660 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

Estimates of Freshwater Discharge from Continents: Latitudinal and Seasonal Variations

AIGUO DAI AND KEVIN E. TRENBERTH National Center for Atmospheric Research,* Boulder, Colorado

(Manuscript received 22 March 2002, in ®nal form 12 July 2002)

ABSTRACT Annual and monthly mean values of continental freshwater discharge into the oceans are estimated at 1Њ resolution using several methods. The most accurate estimate is based on stream¯ow data from the world's largest 921 rivers, supplemented with estimates of discharge from unmonitored areas based on the ratios of runoff and drainage area between the unmonitored and monitored regions. Simulations using a river transport model (RTM) forced by a runoff ®eld were used to derive the river mouth out¯ow from the farthest downstream gauge records. Separate estimates are also made using RTM simulations forced by three different runoff ®elds: 1) based on observed stream¯ow and a water balance model, and from estimates of precipitation P minus evaporation E computed as residuals from the atmospheric moisture budget using atmospheric reanalyses from 2) the National Centers for Environmental Prediction±National Center for Atmospheric Research (NCEP±NCAR) and 3) the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with previous estimates, improvements are made in extending observed discharge downstream to the river mouth, in accounting for the unmonitored stream¯ow, in discharging runoff at correct locations, and in providing an annual cycle of continental discharge. The use of river mouth out¯ow increases the global continental discharge by ϳ19% compared with unadjusted stream¯ow from the farthest downstream stations. The river-based estimate of global continental discharge presented here is 37 288 Ϯ 662 km3 yrϪ1, which is ϳ7.6% of global P or 35% of terrestrial P. While this number is comparable to earlier estimates, its partitioning into individual oceans and its latitudinal distribution differ from earlier studies. The peak discharges into the Arctic, the Paci®c, and global oceans occur in June, versus May for the Atlantic and August for the Indian Oceans. Snow accumulation and melt are shown to have large effects on the annual cycle of discharge into all ocean basins except for the Indian Ocean and the Med- iterranean and Black Seas. The discharge and its latitudinal distribution implied by the observation-based runoff and the ECMWF reanalysis-based P±E agree well with the river-based estimates, whereas the discharge implied by the NCEP±NCAR reanalysis-based P±E has a negative bias.

1. Introduction thereby forcing the oceans regionally through changes In a steady state, precipitation P exceeds evaporation in density (Carton 1991; Nakamura 1996). E (or evapotranspiration) over land and the residual wa- To study the freshwater budgets within the oceans, ter runs off and results in a continental freshwater dis- therefore, estimates of continental freshwater discharge charge into the oceans. Within the oceans, surface net into the ocean basins at each latitude are needed. Baum- freshwater ¯uxes into the atmosphere are balanced by gartner and Reichel (1975, BR75 hereafter) derived this discharge from land along with transports within global maps of annual runoff and made estimates of the oceans. The excess of E over P over the oceans annual freshwater discharge largely based on stream¯ow results in atmospheric moisture that is transported onto data from the early 1960s analyzed by Marcinek (1964). land and precipitated out, thereby completing the land± Despite various limitations of the BR75 discharge data ocean water cycle. While E and P vary spatially, the (e.g., rather limited station coverage, areal integration return of terrestrial runoff into the oceans is mostly con- over 5Њ latitude zones, no seasonal values), these esti- centrated at the mouths of the world's major rivers, thus mates are still widely used in evaluations of ocean and providing signi®cant freshwater in¯ow locally and climate models (Pardaens et al. 2002) and in estimating oceanic freshwater transport (Wijffels et al. 1992; Wijff- * The National Center for Atmospheric Research is sponsored by els 2001), mainly because there have been few updated the National Science Foundation. global estimates of continental discharge. The primary purpose of this study is to remedy this situation. Correct simulations of the world river system and its Corresponding author address: A. Dai, National Center for At- mospheric Research, P.O. Box 3000, Boulder, CO 80307. routing of terrestrial runoff into the oceans has also E-mail: [email protected] become increasingly important in global climate system

᭧ 2002 American Meteorological Society DECEMBER 2002 DAI AND TRENBERTH 661 models (Miller et al. 1994). For evaluating climate mod- spheric moisture budget analyses. For the Mississippi els, a few stream¯ow datasets have been compiled (e.g., River basin, for instance, Milly and Dunne (2001) es- DuÈmenil et al. 1993), although many studies included timate precipitation as 835 mm yrϪ1 and total evapo- only 50 or so of the world's largest rivers. Other ap- ration as 649 mm yrϪ1, of which consumptive use con- proaches (e.g., Hagemann and Gates 2001) utilize daily tributes 12 mm yrϪ1 (1.8%); surface reservoir ®lling precipitation and parameterized evaporation to compute provides a loss of 1 mm yrϪ1; and groundwater mining runoff that is then fed into a hydrological discharge adds 2 mm yrϪ1. Overall, the changes in storage over model. This approach makes no use of actual measure- land, such as in lakes and reservoirs, and through melt- ments of discharge. Perry et al. (1996) gave an updated ing of glaciers are manifested as changes in sea level. estimate of annual-mean river discharge into the oceans Estimates of glacier contributions are 0.2 to 0.4 mm by compiling published, gauge-data-based estimates for yrϪ1 and terrestrial storage Ϫ1.1 to 0.4 mm yrϪ1 (Church 981 rivers. However, although they used the farthest et al. 2001). Note that 1 mm yrϪ1 globally is equivalent downstream gauges, the distances to the river mouths to a volume of 357 km3 yrϪ1 and is approximately the ranged from 100 to 1250 km and they made no ad- mean annual ¯ow of the river Amur in Russia (Table justments for this. They estimated the contributions 2). Nevertheless, these values are small compared with from ungauged small rivers by ®tting a power law to regional P±E values. the dataset and extrapolating. Substantial problems still exist in analyzed ®elds of For years hydrologists have been improving terrestrial precipitation and evaporation based upon four-dimen- runoff ®elds. One such example is the work by Fekete sional data assimilation (Trenberth and Guillemot 1996, et al. (2000, 2002), who used a global river discharge 1998; Trenberth et al. 2001b; Maurer et al. 2000). In dataset from the Global Runoff Data Centre (GRDC) fact problems exist in all precipitation products (e.g., coupled with a simulated river network and a water Yang et al. 2001; Adler et al. 2001; Nijssen et al. 2001), balance model to derive a set of monthly global maps and evaporation is not measured but can only be esti- of runoff at 0.5Њ resolution. This merging approach of- mated from bulk formulas. Potential evapotranspiration fers the best estimate of the geography of terrestrial is measured from pans, but is not a measure of actual runoff, with the magnitude of runoff constrained by the evaporation, and over the United States, estimated observed discharge, while at the same time preserving trends in actual evaporation and pan evaporation are the spatial distribution of the water balance. For oceanic opposite (Milly and Dunne 2001) because increased water budget analyses, however, only the discharges at clouds reduce the latter, while increased soil moisture coastal river mouths are of interest. To estimate conti- from increased precipitation (associated with the cloud nental discharge using the runoff ®elds, a river transport increase) increases the availability of moisture for evap- model that routes the terrestrial runoff into the correct oration. Therefore, we believe that the P±E used here, river mouths is needed. Alternatively, estimates can be which was computed as a residual of the atmospheric made from gauge records of stream¯ow (e.g., Grabs et moisture budget, is potentially much more promising. al. 1996, 2000), but then the issue of ungauged streams Relatively new P±E estimates were derived from the has to be addressed and extrapolation from the gauging atmospheric moisture budget based on the National Cen- station to the river mouth is needed. To our knowledge, ters for Environmental Prediction±National Center for neither of these approaches has been done thoroughly Atmospheric Research (NCEP±NCAR, NCEP hereaf- in estimating continental discharge, and much improved ter) reanalyses (Kalnay et al. 1996) by Trenberth and estimates are the main purpose of this study. Guillemot (1996, 1998) and the European Centre for In a steady state, P±E is a good proxy of runoff over Medium-Range Weather Forecasts (ECMWF) ERA-15 land (BR75). Short-term P±E may, however, differ from reanalyses (Gibson et al. 1997) by Trenberth et al. runoff because of changes in storage and, in particular, (2001a). New P±E estimates will also be produced for snow accumulation and melt and in®ltration of water the ERA-40 reanalyses that are under way. One test for into the ground. On a day-to-day basis, runoff greatly the P±E ®elds is to compute the implied continental depends upon the frequency, sequence, and intensity of discharge and compare with that derived from gauge precipitation and not just amount, as these factors alter data. Unfortunately, the results using model-based P and the extent to which water can in®ltrate and be stored in E are not good (Oki 1999) because these are purely the soil. Changes in soil moisture can be important, and model-predicted ®elds. Although not problem free, P± it is only on annual and longer timescales that conditions E estimates as a residual of the atmospheric moisture may approximate a steady state. budget are better partly because atmospheric wind and Another problem is human interference with the nat- moisture ®elds were calibrated every6hbyatmospheric ural water ¯ows. The mining of groundwater effectively sounding and satellite observations in the reanalyses. changes the subterranean water storage, although it is They depend critically on atmospheric low-level diver- offset to some extent by the ®lling of surface reservoirs. gence ®elds, which have considerable uncertainties, but The biggest human in¯uence comes from irrigation and small-scale errors naturally cancel as averages are taken water usage, although this should be re¯ected in the P± over larger areas, and the global mean is guaranteed to E used in this study, which was derived from atmo- balance. If the reanalysis data are evaluated to be re- 662 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

TABLE 1. Datasets used in this study. All are monthly. Resolution Variables Type and coverage (Њ) Period Source and reference Stream¯ow Station, land 1±100ϩ yr NCAR; Bodo (2001) Runoff Composite, land 0.5 Climatology GRDC±UNH; Fekete et al. (2000) E±P NCEP±NCAR reanalysis, global 2.8 1979±95 NCAR; Trenberth and Guillemot (1998) E±P ECMWF reanalysis, global 2.8 1979±93 NCAR; Trenberth et al. (2001a) P Gauge plus satellite plus reanaly- 2.5 1979±98 CMAP; Xie and Arkin (1997) sis, global P Gauge plus satellite, global 2.5 1979±99 GPCP; Huffman et al. (1997) T Station data, land 0.5 1961±90 CRU; New et al. (1999) River basin at- Simulated river network, STN-30p 0.5 UNH; VoÈroÈsmarty et al. (2000) tributes liable for estimating continental discharge, then they can below. The P±E data were remapped onto the 0.5Њ grid be applied1 to study the interannual to decadal variations for driving the RTM. of continental discharge because the reanalysis data are, Gauge records of stream¯ow rates are the basic data or will be, available for the last 40±50 yr, and are likely for estimating continental discharge. We used monthly to be updated regularly. This could become a valuable stream¯ow datasets compiled by Bodo (2001) and oth- application of the reanalysis data given that many rivers ers that are archived at NCAR (ds552.1, ds553.2, have no or only short records of stream¯ow and that ds550.1; available online at http://dss.ucar.edu/catalogs/ there has been a widespread decline in the number of ranges/range550.html), together with the R-ArcticNET river gauges during the last few decades (Shiklomanov v2.0, a dataset created by C. J. VoÈroÈsmarty [University et al. 2002). of New Hampshire (UNH)] et al. (online at http:// In this study, we analyze station records of stream¯ow www.R-ArcticNET.sr.unh.edu) that contains stream¯ow for 921 of the world's largest rivers to provide new data for the Arctic drainage basins. These datasets were estimates of long-term mean annual and monthly con- created using various sources of stream¯ow data, in- tinental freshwater discharge into the oceans through cluding those collected by the United Nations Educa- each 1Њ latitude ϫ 1Њ longitude coastal box. We make tional Scienti®c and Cultural Organization (UNESCO), use of the composite runoff ®elds of Fekete et al. (2000, the United States Geological Survey (USGS), UNH, 2002), a state-of-the-art database of the world river net- Russia's State Hydrological Institute, the GRDC in Ger- work, and a river transport model (RTM) to translate many (only an earlier version of its collection), and information from the farthest downstream station to the many national archives such as those for South Amer- river mouth, and to estimate contributions from un- ican nations. Data quality controls were applied during monitored river basins. We also derive continental dis- the compilation, primarily for errata and inconsistencies charges using RTM simulations forced with various es- in metadata. Undoubtedly, errors and discrepancies re- timates of terrestrial runoff, including P±E from the main, although the majority of the stream¯ow records reanalyses from NCEP±NCAR and ECMWF. Hence we are thought to have an accuracy to within 10%±20% compare the runoff-implied continental discharges with (Fekete et al. 2000). the gauge-based estimate. After eliminating duplicate stations (mainly through Our goal is to update and improve the continental locations), there were 8878 gauge records of monthly discharge estimates of BR75 and provide an evaluation mean river ¯ow rates with varying lengths, ranging from of the P±E ®elds from the reanalyses, and at the same a few years to over 100 yr. The latest year with data is time provide useful data (e.g., river mouth ¯ow rates) 2000, although most records end earlier (many in the for climate model evaluations. The continental dis- 1990s). For estimating continental discharge, we se- charge can then be applied to produce detailed estimates of mean meridional freshwater transport by the oceans lected 921 near-coastal (i.e., the farthest downstream) that can also be used in climate and ocean model eval- stations (Fig. 1) from this merged global stream¯ow uations. dataset using a procedure described in section 3. Most of the selected stations are close to river mouths and have 10 or more years of data, although a few of them 2. Datasets are located far from the ocean [e.g., Niger (ϳ1000 km), Mekong (ϳ850 km), Irrawaddy (ϳ800 km)] or have We used a number of observational and reanalysis only a few years of data that may not be representative data sets in this study, as listed in Table 1 and described of long-term means (cf. Table 2 and the appendix). The mean stream¯ow and drainage area data from these 1 On annual and seasonal timescales, changes in soil moisture must coastal stations were used to derive the river out¯ow be considred. into ocean at the river mouth, as described in section DECEMBER 2002 DAI AND TRENBERTH 663

FIG. 1. Distribution of the farthest downstream stations for world largest 921 rivers included in this study. Also shown are the world major river systems simulated by a river transport model.

3. Because of the varying time periods of records, the who found substantial discrepancies, usually underes- mean river ¯ow rates derived from these gauge records timates of precipitation, over mountains in GPCP. Nev- may be inconsistent temporally among the rivers. ertheless, the CMAP- and GPCP-averaged precipitation To derive estimates of continental discharge inde- was used to compute the river-basin-integrated precip- pendent of those based on the stream¯ow records and itation and to help evaluate the P±E products. to estimate the discharge from the land areas that were For estimating snow effects on the annual cycle of not monitored by the gauges, we used the composite runoff, we used a simple scheme that requires daily monthly and annual runoff ®elds of long-term means of surface air temperature. The latter was interpolated from Fekete et al. (2000, 2002). Although only 663 gauges the New et al. (1999) monthly climatology. were used and biases are likely over regions with little To perform areal averaging and integration and to calibration, their composite ®elds at 0.5Њ resolution obtain estimates of drainage area for individual river probably represent one of the best estimates of terrestrial basins, a database for the world river network is needed. runoff currently available. We used the simulated river database STN-30p (VoÈ- We used the P±E ®elds derived from atmospheric roÈsmarty et al. 2000), a global simulated topological moisture budget analyses based on the NCEP reanalyses network at 30Ј spatial resolution. The STN-30p contains for 1979±95 (Trenberth and Guillemot 1998) and 397 named large river basins and 5755 unnamed small ECMWF reanalyses for 1979±93 (Trenberth et al. 2001a). river basins for the world river network. In this database, These data are available online from NCAR's Climate each 0.5Њ cell of land surface belongs to a river basin. Analysis Section catalog (http://www.cgd.ucar.edu/cas/ The database includes, among others, the upstream catalog/). We did not use the individual E and P ®elds drainage area for each cell and the exact location where from the reanalysis products. the runoff generated in any given cell enters an ocean We also used monthly and annual precipitation over or sea. land, derived by averaging the Climate Prediction Cen- ter Merged Analysis of Precipitation (CMAP) and the Global Precipitation Climatology Project (GPCP) cli- 3. Analysis procedure matology (Table 1). These products are based mainly a. Selection of coastal stations on rain gauge measurements over land and, therefore, depend on the distribution of gauges. In many open Ignoring discharge of groundwater, the continental areas this is adequate, but it is typically inadequate in freshwater discharge is the sum of river out¯ows into mountainous areas, where gradients of precipitation are the oceans. The farthest downstream gauging station very large. Much more detailed estimates of precipi- (i.e., the station with the largest drainage area) for each tation by the Parameter-elevation Regressions on In- river that reaches the ocean is the obvious choice for dependent Slopes Model (PRISM) at resolution of 2.5Ј our purpose. Selecting the most-downstream stations for for the United States by Daly et al. (1994) have been all the ocean-reaching rivers, while excluding upstream compared with GPCP results by Nijssen et al. (2001), stations, from the 8878 stations in the merged stream- 664 JOURNAL OF HYDROMETEOROLOGY VOLUME 3 Â, Gabon Âne Âa, Cameroon emisphere) for the station. Obidos, Brazil Kinshasa, Congo Pte Angostu, Venezuela Datong, China Bahadurabad, Bangladesh Vicksburg, MS, United States Igarka, Rusia Timbues, Argentina Kusur, Russia Pakse, Laos Tucurui, Brazil Jatoba, Brazil Salekhard, Russia Farakka, India Sagaing, Myanmar (Burma) Cornwall, ON, Canada Komsomolsk, Russia Altamira, Brazil Arctic Red, Canada Wuzhou, China The Dalles, OR, UnitedCalamar, States Colombia Concordia, Argentina Pilot Station, AK, UnitedTagachi, States Colombia Ceatal Izma, Romania Gaya, Niger Lambare Plantain Island, Guyana Hope, Canada Oksino, Russia Upstream of Bladder, Canada Khatanga, Russia Ambunti, Papua New Guinea Kolymskoye, Russia Matundo-Cai, Mozambique Ust Pinega, Russia Kotri, Pakistan Ede Polavaram, India ) Station, Country Њ 2.0 4.3 8.1 3.8 5.2 3.2 6.2 0.7 5.8 4.2 3.8 30.8 25.2 32.3 67.4 32.7 70.7 15.1 66.6 24.5 21.9 45.0 50.5 67.5 23.5 45.6 10.2 31.4 61.9 45.2 11.9 49.4 67.6 54.8 72.0 68.7 16.1 64.1 25.4 16.9 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ ) Lat ( Њ 3.5 55.5 15.3 63.6 89.7 90.9 86.5 60.7 49.7 56.8 66.6 88.1 96.0 74.7 52.2 74.9 58.0 76.7 28.7 10.2 58.6 52.2 97.9 33.6 41.9 68.3 10.1 81.8 ) at the river mouth. The composite annual runoff data of 117.6 127.4 105.8 137.0 133.7 111.3 121.2 162.9 121.4 102.5 142.2 158.7 2 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ km std dev) and river transport model (RTM) simulated river ¯ow 3 Ϯ 6 7 4 4 49 81 66 49 71 60 89 60 20 24 65 21 11 64 54 26 21 46 19 12 21 24 80 29 42 13 79 36 34 13 17 31 37 74 121 112 9 67 41 Stn DA Nyr Lon ( 836 555 545 742 387 952 118 774 446 330 614 257 249 831 807 204 217 312 997 275 526 940 348 975 132 299 4619 3475 1705 2896 2440 2346 2430 2430 1730 1660 1516 34 77 583 774 769 502 956 406 497 409 724 252 356 852 788 210 151 245 302 371 666 367 129 312 5854 3699 1039 1794 3203 2582 2661 2418 2570 1267 2903 1713 2240 1047 1989 1143 River mouth 99 97 Vol DA 944 628 610 599 568 531 525 511 415 412 404 393 363 354 302 290 270 252 231 228 212 204 202 193 186 154 144 140 126 124 123 118 117 112 104 6642 1308 1129 65 33 25 84 69 83 65 86 996 617 458 525 589 456 271 398 545 433 428 324 318 359 325 260 179 194 175 203 166 102 139 121 126 112 404 176 125 5083 1266 1141 9 426 130 51 42 96 63 33 64 32 61 76 29 26 60 44 29 45 33 35 54 18 7 36 9 22 16 3 44 20 9 112 133 130 15 11 17 26 25 32 Vol at station std dev RTM Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ 56 33 78 63 69 86 70 99 89 97 119 613 577 476 526 292 356 337 397 382 258 226 312 272 288 221 172 231 165 203 202 148 135 105 106 984 910 536 5330 1271 Str   ) at the station location, and the estimated annual volume (vol) and drainage area (DA, based on STN-30p, in 10 1 Ϫ 2. World's largest 50 rivers (by the estimated river mouth ¯ow rate, Vol). Listed are long-term mean station (Stn, Amazon Congo Orinoco Changjiang Brahmaputra Mississippi Yenisey Parana Lena Mekong Tocantins Tapajos Ob Ganges Irrawaddy St. Lawrence Amur Xingu Mackenzie Xijiang Columbia Magdalena Uruguay Yukon Atrato Danube Niger Ogooue Essequibo Fraser Pechora Nelson Khatanga Sepik Kolyma Zambeze Severnaya Dvina Indus Sanaga Godavari yr 3 ABLE 1 2 3 4 5 6 7 8 9 T 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 No. Name (in km Fekete et al. (2000) were used in the RTM simulation. Nyr is station record length in yr, lat and lon are latitude and longitude (negative in the Western H DECEMBER 2002 DAI AND TRENBERTH 665

¯ow dataset is, however, not an easy task. We ®rst cre- ated a subset of stations that are not far away from the coastlines using the distance to ocean as the criterion. This subset of coastal stations still included upstream stations for a number of rivers, while some of the large rivers (e.g., Niger, Mekong, Huanghe) were not included because the farthest downstream gauging stations are quite distant from their mouths. We manually deleted

otal volume. the upstream stations. We assigned a class number (1±6) to each river based Kapit Wharf, Malaysia Traipu, Brazil Boca del Ce, Mexico Langa Tabbe, Suriname Lobith, Netherlands Wabo Dam, Papua NewChute Guinea de la, Canada Kaimundi, India Sacramento, CA, United States Paso do Ra, Brazil on the downstream width (ϳ2/3 weight) and total length (ϳ1/3 weight) of the river shown on the Times Atlas of ) Station, Country Њ the World (1999 edition; scales, 1 : 1.0±5.5 million) that 2.0 5.0 7.0 10.0 17.4 51.8 57.4 20.8 38.6 30.0 Ϫ qualitatively represents the size or magnitude of the riv- Ϫ Ϫ er. For example, the Amazon, Congo, Orinoco, and the other top 10 rivers were assigned class 6 (largest rivers), ) Lat ( Њ

6.1 while class 5 (very large rivers) included Tocantins, 37.0 91.5 54.5 69.2 84.0 51.5 112.9 145.1 121.5

Ϫ Ϫ Ϫ Ϫ Ϫ Yukon, Niger, Rhine, and the other top 50 rivers. Ex- Ϫ amples of class 4 (large rivers) include Colorado in North America, Murray in Australia, and Taz in Russia. 4 6 6 6 5

32 56 35 31 31 The majority of world rivers are in class 3 (signi®cant rivers, such as Trinity in Texas, Deseado in Argentina, Limpopo in Mozambique), class 2 (small rivers, e.g., San Antonio in the United States, Neepan in Australia, 34 51 64 29 87 61 71

Stn Orne in France), and class 1 (smallest rivers on the map). DA Nyr Lon ( 623 180 132

) Although the merged stream¯ow dataset is among the most comprehensive available, it was unknown whether it included all the major rivers of the world. Further-

Continued more, we wanted to ensure that we selected all the sig- 56 68 65 34 81 615 165 143 141 193 2. ( ni®cant (classes 3±6) ocean-reaching rivers from the merged dataset. For these purposes, we looked through

ABLE the atlas for all named ocean-reaching rivers on all con- T tinents and large islands. If the name of a river from River mouth

93 90 89 86 75 74 73 73 69 69 the atlas was not in the subset but found in the merged Vol DA dataset, then the farthest downstream station for that river was added to the subset of coastal stations. Through this tedious process, we found out that the merged stream¯ow dataset included all the class 6 and 5 rivers and most class 4 rivers (except for Cuanza in

83 57 11 86 60 11 63 11 50 Angola, and Fuchunjiang and Yalujiang in China). How- 127 ever, a large percentage of the class 3 (ϳ1/3) and class 2(ϳ1/2) rivers were not in the merged stream¯ow da- taset. Apparently, there have either been no gauging stations or the data have not been released to the inter- 552* 18 079 21 152 50 415 42 364 7 10 28 12 14 14 13 13 33 11

Vol at station national community for these rivers. This is particularly std dev RTM Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ true in Indonesia and other south Asian countries. Ϯ 21 70 89 59 59 73 74 43 60 55 For some big rivers, there are signi®cant branch rivers Str entering the main stream below the farthest downstream stations. For example, Obidos, the farthest downstream station available for the Amazon, is ϳ750 km away from the mouth. Below Obidos, two very large (Tapajos and Xingu), two large (Jari and Paru), and several class 3 and 2 rivers enter the Amazon. Although these rivers do not reach the ocean by themselves, they are included Rajang Sao Francisco Usumacinta Maroni Rhine Purari Caniapiscau Mahanadi Sacramento Jacui Total: 17 492 in our subset of ocean-reaching rivers if station data are available. Another example is the BeÂnoue (no. 52 in the appendix), which ¯ows into the Niger (no. 27 in Table * This number, which was estimated as the square root of the sum of the variance of the listed rivers, provides only an estimate for the true std dev of the t 41 42 43 44 45 46 47 48 49 50 No. Name 2) well below the last gauging station at Gaya in Niger. 666 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

The search of a dataset from GRDC containing long- fective water ¯ow velocity [ϭ0.35 m sϪ1, following term mean stream¯ow data for 198 rivers, used by Miller et al. (1994)], and d is the distance between cen- GRDC to estimate freshwater ¯uxes into world oceans ters of the cell and its downstream neighboring cell (m). (Grabs et al. 1996), resulted in an addition of four rivers The water ¯ow direction is one of eight directions to our subset of ocean-reaching rivers. chosen as the downstream direction for each cell, based Finally, we selected 921 ocean-reaching rivers (in- on the steepest downhill slope. Note that stream channel cluding those entering the Mediterranean and Black losses to groundwater recharge, reservoirs, and irriga- Seas), whose station locations are shown in Fig. 1 to- tion are not included in this version of the model. Tests gether with a simulated network of the world's major showed that the choice of ␷ only affects the time for river systems (by the river transport model described the model to reach an equilibrium under a constant run- below). While the coasts in North and South Americas off ®eld. This time is about 6 months with ␷ ϭ 0.35 m and Europe are well monitored, there are large river sϪ1 (for the Amazon) starting from empty river channels. systems not monitored in tropical Africa and south Asia. We used the river ¯ow rates after 200 model days in Australia is a relatively dry continent and there is an annual simulations, and used the data of the second year absence of rivers along the southern coastline, so the in monthly simulations. network there appears to be suf®cient. To simulate the annual-mean stream¯ow of the world river systems, we forced the RTM with the annual runoff from Fekete et al. (2000, 2002). In general, this simu- b. Estimating continental discharge lation, which was run with daily time steps, produced 1) DISCHARGE BASED ON STATION DATA comparable river ¯ows at the coastal stations of most major rivers (see Table 2 and the appendix). We used To estimate continental discharge into each ocean ba- the ratio of the simulated stream¯ow at the river mouth sin at each latitude from the station data of stream¯ow, to that at the farthest downstream station to scale up one can sum up the river out¯ow within each latitude (on monthly and annual timescales) the observed station band for each ocean basin. Before this can be done, ¯ow as the river mouth out¯ow (vol in the tables) for however, we need to estimate the stream¯ow at the river the world's top 200 rivers listed in Table 2 and the mouth from the coastal station data and the contributions appendix. In some cases (e.g., no. 23 Uruguay and no. from rivers (or drainage areas) that are not monitored 29 Essequibo in Table 2), where this simulated-¯ow by the stations. ratio was unreasonable (Ͻ0.9, or Ͼ2.0 except for Ni- Although most of the selected coastal stations are ger), we instead used the ratio of drainage areas (based close to river mouth, it is rare that a gauging station is on STN-30p) at the river mouth to that at the station right on the coast. For many rivers (e.g., Niger, Mekong, for the scaling. The estimated river mouth out¯ow was Amazon), the farthest downstream stations available are then used to compute continental discharge. All branch quite distant from the coast and thus stream¯ow mea- rivers above the mouth of the main river channel were surements are not representative of the actual out¯ow excluded in this calculation. These excluded branch riv- into ocean. Although including the contributions sep- ers are no. 12 Tapajos, no. 18 Xingu, no. 52 BeÂnoueÂ, arately from the rivers that enter downstream partly no. 60 Chindwin, no. 89 Red, no. 105 Jari, and a few solves the problem (e.g., for Amazon), many down- smaller rivers. For other smaller rivers, not listed in stream rivers are not monitored. Table 1 and the appendix, station stream¯ow was treated We used an RTM to estimate river mouth out¯ow as river mouth out¯ow and used in estimating conti- from the upstream station data. The RTM was developed nental discharge. The use of the river mouth out¯ow by M. L. Branstetter and J. S. Famiglietti and is de- increases the estimates of the global continental dis- scribed by Branstetter (2001) and Branstetter and Fa- charge by 18.7% and the total monitored drainage area miglietti (1999). The RTM is used in the NCAR Com- by 20.4%. munity Climate System Model (CCSM; Blackmon et al. The total drainage area monitored by the 921 ocean- 2001) for routing surface runoff into the oceans. Using reaching rivers (at river mouths) is 79.5 ϫ 106 km2, a linear advection scheme at 0.5Њ resolution, the RTM which accounts for about 68% of global nonice, non- routes water from one cell to its downstream neigh- desert land areas [ഠ117 ϫ 106 km2; Dai and Fung boring cell by considering the mass balance of hori- (1993)]. Therefore, approximately one-third of the zontal water in¯ows and out¯ows (Branstetter 2001): world's actively drained land area is not monitored by dS ␷ the available gauging stations. Earlier studies (e.g., Mar- ϭ F Ϫ F ϩ R, F ϭ S, (1) dt͸ in out out d cinek 1964) estimated runoff from these unmonitored areas on the basis of comparable regions (i.e., nearby where S is the storage of stream water within the cell regions having runoff data). Fekete et al. (2000) showed 3 (m ), Fout is the water ¯ux leaving the cell in the down- that simply using the ratio of global actively drained 3 Ϫ1 stream direction (m s ), ⌺Fin is the sum of in¯ows of area to the monitored area to scale up the global con- water from adjacent upstream cells (m3 sϪ1), R is the tinental discharge estimate resulted in a number (28 700 runoff generated within the cell (m3 sϪ1), ␷ is the ef- km3 yrϪ1) that was too low compared with other esti- DECEMBER 2002 DAI AND TRENBERTH 667

TABLE 3. The ratio of mean runoff between unmonitored and monitored river basins for each receiving ocean basin and the global ocean. The monitored basins used in this calculation consist of the world's largest 243 rivers with a total drainage area of 69.8 ϫ 106 km2, or about

59.7% of the total area of actively drained landmass. The global ocean includes the Mediterranean and Black Seas. Also shown (Au/Am)is the ratio of unmonitored to monitored (by the 921-river network) drainage areas. Runoff ratio Area ratio

Jan Apr Jul Oct Annual Au/Am Arctic Ocean 1.514 0.918 1.169 1.634 1.051 0.331 Atlantic Ocean 0.664 0.885 1.167 1.347 0.931 0.300 Indian Ocean 1.158 1.427 0.293 0.336 0.525 1.323 Mediterranean and Black Seas 1.599 2.790 0.556 0.534 1.191 0.735 Paci®c Ocean 4.301 2.350 1.691 2.529 2.300 0.535 Global ocean 1.146 1.000 0.938 1.267 1.022 0.507 mates. Perry et al. (1996) used a power law size dis- very dry unmonitored regions in the summer monsoon, tribution found for large rivers to estimate the total con- to 4.30 for the Paci®c Ocean in January, where the tribution from all small rivers. unmonitored wet regions of Indonesia come into play. Here we made use of the ratio of runoff (based on For the global land areas, however, this ratio is close to Fekete et al. 2000) between unmonitored and monitored one, which supports the notion that the unmonitored areas to account for the contribution of the unmonitored landmass as a whole is unlikely to be signi®cantly wetter areas to continental discharge using the following for- than the monitored land areas (Fekete et al. 2000). Table mula: 3 also shows that the drainage areas into the Atlantic and Arctic oceans are well monitored by the 921 river R(j) R (j)[1 r(j)A (j)/A (j)], (2) ϭ 0 ϩ um network, while the monitoring network is poor for the where R(j) is the continental discharge for 1Њ latitude Indian Ocean [partly due to large unmonitored arid areas where r(j) is small and thus has little effect on R(j); zone j (for individual ocean basins), and R 0(j)isthe contribution from the monitored areas only (i.e., the sum cf. Fig. 1]. Globally, the unmonitored areas (including of river mouth out¯ow for all rivers with data within arid regions) account for about half of the monitored areas, or one-third of the total drainage areas. latitude zone j); Au(j) and Am(j) are the unmonitored and monitored drainage areas, respectively, whose runoff Implicit in our approach is that we are dealing with the actual ¯ows into the oceans, not the natural ¯ows enters ocean in latitude zone j (i.e., Au and Am may be outside latitude zone j), and r(j) is the ratio of mean that would occur without dams and other interference by humans. This aspect does causes complications be- runoff over Au and Am. Equation (2) was ®rst applied to individual ocean basins. To create the discharge from cause comparisons with climate models that do not in- clude or allow for effects of human interference should individual 1Њ coastal boxes, the contribution from Au was further partitioned into the individual coastal boxes expect discrepancies. The gauge records re¯ect the ac- within the 1Њ latitude zone j for each ocean basin based tual ¯ow, regulated or not, and as this may well have changed over time, the more recent records may differ on the allocation of Au on the coasts. For display pur- poses, the 1Њ discharge was aggregated onto a 4Њ latitude from those of previous decades. Moisture from irriga- ϫ 5Њ longitude grid. Note that there are inconsistencies tion can be evaporated, which should be re¯ected in in Table 2 and the appendix between the two drainage measurements of atmospheric moisture and thus in our areas listed, as the station drainage area comes from the P±E estimates. station datasets while the river mouth drainage area Compared with earlier studies, our method for esti- comes from STN-30p; presumably the latter should be mating the discharge from the unmonitored areas has greater. some unique features that should improve our station- In this calculation, the coastal out¯ow location for data-based estimate of continental runoff. For example, the runoff generated over each 0.5Њ land cell was ®rst it makes use of runoff information over the monitored and unmonitored areas. It also integrates the runoff from derived based on STN-30p, and the runoff over Au is put into the ocean at the correct location. Here, r(j) was the unmonitored areas and puts it into oceans at correct computed using monthly and annual runoff ®elds from locations with the help of the river database. Most earlier Fekete et al. (2000). It was found necessary to smooth studies merely scale up the global total discharge de- r(j) over 4Њ latitude zones, because for narrower zones rived from the monitored areas and fail to do this geo- graphically. Am can be zero or small. These r(j) values were then used in monthly and annual calculations of R(j)at1Њ latitude resolution. Typical values of r(j), together with 2) DISCHARGE BASED ON RUNOFF FIELDS Au(j)/Am(j), averaged over the drainage area of each ocean basin, are shown in Table 3. The value of r(j) Continental discharge can also be estimated based on varies from 0.29 for the Indian Ocean in July, owing to runoff ®elds. BR75 used the runoff ®eld (in mm) derived 668 JOURNAL OF HYDROMETEOROLOGY VOLUME 3 by Marcinek (1964) to estimate continental discharge tween the NCEP and ECMWF P±E (right column). for each 5Њ latitude zone using simple areal integration. First, both the NCEP and ECMWF P±E ®elds show However, as many large rivers (e.g., Mississippi, ParanaÂ, negative values over many regions of all continents, Nile, Niger, and most Russian rivers) ¯ow mostly north± especially in central and western Asia and northern Af- south, simple zonal integration of runoff does not pro- rica. While it is physically possible to have negative P± vide correct estimates of continental discharge for a giv- E over land (e.g., due to water ¯owing into the region, en latitude zone. groundwater mining, and evaporation from reservoirs A substantial advance can be made by using the RTM and lakes), negative P±E cannot be used as runoff. Some to route the excess water (runoff) within each 0.5Њ cell of these regions indicate de®ciencies in P±E arising into the ocean and then estimate continental discharge from insuf®cient atmospheric observations (especially by totaling the simulated coastal river out¯ows within in Africa). We set the negative annual P±E to zero in each 1Њ latitude zone. Maps of coastal discharges at 1Њ forcing the RTM. The negative P±E over many coastal and 4Њ latitude ϫ 5Њ longitude resolution were obtained areas, such as eastern Africa, appears to result from from the RTM simulations. Three different runoff ®elds extensions of oceanic P±E patterns. This problem may were used: (i) the composite runoff ®elds of Fekete et stem from the relatively low horizontal resolution of the al. (2000), and runoff estimated using the multiyear reanalysis P±E. On the other hand, some regions, such mean monthly and annual P±E ®elds from the (ii) NCEP as southern California, are heavily irrigated using, for and (iii) ECMWF reanalyses (Table 1). Daily runoff was instance, water from aqueducts. As a result, negative interpolated from monthly mean ®elds and used to drive P±E values are plausible. the RTM. These discharge estimates provide additional Second, the implied E for both the P±E ®elds and, checks for the gauge-based estimate, as well as an eval- to a smaller extent, the Fekete et al. runoff, has negative uation of the reanalysis P±E ®elds. values over a number of land areas. Some of these neg- Fekete et al. (2000) simulated snow accumulation and ative values of E suggest that the Fekete et al. runoff melt using some simple empirical equations in produc- and the P±E are too large since the CMAP and GPCP ing the composite runoff. We adopted a simple scheme precipitation climatologies are generally reliable over from Haxeltine and Prentice (1996) to simulate the ef- nonmountainous land areas and they cover similar data fects of snow on runoff in using the monthly P±E from periods (Table 1). These areas with negative E (espe- the reanalyses for driving the RTM. The climatological cially nonmountainous areas), together with those land daily surface air temperature (td in ЊC), interpolated from areas with negative P±E for the reanalyses cases, con- the monthly climatology of New et al. (1999), is com- tribute to errors in estimating continental freshwater dis- pared with a constant ts. When td Ͻ ts, then positive P± charge. E accumulates as snowpack and runoff is zero; if td Ͼ Third, substantial differences exist on regional scales Ϫ1 ts, then snow melts at a rate sm (mm day ) ϭ k (td Ϫ over both land and ocean between the P±E ®elds derived ts), where k is a constant in millimeters per (day ЊC). from the NCEP and ECMWF reanalyses, even though Haxeltine and Prentice (1996) used Ϫ2.0ЊC for ts and the large-scale patterns are comparable. While the dif- 0.7 for k. We tested a number of values for these two ference patterns are noisy, the NCEP P±E is generally parameters in order to produce the best match between lower than the ECMWF P±E over the southern sub- the simulated and observed annual cycle of continental tropical (20Њ±40ЊS) oceans, the eastern Paci®c and the discharge and selected 0.0ЊC for ts, which is physically Atlantic around 3Њ±12ЊN, and much of the western North reasonable, and 0.7 for k. In reality, of course, temper- Atlantic. Over the continents, the NCEP P±E also has atures vary considerably within a month, so that even a dry bias compared with the ECMWF P±E. On the if the monthly mean is less than 0ЊC, several days could other hand, the ECMWF ERA-15 reanalysis data have be above that threshold, but the simpli®cation we use a major problem over Africa, namely a spurious south- is consistent with the other shortcomings in approxi- ward shift of the intertropical convergence zone (ITCZ) mating runoff by mean P±E, rather than employing after 1987 (Stendel and Arpe 1997; Trenberth et al. hourly, or at least daily, data to properly deal with issues 2001b). discussed in the introduction. The implied E ®elds are spatially correlated with the precipitation ®eld, with the correlation coef®cient r ranging from 0.66 to 0.79 over land and slightly lower 4. Comparison among runoff estimates over ocean for the reanalysis cases. This is expected To help understand discharge differences discussed since precipitation provides the main water source for below, we ®rst compare annual means of the Fekete et evaporation over most land areas. al. runoff and the P±E derived from the NCEP and ECMWF reanalyses. Figure 2 shows the annual-mean 5. Freshwater discharge from the world's major runoff from Fekete et al. (2000), the P±E ®elds, and the rivers mean precipitation (average of CMAP and GPCP) (left column), together with the implied E (i.e., the precip- Table 2 and the appendix present the annual-mean itation minus the runoff or P±E) and the difference be- river ¯ow rate and drainage area at the farthest down- DECEMBER 2002 DAI AND TRENBERTH 669

FIG. 2. Annual runoff from Fekete et al. (2000), P±E derived from the NCEP and ECMWF reanalyses, mean precipitation of CMAP and GPCP (left column), and the implied E (i.e., P±runoff or (P±E)] and the NCEP Ϫ ECMWF difference of P±E (right column). stream station and the river mouth for the world's largest STN-30p. The river ¯ow simulated at the station by the 200 rivers. The river mouth ¯ow was estimated using RTM forced with the annual runoff ®eld of Fekete et the method described in section 3b. The drainage area al. (2000) is also shown next to the observed volume. at the river mouth was estimated using the river database Many of the world's largest river systems, with the 670 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

TABLE 4. Comparison of estimates of mean annual continental freshwater discharge into the individual and global oceans (km 3 yrϪ1). Mediterranean Arctic Atlantic Indian Seac Paci®c Totald Largest 921 riversa 3658 19 168 4532 838 9092 37 288 Fekete runoff 3263 18 594 5393 1173 10 420 38 843 ECMWF P±E 3967 20 585 4989 1144 7741 38 426 NCEP±NCAR P±E 4358 16 823 3162 909 7388 32 640 Fekete et al. (2000) 2947 18 357 4802 1169 11 127 38 402 Korzun et al. (1977)b 5220 20 760 6150 14 800 46 930 Baumgartner and Reichel (1975) 2600 19 300 5600 12 200 37 713 Oki (1999) 4500 21 500 4000 10 000 40 000

a Largest 921 rivers are scaled up by accounting for the unmonitored areas and the runoff ratio at 4Њ lat resolution. b Korzun et al. (1977) include groundwater runoff (2200 km3 yrϪ1 globally) and iceberg runoff (2700 km3 yrϪ1 globally). c Mediterranean Sea includes the Mediterranean and Black Seas. d Total excludes discharge into inland (besides Black) seas and from Antarctica, which puts ϳ2613 km3 yrϪ1 freshwater into the ocean (Jacobs et al. 1992). exception of the Yenisey, Lena, Ob, and Amur, are lo- (e.g., Probst and Tardy 1987; Perry et al. 1996; Grabs cated at low latitudes, where precipitation rates are high- et al. 1996, 2000). These unadjusted estimates not only est. River discharge in general increases with drainage underestimate the true river out¯ow, but also contribute area, but the river ¯ow to drainage area ratio varies from to the large range among various estimates of ¯ow rate about 1 km3 yrϪ1 per 1000 km2 in the Tropics (equiv- for world's major rivers because the distance from the alent to P±E of 1 m yrϪ1) to less than 0.1 km3 yrϪ1 per farthest downstream station to the river mouth varies 1000 km2 in high latitudes and dry areas. among different studies from hundreds to thousands of The total global freshwater discharge, excluding that kilometers (Perry et al. 1996). For example, Perry et al. from Antarctica, is about 37 288 Ϯ 662 km3 yrϪ1 (Table (1996) listed the Amazon with a mean ¯ow rate of 6088 4), which is ϳ7.6% of global precipitation or 35% of Ϯ 465 (std dev) km3 yrϪ1 based on 11 different sources, terrestrial precipitation (excluding Antactica and Green- while it is 6000 km3 yrϪ1 in BR75. Although these are land) based the averaged precipitation of CMAP and higher than our mean ¯ow rate (5330 km3 yrϪ1) at sta- GPCP. Since a time series of global discharge is not tion Obidos, they are about 10% lower than our estimate easy to derive because of a changing number of gauges, of Amazon mouth ¯ow (6642 km2 yrϪ1). this uncertainty range was estimated as the square root When forced with the annual runoff from Fekete et of the sum of the variance of long-term mean annual al. (2000), the RTM simulates the station ¯ow rates ¯ows at the farthest downstream stations of all the 921 reasonably well for most major rivers (Fig. 3a, also see rivers multiplied by 1.187, the global-mean factor for Table 2 and the appendix). This suggests that both the converting the farthest downstream station ¯ows to the Fekete et al. runoff and the RTM routing scheme are river mouth out¯ows. Hence it is based on the assump- likely to be correct over most regions. However, prob- tion that the covariance of stream¯ow among large riv- lems are evident over a few river basins such as the ers is small. The world's 50 largest rivers (Table 2) Nile (no. 83) and Zambeze (no. 36), where the RTM account for ϳ57% of the global discharge, while their greatly overestimates the river channel ¯ow. For the total drainage area is ϳ43% of the global actively Nile, river channel water losses due to evaporation and drained land areas (i.e., excluding glaciers and deserts). human withdrawal contribute to the smaller-than-sim- Adding the next 150 largest rivers (the appendix) in- ulated ¯ow rate. The drainage areas derived from the creases these to 67% and 65%, respectively, while add- STN-30p are close to those given in the station datasets ing the next 721 rivers in our dataset of coastal stations (Fig. 3b), except for a few cases (e.g., Zambeze) where changes the numbers only moderately (to 73% and 68%, drainage areas from the stream¯ow datasets are likely respectively). This suggests that an increasingly large to be incorrect, as the STN-30p provides one of most number (in the thousands) of smaller rivers are needed reliable estimates for the total drainage areas of world's to improve the coverage of the station network for mon- largest rivers. itoring global freshwater discharge. When the RTM is forced by the P±E ®elds derived Table 2 and the appendix show that the farthest-down- from the NCEP and ECMWF reanalyses, the simulated stream station data underestimateÐby 10% to over station ¯ow rate generally agrees with the observed at 100%Ðthe river discharge and drainage area at the river most of the major rivers (Fig. 4). Substantial differences mouth in many cases (e.g., Amazon, Orinoco, Missis- exist, however, for the world's largest rivers. For ex- sippi, ParanaÂ, Mekong, Irrawaddy, St Lawrence, and ample, the simulated ¯ow rate is 3063 and 3833 km3 Niger). Most earlier estimates of stream¯ow and drain- yrϪ1 for the Amazon at Obidos in the NCEP and age area for the world's largest rivers used unadjusted ECMWF cases, respectively, where the observed rate is data from the available farthest downstream station 5330 km3 yrϪ1. In general, the Fekete et al. runoff re- DECEMBER 2002 DAI AND TRENBERTH 671

FIG. 3. (a) Observed (x axis) vs RTM-simulated annual river ¯ow FIG. 4. Same as in Fig. 3a but with the RTM forced by the P±E and (b) the drainage area from the stream¯ow datasets and from the ®elds derived from the (a) NCEP and (b) ECMWF reanalyses. simulated river network STN-30p at the farthest downstream stations for the world largest 200 rivers listed in Table 1 and the appendix. The annual runoff ®eld from Fekete et al. (2000) was used in the RTM simulation. The r is the correlation coef®cient of the data points. precipitation (thin, solid curve), runoff, and P±E for the Note that both of the coordinates are on a logarithmic scale. 10 largest rivers. Note that the RTM is not used in these basinwide summation and therefore the differences be- tween the river discharge and the basin-integrated values sulted in better simulated station ¯ow rates, especially illustrate the effects of snow and the time delay of water for the world's largest rivers. traveling downstream to the river mouth. The Amazon The third column in Table 2 and the appendix lists has the highest ¯ow from May to June, while its basin- the standard deviation (std dev) of year-to-year varia- integrated precipitation, runoff, and P±E peak in early tions of river ¯ow rate at the farthest downstream sta- spring. This lag re¯ects the time needed for surface tion. It can be seen that for the large rivers the std dev runoff to travel to the river mouth. For the Changjiang, is ϳ10%±30% of the mean ¯ow. This ratio increases Brahmaputra/Ganges, Mississippi, Mekong, and smaller to over 100% for smaller rivers (the appendix), sug- rivers, this time lag is shorter (about 1 month or less), gesting that stream¯ow rates of small rivers are much suggesting that the seasonal changes in surface runoff more variable from year to year than those of large occur in the area not far away from the river mouth. rivers. This result arises because river ¯ow rates are The Congo and Parana have relatively small annual cy- areal integrals of surface runoff and an integral over a cles, even though precipitation and runoff (for Parana larger basin is usually less variable than one over a only) exhibit large seasonal variations. There are several smaller region. reasons for this to happen. First, evaporation positively The stream¯ow of many rivers has a large annual correlates with precipitation and thus reduces the sea- cycle. Figure 5 compares the mean annual cycle of dis- sonal variation of surface runoff, which is the case for charge (thick, solid curve) with those of basin-integrated the Congo. Second, for large rivers like the ParanaÂ, 672 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

FIG. 5. Mean annual cycle of river discharge, river-basin-integrated precipitation (read on the right or- dinate), runoff, and reanalysis P±E for world largest 10 rivers. Note that the RTM was not used for this plot. which runs through different climate zones, regional the entire Arctic region (cf. Fig. 12; also see Grabs et variations of surface runoff within the river basin, com- al. 2000) are very similar to those for Yenisey and Lena bined with the time lag due to river channel transport, (Fig. 5). Early spring snowmelt over the Mississippi tend to compensate and thus reduce the seasonal vari- basin also appears to be the main reason for the April ation of downstream river ¯ow. More fundamentally, peak discharge from the Mississippi, whose integrated the relative uniformity of river ¯ows throughout the year precipitation peaks in May±June. in spite of large precipitation variations is a sign of large At low latitudes, the basin-integrated P±E generally and diverse lags and obstructions, such as lakes, that agrees with the Fekete et al. runoff, indicating that the help smooth out and regulate the ¯ow. monthly P±E ®elds are reasonable proxies of monthly The big Russian rivers (Yenisey, Lena, Ob, etc.) have runoff provided the areas are large enough. One excep- large peak ¯ows in June, which result from spring tion is the ECMWF P±E over the Congo River basin, (April±May) snowmelt as precipitation does not peak where it follows the precipitation annual cycle rather until July±August in these regions (Fig. 5). In fact, the than the runoff (Fig. 5). This bias is re¯ected in the high drainage-area-integrated discharge and precipitation for values of P±E in tropical Africa (Fig. 2) and is mainly DECEMBER 2002 DAI AND TRENBERTH 673

FIG. 6. Annual discharge rate (103 m3 sϪ1) from each 4Њ lat by 5Њ lon coastal box. The numbers are the total discharge (in 103 m3 sϪ1) from the coasts behind the solid lines. Blank coastal boxes have zero discharge.

due to the higher-than-observed precipitation in the a. Global ocean ECMWF data resulting from the spurious ITCZ shift over Africa noted earlier. As noted, spring snowmelt Figure 6 shows four estimates of annual freshwater results in peak river ¯ows in late spring or early summer discharge rates from individual 4Њ latitude ϫ 5Њ longi- tude coastal boxes. Also shown are the total discharge at northern mid- and high latitudes. It also disrupts the (in 103 m3 sϪ1) from the coasts behind the solid lines.2 general agreement between surface runoff and the P±E As described in section 3b, these estimates were derived seen at low latitudes. This suggests that on a monthly using long-term mean stream¯ow data from 921 rivers timescale the P±E ®elds at mid- and high latitudes can- through Eq. (2), the composite annual runoff from Fek- not be used as a proxy of runoff without including the ete et al. (2000), and the multiyear-averaged P±E ®elds effects of snow accumulation and melt. from the reanalyses (see Table 1). As indicated by the scale of the color bar, coastal discharge rates vary from Ͻ10 to 215 000 (at the Am- 6. Latitudinal distribution of discharge into the 3 Ϫ1 oceans azon mouth) m s per 4Њϫ5Њ box. Most of the global discharge comes from the world's major rivers, whose In this section, we consider the continental discharge mouths are indicated by the boxes in warm colors in from the ocean perspective, with a view toward their Fig. 6a. The eastern coasts of the Americas, primarily use in oceanic freshwater transport and budgets. We start from the total discharge into the global ocean and then 2 Note that the sum of these numbers differs slightly from that discuss individual ocean basins separately, with a focus listed in Table 4 due to the use of a different (scale dependent) on the latitudinal distribution. drainage-basin mask for the coastal integration. 674 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

FIG. 7. Estimates of annual mean continental freshwater discharge into the global oceans for each 1Њ lat zone (right ordinate, lower stepwise lines and the inset) and the cumulated discharge starting from 90ЊN (upper curves). Each line pattern represents an estimate based either on the largest 921 rivers (thin solid line) or on a runoff ®eld (dashed lines), which was used to force a river transport model to derive the discharge. Also shown is an estimate from Baumgartner and Reichel (1975, thick, solid line). in the Tropics, provide ϳ40% of global discharge (Fig. values of P±E (which was set to zero in the RTM sim- 6a). Discharge from east Asia into the North Paci®c ulations) over these regions (cf. Fig. 2). accounts for another ϳ15% of the total. However, less Figure 7 shows the annual-mean continental fresh- than 10% of the global discharge comes from the west- water discharge into the global oceans for each 1Њ lat- ern coast of South America. itude zone (stepwise lines) and the discharge accumu- While the coastal boxes show the approximate lo- lated starting from 90ЊN (upper curves). For compari- cations of the discharge, it is dif®cult to compare the son, the estimate by BR75 is also shown (thick, solid discharges among the four cases for each grid box in line). As expected, the continental discharge is domi- Fig. 6. The integrated coastal discharge provides an eas- nated by the peak out¯ows from the world's largest ier measure for comparison. It can be seen that the Fek- rivers such as the Amazon (ϳ0.21 Sv at 0.75ЊS, 1 Sv ete et al. runoff implies discharges comparable to the ϭ 106 m3 sϪ1), Congo (0.041 Sv at 5.75ЊS), Orinoco river-based estimates from most of the coastlines except (0.036 Sv at 9.25ЊN), Changjiang (0.030 Sv at 32.25ЊN), for those from east Asia (too low), Indonesia, the Bay Brahmaputra/Ganges (0.033 Sv at 24.25ЊN), Mississippi of Bengal, and the tropical west Paci®c islands (too (0.019 Sv at 30.25ЊN), and Parana (0.018 at 34.75ЊS) high) (Fig. 6b). The ECMWF P±E implied discharges (note that the peaks in Fig. 7 may exceed these numbers are also similar to the river-based estimates, except for because of contributions from small rivers within the the coasts of east Asia, western Australia, and Africa same latitude band). The northern mid- to high latitudes (Fig. 6d). The NCEP case reproduces the discharge into (45Њ±75ЊN) encompass the largest landmass and many the Arctic, South Atlantic, and South Paci®c Oceans, large rivers, including the Yenisey, Lena, Ob, and Amur but has large de®ciencies over eastern Africa, Europe, in Russia; Mackenzie and St. Lawrence in Canada; and and east Asia (Fig. 6c). In general, both the ECMWF Yukon in Alaska. Many of the Russian and Canadian and NCEP cases underestimate discharges from north- rivers run from south to north and enter the Arctic ern Africa and east Asia, largely due to the negative Ocean. Collectively, these rivers provide a large fresh- DECEMBER 2002 DAI AND TRENBERTH 675

FIG. 8. Annual discharge into the global ocean smoothed using a 5Њ lat running mean from four different cases, compared with that of Baumgartner and Reichel (1975). water discharge into the Arctic, North Atlantic, and after smoothing the 1Њ discharge data using 5Њ latitude North Paci®c Oceans (cf. Fig. 2), thereby affecting the running-mean, large differences still exist between the oceanic water budget and circulation, both locally and BR75 and our estimates, whereas the agreement among globally, especially through the thermohaline circula- our four different estimates improves (Fig. 8). tion. Since the northern mid- and high latitudes are ex- The total continental discharge into the oceans is also pected to have the largest temperature and precipitation of interest for the global water cycle. This ¯ux is often increases in the next 100±200 yr due to increases in estimated by totaling the discharge from the world's

CO2 and other greenhouse gases (e.g., Dai et al. major rivers and then adjusting it to account for the 2001a,b), it is vital to establish an observed baseline. contribution from the unmonitored smaller rivers. As The RTM reproduces the peak out¯ows from the discussed in section 3b, our method for estimating the world's largest rivers when forced with the Fekete et al. discharge from the unmonitored areas makes our river- runoff and the reanalysis P±E ®elds, although the mag- based estimate of continental discharge much more re- nitude of the peaks differ somewhat from the estimates liable than in earlier studies (e.g., BR75; Perry et al. based on observations (see the insert in Fig. 7, also see 1996). Table 2). These differences are also shown by the ac- Table 4 compares our estimates of total freshwater cumulated discharges. Note that the accumulated dis- discharge into the ocean basins with four earlier esti- charge, a common measure used in previous studies mates. Discharge from groundwater, which was esti- (e.g., Wijffels 2001), integrates the errors from 90ЊN mated to be around 5% of the total discharge by Lvovich southward, and the differences at southern latitudes do (1970), is included only in the estimates using the P± not re¯ect the actual errors at those latitudes because E data and from Korzun et al. (1977). Small surface contributions are small south of 10ЊS. freshwater discharge from Antarctica (ϭ1987 km3 yrϪ1 The accumulated discharge for the NCEP P±E case according to BR75) and changes in land storage (e.g., is considerably lower than the others, whereas the BR75 effects of melting glaciers, discussed in section 1) are case agrees remarkably well with our estimates based also not included. Our 921-river-based estimate of the on the stream¯ow data, Fekete et al. runoff and ECMWF global discharge is 37 288 Ϯ 662 km3 yrϪ1 (1 km3 yrϪ1 P±E. However, the latitudinal distribution from BR75 ϭ 31.69 m3 sϪ1), which is very close to that of BR75 at 5Њ resolution is too smooth and quite unrealistic. Even (37 713) and Perry et al. (1996) (37 768). However, 676 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

FIG. 9. Estimates of annual-mean continental freshwater discharge into the Atlantic Ocean for each 1Њ lat zone (right ordinate, lower stepwise lines and the inset) and the cumulated discharge starting from 90ЊN (upper curves).

Perry et al. (1996) used stream¯ow rates from the far- higher for the Paci®c and Indian Oceans and the Med- thest downstream stations, which underestimate the true iterranean and Black Seas, whereas the ECMWF P±E river discharge by an average of 18.7% in our analysis implies higher discharge into all but the Paci®c basin. (see Table 2 and the appendix). This result implies that Table 4 shows that both of the reanalysis P±E ®elds the power law size distribution used by Perry et al. underestimate the discharge into the Paci®c Ocean sig- (1996) substantially overestimates the total discharge ni®cantlyÐby 15% for ECMWF and 19% for NCEP from small rivers with an annual ¯ow rate Ͻ250 m3 compared with the river-based estimate. These biases sϪ1. result primarily from the smaller P±E than the Fekete The global discharge implied by the Fekete et al. et al. runoff over China and Southeast Asia (Fig. 2). runoff and ECMWF P±E is only slightly higher than For the Arctic Ocean, estimates range from 2600 km3 that based on stream¯ow data, while the NCEP P±E yrϪ1 by BR75 to 5220 km3 yrϪ1 by Korzun et al. (1977). results in lower global discharge (Table 4), consistent Grabs et al. (2000) obtained an annual discharge of 2603 with the dry bias (cf. Fig. 2). This result suggests that km3 into the Arctic Ocean by totaling discharge data the ECMWF ERA-15 P±E is likely to be more reliable from the 35 farthest downstream stations, which account over global land than the NCEP P±E and it may be considered as a proxy for terrestrial runoff for estimating for 70% of the total Arctic drainage area. Assuming the continental discharge, although problems exist in Africa unmonitored 30% drainage area has similar runoff rates, 3 Ϫ1 and South America. this would imply a total discharge of 3718 km yr , Although the various estimates of total continental which is comparable to our estimates (except for the discharge listed in Table 4 are in good agreement, except NCEP case) (Table 4). for Korzun et al. (1977), their partitioning of the total Figure 6 shows that the right amount of total dis- discharge into individual ocean basins differs substan- charge into a particular ocean basin may result from tially. For example, compared with the 921-river-based discharges coming off the wrong coasts. For example, estimate, the discharge implied by the Fekete et al. run- the total discharge from the western and eastern coasts off is lower for the Arctic and Atlantic Oceans and of the Indian Ocean is similar for the 921-river and DECEMBER 2002 DAI AND TRENBERTH 677

FIG. 10. Estimates of annual-mean continental freshwater discharge into the Indian Ocean for each 1Њ lat zone (right ordinate, lower stepwise lines and the inset) and the cumulated discharge starting from 90ЊN (upper curves).

NCEP cases, but the NCEP P±E implies too much runoff convergence. The ECMWF P±E also overestimates the from Australia and too little from Africa. discharge from the Congo by ϳ0.05 Sv. This approx- imately equals the overall bias in the ECMWF estimate of the accumulated discharge compared with the river- b. Individual oceans based estimate. In general, the discharge implied by the 1) ATLANTIC OCEAN Fekete et al. runoff agrees well with the river-based estimate in the Atlantic basin. The latitudinal distribution of the zonal and accu- mulated discharge into the Atlantic Ocean (Fig. 9) re- veals that the Amazon accounts for more than one-third 2) INDIAN OCEAN of the total discharge (ϳ0.60 Sv). Other major rivers include the Congo, Orinoco, Mississippi, ParanaÂ, and The zonal and accumulated discharge into the Indian St. Lawrence (ϳ0.011 Sv at 48.75ЊN). Most of the dis- Ocean (Fig. 10) reveals that the Brahmaputra/Ganges charge comes from low-latitude America and Africa, Rivers (0.033 Sv at 24.25ЊN) account for about one- although substantial freshwater ¯uxes also occur at 40Њ± quarter of the total discharge. Other large rivers entering 60ЊN, mostly from North America. The Fekete et al. the Indian Ocean include Irrawaddy (0.012 Sv at runoff and the two reanalysis P±E ®elds all slightly 17.75ЊN), Zambeze (0.0037 Sv at 18.25ЊS), Indus underestimate the river discharge at many latitudes with- (0.0033 Sv at 24.00ЊN), Godavari (0.0031 Sv at in 15Њ±55ЊN. The NCEP P±E also underestimates the 16.75ЊN), and Mahanadi (0.0023 Sv at 20.25ЊN). The discharge from rivers between 3Њ and 7ЊN (such as no. Indian subcontinent and the coast of the Bay of Bengal 39 Sanaga) and from the Congo, which adds to its neg- provide the largest freshwater ¯ux into the Indian Ocean ative bias (ഠϪ0.08 Sv for the total accumulated dis- around 15Њ±27ЊN. Substantial discharge also occurs on charge). The ECMWF P±E overestimates discharge the eastern African coast between 6Њ and 24ЊS. There from the Orinoco River (by ϳ0.007 Sv), which com- is little discharge from arid to semiarid western Aus- pensates for the negative bias accumulated to the north, tralia. indicating a dislocation of the atmospheric moisture The NCEP P±E underestimates the out¯ow from the 678 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

FIG. 11. Estimates of annual-mean continental freshwater discharge into the Paci®c Ocean for each 1Њ lat zone (right ordinate, lower stepwise lines and the inset) and the cumulated discharge starting from 90ЊN (upper curves).

Brahmaputra/Ganges by ϳ37% and the Zambeze by sistent with the negative P±E (set to zero in the RTM ϳ50%, but overestimates discharge around 35ЊS (the simulations) or smaller P±E values than the Fekete run- latter bias is also seen in the ECMWF case). Hence, the off over east Asia, the southwestern United States, and NCEP-implied discharge into the Indian Ocean (ϳ0.10 central America (Fig. 2). Sv) is substantially lower than the river-based estimate Within 10ЊS±10ЊN, the RTM simulated discharge is (ϳ0.14 Sv). However, the ECMWF case overestimates more continuous than the river-based estimate, partic- out¯ow from the Irrawaddy by more than 100%, while ularly for the Fekete et al. case, which results in a larger the out¯ow from Zambeze is underestimated. While (by ϳ0.05 Sv) total accumulated discharge. The dif- comparable with the river-based estimate at most lati- ference within 10ЊS±10ЊN results primarily from the In- tudes, the discharge implied by the Fekete et al. runoff donesia±Malaysia±New Guinea region, where a large is also too high for the Irrawaddy. number of class 3 and smaller rivers are not monitored (cf. Fig. 1), making our river-based estimate less reliable for this region. Further, the small islands and varying 3) PACIFIC OCEAN coastlines in this region are dif®cult to simulate accu- The zonal and accumulated discharge into the Paci®c rately and require a resolution higher than 0.5Њ, as used Ocean (Fig. 11) features the Changjiang (ϳ0.030 Sv at by the RTM and the STN-30p river database. The latter 32.25ЊN), Mekong (0.017 Sv around 12.0ЊN), Amur was used in estimating the contribution by the unmon- (0.011 Sv at 51.75ЊN), and Xijiang (0.0086 Sv at itored rivers in the river-based estimate. Therefore, sub- 22.25ЊN). The RTM underestimates the observed out- stantial uncertainties on the order of 0.05 Sv exist for ¯ow from the Xijiang by over 50% when forced with the discharge estimates from this region. the Fekete et al. runoff and the reanalysis P±E ®elds, while it puts higher out¯ows through nearby smaller c. Seasonal variations rivers around 22.25ЊN. The out¯ow from the Changjiang is also underestimated in the NCEP (by 28%) and Figure 12 shows the mean annual cycle of total fresh- ECMWF (by 22%) cases. These negative biases are con- water discharge into the individual and global oceans, DECEMBER 2002 DAI AND TRENBERTH 679

FIG. 12. Mean annual cycle of freshwater discharge into individual and global oceans based on various estimates. The thin, solid line is for the estimate based on the 921 rivers without scaling to account for the unmonitored areas.

as estimated based on the 921 rivers with and without combinations of k and ts (see section 3b) to account for scaling for the contribution from the unmonitored areas, snowmelt but all produced the peak discharge in July. Fekete et al. runoff, and P±E ®elds from the NCEP and This suggests that the simple scheme has limitations, ECMWF reanalyses. The inclusion of the unscaled probably arising from the use of only daily mean tem- gauge-based estimate shows that the contribution from peratures. the unmonitored areas does not signi®cantly alter the The total freshwater discharge into the Atlantic Ocean phase of the mean annual cycle and is large, ranging has a similar peak in May for all but the ECMWF and from ϳ20% of the monitored discharge for the Atlantic the unscaled cases (Fig. 12). For the Atlantic, the to ϳ100% for the Paci®c. ECMWF case has too much discharge in May and too The discharge into the Arctic Ocean has a sharp peak low discharge in August and September and the latter in June arising from snowmelt in late spring, although bias is even larger for the NCEP case. The May peak the Fekete et al. result has a lower maximum but with in all the cases results from the concurrence of high higher values in most other months. Both the NCEP and discharge in May from the Amazon and Mississippi ECMWF P±E ®elds result in too much discharge in July (Fig. 5). The discharge into the Indian Ocean peaks in and too little from January to March. We tested various August, mainly from the heavy Indian summer monsoon 680 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

FIG. 13. Mean annual cycle of freshwater discharge into individual and global oceans estimated using ECMWF and NCEP P±E ®elds with and without the effects of snow. rainfall. For the Paci®c Ocean, the discharge peaks tinental discharge. It can be seen that without the snow around June±July primarily because of heavy monsoon effects, the discharge into the Arctic Ocean would be rainfall over east Asia during these months. Over the radically different and would not have the sharp June Mediterranean and Black Seas, the discharge is low in peak, the peak discharge into the Atlantic around May the warm season and high during the winter and spring would also be considerably lower, the discharge into the seasons. Globally, total freshwater discharge is high Paci®c would have a much lower peak in August instead from May to September with a peak in June and a lull of June, and the global discharge would have little sea- from October to April. This annual cycle results mainly sonal variation. On the other hand, snow has negligible from the discharge from Northern Hemisphere land ar- effects on the discharge into the Indian Ocean and Med- eas. All the cases broadly reproduce the annual cycles iterranean and Black Seas. revealed by the gauge data. Figure 13 shows the annual cycles estimated with and 7. Discussion without the snow scheme (i.e., all P±E was considered as runoff) for the reanalysis P±E cases to illustrate the Our freshwater discharge estimates have various overall effects of snow accumulation and melt on con- sources of errors. For example, the 921-river-based dis- DECEMBER 2002 DAI AND TRENBERTH 681 charge contains uncertainties associated with 1) the es- the large rivers are substantial, use of the unrealistic timate of discharge from the unmonitored areas, 2) the discharge distribution from BR75 in estimating oceanic adjustment for river mouth ¯ow rates, 3) varying length meridional freshwater transport (e.g., Wijffels et al. and periods of stream¯ow records, and 4) varying hu- 1992; Wijffels 2001) is likely to induce signi®cant er- man in¯uences on the natural stream¯ow. We also did rors. Using the BR75 discharge for evaluating climate not include groundwater runoff and the runoff from Ant- models (e.g., Pardaens et al. 2002) is also not a good arctica. For the reanalysis P±E ®elds, there are many choice. documented regional biases such as those related to moisture convergence and the atmospheric circulation (Trenberth and Guillemot 1998; Trenberth et al. 2001a), 8. Summary as well as known problems in ERA-15 over Africa (e.g., the spurious ITCZ shift) and South America (Trenberth We have created and compared several estimates of et al. 2001b). The Fekete et al. runoff has uncertainties continental freshwater discharge into the oceans. The arising from its use of limited stream¯ow data and a ®rst is built on discharge data from 921 ocean-reaching water balance model over many regions (Fekete et al. rivers selected from several comprehensive stream¯ow 2000). The strength of the Fekete et al. (2000) runoff datasets. The drainage area of the 921 rivers is 79.5 ϫ is in its patterns of the distribution, but the calibration 106 km2, or about 68% of global non-ice, nondesert for the total amount contains uncertainties over many land areas. We estimated the river mouth out¯ow from regions. the world's large rivers by adjusting the stream¯ow rate Nevertheless, we believe that the estimates based on the 921-river stream¯ow data, the Fekete et al. runoff, at the farthest downstream station using the ratio of and the ECMWF P±E are likely to be close to the truth. simulated ¯ow rates (or drainage areas in some cases) In particular, we think the 921-river-based estimate is at the river mouth and the station. The discharge from the most reliable, except perhaps for the Mediterranean the unmonitored areas was estimated based on the ratios and Black Seas where the estimate seems to be a bit of runoff and drainage area between the unmonitored too low (Table 4). This conclusion follows because the and monitored areas at each latitude. A river transport 921 rivers cover over two-thirds of world's actively model, the composite runoff ®eld from Fekete et al. drained land areas and the errors in computing the dis- (2000), and a simulated global river database, STN-30p, charge from the unmonitored areas result primarily from were used in this analysis. Long-term mean annual and the uncertainties in the Fekete et al. runoff through r(j) monthly freshwater discharge at each latitude into in- in Eq. (2); that is, these errors are also in the Fekete et dividual and global oceans were derived based on the al. runoff-based estimate. The freshwater discharges im- adjusted river out¯ow and the estimated contribution plied by the reanalysis-derived P±E ®elds generally from unmonitored areas. agree with the 921-river-based estimate, although large Second, we have separately computed annual and regional biases exist (cf. Fig. 2), especially for the NCEP monthly continental discharge at each latitude into the P±E. This result suggests that the P±E products could oceans by forcing the RTM with the Fekete et al. runoff be used as proxies of runoff over most land areas and, and the P±E ®elds derived from the NCEP and ECMWF with some tuning and information about variability of reanalyses with a adjustment for snow effects. These groundwater, may be applied to study the interannual implied discharges were compared with that derived to decadal variations in continental freshwater dis- from the stream¯ow data. The main results are sum- charge. The NCEP reanalysis is available for the last marized as follows. 50 years or so and an improved ECMWF reanalysis (ERA-40) for a similar period will soon become avail- 1) The use of river mouth out¯ow increases the global able. For such applications, however, improvements in continental discharge by ϳ18.7% and the total drain- accounting for the effects of snow accumulation and age area by ϳ20.4% compared with estimates using melt on seasonal to interannual timescales are needed. the unadjusted data from the farthest downstream Furthermore, changes in human in¯uences on the nat- stations (cf. Fig. 1). This result suggests that using ural stream¯ow and evaporation over the last several decades will also be important considerations. unadjusted stream¯ow data from the farthest down- We have shown that comparisons of basinwide dis- stream stations (e.g., Perry et al. 1996; Grabs et al. charges (Table 4) have only limited value. It is obviously 1996, 2000) substantially underestimates global con- a more challenging task to derive correct estimates of tinental freshwater discharge. discharge from all major rivers than just for the total 2) Globally, the annual runoff rate over the unmoni- discharge. For detailed evaluation of climate models and tored areas is comparable to that over the monitored for estimating meridional transport of freshwater in the areas, although their ratio varies from 0.29 for the oceans, discharge from individual rivers (e.g., Table 2 Indian Ocean drainage basin in July to 4.30 for the and the appendix) and spatial distributions of discharge Paci®c Ocean drainage basin in January (Table 3). (Fig. 6) are needed. Because the freshwater ¯uxes from 3) Our 921-river-based estimate of global continental 682 JOURNAL OF HYDROMETEOROLOGY VOLUME 3

freshwater discharge (excluding Antarctica3)is the same seasonal phase as for river discharge, which 37 288 Ϯ 662 km3 yrϪ1, or 1.18 Ϯ 0.02 Sv, which illustrates the important effects of snow accumula- is ϳ7.6% of global P and 35% of terrestrial P. Al- tion and melt and river transport. The total discharge though this value is comparable to earlier estimates, into the Arctic, the Paci®c, and the global oceans large differences exist among the discharges into the peaks in June, whereas the peak is in May for the individual ocean basins. The estimates of global dis- Atlantic Ocean and in August for the Indian Ocean charge based on the Fekete et al. runoff and ECMWF (Fig. 12). Snow accumulation and melt have large P±E are slightly higher than the river-based estimate, effects on the annual cycle of discharge into the Arc- while the NCEP P±E implies lower discharge (Table tic, Atlantic, Paci®c, and global oceans, but little 4). In general, the reanalysis P±E ®elds underesti- in¯uence on the discharge into the Indian Ocean and mate discharge from east Asia and northern Africa the Mediterranean and Black Seas. (Fig. 6). About 57% of the global discharge comes from the world's 50 largest rivers (Table 2). The long-term mean values of river runoff and con- 4) When forced with the Fekete et al. runoff and re- tinental discharge reported here is available for free analysis P±E ®elds, the RTM simulates the station download from NCAR's Climate Analysis Section cat- stream¯ow rates reasonably well for world's major alog (http://www.cgd.ucar.edu/cas/catalog/). rivers (Figs. 3, 4, and 6). This is especially true for the Fekete et al. runoff case (Table 2 and the ap- Acknowledgments. We thank W. G. Large and G. B. pendix) and suggests that river transport models at Bonan for helpful discussions, S. Sorooshian, D. P. Let- 0.5Њ resolution, such as the one used in the NCAR tenmaier, and two reviewers for helpful comments, S. CCSM, can realistically simulate the world river sys- Levis for help with the RTM codes, M. L. Branstetter tem and its routing of terrestrial runoff into the and J. S. Famiglietti for providing the RTM, J. Caron oceans. for help with the reanalysis datasets, and B. M. Fekete, 5) The continental discharges into the oceans within C. J. VoÈroÈsmarty, B. A. Bodo, and others for making each 1Њ latitude band implied by the Fekete et al. the stream¯ow, runoff, and STN-30p datasets available. runoff and reanalysis P±E ®elds agree reasonably This research was sponsored by grants from NOAA's well with the river-based estimates, which we regard Of®ce of Global Programs and jointly by NOAA±NASA as the closest to the truth. 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Listed are aux Chutes, Canada Stiegeler's, Tanzania El Ekhsase, Egypt tete du Lac, Canada Senchi (Hal, Ghana) Salem, OR, United States Usk, Canada Mas-d'Agena, France Alexandria, LA, United States Barakot bri, India Dnepr hydro, Ukraine Gaocung, China Jamanxim, Brazil Harrisburg, PA, United States Shijiao, China Razdorskaya, Russia Susitna Station, AK, UnitedGarudeshwar, States India Charles Fuh, Argentina Maripa, French Guiana Penitas, Colombia Garoua-dono, Cameroon Crooked Creek, AK, UnitedNear States Hat Isla, Canada Bengbu, China en aval de, Canada Baghdad, Iraq Vijayawada, India Chats Falls, Canada Hkamti, Myanmar (Burma) Sidorovsk, Russia Pontelagosc, Italy Mataway (Suriname) Vorontsovo, Russia Beaucaire, France Centrale d', Canada Claiborne, AL, United States Near Wrangell, AK, Unitedtete States de la, Canada ) Station, country Њ 6.2 3.8 4.2 9.3 5.0 7.8 5.5 6.5 58.2 29.7 50.1 44.9 54.6 44.4 31.3 61.9 51.3 32.9 53.7 33.3 16.5 45.5 26.0 21.5 47.9 35.2 40.2 23.6 47.5 21.9 66.6 44.9 69.6 43.8 48.6 31.5 56.7 52.2 50.3 Ϫ Ϫ Ϫ ) Lat ( Њ 0.2 0.1 4.6 92.4 65.8 37.9 31.3 77.4 73.7 71.9 51.9 83.9 78.6 44.4 80.6 76.2 95.7 78.1 85.0 35.2 55.8 76.9 40.7 77.2 13.4 82.3 11.6 57.7 71.6 87.5 123.0 128.4 150.5 158.1 117.4 132.1 114.9 112.9 147.5 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ 1 3 7 7 16 17 12 22 47 84 51 58 55 33 31 33 19 26 38 26 38 47 28 54 19 34 74 72 11 28 80 58 80 70 46 21 21 109 101 ) at the station location, and the estimated annual volume (Vol) and drainage area 1 Ϫ 58 19 42 52 35 50 40 62 38 50 89 16 25 14 61 81 96 90 27 70 52 96 73 57 52 44 yr Stn DA Nyr Lon ( 394 175 158 463 734 378 118 121 134 251 100 305 3 2900 APPENDIX 39 74 31 58 58 57 58 72 34 46 26 31 18 99 68 99 86 51 43 187 398 219 509 894 423 114 339 116 133 244 221 252 133 124 129 102 324 124 3826 River mouth 40 40 40 39 37 37 36 36 36 48 47 47 47 46 46 45 45 44 41 40 65 64 57 57 57 56 56 55 55 55 55 55 54 54 54 53 51 51 49 Vol DA Flow Rates and Drainage Areas of World's Largest Rivers. 7 3 1 15 27 26 46 20 13 19 34 60 22 26 22 24 25 11 11 29 21 25 17 69 37 86 18 69 61 66 10 51 39 50 25 21 32 309 108 552 18 079 21 152 50 415 42 364 3 8 2 5 5 4 6 5 9 6 7 6 4 8 7 5 3 5 8 3 7 9 8 8 18 12 12 10 11 13 14 11 10 14 15 12 17 12 Vol at station std dev RTM Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ ) at the river mouth. The composite annual runoff data of Fekete et al. (2000) were used in the RTM simulation. Nyr is station record length in yr, lon 2 Ϯ 21 24 26 40 33 21 29 19 33 34 46 29 51 29 37 31 45 22 28 65 12 38 30 54 37 34 28 48 50 54 47 45 42 26 39 27 38 52 77 km Stn 3 std dev) and river transport model (RTM) simulated river ¯ow (in km 17 492 Ϯ Â Ânoue George Ru®ji Nile Nottaway Volta Willamette Skeena Garonne Red Taz Po Courantyne Indigirka Rhone Saguenay Alabama Stikine Eastmain San Juan Be Kuskokwim Albany Huai La Grande Tigris Krishna Ottawa Chindwin Brahmani Dnepr Huanghe Jamanxim Susquehanna Beijiang Don Susitna Narmada Santa Cruz Oyapock 81 82 83 84 85 86 87 88 89 61 62 63 64 65 66 67 68 69 51 52 53 54 55 56 57 58 59 60 70 71 72 73 74 75 76 77 78 79 80 No. Name (DA), based on STN-30p,and in lat 10 are longitude and latitude for the station. Total of top 50 rivers from Table 2: World's largest 150 rivers (by the estimated river mouth ¯ow rate, Vol), plus 50 rivers that are among the top 150 rivers by drainage area but do not meet t long-term mean station (Stn DECEMBER 2002 DAI AND TRENBERTH 685 Wittenberge, Germany Near Yakutat, AK, UnitedDemopolis States Lake, AL, UnitedCampos-Pont, States Brazil Porto Formo, Brazil Ahvaz, Iran Cajueiro, Brazil Malonisogor, Russia Balat, Malaysia Porto Plato, Brazil Samburg, Russia u/s Shumal, Canada Regua, Portugal Primera Ang, Argentina Sounda, Congo Montjean, France Carrero de, Chile Boluo, China Kaunia, Bangladesh en aval du, Canada Tainionkosk, Finland Bolshie Sch, Russia Centrale Mc, Canada d.j. Sade, Ecuador Linhares, Brazil Ubileynaya, Russia Sao Francis, Brazil Desembocadu, Chile Limestone R, Canada Ekwok, AK, United States Betomba, Madagascar La Colonia, Chile Temerloh, Malaysia Moose River, Canada Wat Phikun, Thailand Kamaria Fal, Guyana Tczew, Poland Chitina, AK, United States d/s Mactaqu, Canada 7.5 km d/s, Russia ) Station, country Њ 5.3 0.7 0.5 3.4 6.4 3.5 5.6 4.1 0.6 23.2 25.8 31.3 65.0 47.4 53.0 59.4 32.5 67.0 55.2 41.2 55.4 59.3 70.8 51.5 61.2 56.3 49.2 50.8 15.3 54.1 61.5 46.0 71.9 41.5 21.8 40.5 19.7 47.3 36.8 19.4 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ ) Lat ( Њ 7.8 0.8 87.9 41.3 42.4 48.7 54.5 45.6 11.8 63.7 12.1 72.2 89.5 51.4 78.2 45.0 72.9 66.8 76.9 28.8 68.3 79.4 40.1 52.6 73.1 88.3 81.3 58.8 18.8 Ϫ Ϫ 138.1 117.6 129.1 114.3 157.5 144.5 123.7 161.6 136.1 102.4 100.1 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ 8 3 6 5 5 69 67 71 35 72 21 73 34 48 45 37 67 23 43 29 30 47 25 25 23 31 28 21 11 24 22 21 23 44 87 36 18 24 116 138 9 28 40 56 61 35 56 11 25 20 23 95 19 91 95 55 41 61 52 46 19 78 51 24 94 26 45 24 19 61 53 53 40 Stn DA Nyr Lon ( 124 282 110 224 119 194 198 APPENDIX 26 62 63 63 31 65 12 43 92 24 97 62 11 34 19 34 49 67 53 58 51 46 19 90 46 20 35 49 23 149 331 198 118 115 142 181 212 106 235 104 River mouth 27 27 27 26 26 25 25 25 25 30 29 29 29 29 29 28 28 27 27 35 34 34 34 34 34 34 34 33 33 33 33 32 32 32 32 32 32 31 31 30 Vol DA Flow Rates and Drainage Areas of World's Largest Rivers. 8 7 8 8 6 23 23 29 27 24 10 21 18 19 25 10 21 33 12 22 21 21 25 53 13 17 41 26 16 50 30 16 33 22 12 35 21 19 34 100 6 4 6 8 8 8 5 3 2 7 3 2 9 7 5 9 3 6 5 7 5 7 7 4 5 7 3 4 3 6 5 9 9 6 3 3 10 10 10 10 Vol at station std dev RTM Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ 22 27 21 26 24 24 27 20 18 30 28 25 17 27 29 26 21 23 27 18 25 29 33 34 27 32 27 19 29 29 27 32 32 20 21 27 34 32 32 31 Stn Âo Âo Bõ Elbe Alsek Tombigbee Paraiba do S Parnaiba Karun Curua-2 Mezen Kinabatangan Araguari Pur Nass Douro Negro Kouilou Loire Puelo Dongjiang Tista Pahang Moose Chao Phraya Cuyuni Vistula Copper Saint John Olenek Rupert Vuoksi Kamchatka Manicouagan Esmeraldas Doce Yana Jari Bõ Severn Nushagak Tsiribihina Baker ) 90 91 92 93 94 95 96 97 98 99 No. Name 121 122 123 124 125 126 127 128 129 111 112 113 114 115 116 117 118 119 120 100 101 102 103 104 105 106 107 108 109 110 Continued ( 686 JOURNAL OF HYDROMETEOROLOGY VOLUME 3 Ènern, Sweden Chokwe, Mozambique Downstream Ashewei, Canada Tortosa, Spain Narva Ges, Estonia Yuma, AZ, United States Poses, France Coppermine, Canada Bendery, Moldova Almourol, Portugal Clare, Australia Centrale de, Canada Ibanga, Gabon Badajos, Brazil Reforma, Mexico Dagana, Senegal Papaloapan, Mexico Hindiya, Iraq Langnes (So, Norway) Chattahoochee, FL, United States Nadym, Russia Gozdowice, Poland Jekabpils, Latvia d/s Gods Ri, Canada Baleine GR, Canada Bevoay, Madagascar en aval de, Canada pres de la, Canada Churchill River, Canada Guillemard, Malaysia Balclutha, New Zealand Mamfe (Manf), Cameroon Taivalkoski, Finland Va Indogyo, South Korea u/s Hermann, Canada Porog, Russia Kathore, India Saskylakh, Russia Itapebi, Brazil Tikhovskiy, Russia Las Adjunta, Mexico ) Station, country Њ 5.8 5.8 2.7 2.5 54.5 40.8 59.4 32.7 49.3 67.7 46.8 39.5 46.6 18.0 16.5 18.2 32.7 59.6 30.7 65.6 45.2 22.0 58.6 57.7 58.1 65.9 58.4 37.5 66.1 63.8 21.3 72.0 52.8 56.5 56.4 55.2 19.8 24.5 46.2 15.9 21.8 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ ) Lat ( Њ 8.4 1.2 0.5 9.3 29.5 72.7 10.7 47.8 93.2 15.5 96.1 44.3 11.1 84.9 72.7 33.0 87.2 28.2 24.7 12.4 96.5 38.5 72.9 39.5 38.2 98.6 14.3 25.9 92.8 77.0 43.9 70.4 69.6 94.6 Ϫ 115.4 147.2 114.6 127.0 114.1 102.2 169.7 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ 6 3 7 4 84 27 33 65 32 18 83 71 35 26 23 80 72 76 16 47 74 25 29 51 27 35 55 56 23 87 59 18 33 13 25 30 22 39 29 13 178 7 42 20 38 38 21 40 45 48 51 47 25 94 56 62 79 68 48 58 50 84 56 65 51 66 67 70 36 53 42 43 12 20 Stn DA Nyr Lon ( 268 274 342 629 129 110 103 287 6 APPENDIX 38 25 46 50 26 43 55 53 62 83 58 73 49 72 73 83 94 46 60 46 51 15 12 50 51 20 59 66 94 68 64 92 847 221 420 808 121 120 316 106 River mouth 24 23 23 22 22 22 21 21 20 20 14 13 13 12 12 11 11 11 11 10 20 19 19 19 19 19 19 18 18 17 17 17 17 17 17 16 16 16 14 14 14 Vol DA Flow Rates and Drainage Areas of World's Largest Rivers. 6 3 8 2 1 8 6 9 1 6 4 5 61 15 11 56 22 37 19 14 15 26 13 13 18 12 10 12 10 22 11 11 21 14 12 26 12 14 16 12 16 6 3 6 3 5 5 7 3 6 3 4 4 4 4 6 2 3 5 4 4 3 3 3 5 3 3 7 3 5 2 5 4 5 4 3 9 2 3 3 6 10 Vol at station std dev RTM Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ 24 16 18 17 22 22 19 21 20 15 17 16 19 17 19 19 19 18 18 17 17 17 17 17 16 16 16 13 14 11 14 12 13 13 12 11 11 11 10 11 10 Stn Èta Ânuco Saint-Maurice Nyanga Capim Grijalva Senegal Papaloapan Euphrates Glomma Apalachicola Nadym Odra Daugava Hayes, MB Baleine Mangoky Feuilles Melezes Churchill Kelantan Clutha Cross Kemi Go Han Back Onega Tapi Anabar Jequitinhonh Kuban Pa Limpopo Winisk Ebro Narva Colorado Seine Coopermine Dnestr Tagus Burdekin ) No. Name 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 Continued ( DECEMBER 2002 DAI AND TRENBERTH 687 otal volume. Â, Ivory Coast Â, Ivory Coast Ubaitaba, Brazil Alcala del, Spain El Novillo, Mexico Wharton, TX, United States Matamoros, Mexico Coolenar Po, Australia Nine Mile B, Australia Wlyunga, Australia Jimbegnyino, Australia Emu Springs, Australia Peixe Gordo, Brazil Mount Nancar, Australia Bonou, Benin Pulo do Lob, Portugal Downstream Carnwat, Canada Coolibah Ho, Australia Red Rock, Australia Chiling, China Vioolsdrift, South Africa Pedra do Ca, Brazil Cantanhede, Brazil Euston weir, Australia Konstantino, Russia El Capomal, Mexico Tiassale Grand Anicu, India Luug (Lugh), Somalia The Gap, Australia Richmond, TX, United States Aniassue ) Station, country Њ 6.9 6.6 5.9 3.8 5.2 3.6 37.5 29.2 29.3 25.9 42.2 37.8 68.6 29.6 68.2 21.8 10.8 20.3 24.8 31.8 21.3 27.9 14.3 28.8 12.6 15.5 14.7 13.8 23.1 34.6 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ ) Lat ( Њ 6.0 2.5 7.6 4.8 3.7 96.1 97.4 39.3 17.6 39.0 38.2 44.4 78.8 42.5 95.8 Ϫ Ϫ Ϫ Ϫ 109.5 119.2 113.8 116.1 116.2 114.5 128.4 130.9 134.4 123.5 130.7 142.8 161.2 105.1 150.1 Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ Ϫ 4 8 5 51 20 67 67 23 41 30 30 32 21 32 44 24 12 30 42 20 14 30 70 11 26 38 26 10 81 39 56 47 58 50 74 49 85 48 47 47 61 56 45 47 54 51 50 96 74 67 Stn DA Nyr Lon ( 109 457 118 121 851 991 129 180 136 117 18 492 60 856 APPENDIX 48 54 75 20 90 63 72 77 54 52 65 38 60 74 50 79 82 121 805 166 106 107 274 944 192 104 234 138 125 1032 23 237 73 652 River mouth 3.4 3.3 3.3 2.6 1.5 1.4 0.7 0.7 0.3 0.2 6.6 6.3 5.4 5.2 5.0 4.9 4.8 4.6 4.6 4.3 9.9 9.4 8.4 8.4 8.3 7.7 7.5 7.4 7.1 6.8 Vol DA 3939 25 091 Flow Rates and Drainage Areas of World's Largest Rivers. 2.0 2.6 0.1 2.6 1.2 0.0 0.0 0.4 0.0 0.0 3.5 7.1 1.1 0.7 0.2 1.0 2.7 3.4 9.2 3.5 5.5 7.9 4.8 4.5 5.2 5.2 3.4 15.2 14.7 11.5 3230 21 309 1.8 2.3 0.5 1.7 1.7 1.1 0.9 0.3 0.3 0.2 4.1 3.0 3.1 4.1 1.6 2.0 1.5 2.2 3.5 2.5 4.3 7.3 1.9 3.2 5.0 2.7 1.6 4.8 3.9 3.6 Vol at station std dev RTM Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ Ϯ 3.4 3.2 2.5 2.6 1.5 1.4 0.7 0.4 0.3 0.2 3.7 6.3 5.4 4.8 4.6 3.2 2.4 3.5 4.6 4.1 7.4 8.5 8.3 8.4 8.3 7.7 6.1 7.4 6.7 6.0 Stn 558* 79* Ϯ Ϯ Â Â Âme Contas [de] Guadalquivir Yaqui Colorado Rio Grande de Grey Gascoyne Avon Fortescue Murchison Jaguaribe Daly Oue Guadiana Anderson Victoria Roper Liao Orange Paraquacu Itapecuru Murray Bolshoy Anyu Grande de Sa Bandama Cauvery Juba Fitzroy-QX Brazos Comoe ) No. Name 191 192 193 194 195 196 197 198 199 200 181 182 183 184 185 186 187 188 189 190 171 172 173 174 175 176 177 178 179 180 1±200 total: 20 769 * This number, which was estimated as the square root of the sum of the variance of the listed rivers, provides only an estimate for the true std dev of the t 51±200 total: 3277 Continued (