Part A: Hydroclimate and Land-Use Characteristics of the Yongan Watershed

Supplementary materials to “Influence of legacy phosphorus, land use, and climate change on anthropogenic phosphorus inputs and riverine export dynamics” Part A: Hydroclimate and land-use characteristics of the Yongan watershed

The Yongan watershed is located in one of the most developed regions of China, Taizhou area, Zhejiang province (Fig. S1). The Yongan River ultimately flows into the Taizhou Estuary and East China Sea. The downstream outlet examined in this study is 55 km upstream of the Taizhou Estuary. It has an average annual water depth of 5.42 m and discharge of 72.9 m3 s–1 at the downstream BZA sampling point. Six catchments of the Yongan watershed as defined by the location of the monitoring sites at HX, HB, BZA, ZK, HG and XZ, with areas of 547, 1650, 2474, 218, 35 and 357 km2, respectively. Catchments HB and BZA include all area up to the mainstream sampling sites HB and BZA, respectively. In other word, catchment BZA denotes the entire watershed. There is no river regulation, such as artificial dams/reservoirs and transboundary water withdrawal facilities within the Yongan watershed. Fig. S1 Spatial distribution of land use in the Yongan River watershed (2009) Agricultural land (i.e., the sum of paddy field, garden plot, and dry land) averaged ~12% of total watershed area, with developed land (i.e., the sum of rural and urban residential, roads, mining and industrial), woodland, and natural lands contributing ~3%, ~67%, and ~18%, respectively, Fig. S1). Rice, wheat, corn, vegetables, soybean, and potato are major crops cultivated in the agricultural lands. Due to increased development, the areas of woodland and natural lands were decreased, while developed land area was increased over the past 31 years, especially in the 2000s. The climate of the Yongan watershed is subtropical monsoon with the six catchments having a long-term average annual precipitation of 1308–1463 mm during 1980–2010 (Fig. S2). The rainfall mainly occurs in May−September with the typhoon season usually occurring in July−September (Fig. S3). The Yongan watershed has experienced some of the most significant regional climate change in Zhejiang Province (The People’s Government of Zhejiang Province 2010). There has been a ~23 to ~28 day decrease in the number of rainy days, and a ~65% to ~76% increase in the number of storm events (>50 mm per 24 hr) over the 1980–2010 period. However, there were no significant trends in annual precipitation and average river discharge over the study period (1980−2010) (Fig. S2). The long-term trends in these hydroclimate variables were directly determined by regression analysis between each parameter and year number. Daily precipitation data for the study period were obtained from three weather monitoring stations within the Yongan watershed maintained by the Taizhou City Weather Bureau. Precipitation for each catchment was calculated using the Thiessen polygon method to spatially distribute the single point record from rain gauges, and the average precipitation was calculated by the following formula (Richard 1963): A A A P  1 P  2 P ⋯ n P (S1) A 1 A 2 A n where, P is the mean rainfall of the region whose area is A, and P1, P2,…, Pn indicate rainfall samplings within those polygons having areas A1, A2, …，An.

S2 n 2000 36 ) e o a 1 i Precipitation Discharge HX ) - g t 1 r r - a s y a t R² = 0.000 24 i

3 h p c m i 1500 m s c ( i m

e 12 ( D r R² = 0.017 P 1000 0

y Rainy day Storm number 9 b HX r a r e m

e 2 d b

200 R = 0.1502, p<0.05 r

b 6 o m y t m u n S i u 150 3 n a n R 100 R2 = 0.1553, p<0.05 0 n

) 2000 o 120 e

1 a Precipitation Discharge i ) - HB g t 1 r r - a s y t R² = 0.000 80 a i

3 h p c m i 1500 m s c i (

m 40 e ( D r R2 = 0.0350 P 1000 0

y Rainy day Storm number 9

b HB r a r 2 e m e

d R = 0.1424, p<0.05 b

6 b o m y t m u n S i u

150 3 n a n 2 R 100 R = 0.1747, p<0.05 0 n ) o 2500 Precipitation Discharge 150 e 1 i a ) - g

t BZA 1 r r - a s y a t 2000 110

i R² = 0.000 3 h p c m i m s c 1500 70 i ( m e ( D r

P 1000 R² = 0.023 30

y Rainy day Storm number 9

b BZA r a r

2 e m e R = 0.1955, p<0.05 d b

b 6 o m y t m u n S i u 150 3 n a n 2 R 100 R = 0.1587, p<0.05 0 n

) 2800 o 15 e 1 i a Precipitation Discharge -

ZK ) t g 1 r r - a y s t 2200

i R² = 0.000 3 h p m i c m s c i (

e 1600 ( r D P 1000 R² = 0.071 0 250 12 y Rainy day Storm number ZK

b r a r e e d 2 m

200 8 b

b R = 0.1311, p<0.05 y o m m t n u i u 150 S

4 n a n

R 2 100 R = 0.1954, p<0.05 0 n ) o 2500 3 e 1 i a Precipitation Discharge

- HG ) t g r 1 a r - y t

2000 s

2 a i R² = 0.000 3 h p m i c m c s i (

m 1500

e 1 ( r D

P R² = 0.023 1000 0

250 9 y b Rainy day Storm number HG

r a r e e

d 2 200 m

R = 0.1264, p<0.05 b

6 r y o m m t n u i u 150 S

3 n a n 2 R 100 R = 0.1587, p<0.05 0 n ) o 2500 24 e 1 i a - Precipitation Discharge XZ ) t g r 1 a r - y t 2000 16 s a i R² = 0.000 3 h p m i c m c s i ( m 1500 e 8 ( r D

P R² = 0.023 1000 0 250 9 y b Rainy day Storm number XZ

r a r e e

200 R = 0.1264, p<0.05 b

b 6 r y o m m t n u i u S

150 3 n a n 2 R R = 0.1587, p<0.05 100 0 1980 1984 1988 1992 1996 2000 2004 2008 2010 Fig. S2 Changes of hydroclimate variables in each of the six catchments within the Yongan watershed from 1980 to 2010: precipitation and river water discharge (a), annual number of rainy days and number of storm events (b)

S3 ) 350 m m

i 250 t a t

i 200 p i c

e 150 r p

y 100 l h t 50 n o

M 0 Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Fig. S3 Average monthly precipitation in the Yongan watershed in 1980–2010. The error bars refer to standard deviations of measurements from the three weather monitoring sites within the watershed

Part B: Riverine TP flux estimate River water samples were collected once every 4–8 weeks during the 1980–2010 period at six monitoring sites (Fig. S1). Water samples were collected between 8:00 and 9:00 and total phosphorus (TP) was analyzed in the certified laboratory of the Taizhou City Environmental Protection Bureau. Well mixed water samples (surface and bottom layers at three sites along the cross section) were collected and composited. Duplicate water samples were collected from the composite for analysis. The water samples were acidified with H2SO4 in the field (10 ml of concentrated H2SO4 per liter of sample) and analyzed within 4 hr after sampling. TP concentration (all dissolved and particle phosphorus) in the unfiltered water samples was determined by the spectrophotometric ammonium molybdate method (limit of detection: LOD = 0.01 mg P L-1), recommended by The Ministry of Environmental Protection of the People's Republic of China (State Environmental Protection Administration of China 2002). River discharge was measured once every 2 to 12 hours using the rotating-element current-meters method following the standard method recommended by The Ministry of Water Resources of the People's Republic of China (The Ministry of Water Resources of the People's Republic of China 1995). To estimate annual TP flux using the discrete monitoring data from 1980–2010, the widely applied LOADEST model was utilized to predict daily TP flux (Runkel et al. 2004):

2 2 Ln(Lt )  0  1Ln(Qt )  2Ln(Qt )  3t  4tc  5 sin(2t)  6 cos(2t) (S2) where Ln is the natural logarithm function; Qt is daily average river discharge for a given P monitoring 3 –1 –1 date (m s ), Lt is the measured daily P flux (kg P d ), which is estimated by multiplying measured P concentration by Qt; t is the decimal time for the corresponding monitoring date; tc is the center of decimal time for the study period (a constant); β0...β6 are the fitted parameters in the multiple regression;

β1 and β2 describe the relation between flux and discharge; β3 and β4 describe the relationship between flux and time; and β5 and β6 describe seasonal variation in flux data.

In this study, the LOADEST model parameters shown in Eq. S2 (i.e., β0...β6) were calibrated by least squares fit using Matlab software (version 10.0, The MathWorks, Inc., US, 2010) for each of the six catchments. All calibrated parameter values were statistically significant (p<0.05) with average relative

S4 errors of ±4%–±15% and high R2 between the modeled and measured TP concentration (Table S1). These results indicate that the established LOADEST models can be reasonably applied to estimate daily TP concentrations in the Yongan watershed (Fig. S4). Here, daily TP load was estimated by multiplying TP concentration and water discharge, and annual TP load was calculated from the sum of daily TP loads for a corresponding year. Annual TP flux was determined by dividing the annual TP load by the total drainage area upstream of each monitoring station. Table S1 The calibrated LOADEST parameters for TP concentration for each of the six catchments in the Yongan watershed 2 Catchm Parame β0 β1 β2 β3 β4 β5 β6 R ent ter HX Mean -8.838 -0.119 0.015 0.282 0.179 4.501 -0.161 0.76 95% CI -12.842 -0.273 -0.068 0.026 -0.062 2.449 -0.235 (n=176) -5.835 0.036 0.097 0.539 0.420 6.553 -0.086 HB Mean -4.079 0.008 0.015 -0.007 0.169 0.057 0.001 0.98 95% CI -4.475 -0.085 -0.034 -0.191 -0.024 0.046 -0.001 (n=174) -3.683 0.100 0.064 0.178 0.362 0.067 0.003 BZA Mean -3.094 -0.117 -0.032 -0.199 -0.012 0.094 -0.0004 0.78 95% CI -4.165 -0.171 -0.059 -0.293 -0.108 -0.104 -0.009 (n=183) -2.023 -0.062 -0.004 -0.105 0.087 0.292 0.008 ZK Mean -5.458 -0.098 -0.016 -0.057 -0.0211 0.0100 -0.002 0.92 95% CI -8.275 -0.205 -0.069 -0.198 -0.160 -0.348 -0.019 (n=174) -2.640 0.010 0.038 0.084 0.118 0.547 0.016 HG Mean -5.457 -0.201 -0.003 0.038 0.247 0.732 -0.033 0.87 95% CI -16.440 -0.370 -0.097 -0.296 -0.103 -0.971 -0.098 (n=170) 5.527 -0.032 0.092 0.371 0.598 2.436 0.032 XZ Mean -4.912 -0.221 0.019 -0.513 0.239 0.227 -0.005 0.78 95% CI -9.848 -0.447 -0.096 -0.855 -0.088 -0.633 -0.041 (n=151) 0.024 0.005 0.135 -0.171 0.566 1.086 0.031

S5 1000 10000 2 2 100 R =0.72 1000 R =0.98 n=176 n=174 10 100

0.1 HX 1 HB 0.01 0.1 0.01 0.1 1 10 100 1000 0.1 1 10 100 1000 10000 ) 1

- 10000 100 d 2 P 2 R =0.78 R =0.92 g 1000

( n=183

d 100 a o

P 10 T

d 0.1 e

l 1 ZK

e BZA d o 0.1 0.01 M 0.1 1 10 100 1000 10000 0.01 0.1 1 10 100 1000 1000 2 R =0.87 2 100 100 R =0.78 n=170 n=151 10 10

0.1 HG 0.1 XZ 0.001 0.01 0.01 0.1 1 10 100 1000 0.01 0.1 1 10 100 1000 Measured TP load (kg P d-1) Fig. S4 Measured daily riverine TP load versus the modeled values using the LOADEST model for each of the six catchment in the Yongan watershed It is well known that nutrient inputs to rivers from point and nonpoint sources demonstrate significant hydrological differences. Point source nutrient input to rivers is relatively constant and hydrologically independent. In contrast, diffuse source nutrient inputs have a strong hydrologic dependence (Chen et al. 2013; Bowes et al. 2014). To address the role of point and nonpoint source P input to rivers, relationships between measured riverine TP concentration and water discharge in the six catchments were evaluated. Among the six catchments of the Yongan watershed, catchment ZK demonstrated a significant positive relationship between river TP concentrations and river discharge, while catchment HG presented a significant negative correlation (Fig. S5). These results indicate that P inputs from nonpoint (erosion and leaching) and point (domestic sewage) sources are the dominant cause of riverine TP in catchments ZK and HG, respectively (Chen et al. 2013; Bowes et al. 2014). No significant correlations were observed in the other four catchments, implying that P inputs from both point and nonpoint source produced comparable contribution to riverine TP flux (Fig. S5).

S6 0.1 0.09 HX HB 0.08 0.06 0.06 P >0.05 P >0.05 0.04 0.03 0.02 0 0 0.1 10 1000 0.1 10 1000 ) ) 1 1 - - L 0.3 0.03 L

g BZA ZK 0.0395 g m m

( y = 0.0118x (

n 0.2 2 n o

R = 0.12, P <0.01 o i

P >0.05 i t

0.02 t a a r r t

0.1 t n n e e c c n n o o

c 0 0.01 c

P 0.1 1 10 100 1000 P T 0.01 0.1 1 10 100 T 1.2 0.3 HG XZ 0.9 y = -0.0227Ln(x) + 0.2053 0.2 R2 = 0.11, P <0.01 P >0.05 0.6 0.1 0.3

0 0 0.01 0.1 1 10 0.1 1 10 100 River water discharge (m3 s-1)

Fig. S5 The relationship between TP concentration and river discharge for each of the six catchments in the Yongan watershed Part C: Soil characteristics and management

There are 5 soil groups, including 10 soil subgroups and 130 soil species, in the Yongan watershed. The red, yellow, and lithological soil groups were the dominant soil types and accounted for 64.6%, 15.4%, and 1.5% of total soil area in the watershed, respectively (Agricultural Bureau of Xianju County 2011). The dominant red and yellow soil groups correlate with Oxisols and Ultisols in USDA Soil Taxonomy. The alluvial and paddy soil groups were the dominant soil types in the plain area and accounted for 3.9% and 14.6% of total soil area, respectively. Extensive soil property measurements were conducted in 1984 and 2009 by the Agriculture Bureaus of Xianju County in Zhejiang Province, China (Soil Survey Office of Taizhou City 1987; Agricultural Bureau of Xianju County 2011; Chen and Lu 2013). In 1984, soil properties for the 0–20 cm, 20–40 cm, 40–100 cm soil layers of the different soil types and agricultural land types were hierarchically measured, which was supported by the Second National Soil Census of China. In 2009, soil samples were collected from the same locations as in 1984 and soil properties for the

S7 upper 20-cm layer of the different soil and farmland types were measured, which was supported by the National Soil Measurement and Formulated Fertilization Program of China. On average, one composite sample was collected per 15 ha for plain region soils and one sample per 25 ha for hilly region soils. Composite soil samples (20–30 subsamples from each 15–25 ha area) were collected from the upper 20- cm soil layer in October to December following crop harvest. Undisturbed soil samples were simultaneously collected in cylindrical cores for measuring soil bulk density. Soil samples were air-dried, milled, and passed through a 2-mm sieve for chemical analysis. Soil total P (TP) content and Olsen P were measured by the H2SO4-HClO4 degradation (all organic and inorganic P in soil sample were - transformed into PO4 P in the solution)-molybdenum antimony colorimetric method and NaHCO3 extraction-molybdenum antimony colorimetric method (Agricultural Chemistry Specialty Committee of Soil Science Society Chinese 1983), respectively. Soil pH was measured potentiometrically using a pH meter in a 1:5, soil:distilled water suspension. To be comparable, data measured in the same catchment for upper 20-cm layer in both 1984 and 2009 (Table S2) were used to address the changes in soil properties over the 25 year period between sample collections. Table S2 Changes of soil properties in the upper 20-cm soil layer of agricultural lands in the Yongan watershed between 1984 and 2009 Catchment 1984 2009 pH Bulk density Olsen P TP pH Bulk density Olsen P TP (g cm3) (mg P kg–1) (mg P kg–1) (g cm3) (mg P kg–1) (mg P kg–1) HX Mean 5.1a 1.12a 10b 211b 5.3a 1.11b 49a 581a 97.5% 6.1 1.34 100 611 6.4 1.35 341 1220 2.5% 4.2 0.87 2.0 71 4.5 0.76 9 212 HB Mean 5.1a 1.2a 11.1b 220b 5.1a 1.18a 41a 419a 97.5% 6.2 1.54 185 586 6.3 1.46 312 843 2.5% 4.1 0.92 1.2 44 4.2 0.86 5.1 98 BZA Mean 5.0a 1.1a 15.9b 255b 5.1a 1.15a 32a 490a 97.5% 6.4 1.3 117 870 6.3 1.24 453 1232 2.5% 4.2 0.77 1.0 43 4.1 0.8 7.2 109 ZK Mean 4.8a 1.21a 8.8b 144b 4.9a 1.18a 56a 629a 97.5% 6.0 1.45 53 521 5.9 1.51 184 1132 2.5% 4.0 0.82 2.0 46 4.1 0.76 13 123 HG Mean 5.2b 1.21b 15b 157b 5.4a 1.15b 49a 443a 97.5% 6.3 1.72 98 572 6.4 1.64 287 829 2.5% 4.5 0.91 1.2 26 4.7 0.89 2.2 110 XZ Mean 5.5a 1.31a 24.2b 241b 5.1b 1.21a 57.2a 503a 97.5% 6.3 1.81 108 750 6.2 1.77 470 1321 2.5% 4.8 0.86 1.3 50 4.9 0.71 12 128 Lower case letters denote significant differences in soil properties between 1984 and 2009 in each catchment (p<0.01).

–1 Using the measured changes in soil TP content, cumulative P accumulation (S20cm, kg P ha ) in the upper 20-cm layer of agricultural soils between 1984 and 2009 was estimated as follows:

S8 3 3 4 4 C09,i B09,i A09,i10  C84,i B84,i A84,i10   i1 i1 (S3) S20cm  Aw –1 –3 th where C84,i, B84,i, and A84,i are P content (mg P kg ), soil bulk density (g cm ), and area for i land-use -1 type (ha) within the catchment in 1984; C09,i, B09,i, and A09,i are P content (mg N kg ), soil bulk density (g –3 th cm ), and area for i land-use type (ha) within the catchment in 2009; and Aw is catchment area (ha). The majority of agricultural irrigation and drainage systems used in 1980−1999 time period was constructed with stone and mud in the 1950s and progressively lost water delivery capacity after 20−30 years due to silting and collapse. Therefore, agricultural lands became waterlogged during the rainy season (May−September), especially during the typhoon season (Fig. S3), resulting in a considerable crop yield reduction. To cope with more frequent storm events (Fig. S2) and increasing water use conflicts between agriculture and industry, local government investment was increased to renovate old irrigation and drainage systems during the past 31 years. The agricultural land area irrigated and drained with old channels decreased by 40−80% between 1980 and 1999 (Fig. S6). In 2000−2009, new cement channels and pipes established in agricultural lands rapidly increased by 1.5-fold to 3.1-fold. Thus, the efficiency of agricultural drainage conveyance (drained agricultural land area) was greatly improved. The data on the agricultural areas drained by old and renovated drainage systems for each of 21 towns within the Yongan watershed were derived from the annual Statistic Yearbook of Xianju County and Statistic Yearbook of Linhai City.

S9 2000 30% HX Old channel 1500 Renovated channel 20% Drained area percentage 1000 10% 500 0 0% 6000 HB 40% 4000 20%

) 2000 a h ( e

0 0% g a

12000 BZA a

e 40% t r n a 9000

e d c r

n 6000 20% e a l p

3000 l a a e r 0 0% r u a t ZK

l 200 20% d u e c 150 15% i n r i g

100 10% a r a

50 5% D d e

n 0 0% i

a 500 HG 60% r

D 400 300 40% 200 20% 100 0 0% 1500 XZ 30% 1000 20% 500 10% 0 0% 1980 1985 1990 1995 2000 2005 2010 Fig. S6 Changes of agricultural land area and percentage of agricultural lands drained with old and renovated channels in the six catchments of the Yongan watershed in 1980−2010

Part D: NAPI calculation and uncertainty analysis 1. Data source Data sources for estimating the annual NAPI budget for the six catchments of the Yongan watershed and agricultural drainage system from 1980 to 2010 were derived from the annual Statistic Yearbook of Xianju County and Statistic Yearbook of Linhai City. By defining the watershed boundary using a geographical information system (GIS), all towns within Xianju County (~73% of total watershed area) and one town in Linhai City (~12% of total watershed area) were included within the watershed boundary. The remaining ~15% of the catchment area only considered the P input from atmospheric deposition in the NAPI analysis, since it was dominated by forests (~95%) and fell within Panan County

S10 (located in the northwest portion of the watershed) and Jinyun County (located in the southwest portion of the watershed) (Fig. S1). 2. NAPI estimation NAPI was calculated as the sum of four major components: atmospheric P deposition, P fertilizer application, non-food P, and net P in food, feed and seed import/export, where the net food, feed and seed P input was calculated as the sum of human and livestock P consumption and seed input minus the sum of P contained in livestock and crop production (Han et al. 2013). Pesticide P input was also a component for NAPI, but it represented less than 0.001% compared with P fertilizer according to the records in the Statistic Yearbooks; thus, pesticide P input was neglected in this study. NAPI for each of 21 towns within the watershed was estimated for addressing the spatial distribution. Based on the defined boundary for each catchment using GIS, NAPI for a town that belongs to two or more catchments was allocated among catchments using the land-use area ratio method (Han et al. 2011). To match estimated catchment NAPI with riverine TP fluxes at the six sampling sites (Fig. S1), the catchments HB and BZA include all watershed area up to the mainstream sampling sites HB and BZA, respectively (Table 1). 2.1 Fertilizer P application Annual commercial chemical and organic P fertilizer input was estimated from the applied amount of each fertilizer type and corresponding P content. Annual phosphorus fertilizer application amounts, such as superphosphate, calcium-magnesium phosphate, monoammonium phosphate and diammonium phosphate, in 1980−2010 were directly collected from the statistical yearbooks. P fertilizer contained

12−18% of P2O5 (Liang 1999). Total P fertilizer application was converted to kg P by multiplying by

436.4 kg P per ton P2O5 (Han et al. 2013). 2.2 Atmospheric P deposition Atmospheric P deposition rate considered both wet and dry deposition. Wet P deposition was estimated by multiplying TP concentration in rainwater and annual precipitation. Considering <3.5% of the urban area percentage and >65% of the forest area in the Yongan watershed, it can be considered in the Class VI category with TP concentration in rainwater of 0.005 mg L-1 according to the reviewed results concerning TP concentration in precipitation by Zhang and Jøgensen (2005) and Wu and Chen (2013). Then annual wet P deposition was estimated from the product of TP concentration and annual precipitation. The dry P deposition was estimated from the ratios between dry and wet deposition rate observed in the nearby Taihu Lake area (Liu et al. 2012), i.e., 60%, 252% and 299% in 1987−1988, 2002−2003 and 2010, respectively. Linear interpolation was adopted to estimate annual dry P deposition rates between reported values. Finally, atmospheric P deposition for the six catchments was estimated as the sum of wet and dry P deposition rates, which varied from 0.09 to 0.39 kg P ha–1 yr–1 from 1980 to 2010. These estimated results were consistent with previous observations conducted for Eastern Asia over the 1954−2012 period (0.075−1.1 kg P ha–1 yr–1, Tipping et al. 2014) and for the Taihu Lake watershed over the 1987−2011 period (0.024−0.35 kg P ha–1 yr–1, Liu et al. 2012). 2.3 Seed P input In this study, we took seed P as an input for NAPI estimates. Vegetables and the six main agricultural crops, which are major agricultural plants in the watershed, were considered for estimating seed P input. Seed P per unit area for each crop type was adopted from the results of Han et al. (2013) and Wang et al. (2009) as shown in Table S3. Seed P input was then estimated by multiplying the seed P input per unit area for each crop type by the cultivated area of the watershed.

S11 Table S3 Seed P input rate per unit area for each crop type Crop type Seed P (kg P km–2 yr–1) Rice 6.43 Wheat 23.81 Corn 4.53 Potato 79 Soybeans 8.90 Peanuts 0.58 Vegetables 0.03 2.4 Non-food P Non-food P input was largely derived from detergent used in people’s daily lives. Inputs were estimated by multiplying the annual detergent consumption per person by P content in detergent and by the population of each catchment. From 1980 to 2010, the annual detergent consumption per person was increased year by year, with 0.40, 2.41, 2.50, 3.12, 4.12 and 6.16 kg person–1 yr–1 in 1980, 1996, 2000, 2001, 2005 and 2010, respectively (Shu et al. 1998; CCIA 2001; Jin et al. 2001; China Surfactant Network 2007; He et al. 2009; China-Consulting Network 2011). Linear interpolation was adopted to estimate annual detergent consumption for years in which data were not available. Though the government put forward a “phosphorus forbidden” policy for Zhejiang Province in 2004, non-P detergent for consumption is less than 10% of total use due to high production cost (Ji 2007a). Thus, P content in detergent was considered as a constant of 4.25% (Ji 2007b). 2.5 P in net food and feed imports Phosphorus in net food and feed imports was calculated as the sum of human P consumption and livestock P consumption, minus the sum of P contained in livestock and crop production (Han et al. 2013). Human consumption of P in food was estimated by multiplying the number of inhabitants (Fig. S7) by a P intake rate per capita. The value of the per capita intake of phosphorus for each year was derived from Han et al. (2013) and Sprague and Gronberg (2013), i.e., 0.39, 0.46, and 0.52 kg in 1980, 2002, and 2009, respectively. We linearly interpolate values for missing years using the reported values. Domestic animals in China are usually fed according to relatively straightforward dietary prescriptions designed for maintaining or gaining weight. The P mass in animals was calculated as the P consumption per individual animal multiplied by the number of each animal type. The values of P consumption per individual animal were obtained from Han et al. (2013) (Table S4). Using Eq. S3, the average animal population for a year was quantified using data on sales and inventory of livestock (Hong et al. 2013). 1 Sales Cycles 1 AL  inventory    (S3) Cycles Cycles Cycles where AL is the annual average number of livestock, inventory is the number population from the end-of- year inventory data (Figure S7), Sales is the number of livestock slaughtered, which was derived from the Statistics Yearbooks, and Cycles is the duration of the life cycle (the number of days from birth to market) per year, which is estimated as: 365/life cycle. The life cycle for each animal type was adopted from the results reported by Wang et al. (2006) for China. We assumed all animals were completely formula fed, i.e., 80% from corn and 20% from pasture (Li 2007).

S12 ) ) t t

15 3 150 3 1000 30 0 0 1 1 ( HX (

HB n n 800 o o i i t t c 100 10 c 20 u u 600 d d o o r r )

3 0 0 c

c 400 1 i i 1 ( t 50 5 t 10 (

a a s s u u n n q

o 200 q o i a i a t

t , , a ) a ) l l 3 3 0

0 0 0 u 0 0 u 1 1 p ( p 1000 40 ( 60 1.5

o o s s

BZA p p n n

800 i k i c t t c 30 u

ZK a a u l l

d 40 1.0 d u u 600 d p p d n o o n 20 a p p a

n 400 i i e e b b k 20 0.5 k c b b c i i

10 a a h r

200 r h c

, , w g w g o o 0 0 o 0 0 o c c h

h 45 1.2 12 , 120 d d , n

HG n n n

a XZ a a a

e m e l m l t u t u t t H 30 0.8 8 a a 80 H c c

, , t t a a o o g g

0.4 d 15 d 40 4 n n a a

p p e e e e h 0 0 h 0 0 S S 0 0 4 8 2 6 0 4 8 0 0 4 8 2 6 0 4 8 1 8 9 0 8 8 9 0 0 1 8 9 0 8 8 9 0 0 0 9 9 0 9 9 9 0 0 0 9 9 0 9 9 9 0 0 2 1 1 1 1 1 2 2 2 2 1 1 1 1 1 2 2 2 Human Hog Chicken and Duck Aquatic product Cattle and Cow Sheep and Goat Rabbit Fig. S7 Annual population and end-of-year inventory for different types of livestock in each catchment for the Yongan River watershed in 1980−2010 Table S4 Livestock P consumption and excretion rates Livestock type P consumption (kg P P excretion (kg P Production P (kg P individual–1 yr–1) individual–1 yr–1) individual–1 yr–1) Pigs 4.59 3.17 1.42 Horses and cattle 10.99 9.78 1.21 Sheep 1.26 1.06 0.2 Chickens 0.18 0.12 0.06 Duck 0.34 0.22 0.12 Aquatic products 2.16 0.56 1.60 (kg P Mg–1) Livestock P production was the difference between livestock P consumption and livestock P excretion. The values for the percentage of P excreted were obtained from Han et al. (2013) (Table S4). It was assumed that spoilage and inedible components caused a 10% loss of animal product available for consumption (Han et al. 2013).

S13 Crop P production was estimated by multiplying the yield of a specific crop (Fig. S8) by its P content. The P contents (g kg–1) for different crops are listed in Table S5. The P content in different fruits is only slightly different such as apples, grapes, peaches and pears; thus we used the average data for these fruits to represent all fruits (Han et al. 2013). We assume that pests, spoilage and processing cause a 10% loss for all crops (Han et al. 2013). 40 2.4 160 24 HX HB 30 120 1.6 16

) 20 ) 80 g g k k

0 0.8 0 8 1 1 ( ( )

10 40 )

s s g g d d k k l l

e 6 e 6 i i 0 0

y 0 0 y 0 0 1 1

( ( e e

300 15 12 0.6 l l s s

b BZA b ZK d d l l a a t t e e i i e e y 9 y g g

e e n 200 10 0.4 n v v r r

o o d d c c

n n 6 a a d d

n s s n t t a a i i

5 0.2 100 t t u u r r u u f f 3 , , n n o o a a t t e e a a p p t t

, , o 0 0 o 0 0 n n p p

0.12 1.8

75 a 56 a , , t t HG e XZ e a a b b e e y y h h o 42 o S S w w

, 50 0.08 , 1.2 e e c c i i

R R 28 25 0.04 0.6 14

0 0 0 0 8 2 6 0 0 0 4 8 2 0 0 4 4 8 6 0 4 8 8 8 8 9 9 0 0 0 1 8 8 8 9 9 0 0 0 1 9 9 9 0 0 9 9 9 9 0 9 9 0 0 9 0 0 0 1 1 1 1 1 2 2 2 2 1 1 1 1 1 2 2 2 2 Rice Wheat Potato Fruits Vegetable Corn Soybean Peanut Fig. S8 Annual yields of major crops for the six catchments of the Yongan watershed in 1980−2010 Table S5 P content of agricultural crops Crop type P content (g kg–1) Rice 1.10 Corn 2.44 Wheat 1.88 Potato 0.40 Peanuts 2.50 Soybeans 4.65 Vegetable 0.30 Fruits 0.13 2.6 Phosphorus weathering from soil parent material The dominant soils in the Yongan River watershed are highly weathered red and yellow soils (~90% of entire watershed area; Oxisols and Ultisols) that contribute a very limited background P flux compared to

S14 NAPI sources (Zhang et al. 2005). This assumption is supported by a study in the Lake Dianchi watershed in southern China where red soils were found to contribute 0.006 kg P ha–1 yr–1 via natural rock weathering (Liu 2005). Similarly, the low TP export coefficients observed in several natural ecosystems indicate low weathering rates (0.0085 kg P ha–1 yr–1, USEPA 2002). As a result, the weathering P inputs were not considered in this study. Additional studies are required to verify the contribution from natural background P sources. 3. Uncertainty analysis in NAPI estimation To gain insight into the uncertainty in the NAPI estimation, an uncertainty analysis was performed using Monte Carlo simulation, which utilized random sampling from probability distribution functions as inputs (Hendren et al. 2013). The Monte Carlo method assumes that the uncertainty of the model inputs can be characterized by their statistical distribution functions; thus it is useful in modeling systems characterized by uncertainty through giving a measure of uncertainty for calculated quantities. Due to the limited information available for China and surrounding regions, we can not rigorously determine the probability distribution and coefficient of variation for each parameter used in estimating NAPI. Therefore, we assumed that all the parameters used in the NAPI estimation followed a normal distribution with a coefficient of variation equal to 30% for each parameter, which is commonly used in nutrient budgeting studies of nearby watersheds (Yan et al. 2011; Ti et al. 2012). All input parameters (Table S3−S5) for estimating NAPI were set to be independent of each other during the Monte Carlo sampling, since the majority of the parameters have no cause-and-effect relationship with each other in theory and the dependence (correlation) is difficult to quantify due to the limited information. The Monte Carlo sampling method was used to randomly generate 10,000 sets of model parameters according to their normal distribution functions, resulting in 10,000 iterations of NAPI simulation for each year to obtain the mean and 95% confidence interval for annual NAPI values. The NAPI estimation procedure was formulated in Microsoft Excel 2007 embedded with Crystal Ball software (Professional Edition 2000, Oracle Ltd. US., 2000) to run Monte Carlo simulations.

Part E: Regression models for riverine TP export Table S6 Results of multiple regressions between annual riverine TP yield (F, kg P ha–1 year–1) and NAPI (kg P ha–1 year–1), precipitation (P, mm year–1), number of storm events (SE), developed land area percentage (D%), and drained agricultural land area percentage (DA%). All regressions were significant at p<0.001. Model formulas R2 Nash Sutcliffe 0.78 0.77 F  0.0081P1.752 exp(0.430NAPI) 0.77 0.75 F  0.0089SE 0.383 exp(0.433NAPI) 0.81 0.80 F  0.464D%0.719 exp(0.282NAPI) 0.86 0.84 F  0.478DA%1.516 exp(0.272NAPI) 0.88 0.87 F  0.291P1.760DA%1.517 exp(0.256NAPI)

S15 0.84 0.84 F  0.314P1.891D%0.749 exp(0.259NAPI ) 0.89 0.88 F  0.356SE 0.481DA%1.602 exp(0.247NAPI) 0.84 0.83 F  0.500SE 0.540D%0.848 exp(0.236NAPI) 0.94 0.92 F  0.379P1.779DA%1.404 D%0.109 exp(0.245NAPI) 0.89 0.88 F  0.585SE 0.506DA%1.390 D%0.207 exp(0.233NAPI) 0.89 0.87 F  0.468P0.975SE 0.349DA%1.382 D%0.189 exp(0.225NAPI)

Reference Agricultural Bureau of Xianju County (2011) Agricultural ecological environment quality assessment in the Xianju County. http://www.xjlsncp.com/html/main/nyqhView/2011-12/6533.html. (in Chinese) Bowes J, Jarviea HP, Naden PS, Old GH, Scarlett PM, Roberts C, Armstrong LK, Harman SA, Wickham HD, Collins AL (2014) Identifying priorities for nutrient mitigation using river concentration–flow relationships: The Thames basin, UK. J Hydrol 571:1–12. doi: 10.1016/j.jhydrol.2014.03.063 Chen DJ, Dahlgren RA, Lu J (2013) A modified load apportionment model for identifying point and diffuse source nutrient inputs to rivers from stream monitoring data. J Hydrol 501:25–34. doi: 10.1016/j.jhydrol.2013.07.034 Chen HS, Lu LY (2013) Characteristics of soil fertility of farmlands in red earth area in Xianju County, eastern Zhejiang Province. J Henan Agr Sci 42:65–67 (in Chinese) Chen J, Wu XD, Brian LF, Webber M, Wei TY, Li MT, Chen ZY (2014) Variability and trend in the hydrology of the Yangtze River, China: Annual precipitation and runoff. J Hydrol 513:403–412. doi: 10.1016/j.jhydrol.2014.03.044 China Cleaning Industry Association (CCIA) (2001) Detergent with phosphorus is safe to human and environment. Surfactant Soap and Detergent Industry of China 4:5–7 (in Chinese) China Surfactant Network (2007) Industrial status of synthetic detergent and surfactant demand in China. http://www.chinasurfactant.net/index.php/index/detail/id/3128 (in Chinese) China-Consulting Network (2011) Depth analysis report about detergent industry in China, 2010. http://www.china-consulting.cn/article/html/2011/1125/147241.php (in Chinese) Chinese Agricultural Chemistry Specialty Committee of Soil Science Society (1983) Routine Analysis Method of Soil Agricultural Chemistry. Chinese Science Press, Beijing (in Chinese) Feng ZJ, Schilling KE, Chan KS (2013) Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed. Environ Monit Assess 185:4605–4617. doi: 10.1007/s10661-012-2891-7

S16 Han Y, Li X, Nan Z (2011a) Net anthropogenic nitrogen accumulation in the Beijing metropolitan region. Environ Sci Pollut R 18:485–496. doi: 10.1007/s11356-010-0394-z Han YG, Li XY, Nan Z (2011b) Net anthropogenic phosphorus accumulation in the Beijing Metropolitan Region. Ecosystems 14:445–457. doi: 10.1007/s10021-011-9420-3 Han YG, Yu XX, Wang XX, Wang YQ, Tian JX, Xu L, Wang CZ (2013) Net anthropogenic phosphorus inputs (NAPI) index application in Mainland China. Chemosphere 90:329–337. doi: 10.1016/j.chemosphere.2012.07.023 He JN, Kang WX, Yuan ZK (2009) Analysis of the pollutant source in the Dongting Lake. Chinese Agr Sci Bull 25:239–224 (in Chinese) Hendren CO, Badireddy AR, Casman E, Wiesner MR (2013) Modeling nanomaterial fate in wastewater treatment: Monte Carlo simulation of silver nanoparticles (nano-Ag). Sci Total Environ 449:418−425. doi: 10.1016/j.scitotenv.2013.01.078 Hong BG, Swaney DP, Howarth RW (2013) Estimating net anthropogenic nitrogen inputs to U.S. watersheds: comparison of methodologies. Environ Sci Technol 47(10):5199−5207. doi: 10.1021/es303437c Ji GS (2007a) Discussion on eutrophication in water area and prohibition of phosphorous on detergent in our country. China Environ Prot Industry 11:9–11 (in Chinese) Ji GS (2007b) Report about discussion and development proposal on eutrophication in water area and prohibition of phosphorous on detergent in our country. Chin Environ Pollut Contr 384:40–45 (in Chinese) Jin QH, Zhu SJ, Huang WY, Shu JH, Zhong P, Cheng JS (2001) Detergent-phosphorus’ contribution in Hefei region of the Chao Lake. China Environ Pollut Contr 23:320–322 (in Chinese) Li J (2007) Animal Nutrition and Feed. Chongqing University Press, Chongqing (in Chinese) Liang CH (1999) Common fertilizers and their application techniques. Shenyang Press, Shenyang (in Chinese) Liu T ， Yang LY ， Hu ZX ， Sun YN (2012) Spatial-temporal features of atmospheric deposition of nitrogen and phosphorus to the Lake Taihu. The Admin Tech Environ Moni 24:20–24 (in Chinese) Liu XJ, Zhang Y, Han WX, Tang AH, Shen JL, Cui ZL, Vitousek P, Erisman JW, Goulding K, Christie P, Fangmeier A, Zhang FS (2013) Enhanced nitrogen deposition over China. Nature 294:459–462. doi: 10.1038/nature11917 Liu Y (2005) Phosphorus flows in China: Physical profiles and environmental regulation. Dissertation, Wageningen University Richard JK (1963) An alternative method for the construction of Thiessen polygons. The Professional Geographer 15(5):24–26. Runkel RL, Crawford CG, Cohn TA (2004) Load estimator (Loadest): A Fortran program for estimating constituent loads in streams and rivers. U.S. Geological Survey Techniques and Methods Book 4, Chapter

S17 A5. Reston: US Geological Survey. Shu JH, Huang WY, Gao XY, Wu YG (1998) The practices of restricting or banning phosphates in detergents in industralized countries. J Lake Sci 10(1):90–96 (in Chinese) Soil Survey Office of Taizhou City (1987) Soil Species of Taizhou, China, Zhejiang Province Science Press, Hangzhou Sprague LA, Gronberg JM (2013) Estimated anthropogenic nitrogen and phosphorus inputs to the land surface of the conterminous United States—1992, 1997, and 2002: U.S. Geological Survey Scientific Investigations Report 2012–5241, pp 14. State Environmental Protection Administration of China (2002) Standard methods for water and wastewater monitoring and analysis. China Environmental Science Press, Beijing (in Chinese) The Ministry of Water Resources of the People's Republic of China (1995) Calibration method of rotating--element current--meters in straight open tank. http://www.chinawater.net.cn/guifan/new_show_jj.asp?id=157 (in Chinese) The People’s Government of Zhejiang Province (2010) The response program for climate change of Zhejiang Province in China. http://www.zj.gov.cn/art/2010/12/2/art_12460_7561.html (in Chinese) Ti CP, Pan JJ, Xia YQ, Yan XY (2012) A nitrogen budget of mainland China with spatial and temporal variation. Biogeochemistry 108:381–394. doi: 10.1007/s10533-011-9606-y Tipping E, Benham S, Boyle JF, Crow P, Davies J, Fischere U, Guyatt H, Helliwell R, Jackson-Blake L, Lawlor AJ, Monteith DT, Rowe EC, Tobermana H (2014) Atmospheric deposition of phosphorus to land and freshwater. Environ Sci Process Impacts. doi: 10.1039/c3em00641g U. S. Environmental Protection Agency (USEPA) (2006) An approach for using load duration curves in the development of TMDLs. http://www.epa.gov/owow/tmdl/duration_curve_guide_aug2007.pdf U. S. Environmental Protection Agency (USEPA) 2002. National management measures to control nonpoint source pollution from agriculture. Available online at: http://water.epa.gov/polwaste/nps/agriculture/agmm_index.cfm. Accessed 26 June 2014 Wang FH, Ma WQ, Dou ZX, Ma L, Liu XL, Xu JX, Zhang FS (2006) The estimation of the production amount of animal manure and its environmental effect in China. China Environ Sci 26:614–617 (in Chinese) Wang XY, Yan ES, Ou Y (2009) Material flow analysis of the phosphorus cycle in the upper watershed of the Miyun Reservoir in Beijing. Acta Sci Cirumst 29:1549–1560 (in Chinese) Wu SX (2005) The spatial and temporal change of nitrogen and phosphorus produced by livestock and poultry & their effects on agricultural non-point pollution in China. Ph.D. Dissertation, Chinese Academy of Agricultural Science, Beijing (in Chinese) Wu YP, Chen J (2013) Investigating the effects of point source and nonpoint source pollution on the water quality of the East River (Dongjiang) in South China. Ecol Indic 32:294–304. doi: 10.1016/j.ecolind.2013.04.002

S18 Yan XY, Cai ZC, Yang R, Ti CP, Xia YQ, Li FY, Wang JQ, Ma AJ (2011) Nitrogen budget and riverine nitrogen output in a rice paddy dominated agricultural watershed in eastern China. Biogeochemistry 106:489–501. doi: 10.1007/s10533-010-9528-0 Zhang C, Tian HQ, Liu JY, Wang SQ, Liu ML, Pan SF, Shi XZ (2005) Pools and distributions of soil phosphorus in China. Global Biogeochem Cy 19:GB1020. doi: 10.1029/2004GB002296 Zhang JJ, Jøgensen SE (2005) Modelling of point and non-point nutrient loadings from a watershed. Environ Model Softw 20:561–574. doi: 10.1016/j.envsoft.2004.03.003