estimation of global river transport of sediments and ...dust.ess.uci.edu/ppr/ppr_bdb05.pdf ·...

Estimation of global river transport of sediments and associated

particulate C, N, and P

A. H. W. Beusen,1 A. L. M. Dekkers,2 A. F. Bouwman,1 W. Ludwig,3 and J. Harrison4

Received 7 January 2005; revised 25 July 2005; accepted 8 August 2005; published 1 December 2005.

[1] This paper presents a multiple linear regression model developed for describingglobal river export of sediments (suspended solids, TSS) to coastal seas, and approachesfor estimating organic carbon, nitrogen, and phosphorous transported as particulate matter(POC, PN, and PP) associated with sediments. The model, with river-basin spatialscale and a 1-year temporal scale, is based on five factors with a significant influence onTSS yields (the extent of marginal grassland and wetland rice, Fournier precipitation,Fournier slope, and lithology), and accounts for sediment trapping in reservoirs. Themodel generates predictions within a factor of 4 for 80% of the 124 rivers in the data set.It is a robust model which was cross-validated by using training and validation sets ofdata, and validated against independent data. In addition, Monte Carlo simulationswere used to deal with uncertainties in the model coefficients for the five model factors.The global river export of TSS calculated thus is 19 Pg yr�1 with a 95% confidenceinterval of 11–27 Pg yr�1 when accounting for sediment trapping in regulated rivers.Associated POC, PN, and PP export is 197 Tg yr�1 (as C), 30 Tg yr�1 (N), and 9 Tg yr�1

(P), respectively. The global sediment trapping included in these estimates is 13%. Mostparticulate nutrients are transported by rivers to the Pacific (�37% of global particulatenutrient export), Atlantic (28–29%), and Indian (�20%) oceans, and the major sourceregions are Asia (�50% of global particulate nutrient export), South America (�20%),and Africa (12%).

Citation: Beusen, A. H. W., A. L. M. Dekkers, A. F. Bouwman, W. Ludwig, and J. Harrison (2005), Estimation of global river

transport of sediments and associated particulate C, N, and P, Global Biogeochem. Cycles, 19, GB4S05, doi:10.1029/2005GB002453.

1. Introduction

[2] Total suspended solid (TSS) river loads and associatedparticulate nutrients (particulate organic carbon, POC, par-ticulate nitrogen, PN, and particulate phosphorus, PP)greatly influence the ecology and biogeochemistry of fresh-water and coastal marine environments by affecting aquaticfood webs, controlling the availability of dissolvednutrients, and affecting the optical properties of aquaticsystems [Froelich, 1988; Dagg et al., 2004; Stramski etal., 2004]. River transport of particulate nutrients is alsoimportant from regional and global biogeochemical per-spectives, as POC, PN and PP constitute major portions ofthe C, N and P transported from land to sea. In systems forwhich data on both dissolved and particulate species areavailable, POC, PN and PP are frequently more abundant

than dissolved forms of C, N, and P [[Meybeck, 1982;Froelich, 1988; Alexander et al., 1998; Seitzinger et al.,2002].[3] Human perturbations such as land use changes and

river dam construction have greatly influenced fluxes ofparticulate nutrients through rivers. On the global scale themost massive anthropogenic increases in river sediment andassociated particulate nutrient loads are the result of cropfarming, especially where large-scale forest conversion tocropland has occurred [Milliman and Meade, 1983; Meade,1996]. Deforestation and agricultural activities may alter thenatural mechanical erosion rates, mainly through the loss offertile topsoil.[4] Effects of farming activities on river sediment trans-

port are often obscured by the construction of dams. Damsare built to impound water for various purposes, and theymay strongly decrease the particulate nutrient transport bysedimentation in reservoirs and flow diversion [Vorosmartyet al., 2003]. In addition, land use change influences runoff[Bosch and Hewlett, 1982] and large-scale deforestationmay alter climate [Sitch et al., 2005].[5] Past work suggests that river POC and PN loads are

strongly related to river total suspended solid (TSS) loads[Ittekkot and Zhang, 1989; Ludwig and Probst, 1996]. It isalso reasonable to think that river PP load would scale withriver TSS load. It is therefore important to understand the

GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 19, GB4S05, doi:10.1029/2005GB002453, 2005

1Netherlands Environmental Assessment Agency, National Institute forPublic Health and the Environment, Bilthoven, Netherlands.

2Centre for Information Technology and Methodology, NationalInstitute for Public Health and the Environment, Bilthoven, Netherlands.

3Centre de Formation et de Recherche sur l’Environnement Marin,UMR 5110, Perpignan, France.

4Institute of Marine and Coastal Sciences, Rutgers-The State University,New Brunswick, New Jersey, USA.

Copyright 2005 by the American Geophysical Union.0886-6236/05/2005GB002453$12.00

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response of river TSS and associated nutrient fluxes toregional and global changes, and the main factors thatcontrol the fluvial sediment transport to the oceans.[6] There have been several lumped river-basin approaches

to estimating long-term global annual TSS flux to the coastaloceans [Milliman and Meade, 1983; Milliman and Syvitski,1992; Ludwig and Probst, 1996; Hovius, 1998; Ludwig andProbst, 1998; Syvitski et al., 2003]. Common in these studiesis that they all provide models based on average river basincharacteristics. For example, Hovius [1998] used stepwiseregression to analyze the factors that are most effectiveat explaining the global variance of TSS yields (TSSY, inton km�2 yr�1). The factors selected were river basin area,maximum height difference within the river basin, runoff,mean annual temperature and temperature range (differencebetween average daytime temperatures for hottest and coldestmonths).[7] While Hovius [1998] selected the controls on TSSY

by stepwise regression, Syvitski et al. [2003] used an a priorimodel formulation with the factors river basin area, maxi-mum height difference, and basin-average temperatureand derived the model coefficients by using regressiontechniques. Ludwig and Probst [1998] tested various factorsand found no significant influence of anthropogenic ones.On the basis of this analysis they used different a priori setsof factors (runoff, slope, the Fournier index of precipitationand mechanical erodibility of the parent material) to modelTSSY.[8] In this paper we describe, evaluate and apply a new

model for predicting fluxes of TSS, particulate organic C(POC), particulate N (PN), and particulate P (PP) to the

world’s coastal oceans. This model was developed as part ofan international interdisciplinary effort to model river exportof multiple bioactive elements (C, N, P, and Si) andelemental forms (dissolved/particulate, inorganic/organic)called Global Nutrient Export from Watersheds (GlobalNEWS). We hereafter refer to our model as ‘‘NEWS-Particulate Nutrients (PNU)’’. Because NEWS-PNU wasdeveloped as part of a larger system of models withconsistent input data sets and formulation, its output canbe directly compared with output from other NEWS models[e.g., Harrison et al., 2005; Dumont et al., 2005]. NEWS-PNU also represents the first spatially explicit, globalestimates of river PP export.

2. Data and Methods

2.1. General Approach

[9] Past work suggests that river POC loads are stronglyrelated to river total suspended solid (TSS) loads [Ittekkotand Zhang, 1989; Ludwig and Probst, 1996]. There is alsoreasonable evidence that PN and PP are both related to POC[Seitzinger et al.,. 2002] (Figure 1). We therefore modeledTSS as a function of landscape properties, modeled POC asa function of TSS, and modeled PN and PP as a function ofPOC. All data described in this section are available asauxiliary material1 to this paper.

2.2. TSSY, POC, PN, PP, and Ancillary Data

[10] We selected TSS data from global databases forrivers representing a host of environmental conditions andvariability in population density and human economicactivity. Data on the TSS loads or concentrations measuredat or close to the river mouth were taken from the GEMS-GLORI database [Meybeck and Ragu, 1995], Hovius [1998]and GEMS [U.N. Environment Programme (UNEP), 2001](Appendix A). Where necessary, data were converted toTSS yield (TSSY in ton km�2 yr�1 in line with the Global-NEWS approach [e.g. Harrison et al., 2005]) using riverdischarge and basin area. TSSY data obtained from Syvitskiet al. [2003] were used for validation. All TSSYvalues wererecalculated using the river basin areas reported by Fekete etal. [2002]. These combined databases contain relevantinformation for this study, including the location of theriver mouth, areas, natural water discharge, present or actualwater discharge, and long-term natural and actual suspendedload. Various other factors were tested including landuse related ones (Table 1). For trapping of sediments inreservoirs we used estimates for trapping efficiencies for143 individual river basins with large dams reported byVorosmarty et al. [2003] (Appendix A).[11] We selected the data flagged as ‘‘natural’’ TSSY and

excluded data on ‘‘actual’’ TSSY. This is because weintended to exclude effects of dams and reservoirs on waterand TSS fluxes in river basins. We selected rivers with abasin area of >20,000 km2, which is equivalent to about ten0.5 by 0.5 degree grid cells. This was done to avoidproblems caused by the use of spatial information with0.5 by 0.5 degree resolution for small river basins, where

Figure 1. Relationship between POC and PP for 15 riversfrom Meybeck and Ragu [1995] and 80 stations from USGS[1996].

1Auxiliary material is available at ftp://ftp.agu.org/apend/gb/2005GB002453.

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the allocation of land use, soils, climate, relief, hydrologyand geology may not represent the actual conditions. Incontrast, in large river basins the overall spatial patternsmay more realistically describe the geographical distribu-tions. The data set we compiled contains data for 126 rivers(Figure 2a) (Appendix A). The validation data set obtainedfrom data provided by Syvitski et al. [2003] comprises anadditional 31 rivers.[12] The GEMS-GLORI data are very limited with regard

to particulate C, N and P. The database contains 26 riverswith data for both TSS and POC, 21 for both TSS and PP,only 12 with TSS and PN, and 5 river basins for which TSSand the three particulate compounds are reported together.We therefore also used data on PC, PN and PP for 80stations from United States Geological Survey (USGS)[USGS, 1996; Alexander et al., 1998].

2.3. Multiple Regression of TSSY

[13] We reproduced the models of Ludwig and Probst[1998] for different climate zones and tested these againstour more extensive set of TSSY data for a wider range ofriver basin areas. While the original set of about 60 riversgave the same results as Ludwig and Probst [1998], themodel did not perform well for the additional rivers.[14] We therefore started model development without a-

priori selection of factors and assessed the influence of arange of independent factors on ice coverage and river basinarea, and several factors related to climate, land use,lithology, soils, and relief (Table 1) for river basins. Wecollected the most recent data available to us, and alsoincluded data representing the same factor, but from differ-ent sources (see for example, relief and climate-relatedfactors). We analyzed the influence of these factors onthe TSSY using S-PLUS [Mathsoft, 2004]. The analysisincluded a number of steps to develop and validate astatistical model: (1) transformation of the TSSY data;(2) stepwise regression and identification of outliers toselect the important factors which explain the variancein the behavior of ln(TSSY) and obtain the best linearregression model; (3) cross-validation of the model toanalyze the robustness and the uncertainty of the modelpredictions; (4) validation of the model against new data;(5) predictions for global TSS river fluxes based on the fittedregression model and a Monte Carlo simulation to obtainconfidence intervals on the original scale of the global TSSriver flux. The five steps will be elaborated below.2.3.1. Transformation[15] Multiple linear regression requires the observations

to be normally distributed. We found that the naturallogarithm of the TSSY data yields a normal distribution.We therefore transformed the TSSY data and used ln(TSSY)throughout this paper.

Table 1. River-Basin Characteristics Included in the Regression

Analysis

Factor Unit

River basin id no dimensionTSS export ton km�2

Generala

Area covered by ice %Area covered by glaciers/land ice %River basin area km2

Maximum elevation within river basin m

Climatea

Dominant climate grouping based on AEZ climate groupDominant climate grouping based on Holdridge climate groupMean annual temperature �CAnnual runoff mmFournier runoff based on river-basin dischargeb mmFournier runoff based on grid runoffb mmFournier precipitationb mm d�1

Land Usec

Area arable land in extensive systems %Area arable land in intensive systems %Area grassland in extensive systems %Area grassland in intensive systems %Area of marginal and semi-natural grassland %Area total grass %Area wetland rice %Area arable land excluding rice %Area irrigated land %

Parent Materiald

Dominant lithology (eight classes) no dimensionMechanical erodibility index of parent material no dimension

Soil Conditionse

Mean soil silt content %Mean soil sand content %Mean soil clay content %Soil organic carbon content %Mean soil water holding capacity mmArea with texture class

(very fine, fine, medium, coarse, organic)texture class

Mean bulk density ton m�3

Relieff

Average slope based on Digital Elevation Map m km�1

Average slope (based on FAO) m km�1

Average slope (based on Global Agro-Ecological Zones) m km�1

Maximum slope m km�1

Fournier slope based on DEM (m km�1)g m km�1

Notes to Table 1:aData on river basin characteristics are from Fekete et al. [2002] and

Vorosmarty and Fekete [2000]; Agro-Ecological Zones (AEZ) climate dataare from de Pauw et al. [1996]; and GAEZ data Re from Food andAgriculture Organization/International Institute for Applied SystemsAnalysis (FAO/IIASA) [2000].

bThe ‘‘Fournier’’ expression of precipitation and runoff is calculated asthe sum of the square values for all months divided by the annual sum.These expressions provide a representation of the variability within a year(seasonal variation).

cLand cover data are from Bouwman et al. [2005]; irrigated areas arefrom Siebert and Doll [2001].

dLithology is from Amiotte Suchet et al. [2003], mechanical erodibility isfrom Probst [1992]. Classes for lithology: 1, sand/sandstone; 2, carbonaterock; 3, shales; 4, plutonic/metamorphic; 5, gabbros; 6, acid volcanic rock;7, basalt; 8, ice. Ice and gabbros do not occur in the data set of 124 riverbasins. Mechanical erodibility ranges from 1 to 40: 1, plutonic andmetamorphic rocks; 2, volcanic rocks; 4, consolidated sedimentary rocks;10, different rock types in folded zones; 32, nonconsolidated sedimentaryrocks; 40, recent alluvials.

eSoils data are from Batjes [1997, 2002].fSlope is based on DEM [FAO/IIASA, 2000; National Geophysical Data

Center, 2002].gFournier slope is based on grid cells and is calculated as the sum of the

squares for all grid cells divided by the sum of all grid cells within eachriver basin. This provides a representation of the relief and morphology ofthe river basin.


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2.3.2. Stepwise Regression[16] The selection of the relevant independent factors for

the multiple regression was carried out with the S-PLUSfunction step, which alternates between backward selection(with two possibilities for each step; that is, the model isacceptable, or otherwise the least significant factor isexcluded from the model) and forward selection (withoptions; that is, the model is acceptable or otherwise themost significant factor, which is not yet included in themodel, is added). The best model in the function step is thatwith the lowest Akaike’s Information Criterion (AIC). TheAIC consists of the log likelihood of the model plus apenalty for the number of factors included. The penalty isused to ensure only those factors are included in the modelfor which the likelihood decreases sufficiently to gainaccuracy.[17] After each run, the correlation between all indepen-

dent factors was checked as well as the presence of outliers.New outliers were omitted and the stepwise regression withthe same initial model was carried out again. If there were

no outliers, only one factor of a set of correlated factors wasleft in the initial set of independent factors. Models thusdeveloped have the following form:

E ln TSSYð Þ½ � ¼X

biXi; ð1Þ

where E[ln(TSSY)] is the expectation of the prediction onln-scale based on the independent factors Xi listed in Table 1and the estimated regression coefficients bi.2.3.3. Cross-Validation[18] To assess the robustness of the model we estimated

the regression coefficients of the model on the basis of arandomly selected subset of 75% of the data (training set)and made predictions on the remaining 25% (validation set).This step was repeated 100 times, and each time the 95%confidence interval of the predictions was determined.2.3.4. Validation Using Independent Data[19] A further check of the model involved validation

against data obtained from Syvitski et al. [2003]. The

Figure 2. (a) Coverage of river basins with TSSY data used in this study, distribution of (b) wetland riceand (c) marginal grassland, (d) distribution of the Fournier precipitation, (e) Fournier slope, and(f) lithology.


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predictions of the model were compared with the Syvitskydata in two ways: (1) The predictions of the model with thecorresponding 95% confidence interval are compared withthe Syvitski observations; and, (2) a Bland-Altman test[Bland and Altman, 1996] is used to compare the residuals(difference between predictions and observed TSSY) withthe mean values of each pair of observation and prediction.This serves to test possible trends in the residuals.2.3.5. Prediction[20] The final step consists of predicting the global TSS

river export with its 95% confidence interval. From equa-tion (1) we obtain the prediction on ln scale for each riverwith known Xi values. The uncertainty of the model caneasily be assessed since the estimated regression coeffi-cients are multinormal distributed with known mean andcovariance. Monte Carlo simulation enables us to draw5000 equally probable sets of b. Each set can be used topredict the ln(TSSY) based on the known Xi values. Byback-transforming these predictions and calculating the sumof the values in each set, we obtain 5000 estimates for thesum of the TSS flux, representing the complete distributionof the total sum. We use the mean and the 2.5 and 97.5percentiles of this distribution. Note that this mean is not anunderestimation of the real mean of the total sum, since weuse the predictions themselves and not the logarithm of themean.

2.4. POC, PN, and PP Fluxes

[21] Predicting reliable TSS fluxes is important for theflux prediction of many other compounds includingparticulate C, N and P. Particulate organic carbon andnitrogen are among the key elements that are well knownfor their close association with total suspended sediments inrivers. Ludwig and Probst [1996] studied the correlationbetween particulate organic carbon (POC) fluxes anddifferent controlling factors. They found that there is agood correlation between TSS flux and POC flux, althoughthe relationship is not linear. On the basis of a limitednumber of rivers (19) with data for both POC and TSS,Ludwig et al. [1996] found that the fraction of POC inthe suspended solids decreases with increasing TSS con-centration (TSSC),

POCc ¼� 0:160 log TSSCð Þ½ �3þ 2:83 log TSSCð Þ½ �2

� 13:6 log TSSCð Þ þ 20:3; ð2Þ

where POCc is the particulate organic carbon content as %of TSS, and TSSC is the average annual concentration inmg L�1 of TSS in the discharging water (calculated as(TSSY A)/Q, with A being the river basin area and Q thedischarge). The observation of decreasing POCc values withincreasing sediment concentrations is consistent with thegeneral idea of greater erosion rates in high turbid rivers,eroding the soils down to deeper horizons where the organicmatter content is low. Equation (2) is also consistent withdata obtained from Meybeck and Ragu [1995]. Organiccarbon is hence diluted by mineral matter. On the otherhand, high POCc can also be related to in-situ primaryproduction in rivers, which is only possible in rivers where

turbidity is low enough to allow the development of algae.We used equation (2) in combination with our estimates ofTSS load to estimate river POC fluxes.[22] Particulate organic nitrogen contents in world rivers

have been widely analyzed by Ittekkot and Zhang [1989].On the basis of a data set of 378 samples from rivers thatstretch over all turbidity ranges, they found that particulatenitrogen (PN) is closely coupled to POC according to thefollowing relationship:

PNc ¼ 0:166POC� 0:019; ð3Þ

where PNc is the content of particulate nitrogen as percentof TSS. Since nitrogen bearing minerals do not exist insoils, all PN is organic, although some adsorption ofdissolved inorganic nitrogen on sediment particles mayoccur (see below). Hence equation (3), in combination withequation (2), implies that POC:PN ratios (as C:N) rangebetween about 8.8 or less for low turbidity rivers (i.e., withTSSC of <20 mg L�1 and POCc values of >7) and >10.2for high turbidity rivers (i.e., with TSSC of >500 mg L�1

and POCc 1%) on a weight basis. We used equation (3) incombination with our estimates of TSS load to estimateriver PN fluxes.[23] C:N ratios tend to increase with TSS concentrations

up to TSSC values of 500 mg L�1, but decline in rivers withgreater TSSC [Ittekkot and Zhang, 1989]. In other studies,increasing C:N ratios with increasing TSSC have also beenreported. Balakrishna and Probst [2005], for example,report for the Godavari River and its major tributaries thatlow C:N ratios of 8 are typical for less turbid waters,whereas ratios of 10 are normally observed for moreturbid waters. One possible explanation for this behavioris the fact that primary production in the river can lowerC:N ratios since aquatic plants are much poorer in C thanterrestrial plants. When the waters are clear, a considerablepart of PN and POC may hence be derived from in situproduction in the river waters, whereas in turbid waters,almost all of these compounds originate from the erosion ofthe soil and vegetation pools.[24] Many of the rivers with average TSSC greater than

500 mg L�1 in the work by Ittekkot and Zhang [1989] arerivers from India and China, where organic waste pollutionrelated to high population densities can occur. A furtherreason for low C:N ratios is therefore that when the riverssuffer from organic pollution, low oxygen contents andrather high ammonium concentrations could establish,allowing the absorption of the latter on clay minerals, whichmay also lower the C:N ratios [Ittekkot and Zhang, 1989].Nevertheless, the consistently close coupling of POC andPN to TSS in world rivers suggests a general dominantcontrol of erosion processes compared to pollution sourcesfor the overall material transfer to the oceans.[25] For the prediction of the riverine transport of partic-

ulate phosphorous (PP), however, there is no modelingapproach available in the literature. In contrast to PN, Pbearing minerals exist in soils, which means that themineralogical composition of the eroded rocks may havean influence on the PP levels in rivers. Moreover, the


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absorption/desorption of dissolved inorganic phosphorous(DIP) on riverine particles such as hydroxide minerals is awell-known phenomenon, and PP fluxes may therefore alsobe strongly influenced by point sources, which exert animportant control on DIP fluxes [Harrison et al., 2005].Furthermore, application of P fertilizers and animal manuremay lead to elevated P contents of soil material eroded fromagricultural fields.[26] We analyzed reported values for POC and PP dis-

charge for 15 rivers from Meybeck and Ragu [1995] and 80stations from USGS [1996]. We log-transformed the data toattain approximate normal distribution of the data. Thecombined data indicated that there is a close relationship(R2 = 0.96) between reported POC and PP load (kg yr�1)values,

ln PPð Þ ¼ �3:098þ 1:002 ln POCð Þ: ð4Þ

[27] The Meybeck and Ragu [1995] rivers are larger thanthose in the USGS data and have POC values in the higherend of the range (Figure 1). The relationship (R2 = 0.85) forthe USGS stations and the GEMS-GLORI rivers (R2 = 0.84)alone is very similar to equation (4), indicating that therelationship is not different for rivers with low and thosewith high POC loads (Figure 1). Equation (4) indicates thatthe overall POC:P ratio is about 22, which is consistent withthe estimated POC:PP ratio of 22 of Meybeck [1982]. Wetherefore used equation (4) in combination with our esti-

mates of TSS load (equation (1)) to estimate global river PPfluxes.

3. Results and Discussion

3.1. Modeling TSSY Using Multiple Linear Regression

[28] The NEWS-PNU model for describing TSSYobtained with stepwise multiple linear regression was basedon the full set of TSSY data, but two rivers were consistentoutliers, i.e. the Colorado (California) and Cauweri (India)(Appendix A). These two rivers were excluded from themodel development and all further steps.[29] The stepwise regression approach taken for the

development of NEWS-PNU resulted in the selection offive factors with a significant influence on ln(TSSY). Thesefactors include the extent of marginal grassland and wetlandrice, Fournier precipitation, Fournier slope and lithology(Table 2). The multiple regression coefficient (r2) for thismodel is 0.60.[30] The b values of the model factors indicate a strong

influence (but relatively uncertain) of lithology, which ismodified by Fournier precipitation, occurrence of wetlandrice and Fournier slope (Table 2). The extent of marginalgrassland exerts less influence on TSSY than the otherfactors. The correlation coefficients for these modelfactors (Table 3) indicate that these factors are not stronglyintercorrelated.[31] Before testing the robustness of this model we assess

differences and agreements with earlier model approaches.In contrast to Hovius [1998] and Syvitski et al. [2003] wedid not find temperature to be a significant factor inexplaining TSSY. A further major difference between ourresults and those of earlier studies [Milliman and Meade,1983; Milliman and Syvitski, 1992; Ludwig and Probst,1996; Hovius, 1998; Ludwig and Probst, 1998] is theinclusion of anthropogenic factors in our model, i.e., mar-ginal grasslands and wetland rice cultivation. Grasslands,particularly marginal ones which occur mainly in Asia,Africa and Australia (Figure 2), are degraded in many partsof the world and strongly affected by rainfall erosion[Bruinsma, 2003]. Sediments from these areas may resultin high sediment loads in many river basins.[32] Wetland rice is generally one of the crops in rotations

with, for example wheat, within intensive irrigated agricul-tural regions in Asia (Figure 2), and thus represents thedistribution of intensive cropping areas. In wetland ricesystems a dense subsoil to minimize water percolation lossis obtained by wet tillage or puddling [Doorenbos and

Table 2. Factors and Coefficients of the Model Obtained With

Stepwise Regressiona

Factor b Standard Error t Pr (>jtj)Intercept 0.55 0.35 1.56 0.12Marginal grassland 0.03 0.01 4.14 0.00Wetland rice 0.12 0.03 4.34 0.00Fournier precipitation 0.29 0.05 5.52 0.00Fournier slope 0.10 0.01 8.13 0.00Lith. (carbonate) 1.04 0.36 2.85 0.01Lith. (shales) 0.90 0.30 2.97 0.00Lith. (shield) 0.31 0.34 0.92 0.36Lith. (acid volcanic) �2.07 0.90 �2.30 0.02Lith. (basalt) 0.67 0.66 1.02 0.31

aLith. denotes lithology. Ice and gabbros did not occur within the 124river basins in the TSS data set. All lithology classes were related to sand/sandstone, with a coefficient b of 0. Shield rocks include intrusive andmetamorphic acid rocks composing shield regions [Amiotte Suchet et al.,2003].

Table 3. Correlation Between the Model Parameters of the Full Model Based on 124 River Basin

Factor InterceptMarginalGrassland

WetlandRice

FournierPrecipitation

FournierSlope

Lithology(Carbonate)

Lithology(Shales)

Lithology(Shield)

Lithology(Acid Volcanic)

Marginal grassland �0.459Wetland rice �0.054 0.069Fournier precipitation �0.404 0.086 �0.332Fournier slope �0.464 0.144 0.055 �0.123Lithology (carbonate) �0.54 0.26 0.046 0.028 0.045Lithology (shales) �0.627 0.244 0.116 0.029 0.044 0.543Lithology (shield) �0.538 0.266 0.107 �0.074 0.055 0.497 0.582Lithology (acid volcanic) �0.126 0.074 0.048 0.043 �0.201 0.18 0.215 0.191Lithology (basalt) �0.242 0.149 �0.307 �0.139 0.178 0.246 0.258 0.268 0.042


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Kassam, 1986]. The pressure exerted by human feet,animals or machines during tillage, transplanting and weed-ing causes a compacted subsurface layer (plow pan or trafficpan) between 10 and 40 cm depth. Puddled soil materialmay be susceptible to transport with flood waters, particu-larly in areas with rainfed wetland rice systems with limitedwater control.[33] The extent of upland crops did not come out as a

significant factor. Therefore cultivation appears to influenceTSS export only in regions with widespread rice farming,i.e., in tropical Asia. In North and South America, Europeand northern Asia the influence of hydrology, geology andrelief are more dominant than the extent of agriculture inour data. This is in agreement with the findings of earlierstudies which only found physical factors to be significant,i.e., Fournier precipitation, Fournier slope and the lithology.The intensity of precipitation, as represented by Fournierprecipitation, is known to have a major influence on soilloss and was also included as a factor in models describingsoil erosion loss [e.g., Wischmeier and Smith, 1978].[34] Our finding that the Fournier slope has a significant

influence on the ln(TSSY) agrees with other studies. TheFournier slope and mean river basin slope, maximum heightor other relief indicators [Milliman and Meade, 1983;Milliman and Syvitski, 1992; Ludwig and Probst, 1996;Hovius, 1998; Ludwig and Probst, 1998] are all expressionsof the river-basin scale relief. Finally, as with NEWS-PNU,the lithology, an expression of the erodibility of the parentmaterial and the soils developed from it, was also found tobe an important factor in other studies [Hovius, 1998;Ludwig and Probst, 1998].[35] To assess the robustness a randomly selected training

set containing 75% of the data (93 rivers) was used for

Figure 3. Three examples of the model with coefficientsdetermined on the basis of randomly selected training setsof 93 rivers and validation against the remaining 31 rivers inthe data set. Open circles are the training set, solid squaresare the validation set, and solid diamonds are the twosystematic outliers (Colorado, California; Cauweri, India).Other data points outside the confidence intervals are notsystematic outliers, as can be seen by comparing the threeplots. For each prediction on the horizontal axis the 95%confidence interval is presented. This interval is wider thanthat for the expected line, because it also accounts for noisein the data.

Table 4. Minimum and Maximum Values of Coefficients

Obtained With 5000 Simulations and the Value for the Full Modela

Factor Minimum Model Maximum

Intercept 0.09 0.55 1.00Marginal grassland 0.01 0.03 0.04Wetland rice 0.08 0.12 0.18Fournier precipitation 0.22 0.29 0.35Fournier slope 0.08 0.10 0.12Lithology (carbonate) 0.39 1.04 1.56Lithology (shales) 0.53 0.90 1.39Lithology (shield) �0.11 0.31 0.68Lithology (acid volcanic) �2.69 �2.07 �1.19Lithology (basalt) �0.20 0.67 1.48

aSee Table 2.


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estimating the b values for the five model factors selectedabove. Predictions using these b values were made for theremaining 25% of the rivers (31). This exercise wasrepeated 100 times, resulting in plots as shown inFigure 3. The model seems to be robust since only two

rivers were systematic outliers and therefore excluded fromthe model.[36] The minimum and maximum values of each estimated

b are not fixed but change for random selections of91 rivers. The ranges for the coefficients obtained with the100 selections of rivers are generally within a factor of 2 to3 around the original model value (Table 4).[37] Finally the model was validated against data for the

31 ‘‘new’’ rivers in the data set obtained from Syvitski et al.[2003]. First the ln(TSSY) from the Syvistki data set werecompared with the corresponding predictions from the fullmodel with their 95% confidence intervals. The results aresimilar to those presented in Figure 3. Only two predictionswere outside the 95% confidence interval.[38] A second approach for testing the model with the

31 rivers from Syvitski et al. [2003] is the Bland-Altmantest [Bland and Altman, 1996], which involves the compar-ison of the residuals (difference between observation andprediction) with the mean of the predicted and observedvalues (Figure 4). The results of this test show that thepredictions agree with TSSY values, because the line y = 0is clearly within the 95% confidence interval of the fittedregression line, and because of the high p-value (0.37). Onlyone prediction (too high) is outside the 95% confidenceinterval for new observations corresponding to individualdifferences.[39] We illustrate the patterns of TSSY for the model

applied to grid cells (Figure 5). The results show high TSScontributions from mountainous regions and are in agree-ment with the results of Ludwig and Probst [1998]. Forexample, the model confirms that the large sediment loadscarried to the Atlantic Ocean by major rivers of SouthAmerica largely stem from the tectonic regions of the Andes[Meade, 1996]. Figure 5 also shows that a large part of the

Figure 4. Comparison of the difference between predictedand observed TSSY with the mean of predicted andobserved values according to Bland and Altman [1996].Note that the axes have a logarithmic scale. The distancebetween the 95% confidence intervals is about 6, which isequal to a factor of 400.

Figure 5. TSSY values for the model applied to the factor values for the extent of marginal grasslandand wetland rice, Fournier precipitation, Fournier slope, and lithology for individual 0.5 by 0.5 degreegrid cells. See color version of this figure at back of this issue.


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river sediment that reaches the world’s coastline is deliveredby large rivers whose headlines rise in the tectonic collisionzone between the Asian continent and the Indian subconti-nent [Milliman and Meade, 1983]. The high estimates forNorth American arctic rivers are probably the result ofapplication of the model outside of the validity range forFournier precipitation and or slope. Similar effects are seenin northern Scandinavia.[40] We have made no attempt to calibrate these grid-scale

values with the available measurement data for severalreasons. The most important reason is that our main interestis to generate estimates of regional or continental rivertransport of TSS and particulate compounds, and not todescribe within-basin processes. Furthermore, the model isdeveloped on the basis of TSS values at the river mouth,representing the climatic, hydrological, geological and landcover characteristics for the whole river basin. Processes oferosion, transport and resedimentation (and also trapping inreservoirs) are spatially separated, and a lumped river-basinmodel is too limited to describe TSS contributions fromindividual grid cells at the river mouth. In addition, sedi-ment is stored in large river systems at different timescales,and the time lags between erosion and sediment transportare such that the sediments carried by large rivers today mayrepresent episodes of erosion that occurred decades, centu-ries of even millennia ago [Meade, 1996].

3.2. TSS Fluxes to Global Oceans

[41] We can now use the full NEWS-PNU model topredict TSSY (i.e., the back-transformed value of TSSYtimes the river basin area for the river considered) for the124 rivers included in the TSSY data set. This results in anestimate of 7.5 Pg yr�1 (Pg, petagram; 1 Pg = 1015 g) with a95% confidence interval of 5.9 to 13.7 (Table 5). The

estimated value of 7.5 Pg yr�1 is just slightly higher thanthe total observed TSSY of 7.0 Pg yr�1 for the same rivers(Appendix A). Together these 124 river basins cover71 Mkm2 (Table 5 and Appendix A) which is about 54%of the global land area (Figure 2).[42] If we apply the model to all the river basins for which

the factors lie within the range of the river basins that areincluded in the TSSY data set (Table 6), we obtain anestimate of 12.9 Pg yr�1 for the global river TSS export(95% confidence interval 8.7, 18.9). This represents 3109river basins out of a total of 5629 distinguished by Fekete etal. [2002] covering an area of 108 Mkm2 (Table 5), or 82%of the global land area (Figure 6).[43] Hence one or more of the model factors for river

basins covering 18% of the global land area are outside therange for these factors for the rivers that were used todevelop the model (Table 6). This includes 2520 riversoutside the range, with 1623 rivers for which the value ofone factor falls below the lower bound of the validity range,1230 rivers exceeding the upper bound of one factor, and333 rivers with values for one factor falling below the lowerbound and exceeding the upper bound for another factor.[44] If we cut off the values of the coefficients for these

river basins to the minimum and maximum values of themodel (Table 6) we obtain an additional load of 8.5 Pg yr�1.Using the global average for these rivers yields an addi-tional TSS export of 2.6 Pg yr�1. In the absence of data forthese rivers outside the range of the model, we thereforeassumed a range of 2.6 to 8.5 Pg yr�1, recognizing that thisis highly uncertain. The global TSS export estimated thus is11 to 27 Pg yr�1 with a mean of 19 Pg yr�1 (Table 7).[45] Our global estimates are in fair agreement with those

of Ludwig and Probst [1998] (16 Pg yr�1) and Probst[1992] (23–27 Pg yr�1). Asia is by far the largest contrib-

Table 5. Area Covered, TSS River Export From the Model, and Mean, 2.5 and 97.5 Percentiles Obtained With 5000 Monte Carlo

Simulations For the 124 Rivers Included in the TSS Data Set, and For All 3109 Rivers For Which the Factors Are Within the Range of the

TSS Data Set

Number of River Basins Area Covered, Mkm2 Predicted TSS Export, Pg yr�1

Monte Carlo, Pg yr�1

Mean 2.5 Percentile 97.5 Percentile

124 river basins included in the TSS data set 71 7.0a 7.5 5.0 11.13109 river basins within range 108 11.9 12.9 8.7 18.9

aValue is based on TSS observations in the data set.

Table 6. Minimum and Maximum Values of Factors in the TSS Data Set of Observations (124), Minimum and

Maximum for All Global River Basins, and the Number of River Basins Outside the Range of Values For Rivers

Included in the TSS Data Set

Factor Unit

Range ofValues for

Rivers Includedin the TSS Data Set

Range of Values forAll River Basinsa

Number of RiverBasins Outsidethe Range in theTSS Data SetMin. Max. Min. Max.

Marginal grassland % 0 85 0 100 125Wetland rice % 0 25 0 86 72Fournier precipitation mm d�1 1 11 0 29 1536Fournier slope m km�1 1 38 0 319 762Lithologyb . . . n.a. n.a. n.a. n.a. 25

aThis comprises 5629 rivers within the 0.5 by 0.5 degree river network of Fekete et al. [2002].bThis comprises 25 basins with ice coverage according to Amiotte Suchet et al. [2003]; n.a., not applicable.


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utor (>50%) with an estimated river export of 12 Pg yr�1

(Table 7), which confirms earlier estimates [Ludwig andProbst, 1998]. Most of the TSS exported by global riversends in the Pacific (42%), Indian (28%) and Atlantic Oceans(19%) (Table 7).[46] These TSS fluxes represent the ‘‘natural’’ situation,

i.e., not accounting for the trapping of sediment in basinsregulated by dams and reservoirs. Combining our meanestimate with the estimated sediment trapping in reservoirspresented by Vorosmarty et al. [2003] yields a globalanthropogenic reduction of the TSS flux of 13% (Table 7)with large differences between regions. However, sedimenttrapping in river basins such as that of the Nile and

Colorado is almost complete due to reservoir constructionand flow diversion [Vorosmarty et al., 2003].

3.3. POC, PN, and PP Fluxes to Global Oceans

[47] On the basis of equations (1)–(4) we computed theannual input of POC, PN and PP to the coastal oceans. Onthe global scale, our estimated annual flux of POC is 226 Tgyr�1 (Tg, teragram; 1 Tg = 1012g) (Table 7) which compareswell to the estimate of 210 Tg yr�1 of Ludwig et al. [1996]and 180 Tg yr�1 of Meybeck [1982]. When we account forsediment trapping our estimate for the POC flux is reducedto 197 Tg yr�1 (Table 7). A large fraction of the globalPOC input to coastal seas occurs in Asia (43% when

Figure 6. Modeled TSSY for 3109 river basins with river-basin mean factor values for the extent ofmarginal grassland and wetland rice, Fournier precipitation, Fournier slope, and lithology within thevalidity range of the model. See color version of this figure at back of this issue.

Table 7. Predicted TSS, POC, PN and PP Export to the World’s Oceans Aggregated for Continents and Receiving Oceans Without and

With Sediment Trapping in Regulated Basins

Continent/Ocean

Without TrappingTrapping

With Trapping

TSS, Pg yr�1 POCa PNa PPa Efficiency, % TSS, Pg yr�1 POCa PNa PPa

North America 2 24 3.5 1.1 18 2 20 3.0 0.9South America 2 50 7.9 2.3 22 1 40 6.3 1.8Europe 1 9 1.3 0.4 19 1 7 1.1 0.3Africa 2 30 4.5 1.4 18 2 24 3.6 1.1Asia northb 1 10 1.6 0.5 24 0 9 1.3 0.4Asia south 11 98 14.2 4.5 8 10 92 13.3 4.2Oceania 1 6 0.9 0.3 10 0 6 0.8 0.3World 19 226 33.9 10.3 13 17 197 29.6 9.0Arctic Ocean 0 8 1.3 0.4 4 0 8 1.2 0.4Atlantic Ocean 4 70 10.9 3.2 18 3 56 8.7 2.6Indian Ocean 5 43 6.1 2.0 8 5 39 5.6 1.8Mediterranean/Black seas 1 7 1.1 0.3 29 1 4 0.6 0.2Pacific Ocean 8 80 11.8 3.7 11 7 74 10.9 3.4Endoreic 1 18 2.7 0.8 17 1 16 2.6 0.8

aC, N, P are in Tg yr�1.bAsia north is represented by the former Soviet Union.


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excluding the former Soviet Union) and South America(22%) (Figure 7a).[48] Our estimated global annual PN flux is 34 Tg yr�1

of N (Table 7, which exceeds the estimate of 21 Tg yr�1 ofMeybeck [1982]. However, our estimate is similar to that ofSeitzinger et al. [2002] who estimated a global PN flux

of 30 Tg yr�1 of N for the pre-dam situation in all river basins.Similar to POC, most PN inputs to coastal seas are in Asia(41%, when excluding the former Soviet Union) and SouthAmerica (23%). When we correct for sediment trapping,the global estimate for PN is reduced by 4 Tg yr�1 to30 Tg yr�1 of N (Table 7 and Figure 7b). The estimate of

Figure 7. Modeled export of (a) POC, (b) PN, and (c) PP from river basins based on results presented inFigure 6 combined with equations (2)–(4). See color version of this figure at back of this issue.


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Seitzinger et al. [2002] for PN export with trapping is23 Tg yr�1 of N, but this was based on the assumptionthat sediment trapping can be estimated from storage andaging of runoff in large reservoir systems from Vorosmartyet al. [1997]. However, here we use a more recent study onactual sediment trapping [Vorosmarty et al., 2003].[49] Our estimate for PP is 10 Tg yr�1 of P. When

accounting for trapping in river basins, the global PP fluxis reduced to 9 Tg yr�1 (Table 7 and Figure 7c). Meybeck[1982] reported values for global river export of particulateorganic P (8 Tg P yr�1) and total particulate P (20 Tg yr�1).Given the uncertainty in estimates of PP, this difference isregarded acceptable for the scale of this study.[50] Most PP is exported by rivers in Asia South (�45%

of global export), South America (20%) and North America(10%) (Table 7 and Figure 7c). It is clear that within theseworld regions and continents there is considerable variation(Figure 7). For example, PP exported by the Amazon

contributes 43% of the total river PP export from SouthAmerica, the Zaire contributes 21% of the total PP exportfrom Africa, and the Ganges contributes 11% to the total PPexport for Asia South (Figure 7).[51] Most particulate compounds are transported to the

Pacific (37%), Atlantic (28–29%) and Indian (20%) Oceans(Table 7 and Figure 7). However, while the Indian Ocean(29% of global sediment export) receives more sedimentthan the Atlantic Ocean (18%), the latter receives morePOC, PN and PP. This is the result of the relationshipbetween POC and TSS (equation (2)) where POC increaseswith decreasing TSS concentration in river water.

4. Uncertainties and Future Directions

[52] NEWS-PNU explains �60% of the variability in log-transformed TSSY, leaving 40% of the variability stillunexplained. Regarding the uncertainty of the NEWS-PNU model we see that predictions for TSSY (ton km�2

yr�1) are within a factor of 4 of the observations for 80% ofthe rivers in the data set used (124) (Figure 8). By the use ofMonte Carlo simulations to obtain a range of predictions,and not just one, we have tried to account for modeluncertainty. The NEWS-PNU model prediction for the totalTSS flux for 3107 rivers is 12.9 Pg yr�1. The 97.5% upperbound is 18.9 Pg yr�1, which is only a factor of 1.5 of theprediction. A number of factors may be responsible for theuncertainty, including the TSSY data and the ancillary data.Identifying these factors should be the focus of future TSSYmodeling efforts.[53] Considerable uncertainty is associated with existing

TSSY data, as discussed in detail elsewhere [Milliman andSyvitski, 1992; Nittrouer et al., 1995;Meade, 1996; Nixon etal., 1996; Ludwig and Probst, 1998; Goodbred and Kuehl,1999; Syvitski et al., 2003]. The main sources of uncertaintyare: (1) inconsistent measurement techniques; (2) insuffi-cient sampling frequency; and (3) inland location of mostseaward sampling stations. Future improvements in riverTSS and particulate nutrient export modeling would begreatly aided by improvements in sampling procedures thataddress these issues.[54] Syvitski et al. [2003] concluded that their model

grossly underestimated TSS fluxes for small river basinswhere anthropogenic impacts are magnified. Their modelalso grossly overestimated TSS flux from river basins withmajor sediment retention by lakes and reservoirs. Unfortu-nately, none of the other studies [Hovius, 1998; Ludwig andProbst, 1998] include an uncertainty analysis which makescomparison of model uncertainty a difficult task. Since weconcentrated on the larger river basins in the different datasets (river-basin area >20,000 km2) we have avoided thefirst problem of small-sized river basins as much as possi-ble. In our analysis we concentrated on data for rivers withreported ‘‘natural’’ TSS fluxes. Syvitski et al. [2003] madeclear that there are many river basins with where reported‘natural’ TSS fluxes that are in actual fact impacted bysediment retention in reservoirs, and this is probably a majorsource of errors in our analysis too. Hence, although theNEWS-PNU model is robust with respect to the availabledata, there is still considerable uncertainty in the predictedvalues.

Figure 8. (a) Differences between predictions and ob-servations for TSSY for the data set of 124 river basinsplotted against the average of predicted and observed TSSY[Bland and Altman, 1996]; and (b) fraction of rivers withobservations plotted against the ratio prediction: observation(relative error), excluding seven rivers with ratios ofprediction:observation of >10 or <0.1.


12 of 17

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Table

A1.Dataforthe126RiversWithReported

NaturalTSSY

Usedin

Model

Development

Nam

eBasin

Area,

1000km

2

Reported

TSSY,

tonkm

�2yr�

1

Sedim

ent

Trapping

Efficiency

Fournier

Precipitation,

mm

d�1

Fournier

Slope,

mkm

�1

Lithology

Class

aMarginal

Grass,%

WetlandRice,

%

Modeled

Export,

tonkm

�2yr�

1

Sourceb

POC

PN

PP

Amazon

5854

206

0.00

7.3

73

20

2.9

0.5

0.1

1Nile

3826

31

0.92

2.9

14

415

00.6

0.1

0.0

1Zaire

3699

12

0.00

5.6

111

30

1.4

0.2

0.1

1Mississippi

3203

156

0.06

2.5

73

10

0.4

0.1

0.0

1Amur

2903

18

0.15

2.3

10

13

00.2

0.0

0.0

2Parana

2661

30

0.59

4.3

83

11

01.0

0.2

0.0

1Yenisei

2582

50.26

1.8

114

00

0.4

0.1

0.0

1Ob

2570

60.03

1.5

53

12

00.3

0.1

0.0

1Lena

2418

70.01

1.5

12

40

00.3

0.0

0.0

1Niger

2240

18

0.38

4.0

10

18

00.6

0.1

0.0

1Zam

bezi

1989

10

0.76

4.8

12

132

00.8

0.1

0.0

1ChangJiang

1794

268

0.66

4.0

17

25

61.6

0.2

0.1

1Mackenzie

1713

24

0.12

1.3

14

30

00.3

0.0

0.0

1Ganges

1628

320

0.01

7.7

19

30

12

6.4

0.8

0.3

1Chari

1572

20.01

3.4

81

18

00.4

0.1

0.0

3Volga

1463

18

0.93

1.6

33

10

00.2

0.0

0.0

2St.Law

rence

1267

30.80

2.6

64

00

0.7

0.1

0.0

1Indus

1143

219

0.57

1.9

20

10

30.3

0.0

0.0

1Syr-Darya

1070

11

0.57

0.9

111

68

00.3

0.0

0.0

2Orinoco

1039

145

0.83

8.6

12

30

05.1

0.8

0.2

1Murray

1032

29

0.73

1.4

43

41

00.2

0.0

0.0

1Shattel

Arab

967

109

0.78

1.2

12

28

00.4

0.1

0.0

3Orange

944

94

0.96

1.3

14

138

00.6

0.1

0.0

1HuangHe

894

1231

0.95

2.2

16

419

30.3

0.0

0.0

1Yukon

852

70

0.00

1.2

20

30

00.4

0.1

0.0

1Senegal

847

20.43

2.8

51

47

00.4

0.1

0.0

1Colorado(A

ri)

808

149

0.51

1.0

27

66

00.0

0.0

0.0

1Rio

Grande(U

S)

805

25

0.99

1.5

19

225

00.0

0.0

0.0

1Danube

788

86

0.49

2.4

18

33

01.0

0.1

0.0

1Mekong

774

194

0.14

6.8

12

32

12

3.7

0.5

0.2

1Tocantins

769

98

0.85

7.6

64

30

03.5

0.6

0.2

1Columbia

724

21

0.67

2.0

33

66

00.5

0.1

0.0

1Kolyma

666

24

0.00

1.0

15

10

00.3

0.1

0.0

1Sao

Francisco

615

10

0.92

4.7

94

71

0.9

0.1

0.0

1Amu-D

arya

612

153

0.34

1.4

21

138

00.6

0.1

0.0

3Dnepr

509

50.93

1.7

23

50

0.2

0.0

0.0

1Don

423

50.00

1.4

23

10

0.1

0.0

0.0

1Lim

popo

420

79

0.00

2.2

15

434

00.0

0.0

0.0

2Zhujiang

409

169

0.38

5.6

112

06

1.9

0.3

0.1

1Irrawaddy

406

642

0.00

9.8

23

10

96.7

0.9

0.3

1Volta

398

48

0.97

5.2

94

00

0.8

0.1

0.0

1Khatanga

371

50.01

1.1

10

20

00.7

0.1

0.0

1Dvina

367

10

0.03

1.8

33

00

0.4

0.1

0.0

1Uruguay

356

31

0.82

4.1

47

00

0.7

0.1

0.0

2Indigirka

324

43

0.00

1.1

18

10

00.4

0.1

0.0

1Godavari

312

547

0.16

7.0

57

015

1.6

0.2

0.1

1Pechora

302

21

0.00

1.7

63

00

0.5

0.1

0.0

1Ural

296

10

0.22

0.9

33

53

00.2

0.0

0.0

2


13 of 17

GB4S05

Table

A1.(continued)

Nam

eBasin

Area,

1000km

2

Reported

TSSY,

tonkm

�2yr�

1

Sedim

ent

Trapping

Efficiency

Fournier

Precipitation,

mm

d�1

Fournier

Slope,

mkm

�1

Lithology

Class

aMarginal

Grass,%

WetlandRice,

%

Modeled

Export,

tonkm

�2yr�

1

Sourceb

POC

PN

PP

Neva

285

30.64

1.9

44

10

0.3

0.1

0.0

1Liao

274

149

0.40

3.2

10

129

00.1

0.0

0.0

2Salween

273

366

0.00

4.9

23

23

22.3

0.3

0.1

2Magdalena

252

878

0.20

6.4

33

10

14.2

0.6

0.2

1Krishna

252

255

0.90

4.5

54

015

0.4

0.1

0.0

1Fraser

245

81

0.86

2.3

38

30

02.2

0.3

0.1

1Hai

Ho

245

2687

0.00

3.1

12

151

80.0

0.0

0.0

1Huai

244

57

0.12

4.0

51

025

1.4

0.2

0.1

1Yana

235

15

0.00

1.2

17

10

00.3

0.0

0.0

1Kura

219

164

0.46

1.8

35

26

01.7

0.2

0.1

2Olenek

212

50.00

1.1

82

00

0.6

0.1

0.0

1Negro

(Arg)

198

68

0.96

1.0

13

120

00.3

0.0

0.0

1Sacramento

193

12

0.64

2.9

32

36

11.8

0.2

0.1

1GrandedeSantiago

192

50.93

3.5

24

63

00.1

0.0

0.0

2Rufiji

187

91

0.00

5.7

25

40

01.3

0.2

0.1

1Wisla

181

14

0.00

2.0

53

00

0.3

0.0

0.0

1Hong

171

763

0.00

6.2

19

20

83.1

0.4

0.1

1Rhine

165

21

0.00

2.6

17

10

00.9

0.1

0.0

1Essequibo

151

30

0.00

7.3

87

00

2.4

0.4

0.1

1Elbe

149

60.00

1.8

81

00

0.3

0.0

0.0

1Chao

Phraya

142

78

0.26

5.7

12

10

91.6

0.2

0.1

1Sanaga

129

46

0.17

7.0

22

40

05.1

0.8

0.2

2Brazos(Tex)

125

256

0.00

2.2

32

20

0.1

0.0

0.0

1Burdekin

121

25

0.00

2.6

43

40

00.0

0.0

0.0

3Colorado(Tex)

121

107

0.00

1.9

42

22

00.0

0.0

0.0

2Odra

120

10.00

1.9

41

00

0.2

0.0

0.0

1Loire

118

40.00

2.2

94

00

0.4

0.1

0.0

3Kuskowin

116

64

0.00

1.4

15

30

00.7

0.1

0.0

2Narmada

114

1098

0.90

8.3

67

019

3.7

0.5

0.2

1KizilIrmak

110

209

0.37

1.5

23

38

00.4

0.1

0.0

2Po

102

147

0.00

3.4

38

30

24.6

0.6

0.2

1Rhone

99

311

0.09

3.0

35

20

03.2

0.4

0.1

1Tana(K

en)

99

325

0.42

4.1

22

19

00.8

0.1

0.0

1Nem

anus

95

70.00

1.9

33

20

0.4

0.1

0.0

1Anabar

94

40.00

1.0

72

00

0.6

0.1

0.0

1Daugava

83

60.00

1.9

31

80

0.3

0.0

0.0

1Ebro

83

217

0.76

1.9

21

30

10.5

0.1

0.0

1Cauweri

79

10.62

5.4

94

012

1.6

0.2

0.1

3Sepik

77

1043

0.00

8.5

20

30

06.8

1.1

0.3

2Seine

73

15

0.00

2.0

62

00

0.5

0.1

0.0

2Tejo

73

30.23

2.2

17

40

00.3

0.1

0.0

3Gam

bia

72

30.00

7.2

91

00

1.0

0.2

0.0

1Susquehanna

72

25

0.00

2.6

93

00

0.8

0.1

0.0

1Dnestr

72

42

0.85

1.8

63

00

0.2

0.0

0.0

1Mahakam

71

170

0.00

8.8

12

30

06.0

1.0

0.3

2Bug

69

70.00

1.6

33

00

0.1

0.0

0.0

1FuchunJiang

67

98

0.00

4.7

12

20

93.2

0.5

0.1

1Copper

67

1035

0.00

2.1

36

30

02.4

0.3

0.1

2


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Table

A1.(continued)

Nam

eBasin

Area,

1000km

2

Reported

TSSY,

tonkm

�2yr�

1

Sedim

ent

Trapping

Efficiency

Fournier

Precipitation,

mm

d�1

Fournier

Slope,

mkm

�1

Lithology

Class

aMarginal

Grass,%

WetlandRice,

%

Modeled

Export,

tonkm

�2yr�

1

Sourceb

POC

PN

PP

Tapti

66

373

0.78

6.6

67

025

6.0

0.8

0.3

3Mezen

65

14

0.00

1.7

33

00

0.5

0.1

0.0

1Kuban

64

120

0.02

2.5

14

30

11.0

0.2

0.0

1Colville

63

94

0.00

0.9

14

30

00.5

0.1

0.0

2Fly

61

1881

0.00

11.2

15

20

010.2

1.6

0.5

1Dam

odar

60

470

0.22

7.8

33

05

2.3

0.4

0.1

1Onega

59

50.02

1.9

42

30

0.6

0.1

0.0

1Ord

59

374

0.00

3.9

63

85

08.4

1.3

0.4

2Garonne

58

38

0.00

2.5

19

30

00.8

0.1

0.0

1Sakarya

57

154

0.43

1.7

23

37

00.4

0.1

0.0

1Appalachicola

55

30.72

4.1

34

00

0.5

0.1

0.0

2Nadym

53

70.00

1.6

13

00

0.5

0.1

0.0

1Kem

ijoki

50

30.32

1.5

74

00

0.3

0.1

0.0

2Glama

47

321

0.00

2.2

30

40

01.5

0.2

0.1

1Luan

46

430

0.00

3.0

11

171

10.0

0.0

0.0

1Weser

46

70.00

2.0

72

00

0.7

0.1

0.0

1Yesil

45

424

0.53

1.7

31

34

11.8

0.3

0.1

1Meuse

43

16

0.00

2.5

63

00

1.0

0.2

0.0

1Hudson

43

23

0.25

2.7

13

40

01.0

0.2

0.0

1Altam

aha

42

60

0.00

3.6

24

00

0.2

0.0

0.0

1Santee

41

25

0.89

3.5

84

00

0.6

0.1

0.0

1Potomac

38

19

0.00

2.7

16

40

00.7

0.1

0.0

1Purari

34

2379

0.00

8.7

26

40

06.7

1.0

0.3

1Pee

Dee

28

14

0.00

3.2

64

00

0.5

0.1

0.0

1Hanjiang

25

395

0.00

6.2

11

40

82.6

0.4

0.1

1Amguem

a23

22

0.00

1.3

19

30

01.2

0.2

0.1

1Schelde

22

46

0.79

2.2

32

00

0.6

0.1

0.0

1Delaw

are

21

32

0.00

2.8

72

00

0.8

0.1

0.0

1Nag

Dong

20

500

0.10

5.8

18

40

82.6

0.4

0.1

1Drammenselva

20

92

0.00

2.5

30

40

02.0

0.3

0.1

1

aClasses

forlithologyare:

1,sand/sandstone;

2,carbonaterock;3,shales;4,plutonic/m

etam

orphic;5,gabbros;6,acid

volcanic

rock;7,basalt;and8,ice.

bSources

ofreported

TSSY:1,GEMS-G

LORIdata[M

eybeckandRagu,1995];2,Hovius;3,GEMS[U

NEP,2001].


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[55] While the TSSY data used to develop the NEWS-PNU were sparse, the database for the particulate nutrientsis even more limited. The uncertainty in the data for theparticulate compounds is probably larger than for TSSbecause the particulate compounds are often not measureddirectly, but calculated from the difference of the totalconcentration and dissolved C, N or P. Future modelingefforts would be greatly aided by additional, high-qualityparticulate nutrient measurements along with TSS. In addi-tion, future efforts to model PP in particular would also beaided somewhat by improved record keeping regardingwhether PP measurements include both organic and inor-ganic P or just organic P as this is sometimes unclear.[56] A final cause of uncertainty is the ancillary data used.

For example, comparison of data provided by Meybeck andRagu [1995], Fekete et al. [2002] and Syvitski et al. [2003]shows that there is significant disagreement about the riverbasin area and river water discharge, and these discrepan-cies can influence model predictions. The binning of inde-pendent variables can also have an important influence onmodel structure [Bouwman et al., 2002]. Future effortsshould address these possibilities.

5. Conclusions

[57] We succeeded in developing a robust model forTSSY which was cross-validated by using training andvalidation data sets and validated against independent data.Earlier approaches [Hovius, 1998; Ludwig and Probst,1998; Syvitski et al., 2003] did not incorporate anthropo-genic factors as controls of TSS yields. We found asignificant anthropogenic effect of cultivation (wetland riceextent, mainly occurring in Asia, and marginal grasslandswhich occur mainly in Asia and Africa). There is generalagreement about the importance of climate, relief andlithology between all models. A further difference betweenour model and earlier ones is the inclusion of the effect oftrapping of sediments in human-made reservoirs, whichcauses a global reduction of river loads of particulatenutrients of 13%.[58] Models like NEWS-PNU should not be used to

predict the fluxes of sediments and particulate compoundsfor the mouth of individual rivers. They are more appropri-ate to estimate the regional, continental or global fluxes ofsediment and particulate matter to the oceans, and changestherein as a result of climate and land-use change [e.g.,Syvitski et al., 2003].[59] There is of necessity considerable uncertainty asso-

ciated with NEWS-PNU predictions, and future efforts willno doubt improve on our predictions significantly. None-theless, as the first attempt to develop a robust, internallyconsistent, and spatially explicit global model of POC, PN,and PP river export, NEWS-PNU constitutes a significantadvance in its own right.

Appendix A

[60] Data used in the final model for the 126 rivers withreported natural TSSY, including reported TSSY with itsliterature source, sediment trapping efficiency, Fournierprecipitation, Fournier slope, lithology class, and the cov-

erage of marginal grassland and wetland rice as a percent ofthe river basin area, and modeled river export of POC, PNand PP, are provided in Table A1.

[61] Acknowledgments. We gratefully acknowledge the support ofthe UNESCO Intergovernmental Oceanographic Committee for variousworkshops held which formed the basis for the work described in this paper.We thank James Syvitski and Niels Hovius for making their TSS dataavailable for this study, Hans Visser and Peter Heuberger for their assistancein the data analysis and modeling, and Sybil Seitzinger for criticallyreviewing earlier versions of this paper. The work of A. B., A. D., andA. F. B. was part of the project Integrated Terrestrial Modeling (S/550005/01/DD) of the Netherlands Environmental Assessment Agency, NationalInstitute for Public Health and the Environment.

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Harrison, J., S. Seitzinger, A. F. Bouwman, N. Caraco, A. Beusen, andC.Vorosmarty (2005),Dissolved inorganicphosphorus export to the coastalzone: Results from a spatially explicit, global model (NEWS-DIP),GlobalBiogeochem. Cycles, 19, GB4S03, doi:10.1029/2004GB002357.

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��A. H. W. Beusen and A. F. Bouwman, Netherlands Environmental

Assessment Agency, National Institute for Public Health and theEnvironment, P.O. Box 303, NL-3720 AH Bilthoven, Netherlands. ([email protected])A. L. M. Dekkers, Centre for Information Technology and Methodology,

National Institute for Public Health and the Environment, P.O. Box 1, NL-3720 BA Bilthoven, Netherlands.J. Harrison, Institute of Marine and Coastal Sciences, Rutgers, the State

University of New Jersey, New Brunswick, NJ 08901-8521, USA.W. Ludwig, Centre de Formation et de Recherche sur l’Environnement

Marin, UMR 5110, F-66860 Perpignan, France.


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Figure 5. TSSY values for the model applied to the factor values for the extent of marginal grasslandand wetland rice, Fournier precipitation, Fournier slope, and lithology for individual 0.5 by 0.5 degreegrid cells.

Figure 6. Modeled TSSY for 3109 river basins with river-basin mean factor values for the extent ofmarginal grassland and wetland rice, Fournier precipitation, Fournier slope, and lithology within thevalidity range of the model.

GB4S05 BEUSEN ET AL.: RIVER EXPORT OF PARTICULATE MATTER GB4S05

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Figure 7. Modeled export of (a) POC, (b) PN, and (c) PP from river basins based on results presented inFigure 6 combined with equations (2)–(4).

GB4S05 BEUSEN ET AL.: RIVER EXPORT OF PARTICULATE MATTER GB4S05

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estimation of global river transport of sediments and ...dust.ess.uci.edu/ppr/ppr_bdb05.pdf ·...

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