This work identified the major predictor variables and their interactive effects on DIN, NO3− and NH4+ exports. Totally, 35 predictor variables among climatic, landscape setting and human disturbance dimensions were applied using PCA and pRDA analysis based upon data derived from 43 watersheds island-wide in Taiwan. Generally, the PCA identified that SF (Streamflow), SLP (average slope in watershed), AGR (percentage of the agriculture in the watershed), BD (percentage of the buildup area in watershed) and BD100 (percentage of buildup area in the 100 m buffer zone) are the main variables which can mostly explain the variances of DIN, NO3− and NH4+ exports. Because nutrient export is the product of nutrient concentration and streamflow, streamflow, as expected, is the strongest predictor for NO3− export (r = 0.72), but not for NH4+
export, due to active biogeochemical processes. Meanwhile, the SLP (r = -0.75) and BD (r = 0.75) are equally best correlated to NH4 export. Based on the results of the pRDA model, five selected environmental variables can explain NO3 and NH4+
export promisingly, but with different interactive effects. For NO3− export in the wet season, the climatic variable and human-landscape variables are independently responsive to most variances, while the dependent climatic-human variables present high marginal effects on NO3− exports in the dry season. The effective variables shift from human-landscape to climatic-human with seasons showing the mechanistic shift of nutrient transport. For NH4+
export, the residual variances are 0.31 and 0.21 for the wet and dry seasons, respectively, and climatic variables (e.g., streamflow) are not effective variables for NH4+ transport. The human-landscape variables are the major factors to explain the total variance of NH4+
export (over 80%), in both the wet and dry seasons. The shift of interactive effects of variables on nutrient export is important for water quality management at watershed scale
and designing mitigation strategies. Inevitably, the effects of intrinsic collinearity in the human-landscape system cannot be clearly separated due to spurious correlation, though the statistical approach provides some cues. For example, paired AGR and BD or SLP and BD are highly collinear but difficult to single out for estimating nutrient export and for interpretation. Nevertheless, with the accumulation of these studies, it is more possible to clarify the interactive effects, which could be of great help in advancing the understanding of DIN export mechanisms and global synthesized assessment.
References
1. Aber, J.D.; Nadelhoffer, K.J.; Steudler, P.; Melillo, J.M. Nitrogen saturation in northern forest ecosystems. Bioscience 1989, 39, 378–286, doi:10.2307/1311067.
2. Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.;
McNulty, S.; Currie, W.; Rustad, L.; Fernandez, I. Nitrogen saturation in temperate forest ecosystems: Hypotheses revisited. Bioscience 1998, 48, 921–934, doi:10.2307/1313296.
3. Appling, A.P.; Leon, M.C.; McDowell, W.H. Reducing bias and quantifying uncertainty in watershed flux estimates: The R package loadflex. Ecosphere 2015, 6, 1–25, doi:10.1890/es14-00517.1.
4. Aschonitis, V.; Feld, C.; Castaldelli, G.; Turin, P.; Visonà, E.; Fano, E.A.
Environmental stressor gradients hierarchically regulate macrozoobenthic community turnover in lotic systems of Northern Italy. Hydrobiologia 2016, 765, 131–147, doi:10.1007/s10750-015-2407-x.
5. Basnyat, P.; Teeter, L.D.; Flynn, K.M.; Lockaby, B.G. Relationships between landscape characteristics and nonpoint source pollution inputs to coastal estuaries.
Environ. Manag. 1999, 23, 539–549, doi:10.1007/s002679900208.
6. Borcard, D.; Legendre, P.; Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 1992, 73, 1045–1055, doi:10.2307/1940179.
7. Chang, M.; McCullough, J.D.; Granillo, A.B. Effects of land use and topography on some water quality variables in forested east Texas 1. J. Am. Water Resour. Assoc.
1983, 19, 191–196, doi:10.1111/j.1752-1688.1983.tb05313.x.
8. Chang, K.-H.; Jeng, F.-T.; Tsai, Y.-L.; Lin, P.-L. Modeling of long-range transport on Taiwan's acid deposition under different weather conditions. Atmos. Environ. 2000,
34, 3281–3295, doi:10.1016/s1352-2310(00)00072-8.
9. Chang, C.; Hamburg, S.; Hwong, J.; Lin, N.; Hsueh, M.; Chen, M.; Lin, T.-C. Impacts of tropical cyclones on hydrochemistry of a subtropical forest. Hydrol. Earth Syst. Sci.
2013, 17, 3815, doi:10.5194/hess-17-3815-2013.
10. Chang, C.-T.; Wang, S.-F.; Vadeboncoeur, M.A.; Lin, T.-C. Relating vegetation dynamics to temperature and precipitation at monthly and annual timescales in Taiwan using MODIS vegetation indices. Int. J. Remote Sens. 2014, 35, 598–620, doi:10.1080/01431161.2013.871593.
11. Chang, C.-T.; Wang, L.-J.; Huang, J.-C.; Liu, C.-P.; Wang, C.-P.; Lin, N.-H.; Wang, L.; Lin, T.-C. Precipitation controls on nutrient budgets in subtropical and tropical forests and the implications under changing climate. Adv. Water Resour. 2017, 103, 44–50, doi:10.1016/j.advwatres.2017.02.013.
12. Chang, C.T.; Huang, J.C.; Wang, L.; Shih, Y.T.; Lin, T.C. Shifts in stream hydrochemistry in responses to typhoon and non-typhoon precipitation.
Biogeosciences 2018, 15, 2379–2391, doi:10.5194/bg-15-2379-2018.
13. Conley, D.J.; Paerl, H.W.; Howarth, R.W.; Boesch, D.F.; Seitzinger, S.P.; Havens, K.E.; Lancelot, C.; Likens, G.E. Controlling eutrophication: Nitrogen and phosphorus.
Science 2009, 323, 1014–1015, doi:10.1126/science.1167755.
14. Craig, L.S.; Palmer, M.A.; Richardson, D.C.; Filoso, S.; Bernhardt, E.S.; Bledsoe, B.P.; Doyle, M.W.; Groffman, P.M.; Hassett, B.A.; Kaushal, S.S. Stream restoration strategies for reducing river nitrogen loads. Front. Ecol. Environ. 2008, 6, 529–538, doi:10.1890/070080.
15. Ferguson, R. River loads underestimated by rating curves. Water Resour. Res. 1986,
22, 74–76, doi:10.1029/wr022i001p00074.
16. Galloway, J.N.; Aber, J.D.; Erisman, J.W.; Seitzinger, S.P.; Howarth, R.W.; Cowling, E.B.; Cosby, B.J.; The Nitrogen Cascade. BioScience,
2003, 53,
18. Gergel, S.E.; Turner, M.G.; Kratz, T.K. Dissolved organic carbon as an indicator of the scale of watershed influence on lakes and rivers. Ecol. Appl. 1999, 9, 1377–1390, doi:10.1890/1051-0761(1999)009[1377:DOCAAI]2.0.CO;2.
19. Goodale, C.L.; Thomas, S.A.; Fredriksen, G.; Elliott, E.M.; Flinn, K.M.; Butler, T.J.;
Walter, M.T. Unusual seasonal patterns and inferred processes of nitrogen retention in forested headwaters of the Upper Susquehanna River. Biogeochemistry 2009, 93, 197–218, doi:10.1007/s10533-009-9298-8.
20. Graham, M.H. Confronting multicollinearity in ecological multiple regression.
Ecology 2003, 84, 2809–2815, doi:10.1890/02-3114.
21. Groffman, P.M.; Law, N.L.; Belt, K.T.; Band, L.E.; Fisher, G.T. Nitrogen fluxes and retention in urban watershed ecosystems. Ecosystems 2004, 7, 393–403, doi:10.1007/s10021-003-0039-x.
22. Halbfaß, S.; Gebel, M.; Bürger, S. Modelling of long term nitrogen retention in surface waters. Adv. Geosci. 2010, 27, 145–148, doi:10.5194/adgeo-27-145-2010.
23. He, B.; Kanae, S.; Oki, T.; Hirabayashi, Y.; Yamashiki, Y.; Takara, K. Assessment of global nitrogen pollution in rivers using an integrated biogeochemical modeling framework. Water Res. 2011, 45, 2573–2586, doi:10.1016/j.watres.2011.02.011.
24. Hotelling, H. Analysis of a complex of statistical variables into principal components.
J. Educ. Psychol. 1933, 24, 417, doi:10.1037/h0071325.
25. Hough‐Snee, N.; Roper, B.; Wheaton, J.; Lokteff, R. Riparian vegetation communities of the American Pacific Northwest are tied to multi‐scale environmental filters. River
Res. Appl. 2015, 31, 1151–1165, doi:10.1002/rra.2815.
26. Howarth, R.W. An assessment of human influences on fluxes of nitrogen from the terrestrial landscape to the estuaries and continental shelves of the North Atlantic Ocean.
Nutr. Cycl. Agroecosyst. 1998, 52,
213–223, doi:10.1023/A:1009784210657.27. Howarth, R.W.; Sharpley, A.; Walker, D. Sources of nutrient pollution to coastal waters in the United States: Implications for achieving coastal water quality goals.
Estuaries 2002, 25, 656–676, doi:10.1007/bf02804898.
28. Howarth, R.; Swaney, D.; Billen, G.; Garnier, J.; Hong, B.; Humborg, C.; Johnes, P.;
Mörth, C.-M.; Marino, R. Nitrogen fluxes from the landscape are controlled by net anthropogenic nitrogen inputs and by climate. Front. Ecol. Environ. 2012, 10, 37–43, doi:10.1890/100178.
29. Huang, H.; Chen, D.; Zhang, B.; Zeng, L.; Dahlgren, R.A. Modeling and forecasting riverine dissolved inorganic nitrogen export using anthropogenic nitrogen inputs, hydroclimate, and land-use change.
J. Hydrol. 2014, 517,
95–104, doi:10.1016/j.jhydrol.2014.05.024.30. Huang, J.-C.; Kao, S.-J.; Lin, C.-Y.; Chang, P.-L.; Lee, T.-Y.; Li, M.-H. Effect of subsampling tropical cyclone rainfall on flood hydrograph response in a subtropical mountainous catchment.
J. Hydrol. 2011, 409,
248–261, doi:10.1016/j.jhydrol.2011.08.037.31. Huang, J.-C.; Lee, T.-Y.; Kao, S.-J.; Hsu, S.-C.; Lin, H.-J.; Peng, T.-R. Land use effect and hydrological control on nitrate yield in subtropical mountainous watersheds.
Hydrol. Earth Syst. Sci. 2012, 16, 699, doi:10.5194/hess-16-699-2012.
32. Huang, J.-C.; Lee, T.-Y.; Lin, T.-C.; Hein, T.; Lee, L.-C.; Shih, Y.-T.; Kao, S.-J.;
Shiah, F.-K.; Lin, N.-H. Effects of different N sources on riverine DIN export and retention in a subtropical high-standing island, Taiwan. Biogeosciences 2016, 13, 1787, doi:10.5194/bg-13-1787-2016.
33. Hunsaker, C.T.; Levine, D.A. Hierarchical approaches to the study of water quality in rivers. Bioscience 1995, 45, 193–203, doi:10.2307/1312558.
34. legendre, P.; legendre, L. Chapter 9—Ordination in reduced space. In Developments
in Environmental Modelling; Legendre, P., Legendre, L., Eds.; Elsevier: Amsterdam,
The Netherlands, 2012; Volume 24, pp. 425–520.35. Johnson, L.; Richards, C.; Host, G.; Arthur, J. Landscape influences on water chemistry in Midwestern stream ecosystems. Freshw. Biol. 1997, 37, 193–208, doi:10.1046/j.1365-2427.1997.d01-539.x.
36. Johnson, R.K.; Furse, M.T.; Hering, D.; Sandin, L. Ecological relationships between stream communities and spatial scale: Implications for designing catchment‐level
monitoring programmes.
Freshw. Biol. 2007, 52,
939–958, phosphate transport in headwater catchments: The hydrological controls and land use alteration. Biogeosciences 2013, 10, 2617–2632, doi:10.5194/bg-10-2617-2013.40. Lee, T.-Y.; Shih, Y.-T.; Huang, J.-C.; Kao, S.-J.; Shiah, F.-K.; Liu, K.-K. Speciation and dynamics of dissolved inorganic nitrogen export in the Danshui River, Taiwan.
Biogeosciences Discuss. 2014, 11, 5307–5321, doi:10.5194/bg-11-5307-2014.
41. Legendre, P.; Oksanen, J.; ter Braak, C.J. Testing the significance of canonical axes in redundancy analysis.
Methods Ecol. Evol. 2011, 2,
269–277, doi:10.1111/j.2041-210x.2010.00078.x.42. Likens, G.E. Biogeochemistry of a Forested Ecosystem; Springer: Berlin/Heidelberg, Germany, 2013; doi:10.1007/978-1-4614-7810-2_1.
43. Liu, Q. Variation partitioning by partial redundancy analysis (RDA). Environmetrics
1997, 8, 75–85, doi:10.1002/(sici)1099-095x(199703)8:23.0.co;2-n.
44. Lladó, S.; López-Mondéjar, R.; Baldrian, P. Forest Soil Bacteria: Diversity, Involvement in Ecosystem Processes, and Response to Global Change. Microbiol. Mol.
Biol. Rev. 2017, 81, e00063–16, doi:10.1128/mmbr.00063-16.
45. McCrackin, M.L., Harrison, J.A., and Compton, J.E. Factors influencing export of dissolved inorganic nitrogen by major rivers: A new, seasonal, spatially explicit, global model.
Global Biogeochem. Cy. 2014, 28,
269–285, doi:10.1002/2013GB004723.46. Meynendonckx, J.; Heuvelmans, G.; Muys, B.; Feyen, J. Effects of watershed and riparian zone characteristics on nutrient concentrations in the River Scheldt Basin.
Hydrol. Earth Syst. Sci. 2006, 10, 913–922, doi:10.5194/hess-10-913-2006.
47. Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles.
J. Hydrol. 1970, 10,
282–290, doi:10.1016/0022-1694(70)90255-6.48. Nava-López, M.Z.; Diemont, S.A.; Hall, M.; Á vila-Akerberg, V. Riparian buffer zone and whole watershed influences on river water Quality: Implications for ecosystem services near megacities.
Environ. Process. 2016, 3,
277–305, doi:10.1007/s40710-016-0145-3.49. Nielsen, A.; Trolle, D.; Søndergaard, M.; Lauridsen, T.L.; Bjerring, R.; Olesen, J.E.;
Jeppesen, E. Watershed land use effects on lake water quality in Denmark. Ecol. Appl.
2012, 22, 1187–1200, doi:10.1890/11-1831.1.
50. Ohowa, B.; Mwashote, B.; Shimbira, W. Dissolved inorganic nutrient fluxes from two seasonal rivers into Gazi Bay, Kenya. Estuar. Coast. Shelf Sci. 1997, 45, 189–195, doi:10.1006/ecss.1996.0187.
51. Ohte, N. Implications of seasonal variation in nitrate export from forested ecosystems:
A review from the hydrological perspective of ecosystem dynamics. Ecol. Res. 2012,
27, 657–665, doi:10.1007/s11284-012-0956-2.
52. Pajares, S.; Bohannan, B.J. Ecology of nitrogen fixing, nitrifying, and denitrifying microorganisms in tropical forest soils. Front. Microbiol. 2016, 7, 1045, doi:10.3389/fmicb.2016.01045.
53. Parajka, J.; Viglione, A.; Rogger, M.; Salinas, J.L.; Sivapalan, M.; Blöschl, G.
Comparative assessment of predictions in ungauged basins – Part 1:
Runoff-hydrograph studies. Hydrol. Earth Syst. Sci. 2013, 17, 1783–1795, doi:10.5194/hess-17-1783-2013.
54. Porterfield, G. Computation of Fluvial-Sediment Discharge; US Government Printing Office: Washington, DC, USA, 1972; doi:10.3133/twri03C3.
55. Richards, C.; Johnson, L.B.; Host, G.E. Landscape-scale influences on stream habitats and biota. Can. J. Fish. Aquat. Sci. 1996, 53, 295–311, doi:10.1139/f96-006.
56. Robertson, D.M.; Roerish, E.D. Influence of various water quality sampling strategies on load estimates for small streams. Water Resour. Res. 1999, 35, 3747–3759, doi:10.1029/1999wr900277.
57. Rockström, J.; Steffen, W.; Noone, K.; Persson, Å .; Chapin, F.S.; Lambin, E.F.;
Lenton, T.M.; Scheffer, M.; Folke, C.; Schellnhuber, H.J. A safe operating space for humanity. Nature 2009, 461, 472–475, doi:10.1038/461472a.
58. Seitzinger, S.P.; Mayorga, E.; Bouwman, A.F.; Kroeze, C.; Beusen, A.H.W.; Billen, G.; Van Drecht, G.; Dumont, E.; Fekete, B.M.; Garnier, J., et al. Global river nutrient export: A scenario analysis of past and future trends. Glob. Biogeochem. Cycle 2010,
24, GB0A08, doi:doi:10.1029/2009GB003587.
59. Shih, Y.-T.; Lee, T.-Y.; Huang, J.-C.; Kao, S.-J. Apportioning riverine DIN load to export coefficients of land uses in an urbanized watershed. Sci. Total Environ. 2016,
560, 1–11, doi:10.1016/j.scitotenv.2016.04.055.
60. Sliva, L.; Williams, D.D. Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Res. 2001, 35, 3462–3472, doi:10.1016/s0043-1354(01)00062-8.
61. Sutter, J.M.; Kalivas, J.H. Comparison of forward selection, backward elimination, and generalized simulated annealing for variable selection. Microchem. J. 1993, 47, 60–66, doi:10.1006/mchj.1993.1012.
62. ter Braak, C.J. CANOCO-a FORTRAN Program. for Canonical Community
Ordination By [Partial][Etrended][Canonical] Correspondence Analysis, Principal Components Analysis and Redundancy Analysis (Version 2.1);
Wageningen University: Wageningen, The Netherlands, 1988.
63. Tong, S.T.; Chen, W. Modeling the relationship between land use and surface water quality. J. Environ. Manag. 2002, 66, 377–393, doi:10.1006/jema.2002.0593.
64. Tørseth, K.; Aas, W.; Breivik, K.; Fjæ raa, A.M.; Fiebig, M.; Hjellbrekke, A.-G.; Lund Myhre, C.; Solberg, S.; Yttri, K.E. Introduction to the European Monitoring and Evaluation Programme (EMEP) and observed atmospheric composition change during 1972–2009.
Atmos. Chem. Phys. 2012, 12,
5447–5481, doi:10.5194/acp-12-5447-2012.65. Uriarte, M.; Yackulic, C.B.; Lim, Y.; Arce-Nazario, J.A. Influence of land use on water quality in a tropical landscape: A multi-scale analysis. Landsc. Ecol. 2011, 26, 1151, doi:10.1007/s10980-011-9642-y.
66. Varanka, S.; Luoto, M. Environmental determinants of water quality in boreal rivers based on partitioning methods. River Res. Appl. 2012, 28, 1034–1046, doi:10.1002/rra.1502.
67. Vet, R.; Artz, R.S.; Carou, S.; Shaw, M.; Ro, C.-U.; Aas, W.; Baker, A.; Bowersox, V.C.; Dentener, F.; Galy-Lacaux, C. A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH,
and phosphorus.
Atmos. Environ. 2014, 93,
3–100,doi:10.1016/j.atmosenv.2013.10.060.
68. Xiao, R.; Wang, G.; Zhang, Q.; Zhang, Z. Multi-scale analysis of relationship between landscape pattern and urban river water quality in different seasons. Sci. Rep. 2016, 6, 1–10, doi:10.1038/srep25250.
SUPPLEMENTARY MATERIALS
Table S1. The basic landscape characteristics of the 43 sampling sites.
Station Name
Rainfall Forest Agri. Buildup
(km2) (℃) (%) (mm) (mm) (%) (%) (%)
Table S2. Estimated annual and seasonal DIN, NO3− and NH4+ concentrations for 43 sampling sites in
Table S3. Estimated annual and seasonal DIN, NO3− and NH4+ concentrations for 43 sampling sites in
Table S4. Estimated annual and seasonal DIN, NO3− and NH4+ exports for 43 sampling sites in 2015 (unit: 12. Chi-Nan Bridge 5703.13 2319.58 3383.55 3031.06 973.38 2057.68 2262.53 1179.46 1083.07 13. Yu-Feng Bridge 956.11 234.62 721.49 591.48 120.87 470.61 332.82 103.17 229.64
14. Chi-Chou
Bridge 1467.35 78.32 1389.03 909.48 23.23 886.25 526.75 50.96 475.79 15. Pei-Kang-2 8655.88 2336.25 6319.63 2495.31 446.42 2048.90 5757.24 1815.02 3942.21
16. Tun-Kun
Bridge 10228.53 2928.19 7300.34 3069.35 471.44 2597.91 5295.93 2372.08 2923.85 17 Chun-Huei
Table S5. Estimated annual and seasonal DIN, NO3− and NH4+ exports for 43 sampling sites in 2016 (unit: 12. Chi-Nan Bridge 6602.35 3093.40 3508.95 3997.99 1714.69 2283.29 2138.87 1164.33 974.54 13. Yu-Feng Bridge 1925.92 637.74 1288.18 1551.78 503.19 1048.59 333.45 118.68 214.77 14. Chi-Chou Bridge 2135.77 410.36 1725.42 1824.86 313.57 1511.29 263.57 85.13 178.44 15. Pei-Kang-2 8689.27 2724.01 5965.27 3381.86 841.47 2540.39 4824.23 1752.17 3072.06 16. Tun-Kun Bridge 8753.00 2383.04 6369.96 2744.42 560.30 2184.13 3432.72 1738.66 1694.06
17. Chun-Huei
Bridge 3775.87 634.74 3141.13 2990.73 405.71 2585.01 727.57 209.75 517.82 18. Chu-Kuo 5845.26 558.26 5287.00 5207.82 299.54 4908.28 627.92 257.03 370.90 19. Ho-Sung Bridge 9923.87 2617.95 7305.92 5801.49 1005.11 4796.38 3290.64 1380.35 1910.30
20. Shin-Ying 6597.14 1869.92 4727.22 4533.52 958.08 3575.44 1646.04 793.90 852.14 21. Yu-Tien 2015.85 144.97 1870.88 1928.94 140.19 1788.75 58.14 2.42 55.71 22. Hsin-Shih 9866.62 2339.83 7526.79 3219.43 427.36 2792.07 5131.80 1789.08 3342.72
23. A-Lien-2 7407.47 1558.67 5848.80 3017.74 446.49 2571.26 3724.09 1013.29 2710.80
Figure S1. Scatterplot matrix among streamflow [SF; mm], slope [SLP; %], the proportion of agriculture [AGR; %], the proportion of buildup [BD; %] of various scales and annual NO3−, and NH4+, and DIN exports at (a) 100 m, (b) 200 m, (c) 500 m, (d) 1000 m, (e) 2000 m, and (f) entire watershed scales. The asterisk indicates that the correlation is statistic significant (p-value: ** < 0.01 < * < 0.05), and the red lines indicate smooth transition regressions.
Figure S2. Scatterplot matrix among streamflow [SF; mm], slope [SLP; %], the proportion of agriculture [AGR; %], the proportion of buildup [BD; %] of various scales and NO3− (Ni), and NH4+ (Am), and DIN exports during wet season at (a) 100 m, (b) 200 m, (c) 500 m, (d) 1000 m, (e) 2000 m, and (f) entire watershed scales. The asterisk indicates that the correlation is statistic significant (p-value: ** < 0.01 < * <
0.05), and the red lines indicate smooth transition regressions.
Figure S3. Scatterplot matrix among streamflow [SF; mm], slope [SLP; %], the proportion of agriculture [AGR; %], the proportion of buildup [BD; %] of various scales and NO3− (Ni), and NH4+ (Am), and DIN exports during dry season at ((a) 100 m, (b) 200 m, (c) 500 m, (d) 1000 m, (e) 2000 m, and (f) entire watershed scales. The asterisk indicates that the correlation is statistic significant (p-value: ** < 0.01 < * <
0.05), and the red lines indicate smooth transition regressions.
Figure S4. Principal components analysis of environmental variables for 43 catchments (gray dots) for NO3− export (left panel) and NH4+ export (right panel) at different buffer zones: (a, b) 100 m, (c, d) 200 m, (e, f) 500 m, (g, h) 1000 m, (i, j) 2000 m and (k, l) entire watershed. Red-labeled variables are main components for PC1 and PC2. Blue-labeled variables indicate annual nitrate (Ni), dry season nitrate (NiDry), and wet season nitrate export (NiWet) in (left panel) and annual ammonium (Am), dry season ammonium (AmDry) and wet season ammonium export (AmWet) in (right panel).
Figure S5. The relationship between the observed concentration (y-axis) and the simulated discharge (x-axis) in site no.38 (a) and no.1 (b) during the study period. Obs_NO3 is the observed NO3−
concentration; Obs_NH4 is the observed NH4+ concentration.