Fluvial sediment supply to a mega-delta reduced by shifting tropical-cyclone activity

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Abstract

The world’s rivers deliver 19 billion tonnes of sediment to the coastal zone annually1, with a considerable fraction being sequestered in large deltas, home to over 500 million people. Most (more than 70 per cent) large deltas are under threat from a combination of rising sea levels, ground surface subsidence and anthropogenic sediment trapping2,3, and a sustainable supply of fluvial sediment is therefore critical to prevent deltas being ‘drowned’ by rising relative sea levels2,3,4. Here we combine suspended sediment load data from the Mekong River with hydrological model simulations to isolate the role of tropical cyclones in transmitting suspended sediment to one of the world’s great deltas. We demonstrate that spatial variations in the Mekong’s suspended sediment load are correlated (r = 0.765, P < 0.1) with observed variations in tropical-cyclone climatology, and that a substantial portion (32 per cent) of the suspended sediment load reaching the delta is delivered by runoff generated by rainfall associated with tropical cyclones. Furthermore, we estimate that the suspended load to the delta has declined by 52.6 ± 10.2 megatonnes over recent years (1981–2005), of which 33.0 ± 7.1 megatonnes is due to a shift in tropical-cyclone climatology. Consequently, tropical cyclones have a key role in controlling the magnitude of, and variability in, transmission of suspended sediment to the coast. It is likely that anthropogenic sediment trapping in upstream reservoirs is a dominant factor in explaining past5,6,7, and anticipating future8,9, declines in suspended sediment loads reaching the world’s major deltas. However, our study shows that changes in tropical-cyclone climatology affect trends in fluvial suspended sediment loads and thus are also key to fully assessing the risk posed to vulnerable coastal systems.

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Figure 1: The gauging network of the Mekong River.
Figure 2: Daily flow discharge and suspended solids load at Kratie from 1 January 1995 to 31 December 1999.
Figure 3: Time series of annual suspended solids loads at Kratie from 1982 to 2004.

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Acknowledgements

This study was supported by awards NE/JO21970/1, NE/JO21571/1 and NE/JO21881/1 from the UK Natural Environmental Research Council (NERC) and the Academy of Finland funded project SCART (grant number 267463). We thank the Mekong River Commission for access to hydrological and suspended sediment data and the Department for Hydrology and Water Resources in Cambodia for aDcp data and their logistical support. J.L.B. was also in receipt of a University of Southampton Diamond Jubilee Fellowship and National Great Rivers Research and Education Centre Fellowship that aided completion of this work.

Author information

S.E.D., J.L., C.R.H., D.R.P., J.L.B., A.P.N. and R.A. jointly conceived the study. C.R.H., S.E.D., J.L., J.L.B. and D.R.P. collected and processed the field data. C.R.H. constructed the sediment rating curves and, with S.E.D., undertook the data analysis. M.K. and H.L. conducted the model simulations, with the TC track data and rainfall anomalies being computed by J.L. S.E.D. drafted the paper, which was then edited by all co-authors.

Correspondence to Stephen E. Darby.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks L. Giosan and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Locations of the world’s 30 largest (by drainage area) rivers.

The numbers identify the basins listed in Extended Data Table 1. Note that the Ganges (basin 19) and Brahmaputra (basin 28) catchments are outlined as a single basin in the figure. Also shown is the density of all TC tracks from 1842 to 2015 as recorded in the IBTrACS42 database. Track density was calculated using the point density function in ArcGIS 10.1.

Extended Data Figure 2 Sediment rating curves for the five river gauging stations on the Lower Mekong River.

a, c, e, g, i, The relationship between flow discharge (Q) and suspended solids concentration (C) at: Luang Prabang (pre-dam: n = 187, r2 = 0.338; post-dam: n = 49, r2 = 0.648) (a); Mukdahan (n = 1,159, r2 = 0.497) (c); Pakse (n = 60, r2 = 0.591) (e); Stung Treng (n = 95, r2 = 0.870) (g); and Kratie (n = 140, r2 = 0.850) (i). b, d, f, h, j, These panels show how the relationships in a, c, e, h and i propagate through to give the relationship between flow discharge (Q) and instantaneous sediment load (Qs) at the same stations: Luang Prabang (pre-dam: n = 187, r2 = 0.791; post-dam: n = 49, r2 = 0.864) (b); Mukdahan (n = 1,159, r2 = 0.693) (d); Pakse (n = 60, r2 = 0.780) (f); Stung Treng (n = 95, r2 = 0.900) (h); and Kratie (n = 140, r2 = 0.931) (j). All the fits shown are significant at P < 0.00001. Note that the scales for a and b (Luang Prabang) differ from those for the other panels. We recognize that the fits for Q versus Qs in b, d, f, h and j are stronger than the fits between Q and C because of the auto-correlation arising when transforming C to Qs (Qs = C × Q/1,000). For the stations at Mukdahan, Pakse, Stung Treng and Kratie, a single rating curve is employed (black lines), as there is no evidence of hysteresis between the rising (filled circles) and falling (open circles) limbs of the hydrograph (see Methods). At Luang Prabang, there is likewise no evidence of hysteresis between the rising (coloured filled symbols) and falling (coloured open symbols) limbs. However, two rating functions are employed at this station, one for the pre-dam (orange coloured lines) and post-dam (green coloured lines) periods (see Methods). Source data

Extended Data Figure 3 Daily flow discharge and suspended solids load at selected Mekong River gauging stations from 1 January 1995 to 31 December 1999.

a, c, e, g, Daily simulated (Qsim) and observed (Qobs) water flows, along with the daily water flows attributable to tropical cyclones (Qsim_TC) at Luang Prabang (a), Mukdahan (c), Pakse (e) and Stung Treng (g). b, d, f, h, Daily total suspended solids load (Qs; in Mt per day) and daily suspended solids load attributable to TCs (Qs_TC; also in Mt per day) at Luang Prabang (b), Mukdahan (d), Pakse (f) and Stung Treng (h). Note that the period 1995 to 1999 encompasses the years during the 1981–2005 study period that are the most (1996) and least (1999) strongly affected by TCs. i, Goodness-of-fit measures comparing VMod simulated and observed water flows at five river gauging stations on the Lower Mekong River. Note that the goodness-of-fit metrics are all based on the mean daily flows for the full simulation period (1 May 1981 to 31 March 2005), with the exception of the mean discrepancy ratio for the annual flood peaks (Mep). The Mep metric is computed using the ratio of simulated maximum daily discharge to observed maximum daily discharge in each year of the record (1981–2004 inclusive) studied here. Source data

Extended Data Figure 4 Time series of annual suspended solids load at selected river gauging stations during 1982 to 2004.

a, Luang Prabang; b, Mukdahan; c, Pakse; d, Stung Treng. The symbols indicate the total suspended solids load (Qs; open circles) and suspended solids load attributable to TCs (Qs_TC; filled squares). Significant (P ≤ 0.05) trends as identified by Mann–Kendall analysis are indicated by the dashed lines, with the corresponding time rate of change of annual suspended solids load annotated on the plot. Source data

Extended Data Figure 5 Spatial distributions of mean annual rainfall contributed from TCs over the Mekong Basin.

a, 1981–1985; b, 1986–1990; c, 1991–1995; d, 1996–2000; e, 2001–2005. Note the pronounced declines in rainfall associated with TCs at Stung Treng and Kratie in particular.

Extended Data Figure 6 Strike counts for TCs tracking across the Mekong basin during 1950–2013.

The strike count data plotted are extracted from the IBTrACS42 database and normalized by the maximum count (199) observed in 1964. We employ strike count, rather than precipitation, data in this longer-term historical analysis because reliable precipitation data are not available outside of the 1981–2005 period that is the main focus of the study. Similarly, mean wind speed data, which in principle could be used to estimate variations in ACE as a proxy for precipitation, are available only sporadically outside of 1981–2005. In terms of strike counts, the data suggest that there is a periodicity in the long-term cyclone climatology, with the most recent data (2006–2013) having annual strike counts similar to the 1950–2013 mean of 87 ± 37. However, these data must be treated with caution since strike count data do not report the intensity or locations of cyclone tracks, both of which are important controls on the precipitation delivered to the basin by these TCs. Source data

Extended Data Figure 7 Procedures used to determine cross-section mean suspended solids concentration from acoustic Doppler current profiler data.

a, Calibration function (solid line; n = 54, r2 = 0.9306, P < 0.0001) linking the suspended solids concentration (SSC) to acoustic backscatter (ABS) for the 600 kHz (RD Instruments) acoustic Doppler current profiler (aDcp) instrument employed in this study (dashed lines indicate 95% prediction intervals). b, Example of quasi-synoptic ABS field obtained from the aDcp survey at the Kratie gauging station on 23 September 2013 (flow discharge, Q = 57,000 m3 s−1). Note that there is a small blanking distance close to the water surface and a zone of side-lobe interference near the bed (indicated by the dashed black lines) where no ABS values are returned, and the ABS values in these zones are therefore determined by interpolation. c, SSC field obtained on the basis of the ABS values in b and using the calibration function in a. Note how the locations of the nine point-based SSC estimates collected using the sampling procedure adopted at Luang Prabang and Pakse lead to a deviation of the cross-section mean SSC derived from the aDcp-estimated SSC field in c and the point-based sampling procedure. We compared 11 cross-section mean SSCs obtained using point-based versus aDcp sampling procedures at locations throughout the Mekong River south of Kratie to correct (by 26%) the consequent bias arising from cross-section averaging of point-based samples. Source data

Extended Data Table 1 Characteristics of the world’s 30 largest rivers with data from ref. 1
Extended Data Table 2 Hydrometeorological data (1982–2004) for hydrological stations on the Lower Mekong River
Extended Data Table 3 Data sources used for sediment rating curves derived herein

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Darby, S., Hackney, C., Leyland, J. et al. Fluvial sediment supply to a mega-delta reduced by shifting tropical-cyclone activity. Nature 539, 276–279 (2016) doi:10.1038/nature19809

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