; proteins: r = 0.83, p < 0.05; lignins: r = 0.96, p < 0.001; tannins: r = 0.87, p < 0.05; CASs: r = 0.76, p < 0.05).

The significant relationships were not found between runoff and the peak-intensity-weighted average values of double bond equivalent (DBE_wa), aromaticity index (AI_wa), oxygen-and-carbon ratio (O/C_wa), or hydrogen-to-carbon ratio (H/C_wa) in stream DOM (Figure 8a–d). On the other hand, DBE_wa, AI_wa, and O/C_wa significantly increased with increasing DOC concentration, whereas H/C_wa significantly decreased (Figure 8e–h; test for the slope of the regression, p < 0.05 in all cases). AI_wa was significantly higher in the A catchment than in the other two catchments (Figure 8f; ANCOVA, p < 0.05), as well as in the B catchment than in the C catchment at a given DOC level (ANCOVA, p < 0.05). Additionally, DBE_wa and AI_wa were significantly higher in the A catchment than in the other two catchments at a given runoff level (Figure 8a,b; ANCOVA, p < 0.05). Conversely, H/C_wa was significantly higher in the C catchment than in the other two catchments at a given DOC level (Figure 8h; ANCOVA, p < 0.05).

4. Discussion

Differences in dissolved organic carbon (DOC) concentration among the three catchments reflected differences in vegetation conditions in soil surfaces (Figure 2b). A higher abundance of vegetation results in higher content of soil carbon because vegetation supplies organic matter, such as litter, to soil surfaces (Figure 2a) [31]. This could explain why the DOC concentration tended to be higher in the A catchment than in the other two catchments regardless of the runoff level (Figure 3a). A larger amount of organic matter could be supplied from soil surfaces to groundwater in a dissolved form, which resulted in the higher DOC concentration during base flows in the A catchment. Higher peak-intensity-weighted average values of double bond equivalent (DBE_wa) and aromaticity index (AI_wa) in the A catchment than in the other two catchments at a given runoff level (Figure 8a,b) also implied that the quality of dissolved organic matter (DOM) contained in groundwater was different in the A catchment than in the other two catchments. Koch and Dittmar [29] showed that molecular compounds with high aromaticity index (AI) could have complicated aromatic structures. Therefore, the results in the present study suggest that molecular compounds with aromatic structures were more abundant in stream DOM in the A catchment than in the other two catchments.

Significant differences in molecular species among the three catchments were attributable to the fact that molecular species inherent to each catchment accounted for the majority of the total molecular species (Figure 4 and Figure 5). On the other hand, a major proportion of lignin-like molecules (lignins) in the total molecular species in all catchments (Figure 6) suggests that a large number of molecular species derived mainly from vascular plants, because lignins are phenolic polymers that originate mainly from vascular plants in terrestrial ecosystems [43,44]. In particular, the numbers of total molecular species (molecular diversity) and lignins increased with DOC concentration in the A catchment (Figure 7). This suggests that stream DOM derived mainly from vascular plants, i.e., litter and humic substances, during base flows in the A catchment, which was covered by tree canopies. This was also supported by the fact that AI_wa was higher in the A catchment than in the B and C catchments, regardless of the runoff and DOC levels (Figure 8b,f). Conversely, molecular diversity was not related to DOC concentrations in the B and C catchments, where vegetation and soils were lost in the landslide (Figure 7). Given that DBE_wa and AI_wa were lower in the B and C catchments than in the A catchment (Figure 8a,b,f), this result suggests that vegetation disturbance has a considerable impact on the quality of stream DOM. This was supported by the study conducted by Liu et al. [45], which showed that organic matter had higher aromaticity, hydrophobic fraction, and proportion of humic-like substances in forested soils than in the gully banks in the hilly area of Loess Plateau.

Significant relationships between DOC concentration and molecular parameters, i.e., DBE_wa, AI_wa, O/C_wa, and H/C_wa (Figure 8e–h), indicated that molecular species in stream DOM had higher aromaticity as the DOC concentration increased. On the other hand, relationships between DOC concentration and AI_wa and H/C_wa (Figure 8e,f) showed that the quality of stream DOM varied depending on vegetation conditions within a forest catchment even at a given DOC level. The H/C is related to biogeochemical reactions of hydrogenation or dehydrogenation, and a higher H/C reflects a higher degree of hydrogen saturation of the molecular compound [46]. Additionally, the lower H/C in groundwater and stream water indicates that DOM has higher aromaticity in aquatic and terrestrial surface environments [38,47,48,49,50]. Litter supplies larger amounts of lignins, organic acids, and other recalcitrant DOM with aromatic structures, to soils in forests than in other land cover types [45,51]. Consequently, high levels of DOM, chelating organic acids, and organic metal complexes accumulate in forest soils [52,53]. Conversely, soil disturbances induced by natural disasters, such as landslides, can reduce recalcitrant DOM supplied from forest soils [45,54]. In the present study, differences in AI_wa and H/C_wa (Figure 8) among the three catchments reflected the extent to which recalcitrant DOM from soils was reduced in response to the tree loss caused by landslides. This was also supported by the fact that stream DOM had lower AI_wa and higher H/C_wa in the C catchment, where the landslide coverage was the highest among the three catchments, compared with those in the other two catchments regardless of the DOC level (Figure 8).

The DOC concentration and the relationship between the DOC concentration and runoff (Figure 2b and Figure 3a) did not clearly reflect hierarchical differences in vegetation conditions among the three catchments. Additionally, the DOC concentration was not related to runoff even at the undisturbed A catchment. On the other hand, the DOC concentration was positively related to the quality of DOM, i.e., AI_wa, which reflected hierarchical differences in vegetation conditions among the three catchments. This result indicated that undisturbed cold-region forests could provide high levels of DOM rich in molecular compounds with aromatic structures to stream water. Surface waters in wet- and cold-region forests in the northern hemisphere are often rich in DOM, the origin of which is terrestrial rather than aquatic, because high precipitation and low temperatures favor the leaching of DOM from soils to watercourses [55,56]. Thus, stream DOM in those forests often reflects the properties of the surrounding vegetation and soils, and consequently, can show high aromaticity [57]. The AI_wa calculated based on an ultrahigh-resolution mass spectral analysis should provide more detailed information on DOM derived from vascular plants than DOC concentration and optical spectroscopy, such as specific ultraviolet absorbance (SUVA) and three-dimensional excitation-emission matrix fluorescence spectroscopy [58]. The AI can be used to deduce information on molecular structures and thereby the UV-inactive part of DOM that cannot be evaluated by the optical spectroscopy, i.e., SUVA [58,59]. Conversely, the combination of optical spectral analyses with AI would provide more complementary information for DOM characterization in stream water [36,60]. Taken together, the AI_wa may be a useful indicator for evaluating changes in biogeochemical cycles associated with vegetation recovery in cold-region forests. This would also be supported by several previous studies, which have shown that a large amount of litter is supplied from deciduous broad-leaved trees to soils, a large amount of organic matter is retained in the soils, and consequently, plant-derived DOM with abundant humic-like substances is supplied from the soils to stream water in cold-region forests [14,61,62]. Our results indicate that molecular species in stream DOM reflect vegetation conditions and could change depending on the DOC concentration in cold-region forest catchments.

Disturbances, such as landslides, strongly affect soil properties and consequent flow paths through changes in vegetation conditions, which in turn may alter the molecular composition of stream DOM in forest headwaters [63,64,65]. Therefore, exploring the relationships between molecular parameters and observation items regarding the physical environment, such as soil properties, water and sediment runoff, could enhance the evaluation of the recovery of vegetation and subsequent ecosystem services following a disturbance. Several studies have implied that the magnitude and frequency of precipitation change in response to climate change in cold-region forests [66,67,68] and, in particular, showed that the frequency of intensive rainfall has been increasing in Japan [69,70]. Thus, further research is needed to understand how vegetation conditions affect changes in the molecular composition of stream DOM during rain events in cool-temperate forest headwaters through the combination of physical environment observations with ultrahigh-resolution mass spectral analyses.

5. Conclusions

This study applied ultrahigh-resolution mass spectral analyses to the detection of the molecular composition of dissolved organic matter (DOM) in stream water from three forest catchments with different landslide coverage in northern Japan to examine whether the quality of stream DOM was affected by vegetation conditions in the cold-region forest catchments. The results revealed that the peak-intensity-weighted average value of aromaticity index (AI_wa) significantly increased with increasing dissolved organic carbon (DOC) concentration in stream water, whereas that of hydrogen-to-carbon ratio (H/C_wa) decreased. Additionally, we found that AI_wa was higher in the less disturbed catchments, whereas H/C_wa was the highest in the catchment with the highest landslide coverage at a given DOC level. Since these molecular parameters are related to the abundance of molecular compounds with aromatic structures derived from vascular plants, our finding suggests that biogeochemical cycles changed depending on vegetation conditions in the catchments. We concluded that DOM molecular species in stream water reflected vegetation conditions and therefore may be a useful indicator for evaluating the recovery of vegetation and subsequent ecosystem services following a disturbance in cold-region forest catchments.

Author Contributions

Conceptualization, J.I. and Y.A.; field investigation, J.I., K.H., Y.A. and I.E.; chemical analysis, J.I., K.H. and H.N.; data analysis, J.I.; writing—original draft preparation, J.I.; writing—review and editing, J.I., Y.A., I.E., M.O., H.N. and T.G. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Any data used in this study may be obtained by sending a written request to the corresponding author.

Acknowledgments

We thank Karibu Fukuzawa, Wataru Ishizuka, Hisayuki Wada, and Toshiro Yamada for helping our chemical analyses. We also thank our laboratory members at the Chitose Institute of Science and Technology for helping with the field surveys.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures

Figure 1. Location and map of the study catchments. A, B, and C represent the catchment names.

Figure 2. (a) Soil carbon content and (b) dissolved organic carbon (DOC) concentration in the A, B, and C catchments. Values and error bars in (a–b) represent averages and standard deviations, respectively. Values with the same letter in (a,b) indicate no significant difference (p [greater than] 0.05).

Figure 3. (a) Relationships between dissolved organic carbon (DOC) concentration and runoff and (b) between the number of m/z peaks detected by FT-ICR-MS and runoff in the A, B, and C catchments.

Figure 4. The average numbers of m/z peaks shared and not shared between stream water samples in the A, B, and C catchments. Error bars represent standard deviations in each of the numbers of m/z peaks shared and not shared.

Figure 5. Two-dimensional ordination of nonmetric multidimensional scaling (NMDS) of different samples for all molecular species identified in the A, B, and C catchments.

Figure 6. The average numbers of m/z peaks classified into specific biomolecular classes in the A, B, and C catchments. See Section 2 for the abbreviations of biomolecular classes.

Figure 7. (a) Relationships between the dissolved organic carbon (DOC) concentration and the number of m/z peaks for all molecular species identified and (b–h) for the biomolecular classes in stream water in the A, B, and C catchments. Broken lines represent regression lines in the A catchment ((a) y = 61.9x + 245.7, R2 = 0.97; (c) y = 12.7x + 8.5, R2 = 0.84; (f) y = 37.6x + 71.4, R2 = 0.91; (g) y = 5.6x + 3.1, R2 = 0.75; (h) y = 5.4x + 25.1, R2 = 0.58). See Section 2 for the abbreviations of biomolecular classes.

Figure 8. Relationships of the peak-intensity-weighted average values of double bound equivalent (DBEwa), aromaticity index (AIwa), oxygen-to-carbon ratio (O/Cwa), and hydrogen-to-carbon ratio (H/Cwa) to runoff or dissolved organic carbon (DOC) concentration in the A, B, and C catchments. Broken lines represent regression lines ((e) y = 0.4logx − 0.26, R2 = 0.23; (f) y = 0.02logx + 0.02, R2 = 0.35; (g) y = 0.003x + 0.124, R2 = 0.42; (h) y = −0.08logx + 1.89, R2 = 0.52).

References

1. Borga, M.; Stoffel, M.; Marchi, L.; Marra, F.; Jakob, M. Hydrogeomorphic Response to Extreme Rainfall in Headwater Systems: Flash Floods and Debris Flows. J. Hydrol.; 2014; 518, pp. 194-205. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2014.05.022]

2. Kato, A.; Ben-Zion, Y. The Generation of Large Earthquakes. Nat. Rev. Earth Environ.; 2021; 2, pp. 26-39. [DOI: https://dx.doi.org/10.1038/s43017-020-00108-w]

3. Marc, O.; Hovius, N.; Meunier, P.; Uchida, T.; Hayashi, S. Transient Changes of Landslide Rates After Earthquakes. Geology; 2015; 43, pp. 883-886. [DOI: https://dx.doi.org/10.1130/G36961.1]

4. Nelson, E.J.; Kareiva, P.; Ruckelshaus, M.; Arkema, K.; Geller, G.; Girvetz, E.; Goodrich, D.; Matzek, V.; Pinsky, M.; Reid, W. et al. Climate Change’s Impact on Key Ecosystem Services and the Human Well-being They Support in the US. Front. Ecol. Environ.; 2013; 11, pp. 483-893. [DOI: https://dx.doi.org/10.1890/120312]

5. Wu, Z.; Fang, H. Snowmelt Erosion: A Review. Earth-Sci. Rev.; 2024; 250, 104704. [DOI: https://dx.doi.org/10.1016/j.earscirev.2024.104704]

6. Eisenbies, M.H.; Aust, W.M.; Burger, J.A.; Adams, M.B. Forest Operations, Extreme Flooding Events, and Considerations for Hydrologic Modeling in the Appalachians—A Review. For. Ecol. Manag.; 2007; 242, pp. 77-98. [DOI: https://dx.doi.org/10.1016/j.foreco.2007.01.051]

7. Thom, D.; Seidl, R. Natural Disturbance Impacts on Ecosystem Services and Biodiversity in Temperate and Boreal Forests. Biol. Rev.; 2016; 91, pp. 760-781. [DOI: https://dx.doi.org/10.1111/brv.12193]

8. Seidl, R.; Spies, T.A.; Peterson, D.L.; Stephens, S.L.; Hicke, J.A. Searching for Resilience: Addressing the Impacts of Changing Disturbance Regimes on Forest Ecosystem Services. J. Appl. Ecol.; 2016; 53, pp. 120-129. [DOI: https://dx.doi.org/10.1111/1365-2664.12511]

9. Koenings, J.P.; Hooper, F.F. The Influence of Colloidal Organic Matter on Iron and Iron-Phosphorus Cycling in an Acid Bog Lake1. Limnol. Oceanogr.; 1976; 21, pp. 684-696. [DOI: https://dx.doi.org/10.4319/lo.1976.21.5.0684]

10. Francko, D.A. Epilimnetic Phosphorus Cycling: Influence of Humic Materials and Iron on Coexisting Major Mechanisms. Can. J. Fish. Aquat. Sci.; 1986; 43, pp. 302-310. [DOI: https://dx.doi.org/10.1139/f86-039]

11. Jones, R.I.; Salonen, K.; Haan, H. Phosphorus Transformations in the Epilimnion of Humic Lakes: Abiotic Interactions Between Dissolved Humic Materials and Phosphate. Freshw. Biol.; 1988; 19, pp. 357-369. [DOI: https://dx.doi.org/10.1111/j.1365-2427.1988.tb00357.x]

12. Mierle, G.; Ingram, R. The Role of Humic Substances in the Mobilization of Mercury from Watersheds. Water Air Soil Pollut.; 1991; 56, pp. 349-357. [DOI: https://dx.doi.org/10.1007/BF00342282]

13. Winch, S.; Ridal, J.; Lean, D. Increased Metal Bioavailability Following Alteration of Freshwater Dissolved Organic Carbon by Ultraviolet B Radiation Exposure. Environ. Toxicol.; 2002; 17, pp. 267-274. [DOI: https://dx.doi.org/10.1002/tox.10057] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12112635]

14. Bolan, N.S.; Adriano, D.C.; Kunhikrishnan, A.; James, T.; McDowell, R.; Senesi, N. Dissolved Organic Matter: Biogeochemistry, Dynamics, and Environmental Significance in Soils. Advances in Agronomy (Volume 110); Sparks, D.L. Elsevier: Burlington, NJ, USA, 2011; pp. 1-75. ISBN 978-0-12-385531-2

15. Qualls, R.G.; Haines, B.L. Biodegradability of Dissolved Organic Matter in Forest Throughfall, Soil Solution, and Stream Water. Soil Sci. Soc. Am. J.; 1992; 56, pp. 578-586. [DOI: https://dx.doi.org/10.2136/sssaj1992.03615995005600020038x]

16. Ward, N.D.; Keil, R.G.; Medeiros, P.M.; Brito, D.C.; Cunha, A.C.; Dittmar, T.; Yager, P.L.; Krusche, A.V.; Richey, J.E. Degradation of Terrestrially Derived Macromolecules in the Amazon River. Nat. Geosci.; 2013; 6, pp. 530-533. [DOI: https://dx.doi.org/10.1038/ngeo1817]

17. Oliver, B.G.; Thurman, E.M.; Malcolm, R.L. The Contribution of Humic Substances to the Acidity of Colored Natural Waters. Geochim. Cosmochim. Acta; 1983; 47, pp. 2031-2035. [DOI: https://dx.doi.org/10.1016/0016-7037(83)90218-1]

18. Meili, M. Sources, Concentrations and Characteristics of Organic Matter in Softwater Lakes and Streams of the Swedish Forest Region. Hydrobiologia; 1992; 229, pp. 23-41. [DOI: https://dx.doi.org/10.1007/978-94-011-2474-4_2]

19. Kortelainen, P. Content of Total Organic Carbon in Finnish Lakes and Its Relationship to Catchment Characteristics. Can. J. Fish. Aquat. Sci.; 1993; 50, pp. 1477-1483. [DOI: https://dx.doi.org/10.1139/f93-168]

20. Green, S.A.; Blough, N.V. Optical Absorption and Fluorescence Properties of Chromophoric Dissolved Organic Matter in Natural Waters. Limnol. Oceanogr.; 1994; 39, pp. 1903-1916. [DOI: https://dx.doi.org/10.4319/lo.1994.39.8.1903]

21. 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, pp. 57-64. [DOI: https://dx.doi.org/10.1016/S0269-7491(03)00259-8]

22. Likens, G.E.; Bormann, F.H. Biogeochemistry of a Forested Ecosystem; Springer: New York, NY, USA, 1995; ISBN 978-0-387-94351-0

23. Ide, J.; Ohashi, M.; Takahashi, K.; Sugiyama, Y.; Piirainen, S.; Kortelainen, P.; Fujitake, N.; Yamase, K.; Ohte, N.; Moritani, M. et al. Spatial Variations in the Molecular Diversity of Dissolved Organic Matter in Water Moving Through a Boreal Forest in Eastern Finland. Sci. Rep.; 2017; 7, 42102. [DOI: https://dx.doi.org/10.1038/srep42102]

24. Ide, J.; Makita, N.; Jeong, S.; Yamase, K.; Ohashi, M. The Contribution of Coniferous Canopy to the Molecular Diversity of Dissolved Organic Matter in Rainfall. Water; 2019; 11, 167. [DOI: https://dx.doi.org/10.3390/w11010167]

25. Saarela, T.; Jäntti, H.; Ohashi, M.; Ide, J.; Berninger, F.; Ojala, A. The Molecular Composition of Dissolved Organic Matter (DOM) and Its Effects on the Greenhouse Gas Production in Pristine Subarctic Rivers. Proceedings of the 22nd EGU General Assembly; Online, 4–8 May 2020; 7936. [DOI: https://dx.doi.org/10.5194/egusphere-egu2020-7936]

26. Ide, J.; Ohashi, M.; Köster, K.; Berninger, F.; Miura, I.; Makita, N.; Yamase, K.; Palviainen, M.; Pumpanen, J. Molecular Composition of Soil Dissolved Organic Matter in Recently-Burned and Long-Unburned Boreal Forests. Int. J. Wildl. Fire; 2020; 29, 541. [DOI: https://dx.doi.org/10.1071/WF19085]

27. Sleighter, R.L.; Hatcher, P.G. The Application of Electrospray Ionization Coupled to Ultrahigh Resolution Mass Spectrometry for the Molecular Characterization of Natural Organic Matter. J. Mass Spectrom.; 2007; 42, pp. 559-574. [DOI: https://dx.doi.org/10.1002/jms.1221] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17474116]

28. Kim, S.; Kramer, R.W.; Hatcher, P.G. Graphical Method for Analysis of Ultrahigh-Resolution Broadband Mass Spectra of Natural Organic Matter, the Van Krevelen Diagram. Anal. Chem.; 2003; 75, pp. 5336-5344. [DOI: https://dx.doi.org/10.1021/ac034415p]

29. Koch, B.P.; Dittmar, T. From Mass to Structure: An Aromaticity Index for High-Resolution Mass Data of Natural Organic Matter. Rapid Commun. Mass Spectrom.; 2006; 20, pp. 926-932. [DOI: https://dx.doi.org/10.1002/rcm.2386]

30. Ritonga, R.P.; Gomi, T.; Tarigan, S.D.; Kaswanto, R.L.; Kharismalatri, H.S.; Noviandi, R.; Arata, Y.; Ishikawa, Y. Land Cover and Characteristics of Landslides Induced by the 2018 Mw 6.7 Eastern Iburi Earthquake, Hokkaido. Int. J. Eros. Control Eng.; 2021; 13, pp. 76-83. [DOI: https://dx.doi.org/10.13101/ijece.13.76]

31. Ide, J.; Noguro, T.; Yokoyama, R.; Arata, Y.; Gomi, T.; Endo, I. A Preliminary Investigation of the Effects of Vegetation Coverage on Carbon and Nitrogen Exports from Forest Catchments with Landslide Area. Boreal For. Res.; 2023; 71, pp. 67-70. [DOI: https://dx.doi.org/10.24494/jfsh.71.0_67]

32. Nakata, Y.; Hayamizu, M.; Ishiyama, N. Assessing Primary Vegetation Recovery from Earthquake-Induced Landslide Scars: A Real-Time Kinematic Unmanned Aerial Vehicle Approach. Ecol. Eng.; 2023; 193, 107019. [DOI: https://dx.doi.org/10.1016/j.ecoleng.2023.107019]

33. Ide, J.; Naito, R.; Arata, Y.; Hirokawa, R.; Endo, I.; Gomi, T. Vegetation Effects on Phosphorus Runoff from Headwater Catchments in a Cool-Temperate Region with Landslides, Northern Japan. Forests; 2024; 15, 229. [DOI: https://dx.doi.org/10.3390/f15020229]

34. Dittmar, T.; Koch, B.; Hertkorn, N.; Kattner, G. A Simple and Efficient Method for the Solid-Phase Extraction of Dissolved Organic Matter (SPE-DOM) from Seawater. Limnol. Ocean. Methods; 2008; 6, pp. 230-235. [DOI: https://dx.doi.org/10.4319/lom.2008.6.230]

35. Saarela, T.; Zhu, X.; Jäntti, H.; Ohashi, M.; Ide, J.; Siljanen, H.; Pesonen, A.; Aaltonen, H.; Ojala, A.; Nishimura, H. et al. The Influence of Dissolved Organic Matter Composition on Microbial Degradation and Carbon Dioxide Production in Pristine Subarctic Rivers. Boreal Environ. Res.; 2024; 29, pp. 131-148.

36. Herzsprung, P.; Von Tümpling, W.; Hertkorn, N.; Harir, M.; Büttner, O.; Bravidor, J.; Friese, K.; Schmitt-Kopplin, P. Variations of DOM Quality in Inflows of a Drinking Water Reservoir: Linking of van Krevelen Diagrams with EEMF Spectra by Rank Correlation. Environ. Sci. Technol.; 2012; 46, pp. 5511-5518. [DOI: https://dx.doi.org/10.1021/es300345c] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22524193]

37. Riedel, T.; Biester, H. Molecular Fractionation of Dissolved Organic Matter with Metal Salts. Environ. Sci. Technol.; 2012; 46, pp. 4419-4426. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22414136]

38. Seifert, A.-G.; Roth, V.-N.; Dittmar, T.; Gleixner, G.; Breuer, L.; Houska, T.; Marxsen, J. Comparing Molecular Composition of Dissolved Organic Matter in Soil and Stream Water: Influence of Land Use and Chemical Characteristics. Sci. Total Environ.; 2016; 571, pp. 142-152. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2016.07.033] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27470673]

39. Kujawinski, E.B.; Behn, M.D. Automated Analysis of Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectra of Natural Organic Matter. Anal. Chem.; 2006; 78, pp. 4363-4373. [DOI: https://dx.doi.org/10.1021/ac0600306]

40. Wozniak, A.S.; Bauer, J.E.; Sleighter, R.L.; Dickhut, R.M.; Hatcher, P.G. Technical Note: Molecular Characterization of Aerosol-Derived Water Soluble Organic Carbon Using Ultrahigh Resolution Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Atmos. Chem. Phys.; 2008; 8, pp. 5099-5111. [DOI: https://dx.doi.org/10.5194/acp-8-5099-2008]

41. Wagner, S.; Riedel, T.; Niggemann, J.; Vähätalo, A.V.; Dittmar, T.; Jaffé, R. Linking the Molecular Signature of Heteroatomic Dissolved Organic Matter to Watershed Characteristics in World Rivers. Environ. Sci. Technol.; 2015; 49, pp. 13798-13806. [DOI: https://dx.doi.org/10.1021/acs.est.5b00525]

42. R Core Team. R: A Language and Environment for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 4 October 2024).

43. Martin, J.P.; Haider, K.; Kassim, G. Biodegradation and Stabilization After 2 Years of Specific Crop, Lignin, and Polysaccharide Carbons in Soils. Soil Sci. Soc. Am. J.; 1980; 44, 1250. [DOI: https://dx.doi.org/10.2136/sssaj1980.03615995004400060024x]

44. Haider, K. Problems Related to the Humification Processes in Soils of Temperate Climates. Soil Biochemistry 7; Stotzky, G.; Bollag, J.-M. CRC Press: Boca Raton, FL, USA, 1992; pp. 55-94.

45. Liu, C.; Hu, B.X.; Li, Z.; Yu, L.; Peng, H.; Wang, D.; Huang, X. Linking Soils and Streams: Chemical Composition and Sources of Eroded Organic Matter During Rainfall Events in a Loess Hilly-Gully Region of China. J. Hydrol.; 2021; 600, 126518. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2021.126518]

46. D’Andrilli, J.; Cooper, W.T.; Foreman, C.M.; Marshall, A.G. An Ultrahigh-Resolution Mass Spectrometry Index to Estimate Natural Organic Matter Lability. Rapid Commun. Mass Spectrom.; 2015; 29, pp. 2385-2401. [DOI: https://dx.doi.org/10.1002/rcm.7400]

47. Hu, A.; Han, L.; Lu, X.; Zhang, G.; Wang, J. Global Patterns and Drivers of Dissolved Organic Matter Across Earth Systems: Insights from H/C and O/C Ratios. Fundam. Res.; 2024; in press [DOI: https://dx.doi.org/10.1016/j.fmre.2023.11.018]

48. Kovacs, K.; Gaspar, A.; Sajgó, C.; Schmitt-Kopplin, P.; Tombacz, E. Comparative Study on Humic Substances Isolated in Thermal Groundwaters from Deep Aquifers Below 700 M. Geochem. J.; 2012; 46, pp. 211-224. [DOI: https://dx.doi.org/10.2343/geochemj.1.0168]

49. Qiao, W.; Guo, H.; He, C.; Shi, Q.; Zhao, B. Unraveling Roles of Dissolved Organic Matter in High Arsenic Groundwater Based on Molecular and Optical Signatures. J. Hazard. Mater.; 2021; 406, 124702. [DOI: https://dx.doi.org/10.1016/j.jhazmat.2020.124702] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33296763]

50. Maurischat, P.; Seidel, M.; Dittmar, T.; Guggenberger, G. Complex Dissolved Organic Matter (DOM) on the Roof of the World—Tibetan DOM Molecular Characteristics Indicate Sources, Land Use Effects, and Processing Along the Fluvial–Limnic Continuum. Biogeosciences; 2023; 20, pp. 3011-3026. [DOI: https://dx.doi.org/10.5194/bg-20-3011-2023]

51. Chantigny, M.H. Dissolved and Water-Extractable Organic Matter in Soils: A Review on the Influence of Land Use and Management Practices. Geoderma; 2003; 113, pp. 357-380. [DOI: https://dx.doi.org/10.1016/S0016-7061(02)00370-1]

52. Lee, Y.; Hur, J.; Shin, K.H. Characterization and Source Identification of Organic Matter in View of Land Uses and Heavy Rainfall in the Lake Shihwa, Korea. Mar. Pollut. Bull.; 2014; 84, pp. 322-329. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2014.04.033] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24841714]

53. Li, Z.; Peng, H.; Xie, B.; Liu, C.; Nie, X.; Wang, D.; Huang, M.; Xiao, H.; Shi, L.; Zhang, X. et al. Dissolved Organic Matter in Surface Runoff in the Loess Plateau of China: The Role of Rainfall Events and Land-Use. Hydrol. Process.; 2020; 34, pp. 1446-1459. [DOI: https://dx.doi.org/10.1002/hyp.13660]

54. Chen, W.; Hu, H.; Heal, K.; Sohi, S.; Tigabu, M.; Qiu, W.; Zhou, C. Linking Microbial Decomposition to Dissolved Organic Matter Composition in the Revegetation of the Red Soil Erosion Area. Forests; 2023; 14, 270. [DOI: https://dx.doi.org/10.3390/f14020270]

55. Lobbes, J.M.; Fitznar, H.P.; Kattner, G. Biogeochemical Characteristics of Dissolved and Particulate Organic Matter in Russian Rivers Entering the Arctic Ocean. Geochim. Cosmochim. Acta; 2000; 64, pp. 2973-2983. [DOI: https://dx.doi.org/10.1016/S0016-7037(00)00409-9]

56. Hedges, J.; Oades, J. Comparative Organic Geochemistries of Soils and Marine Sediments. Org. Geochem.; 1997; 27, pp. 319-361. [DOI: https://dx.doi.org/10.1016/S0146-6380(97)00056-9]

57. Jaffé, R.; Yamashita, Y.; Maie, N.; Cooper, W.T.; Dittmar, T.; Dodds, W.K.; Jones, J.B.; Myoshi, T.; Ortiz-Zayas, J.R.; Podgorski, D.C. et al. Dissolved Organic Matter in Headwater Streams: Compositional Variability Across Climatic Regions of North America. Geochim. Cosmochim. Acta; 2012; 94, pp. 95-108. [DOI: https://dx.doi.org/10.1016/j.gca.2012.06.031]

58. da Silva, M.P.; Blaurock, K.; Beudert, B.; Fleckenstein, J.H.; Hopp, L.; Peiffer, S.; Reemtsma, T.; Lechtenfeld, O.J. Delineating Source Contributions to Stream Dissolved Organic Matter Composition Under Baseflow Conditions in Forested Headwater Catchments. J. Geophys. Res. Biogeosci.; 2021; 126, e2021JG006425. [DOI: https://dx.doi.org/10.1029/2021JG006425]

59. Shi, W.; Zhuang, W.-E.; Hur, J.; Yang, L. Monitoring Dissolved Organic Matter in Wastewater and Drinking Water Treatments Using Spectroscopic Analysis and Ultra-High Resolution Mass Spectrometry. Water Res.; 2021; 188, 116406. [DOI: https://dx.doi.org/10.1016/j.watres.2020.116406] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33010601]

60. Liu, S.; He, Z.; Tang, Z.; Liu, L.; Hou, J.; Li, T.; Zhang, Y.; Shi, Q.; Giesy, J.P.; Wu, F. Linking the Molecular Composition of Autochthonous Dissolved Organic Matter to Source Identification for Freshwater Lake Ecosystems by Combination of Optical Spectroscopy and FT-ICR-MS Analysis. Sci. Total Environ.; 2020; 703, 134764. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.134764] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31726300]

61. Balcarczyk, K.L.; Jones, J.B.; Jaffé, R.; Maie, N. Stream Dissolved Organic Matter Bioavailability and Composition in Watersheds Underlain with Discontinuous Permafrost. Biogeochemistry; 2009; 94, pp. 255-270. [DOI: https://dx.doi.org/10.1007/s10533-009-9324-x]

62. Schumacher, M.; Christl, I.; Vogt, R.D.; Barmettler, K.; Jacobsen, C.; Kretzschmar, R. Chemical Composition of Aquatic Dissolved Organic Matter in Five Boreal Forest Catchments Sampled in Spring and Fall Seasons. Biogeochemistry; 2006; 80, pp. 263-275. [DOI: https://dx.doi.org/10.1007/s10533-006-9022-x]

63. Bogaard, T.A.; Greco, R. Landslide Hydrology: From Hydrology to Pore Pressure. WIREs Water; 2016; 3, pp. 439-459. [DOI: https://dx.doi.org/10.1002/wat2.1126]

64. Cawley, K.M.; Campbell, J.; Zwilling, M.; Jaffé, R. Evaluation of Forest Disturbance Legacy Effects on Dissolved Organic Matter Characteristics in Streams at the Hubbard Brook Experimental Forest, New Hampshire. Aquat. Sci.; 2014; 76, pp. 611-622. [DOI: https://dx.doi.org/10.1007/s00027-014-0358-3]

65. Yamashita, Y.; Kloeppel, B.D.; Knoepp, J.; Zausen, G.L.; Jaffé, R. Effects of Watershed History on Dissolved Organic Matter Characteristics in Headwater Streams. Ecosystems; 2011; 14, pp. 1110-1122. [DOI: https://dx.doi.org/10.1007/s10021-011-9469-z]

66. Likens, G.E. A Long-Term Record of Ice Cover for Mirror Lake, New Hampshire: Effects of Global Warming?. Int. Ver. Theor. Angew. Limnol. Verhandl.; 2000; 27, pp. 2765-2769. [DOI: https://dx.doi.org/10.1080/03680770.1998.11898169]

67. Stottlemyer, R.; Toczydlowski, D. Effect of Reduced Winter Precipitation and Increased Temperature on Watershed Solute Flux, 1988–2002, Northern Michigan. Biogeochemistry; 2006; 77, pp. 409-440. [DOI: https://dx.doi.org/10.1007/s10533-005-1810-1]

68. Pendergrass, A.G.; Knutti, R. The Uneven Nature of Daily Precipitation and Its Change. Geophys. Res. Lett.; 2018; 45, pp. 11980-11988. [DOI: https://dx.doi.org/10.1029/2018GL080298]

69. Fujibe, F.; Yamazaki, N.; Katsuyama, M.; Kobayashi, K. The Increasing Trend of Intense Precipitation in Japan Based on Four-Hourly Data for a Hundred Years. SOLA; 2005; 1, pp. 41-44. [DOI: https://dx.doi.org/10.2151/sola.2005-012]

70. Fujibe, F.; Yamazaki, N.; Kobayashi, K. Long-Term Changes of Heavy Precipitation and Dry Weather in Japan (1901–2004). J. Meteorol. Soc. Jpn.; 2006; 84, pp. 1033-1046. [DOI: https://dx.doi.org/10.2151/jmsj.84.1033]